A. Theory and Prior Evidence

The effect of structural policies on current account adjustment is theoretically ambiguous. Reforms can affect saving and investments of firms and households (thus trade flows) through a variety of channels. Indirectly, structural policies can impact the current account balance through increasing productivity. Reforms can also have a direct impact. For example, strengthening property rights reduces uncertainty, which should raise the investment rate, reduce precautionary savings, and worsen the current account balance. By increasing competition in the capital goods sector and lowering the price of investment goods, product market reforms can also boost investment (OECD, 2011).

The literature has used DSGE models to capture the complex reform interactions and dynamics. In a two country DSGE, Fournier and Koske (2010) find that the impact of a positive productivity shock on savings (and thus the current account) depends on several considerations—namely, consumers’ preferences for consumption smoothing; if the rise in productivity and income is believed to be permanent or temporary; and whether the productivity shock is in the tradable or non-tradable sectors. Cacciatore et al. (2016) show in a DSGE set-up with micro-founded labor and product market frictions that unanticipated and permanent product market reforms generally deteriorate the current account balance, while labor market reforms tend to strengthen it. Testable hypotheses and theoretical considerations emerge from the literature that we take to the data.

Under our first hypothesis, productivity increases due to structural reform in the tradable sector will improve the current account balance. This is most easily derived from a static trade model, in which a productivity increase leads to an increase in tradable output and exports. Here, the current account may strengthen in the immediate aftermath of reforms if (i) consumers have a low propensity for inter-temporal consumption smoothing, so they postpone their consumption to benefit from lower prices in the future as productivity rises and/or (ii) reform increases productivity temporarily or agents perceive it as temporary or not credible. In these instances, the current account balance strengthens temporarily as households save part of the higher income for the future. The same result is derived in Fournier and Koske (2010).

In our second hypothesis, productivity increases lead to a decline in the current account balance in the short-run but an increase over the long-run. The standard infinite horizon open economy model predicts that, following a productivity increase, consumption and investment will increase instantaneously, as agents are adjusting to the new equilibrium (a higher sum of discounted future incomes). Output, on the other hand, increases gradually, reflecting adjustment costs. The timing difference results in a current account deficit in the initial years following reform. Glick and Rogoff derive a theoretical model of the negative link between total factor productivity and the current account balance. A similar hypothesis is derived in Fournier and Koske (2010) in a two-sector model, which links increased productivity in the tradable sector to higher consumption (and therefore worsening external balance). They identify that the standard consumption smoothing channel (consume more today in response to expected higher income tomorrow) is supplemented by frontloading consumption on the expectation of a rising relative price of non-tradable goods on the account of the Balassa-Samuelson effect.³

The overall impact of structural reforms on the current account balance therefore depends on the speed at which consumption adjusts to a higher permanent income and the speed of factor mobility. The current account balance will improve the slower consumption responds to income and the faster factors move to the tradable sector. Other factors affecting the sign and magnitude of the impact of reforms on the current account balance relate to the characteristics of the reform shock, e.g., permanent versus temporary, anticipated versus unanticipated, affecting supply in the short term versus only the longer term.

There are further complexities in the relationship between structural policies and the current account balance that depend on country-specific circumstances. Structural policies can interact with other policies, macroeconomic conditions (output gap), or features of the economy (structure of the export basket) to influence the S-I behavior of firms and households. Wealth effects on saving could be larger in countries with more developed financial markets (Boone et al., 2001). And while structural reforms contribute to reducing CA imbalances in countries running surpluses, their potential to reduce imbalances in deficit countries is found to be more limited (IMF, 2012). Ultimately, the impact of structural reforms on the current account is an empirical question.

The empirical literature finds that structural reforms have a positive impact on productivity and growth (IMF 2008 and 2016, OECD 2008 and 2012, Bouis et al. 2016). Importantly, the payoff from reforms varies across income group (IMF 2015b), how far a country is from the technology frontier (IMF2013), as well as institutional quality (IMF 2008).

In turn, productivity shocks have been identified—since the seminal work of Glick and Rogoff (1995)—as one of the driving forces behind current account movements. In their framework, country-specific productivity shocks negatively affect the current account balance, while global productivity shocks do not have any significant impact. Overall, the inter-temporal model is shown to perform well if confronted with the data. Since then, several empirical papers have confirmed the validity of the Glick and Rogoff results and studied the incidence of productivity shocks on the current account in an open economy.⁴ More recently, Bussière, Fratzscher and Müller (2005) investigate the role of productivity shocks compared to budget deficits in determining current account balances in 21 OECD countries from 1960 to 2003. They find no evidence of a contemporaneous effect of budget deficits, while productivity shocks appear to play an important role.

Empirical work on the direct impact of reform on the current account is limited, and few studies find a robust link. Using pooled time series (controlling for cyclical factors), Kennedy and Sløk (2005) find that while product and financial market reforms exhibit a significant negative relationship with the current account, their contribution to explaining current account positions is limited.

Jaumotte and Sodsriwiboon (2010) focus on the impact of labor market indicators (minimum wages, unemployment benefits and employment protection legislation). They find that higher minimum wages lower the current account balance, presumably by raising wage costs and reducing competitiveness. Other indicators are insignificant.

Cheung et al. (2010) and IMF (2012)⁵ find that institutional quality negatively impacts current account balances. This negative relationship can be interpreted in several ways. For some it may reflect capital flowing from emerging economies towards countries perceived to possess more efficient institutions. Or it may be that improved institutions lower the need for precautionary savings, reducing current account balances. Kerdrain, Koske and Wanner (2010) disentangle the transmission channels and investigate separate impacts on savings and investment. They find that (i) product market liberalization boosts investment and weakens the current account balance; (ii) financial market deregulation lowers savings; (iii) stricter employment protection is associated with lower saving rates if unemployment benefits are low.

B. Empirical Approach

We estimate the impact of structural reform on the current account using a sample of 108 countries from 1970 to 2011; data sources are detailed in Annex 1. The focus is mainly on structural policies that affect domestic settings but also include policies that directly affect capital flows (e.g., removal of restrictions on trade and foreign direct investment). As discussed in the previous section, interpreting the estimated coefficient of the structural indicator on the current account is challenging. We proceed by disentangling the impact and looking separately at import and export equations.⁶

We use Jordà (2005) local projection (LP) technique to estimate current account, import, and export impacts of structural reforms dynamically for up to 5 years after the reform occurred. This specification allows us to capture year-by-year impacts in the event reforms have a J-curve effect on the current account balance. For example, the initial deterioration due to higher imports of capital goods in response to the productivity can be followed by an improvement as exports gradually increase on the back of higher productivity and investment. Aside from tracking such dynamics over time, LP is flexible in accommodating nonlinear impacts. Here, we investigate if reform effects differ depending on the structure of the export basket, i.e., if a country is a commodity exporter. The LP technique also allows to control for endogeneity of the the reform variable by including crises and controlling for several time-varying and time-invariant factors including country-fixed effects. However, including crises may not be enough. Countries could share other characteristics beyond the occurrence of crises that also determine current account changes. For example, countries with more efficient governments may be less likely to adopt reforms and yet achieve external adjustment if needed.

Reform shocks are identified using structural variables (described in Annex 1). An improvement in the variable is assumed to signal a “reform” which depending on the magnitude of the change can be small or large. The reform shock is derived as the year-on-year change in reform indicator (if it is positive) and takes the value 0 if no reform was identified. We estimate the impact of the reform shock on the current account, imports, and exports in the next five years using impulse response functions. The following cross-country time series equation is estimated for each of the five years h =1…5 after the reform shock has occurred:

where y_i,t+h = Y_i,t+h − Y_i,t−1 and Y_it is the current account in country i observed at year t. We estimate the model at each horizon h = 0, 1, … 5. The approach estimates a set of five independent equations. R_i,t−1 is a shock across each reform indicator or institutional variable. The estimated β_h coefficients give the impact at horizon h and capture short and medium-term impacts of reforms on three dependent variables: the current account, exports and imports in percent of GDP. Current account balances are the outcome of general equilibrium processes and depend not only on domestic economic conditions but also on conditions abroad. For simplicity, our analysis assumes no reforms take place in the rest of the world or at least that any such reforms have smaller effects than those at home. We only include trade flows and exclude the income account.

The equation includes lags of the dependent variable to control for persistence and a set of controls for the cycle X_it, growth in domestic and foreign demand and terms of trade.⁷ Control variables are lagged one year to minimize endogeneity concerns. Five lags of crisis dummies, taking the value one if the country experienced a banking or currency crisis, or a recession are included. Economic crises can affect the dependent variables (e.g., current account adjustment through import compression) while being correlated with the probability of observing a reform shock. Hence, not controlling for them could introduce an omitted variable bias (Duval 2008). The model includes country fixed effects to account for unobserved time-invariant factors (i.e., cross-country heterogeneity such as differences in institutions) as well as time fixed effects, to account for unobserved global shocks and country-specific time trends. Country fixed effects may dampen the estimated link between slow-changing structural indicators and the current account balance.

Standard errors are clustered by country. The specification is estimated over the total sample rather than by income group. Our prior, confirmed in robustness tests, is that the impact of reform does not vary directionally across countries by income group. Furthermore, we do not always have sufficient reform shocks in each group to look for differential impacts in a dynamic specification. In this exercise, we leave the REER out of from the estimated equation, as the REER is not an exogenous driver of the current account. Some reforms could affect the current account through the REER, or might affect the REER at the same time as the current account, with the risk that the coefficient on structural variables becomes insignificant. In an economy starting at full employment, a reform shock that shifts consumption on a sustained basis will first directly affect the CA and output—but such a shock will also induce macroeconomic adjustment involving changes in relative prices, including the REER.

This analysis has shortcomings that cannot be overcome. First, it captures an average effect across heterogeneous reform experiences, which reduces chances of obtaining significant and robust results. Second, structural policies are likely to influence saving and investment decisions through changes in the macroeconomic control variables, reducing the chances of finding significant coefficients on the policy variables themselves. Third, IMF’s surveillance on select countries has found that changes in structural indicators do not always neatly map to known reform episodes, and vice-versa. Finally, countries usually do not embark on a single reform—instead, reforms are usually implemented as part of a package. Given that we look at one reform at a time, reform impacts could be underestimated. However, the last two points are not specific to our findings.

C. Baseline Results

Evidence of a positive impact of structural reforms on current account balances is limited. Rather, results support the hypothesis that in the short-run, most reforms—financial, trade openness, institutions, product market, labor and higher R&D spending—have no impact. (The results are summarized in Table 1, and detailed results are presented in Table A1–Table A3). If anything, some reforms—banking sector, business regulations and improvements in legal system—are associated with a deterioration of the current account balance in the short-term as imports and possibly investment respond to higher levels of productivity. In the medium-term, exports do respond to large reform episodes. Though capturing a positive impact on the current account balance remains elusive for most reforms—except for reform of securities market, product market reform in the telecom sector, and higher research and development spending. Controls for the relative cyclical position are significant. In the current account equation, higher GDP growth relative to trading partners comes in significant. In the exports equation, partner growth increases exports while domestic demand growth significantly predicts import growth.

Table 1.

Direct Impact on the Current Account: Summary of Main Results

Note: Table shows the sign on the lagged structural indicator. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects and cluster standard errors on country.

Table 1.

Direct Impact on the Current Account: Summary of Main Results

		Year 1	Year 2	Year 3	Year 4	Year 5
Financial sector
Banking		–**	–	–	–	+
	Interest rate controls	–	–	–*	–*	–
	Directed credit/reserve requirements	–	–	–	–	+
	Privatization	–	+	+	+	+
	Banking Supervision	–	+	–	+	+
Security Markets		+	+**	+**	+***	+**
Openness
Mean tariff rate		–	–*	–***	–**	–**
Regulatory trade barriers		–	–	+	–	–
Restrictions on current account transactions		+	+	+	–	–
Capital flows restrictions		–*	+	+	–	–
Institutions
Legal System & Property Rights		–*	–**	–	–	–
Protection of property rights		–	–	+	–	–
Product market regulation
Business regulations		–*	–	–	–	–
Product: telecom and electricity		–	+**	+	+*	+
	Product: telecom	+	+**	+**	+	–*
	Product: electricity	–	+	+	+	+*
Infrastructure
Electricity production capacity		+	+	+	+	–
Telephone lines		–	–	–	–	–
Roads density		–	–	+	+	–
Labor
Hiring and firing regulations		–	–	–	–	–
Centralized collective bargaining		–	+	+	+	+
R&D
Basic R&D spending		+	+	+	+*	+**

Note: Table shows the sign on the lagged structural indicator. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects and cluster standard errors on country.

View Table

Table 1.

Direct Impact on the Current Account: Summary of Main Results

		Year 1	Year 2	Year 3	Year 4	Year 5
Financial sector
Banking		–**	–	–	–	+
	Interest rate controls	–	–	–*	–*	–
	Directed credit/reserve requirements	–	–	–	–	+
	Privatization	–	+	+	+	+
	Banking Supervision	–	+	–	+	+
Security Markets		+	+**	+**	+***	+**
Openness
Mean tariff rate		–	–*	–***	–**	–**
Regulatory trade barriers		–	–	+	–	–
Restrictions on current account transactions		+	+	+	–	–
Capital flows restrictions		–*	+	+	–	–
Institutions
Legal System & Property Rights		–*	–**	–	–	–
Protection of property rights		–	–	+	–	–
Product market regulation
Business regulations		–*	–	–	–	–
Product: telecom and electricity		–	+**	+	+*	+
	Product: telecom	+	+**	+**	+	–*
	Product: electricity	–	+	+	+	+*
Infrastructure
Electricity production capacity		+	+	+	+	–
Telephone lines		–	–	–	–	–
Roads density		–	–	+	+	–
Labor
Hiring and firing regulations		–	–	–	–	–
Centralized collective bargaining		–	+	+	+	+
R&D
Basic R&D spending		+	+	+	+*	+**

Note: Table shows the sign on the lagged structural indicator. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects and cluster standard errors on country.

Labor markets: The sign on lower collective bargaining power suggests a positive link with the current account balance, though the estimated coefficient is not significant.

Product markets: Indicators of product market regulation are significant in the current account and export specifications. In the short-run, a better regulated business environment weakens the current account and increases imports. Deregulating product markets leads to increasing entry into domestic markets including foreign competitors. This should weaken the current account, in part through increased capital inflows before equilibrating mechanisms gradually set in. Indeed, in the medium-term less regulation of networks improves the current account balance; in particular, deregulating telecom and electricity markets increases the current account balance two to three years after the reforms are implemented. This result is mirrored in an increase in non-commodity exports, which gets increasingly larger over the medium-term projection horizon (years 2, 3, 4).

Infrastructure: Improvements in infrastructure have no significant impact on the current account. Increasing electricity production capacity, however, does increase non-commodity exports starting in the second year, with gains plateauing by the fourth year.

Institutions: Improvements in the institutional environment, specifically in the legal system and property rights, are associated with a weaker current account balance in the short-term. This may be due to a safer institutional environment both raising investment (including investment related imports) and lowering incentives for precautionary savings. This result is in line with EBA findings, where an improvement in the institutional environment is associated with a weakening of the current account (IMF 2012, Annex 4).

Financial sector: Deregulating domestic financial and capital markets can stimulate the entry of foreign capital with associated upward pressures on the exchange rate, downward pressures on the interest rate, and a weakening current account. In our data, banking sector reform is associated with a deterioration in the current account balance one year after implementation. In particular, reducing interest rate controls (which should lead to upward pressure on the exchange rate) deteriorates the current account over the medium term. This impact is reflected in an increase in imports (and a decrease in exports) throughout most of the projection horizon. If imports are investment goods, higher capital accumulation could lead to better productivity and competitiveness, which would strengthen the current account in the medium to long-term. This effect, however, is not observed over the medium-term. Securities market development improves the current account after two years, with effects increasing in outer years. This result is somewhat counterintuitive, as a more developed securities market could be expected to attract capital inflows and therefore deteriorate the current account balance.

Openness: Trade reform and capital account liberalization do not improve the current account balance. The link between capital account liberalization and the current account is not straightforward as effects work through both trade and financial flows. In theory, lowering capital flow restrictions should lead to stronger net inflows and an appreciating exchange rate that would weaken, all else equal, the current account. Lowering capital account restrictions on non-residents deteriorates the current account in the short and increasingly in the medium-term, with the impact through the financial account. As expected, lifting restrictions on FDI increases imports. Lowering trade barriers, specifically decreasing import tariffs deteriorates the current account. The mechanism works through an increase in imports which negatively impacts the current account in the short and medium-term.

The next section investigates several extensions relating to large reform episodes, phase of economic cycle, and export basket composition.

D. Extensions

Large Reform Episodes

In the first extension, we estimate the impact of only large reform episodes on external adjustment. Large reform episodes are better identified, given the difficulties involved in measuring incremental reforms based on small changes in available policy indicators. In addition, focusing on large episodes allows us to treat them as a shock and estimate impulse response functions using a dynamic specification. We identify a large reform episode as changes in reforms that are larger than two standard deviations. The episode takes the value 1, if the calculated as the year-on-year change in the reform index is larger than two standard deviations and zero otherwise.

We find that even large reform episodes are not associated with changes in the current account balance. However, we do find that that that large reforms – in particular product market regulation (in energy, transport and communication and the policy environment for investment), capital controls, and labor markets are associated with a positive impact on exports.

Table 2.

Effect of Reform on the Current Account, Large Reform Episodes

Note: The table shows regressions of the change in the current account, non-commodity exports and imports in percent of GDP on dummies for large reform episodes for year 1(Y1) through 5 (Y5). Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively.

Table 2.

Effect of Reform on the Current Account, Large Reform Episodes

			Current account to GDP					Non-commodity exports to GDP					Imports to GDP
			(Y1)	(Y2)	(Y3)	(Y4)	(Y5)	(Y1)	(Y2)	(Y3)	(Y4)	(Y5)	(Y1)	(Y2)	(Y3)	(Y4)	(Y5)
Trade reform
		Number of tariffs	−3.902**	−4.074*	−4.528*	−2.697	−1.014	0.512	1.735	1.248	2.43	1.135	4.021**	4.711**	3.27	3.931	3.915
			(1.375)	(2.149)	(2.367)	(2.311)	(2.159)	(1.087)	(1.163)	(1.422)	(1.813)	(1.642)	(1.946)	(2.354)	(3.670)	(2.643)	(2.570)
		Capital account restrictions (Quinn-Ito)	−3.52**	−4.376	−1.275	−2.523	0.731	0.883	2.987*	4.935*	6.234**	6.629***	−0.9	1.279	6.358	4.851	5.23
			(1.117)	(2.936)	(2.594)	(2.608)	(2.682)	(1.607)	(1.579)	(2.314)	(2.302)	(2.147)	(2.901)	(2.806)	(6.823)	(3.294)	(3.198)
Product market
	Regulation in energy, transport & com.		−0.198	−0.017	−0.569	−0.343	0.221	0.616*	1.079*	0.712	0.487	0.648	1.235***	2.032***	1.976**	1.381	0.984
			(0.441)	(0.582)	(0.637)	(0.718)	(0.727)	(0.370)	(0.553)	(0.681)	(0.773)	(0.836)	(0.476)	(0.680)	(0.780)	(0.860)	(0.907)
	Policy environment for foreign investment		1.867	0.303	−1.393	−0.274	0.095	1.430**	1.436*	2.112**	1.151	1.591	0.016	1.902	3.991**	3.159*	2.461
			(1.138)	(1.552)	(1.450)	(1.493)	(1.464)	(0.623)	(0.818)	(0.965)	(1.054)	(1.131)	(1.301)	(1.544)	(1.770)	(1.861)	(1.913)
Labor market reform
	Collective bargaining		0.258	0.163	−0.044	−0.519	−0.129	1.638	1.866	2.336	1.790	1.172	0.789	1.082	0.683	1.126	0.647
			(0.624)	(0.764)	(0.833)	(0.817)	(0.804)	(0.508)	(0.723)	(0.867)	(0.997)	(1.123)	(0.707)	(0.931)	(1.052)	(1.120)	(1.140)

Note: The table shows regressions of the change in the current account, non-commodity exports and imports in percent of GDP on dummies for large reform episodes for year 1(Y1) through 5 (Y5). Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively.

View Table

Table 2.

Effect of Reform on the Current Account, Large Reform Episodes

			Current account to GDP					Non-commodity exports to GDP					Imports to GDP
			(Y1)	(Y2)	(Y3)	(Y4)	(Y5)	(Y1)	(Y2)	(Y3)	(Y4)	(Y5)	(Y1)	(Y2)	(Y3)	(Y4)	(Y5)
Trade reform
		Number of tariffs	−3.902**	−4.074*	−4.528*	−2.697	−1.014	0.512	1.735	1.248	2.43	1.135	4.021**	4.711**	3.27	3.931	3.915
			(1.375)	(2.149)	(2.367)	(2.311)	(2.159)	(1.087)	(1.163)	(1.422)	(1.813)	(1.642)	(1.946)	(2.354)	(3.670)	(2.643)	(2.570)
		Capital account restrictions (Quinn-Ito)	−3.52**	−4.376	−1.275	−2.523	0.731	0.883	2.987*	4.935*	6.234**	6.629***	−0.9	1.279	6.358	4.851	5.23
			(1.117)	(2.936)	(2.594)	(2.608)	(2.682)	(1.607)	(1.579)	(2.314)	(2.302)	(2.147)	(2.901)	(2.806)	(6.823)	(3.294)	(3.198)
Product market
	Regulation in energy, transport & com.		−0.198	−0.017	−0.569	−0.343	0.221	0.616*	1.079*	0.712	0.487	0.648	1.235***	2.032***	1.976**	1.381	0.984
			(0.441)	(0.582)	(0.637)	(0.718)	(0.727)	(0.370)	(0.553)	(0.681)	(0.773)	(0.836)	(0.476)	(0.680)	(0.780)	(0.860)	(0.907)
	Policy environment for foreign investment		1.867	0.303	−1.393	−0.274	0.095	1.430**	1.436*	2.112**	1.151	1.591	0.016	1.902	3.991**	3.159*	2.461
			(1.138)	(1.552)	(1.450)	(1.493)	(1.464)	(0.623)	(0.818)	(0.965)	(1.054)	(1.131)	(1.301)	(1.544)	(1.770)	(1.861)	(1.913)
Labor market reform
	Collective bargaining		0.258	0.163	−0.044	−0.519	−0.129	1.638	1.866	2.336	1.790	1.172	0.789	1.082	0.683	1.126	0.647
			(0.624)	(0.764)	(0.833)	(0.817)	(0.804)	(0.508)	(0.723)	(0.867)	(0.997)	(1.123)	(0.707)	(0.931)	(1.052)	(1.120)	(1.140)

Note: The table shows regressions of the change in the current account, non-commodity exports and imports in percent of GDP on dummies for large reform episodes for year 1(Y1) through 5 (Y5). Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively.

Reform Interactions

In the second and third extensions, we test the interaction between reforms and the current account, conditional on (i) business cycle phase at the time of reforms and (ii) commodity exporter status of the country. The effects of reforms may be affected by the state of the cycle. Conditional on all remaining equal across countries, the path of the current account in the aftermath of reform implementation could be different in good times or in times of crisis. Similarly, the response of the current account—and especially exports—to reforms could depend on the structure of the export basket. An export basket dominated by commodities, and particularly fuel, may not be sensitive to reforms. As an example, oil export output may not dependent on local infrastructure if it has its own network of roads and port infrastructure.

We augment the baseline LP model to include these two different interactions. The state of the business cycle is captured by a dummy variable taking the value 1 whenever the output gap is lower than −0.5 percent of potential output, and 0 otherwise. Data for advanced and emerging markets are from the IMF’s WEO database. The output gap for LICs is calculated as the deviation of actual output from trend GDP, computed using a standard Hodrick-Prescott filter. In our second test, we interact the share of commodities in total exports with our reform shock. The specification is as follows:

$y_{i,t+h}=({\beta }_{1h}+{\beta }_{2h}{I}_{i,t})R_{i,t}+{\theta }_h{I}_{i,t}+{\mu }_h\left(L\right)e_{i,t+h-1}+X_{i,t-1}^{\prime }{\phi }_h+u_i+{\lambda }_t+{\epsilon }_{i,t+h}$

I_i,t is a dummy variable capturing periods of economic slack and defined as follows:

$I_{i,t}=1[outputgap<-0.5]$

The coefficients of interest are β_1h and (β_1h + β₂h). They measure the association of reforms with cumulative changes in the current account, imports and exports to GDP at each horizon in good and bad times, respectively. The interaction with commodity dependence is specified in the same way, except we use a continuous variable (share in total exports) to avoid using arbitrary thresholds to label countries as commodity exporters. The model is again estimated using the LP method with corrected standard errors.

Evidence on interaction effects is mixed. Table 3 shows that the impact of reforms in financial and product markets is affected by the cyclical position of the economy. More specifically, the effect of structural reforms (banking sector reform and lower entry barriers) launched during bad times is positive and statistically significant. In contrast, lower regulation of networks (telecom, in particular) deteriorates the current account in periods of slack, with the result possibly driven by lower exports which are also seen to worsen. Results on the interaction with the share of commodity exports are similarly weak, with only interactions on banking sector reform and network reforms significant (Table 4). Here, the higher the share of commodity exports is associated with larger deteriorations in the current account from reforms.⁸

Table 3.

Effect of Reform on the Current Account, Accounting for Economic Cycle

Dependent variable: Deviation in current account relative to pre-reform year

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

Table 3.

Effect of Reform on the Current Account, Accounting for Economic Cycle

Dependent variable: Deviation in current account relative to pre-reform year

	Year 1	Year 2	Year 3	Year 4	Year 5
Banking, no slack	−3.657	−14.653*	−18.358**	−14.698**	−16.362***
	(0.568)	(1.856)	(2.453)	(2.556)	(3.278)
Banking sector, slack	1.188	10.731	19.505**	13.906*	18.921***
	(0.151)	(1.148)	(2.173)	(1.722)	(2.815)
Observations	364	364	364	364	364
Countries	65	65	65	65	65
R2	0.169	0.208	0.318	0.311	0.274
Business entry, slack	2.197	6.353**	3.509**	−4.847**	6.446***
	(0.954)	(2.106)	(2.185)	(2.468)	(3.043)
Observations	98	98	98	98	98
Countries	48	48	48	48	48
R2	0.917	0.944	0.967	0.918	0.918
Telecom regulation, no slack	14.988	39.619***	43.202***	33.971***	37.254**
	(1.259)	(3.154)	(4.381)	(4.472)	(2.659)
Telecom regulation, slack	−13.422	−37.317***	−37.319***	−25.238***	−31.887**
	(1.128)	(2.976)	(4.043)	(3.416)	(2.564)
Observations	100	100	100	100	100
Countries	56	56	56	56	56
R2	0.888	0.929	0.957	0.967	0.918

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

View Table

Table 3.

Effect of Reform on the Current Account, Accounting for Economic Cycle

Dependent variable: Deviation in current account relative to pre-reform year

	Year 1	Year 2	Year 3	Year 4	Year 5
Banking, no slack	−3.657	−14.653*	−18.358**	−14.698**	−16.362***
	(0.568)	(1.856)	(2.453)	(2.556)	(3.278)
Banking sector, slack	1.188	10.731	19.505**	13.906*	18.921***
	(0.151)	(1.148)	(2.173)	(1.722)	(2.815)
Observations	364	364	364	364	364
Countries	65	65	65	65	65
R2	0.169	0.208	0.318	0.311	0.274
Business entry, slack	2.197	6.353**	3.509**	−4.847**	6.446***
	(0.954)	(2.106)	(2.185)	(2.468)	(3.043)
Observations	98	98	98	98	98
Countries	48	48	48	48	48
R2	0.917	0.944	0.967	0.918	0.918
Telecom regulation, no slack	14.988	39.619***	43.202***	33.971***	37.254**
	(1.259)	(3.154)	(4.381)	(4.472)	(2.659)
Telecom regulation, slack	−13.422	−37.317***	−37.319***	−25.238***	−31.887**
	(1.128)	(2.976)	(4.043)	(3.416)	(2.564)
Observations	100	100	100	100	100
Countries	56	56	56	56	56
R2	0.888	0.929	0.957	0.967	0.918

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

Table 4.

Effect of Reform on the Current Account, Accounting for High Commodity Shares in Exports

Dependent variable: Deviation in current account relative to pre-reform year

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

Table 4.

Effect of Reform on the Current Account, Accounting for High Commodity Shares in Exports

Dependent variable: Deviation in current account relative to pre-reform year

	Year 1	Year 2	Year 3	Year 4	Year 5
Banking	2.277	5.847	4.202	−8.749*	1.882
	(0.667)	(1.288)	(0.868)	(1.672)	(0.450)
Banking (high commodity share)	−18.512	−46.907***	−42.494**	2.474	−28.845***
	(1.442)	(2.814)	(2.457)	(0.168)	(2.828)
Observations	426	426	426	426	426
Countries	81	81	81	81	81
R2	0.219	0.287	0.358	0.330	0.340
Legal system and property rights	−3.809*	−4.801*	−4.596*	−4.255	−4.896*
	(1.684)	(1.837)	(1.732)	(1.584)	(1.665)
Legal system and property rights (high commodity share)	11.880	12.655	10.366	9.181	12.790
	(1.316)	(1.231)	(0.985)	(0.895)	(1.202)
Observations	1,295	1,295	1,295	1,294	1,293
Countries	106	106	106	106	106
R2	0.563	0.550	0.517	0.489	0.506
Business regulation	−1.084	−1.952*	−1.780	−2.431*	−2.876**
	(1.303)	(1.680)	(1.378)	(1.803)	(2.057)
Business regulation (high commodity share)	3.643	6.022	5.466	8.362	11.686
	(0.683)	(0.825)	(0.609)	(0.953)	(1.251)
Observations	468	468	467	466	466
Countries	92	92	92	91	91
R2	0.187	0.288	0.247	0.215	0.225

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

View Table

Table 4.

Effect of Reform on the Current Account, Accounting for High Commodity Shares in Exports

Dependent variable: Deviation in current account relative to pre-reform year

	Year 1	Year 2	Year 3	Year 4	Year 5
Banking	2.277	5.847	4.202	−8.749*	1.882
	(0.667)	(1.288)	(0.868)	(1.672)	(0.450)
Banking (high commodity share)	−18.512	−46.907***	−42.494**	2.474	−28.845***
	(1.442)	(2.814)	(2.457)	(0.168)	(2.828)
Observations	426	426	426	426	426
Countries	81	81	81	81	81
R2	0.219	0.287	0.358	0.330	0.340
Legal system and property rights	−3.809*	−4.801*	−4.596*	−4.255	−4.896*
	(1.684)	(1.837)	(1.732)	(1.584)	(1.665)
Legal system and property rights (high commodity share)	11.880	12.655	10.366	9.181	12.790
	(1.316)	(1.231)	(0.985)	(0.895)	(1.202)
Observations	1,295	1,295	1,295	1,294	1,293
Countries	106	106	106	106	106
R2	0.563	0.550	0.517	0.489	0.506
Business regulation	−1.084	−1.952*	−1.780	−2.431*	−2.876**
	(1.303)	(1.680)	(1.378)	(1.803)	(2.057)
Business regulation (high commodity share)	3.643	6.022	5.466	8.362	11.686
	(0.683)	(0.825)	(0.609)	(0.953)	(1.251)
Observations	468	468	467	466	466
Countries	92	92	92	91	91
R2	0.187	0.288	0.247	0.215	0.225

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

A. Motivation

Expenditure-switching policies – achieved through a correction in the real exchange rate – are a key ingredient of macroeconomic adjustment programs. A real devaluation, whether achieved through a nominal depreciation or internal deflation, raises the relative price of tradables vis-à-vis non-tradables, thus improving external balance by reducing consumption of tradables and simultaneously shifting resources to the tradable sector. It is well documented that most of the adjustment comes from consumption and imports compression.⁹ However, the promise of a stronger tradable sector down the road is often seen as the silver lining of such adjustment programs. The same logic applies when the devaluation is triggered by an exogenous event, such as a term-of-trade shock in the case of a commodity exporter or a capital outflow caused by contagion in the case of an emerging market with an open capital account. A quick response of the tradable sector to a change in the real exchange rate can mitigate the shock, thus contributing to economic stability.

Countries vary in their success to boost exports on the back of a real devaluation. The varied experience of Euro Area deficit countries during and following the global financial crisis is a notable recent example. The left chart in Figure 1 shows that Greece saw a much smaller export dividend to its devaluation – as measured by unit labor cost (ULC) – than other countries in this group. Some explanations put forward for this disconnect include differences in trading partners (e.g., Ireland’s sronger ties with the healthier, at the time, US and UK economies) and differences in the export basket (e.g., Greece’s services are dominated by shipping, which collapsed during the GFC). However, the right chart in the figure supports an alternative explanation: cheap labor did not result in improved competitiveness.¹⁰ Using monthly data, we see that a given reduction in unit labor translated into a smaller CPI REER reduction than expected for an advanced economy. In Greece, a 1 percent ULC real depreciation is associated with a 0.35 depreciation in the CPI REER, compared to 0.85 for the aveage Advanced Economy.¹¹ IMF (2015a) concludes that “the benefits of lower labor costs for competitiveness [were] blunted by limited adjustment in producer prices and supply response because of barriers to new entry”.

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Export and REER Developments in Euro Area Periphery Countries

Citation: IMF Working Papers 2017, 182; 10.5089/9781484313732.001.A001

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The example above highlights the importance of product markets – ease of entry in particular – for a real devaluation to successfully spur exports. However, the argument can be broadened: facing a change in the real effective exchange rate, an economy with fewer frictions should be able to reallocate resources faster towards tradable activities and – within tradable activities – towards higher-productivity industries and firms. Labor and product market reforms remove impediments to the allocation of resources to the new equilibrium. A sound financial sector facilitates this reallocation by channeling the resources needed to finance the adjustment. Strong legal systems can mitigate risks associated with the transition, which would otherwise lead to underinvestment. Trade openness ensures inputs needed by new industries are readily available, and human capital and infrastructure endowments can reduce bottlenecks. Overall, a country with better structural indicators should exhibit a higher export response to movements in the exchange rate. The question is which reforms are more likely to affect the export response to exchange rate movements and how the response differs by country groups.

In what follows, we analyze the impact of structural indicators on the exports’ response to REER movements using cross-country panel regressions. This allows for a robust estimation of the slope of the regression line in the left chart of Figure 1, which represents the percentage change in exports in response to a one percent change in the REER (e.g., around 0.5 in the figure). This slope – the elasticity of exports with respect to the REER – denotes the sensitivity of exports to movements in the exchange rate. Multivariate regression analysis allows controlling for some other factors that can influence this relationship, such as the pattern of trading partners. The hypothesis is that the slope (elasticity) should increase as structural indicators improve, i.e., countries with better structural indicators should exhibit a higher export response to a given REER depreciation. Since different structural indicators are likely to be binding across economic structures, we also expect different types of indicators to matter across income groups.

B. Econometric Specification

We follow the general setup used by Eichengreen and Gupta (2013) for estimating the elasticity of exports with respect to the real effective exchange rate:

where X is real exports, Z_it is a 1×k vector of other factors affecting exports (Eichengreen and Gupta limit their controls to lagged GDP per capita), ω is a country dummy and η is a time dummy. β₁ represents the estimated export elasticity with respect to the REER. For presentation purposes, we flip the sign of the first difference of the REER. Thus, a depreciation enters the regression with a positive sign, and the expected sign on β₁ is thus positive. We also introduce several changes to obtain the following specification:

First and foremost, we introduce the lagged level of a structural indicator (S_it−1) and its interaction with REER depreciation in order to estimate the differentiated elasticity at different values of the structural indicator, with the overall elasticity computed as β₁ + β₂S_it−1.

Second, we use fairly narrow measures of exports as dependent variable. We focus on manufactured goods and services exports. The use of manufactured exports, as opposed to all goods exports, is determined by the desire to abstract from commodities, for which domestic price competitiveness play a relatively minor role (as the share of domestically-priced inputs is relatively low). As for services exports, Eichengreen and Gupta have already shown that they are more susceptible to exchange rate movements, and therefore more likely to exhibit measurably different responses as a function of structural indicators. The use of narrow measures of exports diminish concerns about reverse causality as manufacturing exports and services exports separately represent small shares of total current account flows (around 18 and 12 percent respectively for non-LICs). Therefore, their potential to have a significant impact on the REER is correspondingly smaller. This is an important factor behind using the REER on the right-hand side in this section, unlike in the previous one (which operated with much broader current account components).

Third, we lag the REER variable by one period, to further minimize reverse causality concerns. In any event, the reverse causality link would only tend to attenuate the results. An exogenous positive shock to exports should appreciate the exchange rate, thus dampening the measured correlation between depreciation and export growth.

Fourth, we use nominal measures of exports, as opposed to deflating all export prices by the US CPI. Although standard practice in the trade literature, this step is superfluous in a setup with time effects, as dollar inflation is captured by period dummies.

Fifth, we expand the set of controls. We introduce a country-specific deflator of goods export prices (when dependent variable is manufactured services) to proxy for changing price conditions on world markets, and we use the export-weighted real GDP growth in trading partners to control for external demand effects.

Finally, the use of lagged REER motivates the use of averaged data over shorter periods – three years instead of five used by Eichengreen and Gupta and other studies.

C. Results

Benchmark results

Our benchmark regressions estimate export elasticities with respect to the REER without accounting for structural indicators. For brevity, Table 5 presents only regressions with manufacturing exports as the dependent variable.

Table 5.

Benchmark Regressions for Manufacturing Exports

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

Table 5.

Benchmark Regressions for Manufacturing Exports

	All countries			Non-LICs			Advanced Economies			Emerging Markets			LICs
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
Lagged log change REER	0.141**	0.153**	0.135	0.11	0.193**	0.214***	0.359***	0.343***	0.348***	0.0697	0.167*	0.180*	0.160*	0.187**	0.156
	(0.0593)	(0.0683)	(0.0818)	(0.0881)	(0.0734)	(0.0752)	(0.0645)	(0.0714)	(0.0735)	(0.0999)	(0.0937)	(0.0984)	(0.0776)	(0.0823)	(0.0952)
Log change partner GDP		15.21**	14.39**		9.620**	9.257**		4.024	3.983		11.90**	11.54**		10.33	10.08
		(6.841)	(7.028)		(3.873)	(3.899)		(4.168)	(4.135)		(5.553)	(5.483)		(21.25)	(21.6)
Log change export deflator		−0.173	−0.187		0.0198	0.0242		0.179**	0.175**		−0.0125	0.00246		−0.679	−0.68
		(0.147)	(0.153)		(0.0903)	(0.0906)		(0.0652)	(0.0662)		(0.115)	(0.118)		(0.448)	(0.455)
Lagged GDP/capita, PPP			−0.0034			0.00079			−0.005			0.00058			−0.0208
			(0.00371)			(0.002)			(0.0137)			(0.00213)			(0.0132)
Observations	665	628	619	543	512	506	221	210	210	322	302	296	122	116	113
Countries	81	79	79	62	61	61	23	23	23	39	38	38	19	18	18
R2	0.142	0.164	0.160	0.219	0.247	0.254	0.671	0.690	0.690	0.163	0.199	0.206	0.227	0.233	0.227

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

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Table 5.

Benchmark Regressions for Manufacturing Exports

	All countries			Non-LICs			Advanced Economies			Emerging Markets			LICs
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
Lagged log change REER	0.141**	0.153**	0.135	0.11	0.193**	0.214***	0.359***	0.343***	0.348***	0.0697	0.167*	0.180*	0.160*	0.187**	0.156
	(0.0593)	(0.0683)	(0.0818)	(0.0881)	(0.0734)	(0.0752)	(0.0645)	(0.0714)	(0.0735)	(0.0999)	(0.0937)	(0.0984)	(0.0776)	(0.0823)	(0.0952)
Log change partner GDP		15.21**	14.39**		9.620**	9.257**		4.024	3.983		11.90**	11.54**		10.33	10.08
		(6.841)	(7.028)		(3.873)	(3.899)		(4.168)	(4.135)		(5.553)	(5.483)		(21.25)	(21.6)
Log change export deflator		−0.173	−0.187		0.0198	0.0242		0.179**	0.175**		−0.0125	0.00246		−0.679	−0.68
		(0.147)	(0.153)		(0.0903)	(0.0906)		(0.0652)	(0.0662)		(0.115)	(0.118)		(0.448)	(0.455)
Lagged GDP/capita, PPP			−0.0034			0.00079			−0.005			0.00058			−0.0208
			(0.00371)			(0.002)			(0.0137)			(0.00213)			(0.0132)
Observations	665	628	619	543	512	506	221	210	210	322	302	296	122	116	113
Countries	81	79	79	62	61	61	23	23	23	39	38	38	19	18	18
R2	0.142	0.164	0.160	0.219	0.247	0.254	0.671	0.690	0.690	0.163	0.199	0.206	0.227	0.233	0.227

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

These results corroborate a number of empirical findings in the recent literature. Some of these are attributable to all countries: the estimated elasticity of exports with respect to the real exchange rate is broadly in line with estimates in IMF (2015c), and exports are highly dependent on partner growth.

Heterogeneity across country groups is also in line with the literature. First, export elasticities exhibited by emerging markets (EMs) are lower than those for advanced economies (AEs), and the exchange rate has a lower predictive power (comparing the R² in equations 7 and 10). This is compatible with the theoretical argument and empirical findings that participation in global value chains – relatively more important in EM export industries – tends to attenuate the price effect on exports.¹² Emerging markets also exhibit a higher elasticity with respect to export partner growth, which is in line with the Cubeddu et al. (2014) finding that GDP growth in EMs is more dependent on partner growth than AEs. We find that, unlike AEs, EMs exports exhibit no response to changes in the export deflator—another difference that can be traced to the prevalence of global value chains in their export industries.¹³

Results for Low Income Countries (LICs) should be interpreted with greater care. LICs exhibit the expected sign for the exchange rate. However, LICs suffer from data limitations, constraining the set of usable countries to less than twenty, and the number of observations to barely above one hundred. Second, manufactured exports generally constitute a small subset of LICs’ export baskets. This means, among other things, that the WEO-derived export deflator is a highly imperfect proxy for the price of manufactured exports.

Introducing Structural Reforms

We now augment the benchmark regressions above with two more terms: the lagged level of structural reform and its interaction with the REER. We focus on the same set of structural indicators as IMF (2015b), and test them one by one.

Table 6 presents a subset of results for advanced economies. The dependent variable is the same across all regressions – manufacturing exports. What changes is the structural indicator used in the second and third rows (banking in equation 1, security markets in equation 2, etc.). The table shows that several types of structural reforms are associated with higher export elasticities at standard levels of significance: security markets, legal systems and property rights, business regulation and labor market regulation.

Table 6.

Manufacturing Exports Fixed Effects Regressions for Advanced Economies

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

Table 6.

Manufacturing Exports Fixed Effects Regressions for Advanced Economies

	(1) Security Markets	(2) Average tariff rate	(3) Regulatory trade barriers	(4) Legal System & Property Rights	(5) Business regulations	(6) Hiring and firing regulations	(7) Percentage tertiary edu
Lagged log change REER	−0.635*	−1.322	−0.268	−1.150*	−0.0846	−0.261	0.0896
	(0.355)	(2.408)	(0.490)	(0.659)	(0.168)	(0.276)	(0.185)
Lagged structural indicator	0.0119	0.0300	0.0125	0.00439	0.00812	−0.00230	−0.00270**
	(0.0204)	(0.0577)	(0.00834)	(0.00569)	(0.00510)	(0.00648)	(0.00113)
Lagged log change REER * Lagged structural indicator	1.008**	1.838	0.0897	0.187**	0.0775**	0.146**	0.0111*
	(0.368)	(2.667)	(0.0650)	(0.0805)	(0.0330)	(0.0606)	(0.00548)
Log change partner GDP	8.536**	6.889	1.096	3.954	1.715	3.396	3.489
	(3.574)	(4.028)	(2.953)	(4.084)	(2.556)	(3.429)	(3.546)
Log change export deflator	0.177**	0.183**	0.318*	0.187***	0.237*	0.207**	0.188**
	(0.0740)	(0.0681)	(0.161)	(0.0641)	(0.138)	(0.0826)	(0.0708)
Observations	184	187	108	210	129	149	210
Countries	21	22	23	23	23	23	23
R2	0.723	0.704	0.732	0.697	0.698	0.636	0.706

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

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Table 6.

Manufacturing Exports Fixed Effects Regressions for Advanced Economies

	(1) Security Markets	(2) Average tariff rate	(3) Regulatory trade barriers	(4) Legal System & Property Rights	(5) Business regulations	(6) Hiring and firing regulations	(7) Percentage tertiary edu
Lagged log change REER	−0.635*	−1.322	−0.268	−1.150*	−0.0846	−0.261	0.0896
	(0.355)	(2.408)	(0.490)	(0.659)	(0.168)	(0.276)	(0.185)
Lagged structural indicator	0.0119	0.0300	0.0125	0.00439	0.00812	−0.00230	−0.00270**
	(0.0204)	(0.0577)	(0.00834)	(0.00569)	(0.00510)	(0.00648)	(0.00113)
Lagged log change REER * Lagged structural indicator	1.008**	1.838	0.0897	0.187**	0.0775**	0.146**	0.0111*
	(0.368)	(2.667)	(0.0650)	(0.0805)	(0.0330)	(0.0606)	(0.00548)
Log change partner GDP	8.536**	6.889	1.096	3.954	1.715	3.396	3.489
	(3.574)	(4.028)	(2.953)	(4.084)	(2.556)	(3.429)	(3.546)
Log change export deflator	0.177**	0.183**	0.318*	0.187***	0.237*	0.207**	0.188**
	(0.0740)	(0.0681)	(0.161)	(0.0641)	(0.138)	(0.0826)	(0.0708)
Observations	184	187	108	210	129	149	210
Countries	21	22	23	23	23	23	23
R2	0.723	0.704	0.732	0.697	0.698	0.636	0.706

Note: Standard errors in parentheses are robust and clustered by country. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

The above tabular presentation is not conducive to gauging the impact on the elasticity associated with a specific improvement in a given structural indicator. We propose a novel (to the best of our knowledge) and parsimonious presentation of marginal effects.

In chart A of Figure 2, we start with a standard graphic interpretation, where the intercept is given by the coefficient on the “lagged log change in REER” and the slope – by its interaction with the structural indicator; the hiring and firing regulations from the Frasier Institute in this example. This chart shows that moving from 1 to 8 on the scale is associated with the increase in elasticity from nil to 1. However, this information is of limited use without knowing the current distribution of countries across the scale – we add the relevant histogram in chart B. Finally, in chart C, we combine the two series by replacing the linear horizontal axis with common percentile benchmarks (5, 10, 25, 50, 75, 90 and 95), which – given the usual bell-shaped distribution of countries across the index scale – transforms the straight line into an S-shaped curve. From chart C, it is easy to read that, for example, moving from the 25^th to 75^th percentile on the hiring and firing regulations index is associated with an increase of the export elasticity from 0.2 to nearly 0.5.

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Linear vs. Percentile Presentation of Marginal Effects

Citation: IMF Working Papers 2017, 182; 10.5089/9781484313732.001.A001

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This presentation has multiple advantages. First, it helps compare impact of structural indicators measured on different scales.¹⁴ Second, the approach allows for comparing countries against the relevant benchmark group – Figure 2 only compares AEs. Third, for policy purposes, the absolute value of the indicator is generally less relevant than benchmarking, which is why country rankings is often the preferred method for presenting such datasets (e.g., World Bank’s Doing Business Indicators). Finally, the approach helps address at least one aspect of the perennial issue of economic vs. statistical significance – a variable may be statistically significant, but it may hold little policy value. Note that the percentiles are calculated based on the most recent distribution (the last five-year period for which data is available). It helps, in particular, to show that although the export REER elasticity exhibits a statistically significant relationship with some structural indicators, there is little (if any) room for further improvement on account of these indicators. For example, equation 2 in Table 6 shows that security markets are associated with higher export elasticities. However, this result is based on a regression that incorporates historical data going back to 1973, when advanced economies differed significantly in the level of development (most scoring lower than currently). However, all AEs converged to 1 (the indicator’s maximum value) by 1992, reflected in the horizontal marginal effect line.

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Marginal Effects for Securities Markets

Citation: IMF Working Papers 2017, 182; 10.5089/9781484313732.001.A001

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Main Results

The specific reforms that matter differ across income groups. Figure 4 offers selected highlights, and Table 7 summarizes the sign and statistical significance of various structural indicators. Full regression results are presented in Table A4–Table A8, and the full set of marginal effects in Figure A1–Figure A5.

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Highlights of Main Results

Citation: IMF Working Papers 2017, 182; 10.5089/9781484313732.001.A001

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In advanced economies, labor and business regulations are the primary facilitators of the manufactured exports’ response to REER movements. As discussed above, historically, securities markets were also an important facilitator of reallocation of resources to the tradable sector following depreciation episodes. However, as all AEs have developed sufficiently strong securities markets, their further development is unlikely to improve the resilience of the economy any further. We also find a positive and significant result on the share of population with tertiary education, which is an important factor of production in the export basket of a typical AE.

Institutional indicators are important in emerging markets, which is consistent with the prominent role played by cross-border supply chains, which rely heavily on a strong contracting environment. There is also a strong correlation with the share of population with secondary education, a factor of production more dominant in the export basket of a typical EM.

In low income countries, labor and banking sector regulations have a strong effect on export elasticities. However, as discussed above, the sample of LICs is limited, and manufacturing currently exports play a relatively smaller role in their export baskets, so these results should be treated with more caution.

Table 7.

Indirect Impact: Summary of Main Results

Note: Table shows the sign on the interaction term between the lagged log change in REER and the lagged structural indicator. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

Table 7.

Indirect Impact: Summary of Main Results

	Manufacturing					Services
	All	Non-LIC	AE	EM	LIC	Non-LIC
Financial sector
Banking	+	−	+	−	+*	+**
Security Markets	+	−	+**	−	+	+***
Openness
Tariff Rates (average)	−	−	+	−	+	+**
Regulatory trade barriers	−*	+	+	−	−	+***
Institutions
Protection of property rights	−	+***	+	+**	−	+**
Legal enforcement of contracts	+	+	−	+	+	+**
Product market regulation
Business regulations	−	+	+**	+	−	+**
Product: telecom and electricity	−	−*	+	−	+	+
Labor
Hiring and firing regulations	+***	+	+**	+	+***	−
Centralized collective bargaining	+*	+	+	+	+	−
Human capital and R&D
Percentage of secondary	+*	+*	−	+**	+	−
Percentage of tertiary	−	+	+ *	−	−*	−
Basic R&D spending	+	+	+	−		−

Note: Table shows the sign on the interaction term between the lagged log change in REER and the lagged structural indicator. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

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Table 7.

Indirect Impact: Summary of Main Results

	Manufacturing					Services
	All	Non-LIC	AE	EM	LIC	Non-LIC
Financial sector
Banking	+	−	+	−	+*	+**
Security Markets	+	−	+**	−	+	+***
Openness
Tariff Rates (average)	−	−	+	−	+	+**
Regulatory trade barriers	−*	+	+	−	−	+***
Institutions
Protection of property rights	−	+***	+	+**	−	+**
Legal enforcement of contracts	+	+	−	+	+	+**
Product market regulation
Business regulations	−	+	+**	+	−	+**
Product: telecom and electricity	−	−*	+	−	+	+
Labor
Hiring and firing regulations	+***	+	+**	+	+***	−
Centralized collective bargaining	+*	+	+	+	+	−
Human capital and R&D
Percentage of secondary	+*	+*	−	+**	+	−
Percentage of tertiary	−	+	+ *	−	−*	−
Basic R&D spending	+	+	+	−		−

Note: Table shows the sign on the interaction term between the lagged log change in REER and the lagged structural indicator. *, **, *** indicate that coefficients are significant at 10, 5 and 1 percent levels respectively. All regressions inlcude country and time fixed effects.

The table also presents results for services exports using Balance of Payments data for AMs and EMs. We find that services exhibit strong responses to REER movements in the presence of flexible financial sectors, trade openness, strong legal systems, and property rights.

Table A9 presents extended results for a larger set of 24 variables, the most notable additions being indicators of infrastructure endowment.¹⁵ Here, we find that the positive link between structural reforms and REER export elasticities is most consistently established in the case of advanced economies – of the 24 structural variables tested in the wider set, 22 have the expected sign and seven are significant at least at the 10 percent level. For comparison, only seven structural indicators produce the expected positive sign in the case of emerging markets, although only one of the negative signs (restrictions on current account transactions) shows significance at the 10 percent level. LICs results are more in line with expectations – most indicators exhibit the expected sign. However, they show a statistically significant negative correlation between infrastructure (electricity generation capacity and phone lines) and manufacturing export elasticity.¹⁶

D. Extensions and Robustness

Robustness checks include alternative measures of real exchange rates, annual data, and sensitivity to additional controls.