rafmacalaba/datause-encoder-data huggingface dataset:
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12 **-a** FIGURE 2

# COTE d'IVOIRE. SENEGAL & BENIN

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statistically significant, while colonial links and a common official language are both positive and statistically significant.

Interestingly, we find that non-harmonized European standards exert a negative and statistically significant influence on export propensity. However, standards that are aligned with ISO standards carry a statistically insignificant and (slightly) positive coefficient. These standards impact export propensity only very weakly, but to the extent that we can measure such an impact, it would appear that these types of EU standards actually increase export propensity (cf. Moenius, 2004).

The combined results from the selection and outcome equations therefore suggest that EU standards not harmonized to international norms tend to impose significant added costs on exporters, both fixed and variable. In both the selection and outcome equations, the difference in impact between ISO and non-ISO standards is statistically significant at the 1% level.

# **_4.3 Robustness Checks_**

The above discussion is subject to a well-known caveat: the estimated coefficients from a justidentified Heckman model like the one presented in Columns 2-3 of Table 13 tend to exhibit considerable instability (Davidson and MacKinnon, 2004). It is preferable to specify an overidentified form of the same model, which can be achieved by including at least one variable in the selection equation that does not appear in the outcome equation. However, the existing literature has highlighted the difficulty of finding such a variable in practice. On empirical grounds, Baller (2007) uses WTO membership. Helpman et al. (2007) use (alternately) common religion, and a measure of the cost of starting a business taken from the World Bank’s _Doing Business Report_ . The disadvantage of using data from _Doing Business_ is that they are only

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|Petroleum Exploration and Essouira Appraisal Project|
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|Petroleum Exploration and Essouira Appraisal Project|
|Petroleum Exploration and Essouira Appraisal Project|
|HighwayProject(04)|
|HighwayProject(04)|
|HighwayProject(04)|
|HighwayProject(04)|
|HighwayProject(04)|
|HighwayProject(04)|
|HighwayProject(04)|
|HousingDevelopment Project|
|HousingDevelopment Project|
|HousingDevelopment Project|
|HousingDevelopment Project|
|HousingDevelopment Project|
|HousingDevelopment Project|
|Agriculture Credit Project(05)|
|Agriculture Credit Project(05)|
|Agriculture Credit Project(05)|
|Agriculture Credit Project(05)|
|Agricultural Sector Adjustment Loan Project|
|Agricultural Sector Adjustment Loan Project|
|Agricultural Sector Adjustment Loan Project|
|Electrical and Mechanical Industries Project|
|Electrical and Mechanical Industries Project|
|Electrical and Mechanical Industries Project|
|Electrical and Mechanical Industries Project|
|Electrical and Mechanical Industries Project|
|Electrical and Mechanical Industries Project|
|Electrical and Mechanical Industries Project|
|Industrial and Trade PolicyAdjustment Loan Project(02)|
|Industrial and Trade PolicyAdjustment Loan Project(02)|
|Vocational TrainingProject(01)|
|Vocational TrainingProject(01)|
|Vocational TrainingProject(01)|
|Vocational TrainingProject(01)|
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|Vocational TrainingProject(01)|
|Jerada Coal MiningModernization and Expansion Project|
|Jerada Coal MiningModernization and Expansion Project|
|Jerada Coal MiningModernization and Expansion Project|
|Jerada Coal MiningModernization and Expansion Project|
|Jerada Coal MiningModernization and Expansion Project|
|Jerada Coal MiningModernization and Expansion Project|
|Casablanca Mohammedia Port Project|
firms, and in states and industries that were relatively less exposed to external competition (through proximity to ports and trade exposure) prior to the reforms.

Finally, we use our panel dataset to isolate the impacts of the policy changes on within-firm changes in productivity. Once we control for time-invariant, firm-specific characteristics, the magnitude of the policy impacts is somewhat attenuated, suggesting that the changing composition of firms, and unobservable, firm-level characteristics, may play important roles in determining productivity growth. Nonetheless, our findings confirm that the trade and FDI liberalizations are associated with increased within-firm productivity.

Our study was motivated by the emphasis that the new trade theory places on the importance of market-share reallocations in increasing aggregate productivity. Although a number of papers have tested various implications of this literature (see, for example, Arkolakis (2010), Bernard et al. (2003), Bernard, Jensen and Schott (2006), Berthou and Fontagne (2010), Eaton, Kortum and Kramarz (2008), Helpman, Melitz and Yeaple (2004), Manova and Zhang (2010)), few are able to directly test the effect of a trade liberalization episode on market-share reallocations, and existing evidence on the role of reallocation is mixed. For example, Tybout and Westbrook (1995) find that the reallocation of market share to relatively low-cost firms explained little of the overall change in productivity following Mexico’s trade liberalization; however, Pavcnik (2002) and Menezes-Filho and Muendler (2007) find that market-share reallocation was an important driver of productivity growth following trade reforms in Chile and Brazil, respectively. Trefler (2004) documents that a fall in Canadian tariffs increased industry-level labor productivity, but not within-plant labor productivity, which he interprets as evidence that reallocation was more important than withinplant improvements. In Colombia, Fernandes (2007) finds that average productivity gains were more important than reallocation, but that reallocation became important in many industries during periods of tariff liberalization.

Our study also contributes to the substantial body of work examining India’s 1991 reforms. Topalova and Khandelwal (forthcoming) establish that the reductions in final goods and input tariffs increased productivity among approximately 4,000 large, publicly listed manufacturing firms.

4
costs of avoiding the less burdensome formal requirements. Simplifying the rule could even be associated with longer times reported by firms if compliance actually goes up in the wake of reforms.<sup>4</sup> In looking for impact, the role of institutional quality appears to help predict the range of reported firm outcomes. Where institutional quality is higher, rules are more likely to be implemented, and so changes in rules will more likely be reflected in the conditions firms face. This insight helps reconcile the “institutions rule” evidence on aggregate performance with the mixed success of “policy reform.” The paper is organized as follows. The second section elaborates on the distinctions between policy, policy implementation and policy actions. The third section then develops in greater detail the framework for thinking about policy uncertainty, focusing on the gap between de jure rules and actual implementation, and the behaviors firms engage in to shape the actions taken by agents of the state.

The third and fourth sections of the paper look at the evidence provided by firm surveys of 11,150 firms in 34 countries in Africa. It looks at the variation in policy outcomes as reported by firms, and relates that variation to deal making activities and to firm performance. Section four focuses on three specific regulatory areas: getting a construction permit, getting an operating license and clearing customs. It shows the lack of relationship between the _de jure_ and _de facto_ policies indicating the prevalence of deals, and that this gap grows with the _de jure_ delays and burdens of the regulations. It also relates the findings to broader debates about institutional capacity and discusses possible avenues for reducing implementation uncertainty.

> 4 In additional work we are testing the impact using the time series data; here the results are based on crosssectional variation. (Hallward-Driemeier and Pritchett 2010).

6
risk by TTLs (World Bank, 2012). Next, as explicit knowledge and evidence are lacking, the Bank and other donors should invest in learning about the dynamics of public sector reform and the impact of PSG interventions.<sup>19</sup> To achieve this, more and better data on the functioning of public sector institutions are required.

Finally, the Bank’s new diagnostic, “best fit” approach envisions a more continuous dialogue with recipient country counterparts in government, and greater participation of other stakeholders including the public. A more continuous and broadened engagement of this sort by Bank task teams can generate mutual trust and help build agreement on a convincing diagnostic story and possible solutions that take into account incentives of key actors including politicians (World Bank, 2012).

# **Acknowledgement**

We would like to thank Vincenzo Verardi, Adam Wagstaff, Peter Moll and Deon Filmer for useful comments and suggestions. Lodewijk is also indebted to the Institute of Development Policy and Management (University of Antwerp) and the Research Foundation Flanders (FWO) for financial support.

# **References**

- Alesina, A., Weder, B., 2002. Do corrupt governments receive less foreign aid? The American Economic Review 92 (4), pp. 1126–1137.

- Andrews, M., 2009. Isomorphism and the limits to african public financial management reform. Harvard Kennedy School of Government Working Paper Series RWP09-012.

- Andrews, M., 2013. Explaining positive deviance in public sector reforms in development. Harvard Kennedy School of Government Working Paper Series RWP13-040.

> 19Already several initiatives are set up to improve the effectiveness of PSG lending. For instance, in 2005 the development impact evaluation initiative (DIME) was created to generate knowledge on policy effectiveness. Currently, new impact evaluations are being developed to address specific PSG bottlenecks in civil service and PFM reform.

25
|**Table V. Regression A**|**nalysis: Actua**<br>**Pan**<br>**Basic Specifi**|**l Bilateral C**<br>**el B. Japane**<br>**cation (BS)**|**urrent Accou**<br>**se Investors**<br>**BS + Tim**|**nt vs. Optim**<br>**e Trend**|**al Portfolio**<br>**BS + Lag**|**Shares**<br>**ged BCA**|
|---|---|---|---|---|---|---|
||**Alpha**|**L.Alpha**|**Alpha**|**L.Alpha**|**Alpha**|**L.Alpha**|
|**Relative Risk Aversion: 5**|||||||
|Basic VAR|-0.002 ***|0.000|-0.001 **|0.001 *|-0.000|0.000|
|+ RER in Levels|-0.002 ***|0.000|-0.001 **|0.001 *|-0.000|0.000|
|+ RER in Differences|-0.002 ***|0.000|-0.001 **|0.001|-0.000|0.000|
|+ Infl., NER in Levels|-0.002 ***|0.000|-0.001 **|0.001 *|-0.000 **|0.000 **|
|+ Infl., NER in Differences|-0.002 ***|0.000|-0.001 **|0.001|-0.000 **|0.000 **|
|**Relative Risk Aversion: 10**|||||||
|Basic VAR|-0.004 ***|0.000|-0.002 **|0.002 *|-0.001|0.001|
|+ RER in Levels|-0.004 ***|0.000|-0.002 **|0.002 *|-0.001|0.000|
|+ RER in Differences|-0.004 ***|0.000|-0.002 **|0.002|-0.001|0.001|
|+ Infl., NER in Levels|-0.003 ***|0.000|-0.002 **|0.001 *|-0.001 **|0.001 **|
|+ Infl., NER in Differences|-0.004 ***|0.000|-0.002 **|0.001|-0.001 **|0.001 **|
|**Relative Risk Aversion: 30**|||||||
|Basic VAR|-0.012 ***|0.000|-0.007 **|0.005|-0.002|0.002|
|+ RER in Levels|-0.010 ***|0.002|-0.007 **|0.005 *|-0.002|0.001|
|+ RER in Differences|-0.011 ***|0.000|-0.007 **|0.005|-0.002|0.002|
|+ Infl., NER in Levels|-0.009 ***|0.001|-0.006 ***|0.004 *|-0.002 **|0.002 **|
|+ Infl., NER in Differences|-0.011 ***|0.000|-0.006 **|0.004|-0.003 **|0.003 **|
|**Relative Risk Aversion: 100**|||||||
|Basic VAR|-0.040 ***|0.001|-0.023 **|0.017|-0.006|0.006|
|+ RER in Levels|-0.034 ***|0.006|-0.024 ***|0.016 *|-0.006|0.005|
|+ RER in Differences|-0.038 ***|0.001|-0.022 **|0.016|-0.006|0.006|
|+ Infl., NER in Levels|-0.030 ***|0.005|-0.021 ***|0.014 *|-0.008 **|0.007 **|
|+ Infl., NER in Differences<br>This table shows the regression <br>results for a Japanese investor. Th<br>account on contemporaneous and<br>time trend, and the basic specific<br>relative risk aversion parameter: 5<br>our five VAR specifications use<br>percentage of GNP. * means signi|-0.035 ***<br>results of the bila<br>ree different regre<br>lagged optimal po<br>ation including a <br>, 10, 30, and 100.<br>d to calculate ex<br>ficant at 10%, **|-0.001<br>teral current ac<br>ssions are repor<br>rtfolio shares (a<br>lagged depende<br>Different serie<br>pected asset re<br>at 5%, and ***|-0.021 **<br>count on the m<br>ted: our basic sp<br>lpha and L.alph<br>nt variable. The <br>s of model-predi<br>turns. The bilat<br>at 1% levels.|0.014<br>odel-predicted <br>ecification that<br>a, respectively),<br> results are sho<br>cted portfolio s<br>eral current ac|-0.009 **<br>portfolio shares<br>regresses the bi<br>the basic speci<br>wn for differen<br>hares are consid<br>count data is e|0.009 **<br>. It shows the<br>lateral current<br>fication with a<br>t values of the<br>ered based on<br>xpressed as a|

51
||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
||**Benchmark**<br>(levels)|**Count 1**<br>% change|**Count 2**<br>% change|
|Output||||
|_Yp_|6.035|1.21%|-0.97%|
|_Yg_|0.904|-8.55%|0.05%|
|GDP|6.850|1.07%|-0.85%|
|real GDP|6.850|0.05%|-0.85%|
|Capital||||
|_Kp_|23.395|1.11%|-1.07%|
|_Kg_|2.956|-1.82%|3.24%|
|_Kp_+_Kg_|26.351|0.78%|-0.59%|
|Productivity||||
|TFP_p_|0.258|0.09%|0.10%|
|TFP_g_|0.306|-6.85%|-3.09%|
|TFP|0.260|0.28%|-0.27%|
|real TFP|0.534|-3.49%|-1.55%|
|MRPK_p_|0.258|0.07%|0.05%|
|MRPK_g_|0.276|1.89%|-3.10%|
|TFP_p_ gain|0.057|-1.76%|-1.87%|
|TFP_g_ gain|0.066|9.14%|-12.09%|
|Prices/tax||||
|_Pg_/_Pp_|0.901|9.38%|-0.01%|
|_r_|0.050|0.0010|0.0001|
|_τ_|-0.079|0.103|-0.032|
|Size of procurement||||
|% frms|0.120|0.000|0.080|
|Share GDP|0.119|-0.001|0.001|
|_η_0|0.783|-0.212|0.563|
|_η_1|0.624|-0.128|0.000|
|Selection||||
|ratio mean _s_|1.289|-7.98%|-2.70%|
|ratio mean _a_|1.776|-14.78%|-8.52%|
|ratio mean _λ_|2.082|12.32%|-20.13%|

**Notes:** Column (1) shows the main results from the benchmark economy. Column (2) shows the results from running counterfactual 1, which consists in changing _η_ 0, _η_ 1, and _Yg_ so that the procurement size premium decreases from 72% to 50%, while keeping unchanged the % of procurement firms and the procurement expenditure _PgYg_ . Column (3) shows the results from running counterfactual 2, which consists in changing _η_ 0 and _Yg_ so that the model generates an increase of procurement firms from 12% to 20% while keeping unchanged the procurement expenditure _PgYg_ , which decreases the average size of contracts accordingly.

**Table 3.** Counterfactuals

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reasons make the Japanese market particularly suitable for this analysis.

First, Japan is the most important regional trading partner for Indonesia (excluding China) and is among Indonesia’s top three export destinations in our sample period. Exports to Japan represent about 10% of all export value for Indonesia.

More importantly, Indonesian firms face a high degree of competition from Chinese firms in Japan as China is Japan’s top trading partner in terms of imports, with over 20% of Japanese imports originating from China in 2018.<sup>11</sup> Indeed, Japan is also among the top 3 export destinations for China. A total of 8,353 Indonesian firms exported to Japan between 2014-18 and, among these firms, a total of 5,425 imported and exported.

A further reason to focus on Japan is the high quality of the data. We are able to obtain information on total Japanese imports and imports from China into Japan at monthly frequency at the HS 9-digit product level from the Ministry of Finance in Japan. As our empirical analysis uses demand shocks faced by Indonesian exporters in Japan that are driven by fluctuations in exchange rates of yuan per USD, in section 3.4 below we show the extent of exchange rate fluctuations during our period of analysis.

# **3.4 Exchange rates**

We use data from the International Monetary Fund (IMF) International Finance Statistics (IFS) for our exchange rate data. The data is the monthly average of bilateral exchange rates between the yuan and the USD. We also use IFS as a source for the Consumer Price Index (CPI) for the real exchange rate calculation. Figure 1 plots the log value of real exchange rate between yuan and USD over the period of analysis, 2014-2018. While there have been significant short-term fluctuations in the yuan/ USD exchange rate during this time period, which we will exploit in our analysis, the overall trend has been towards depreciation.

In all our specifications, we use the log real exchange rate between the yuan and the USD for our analysis as the relevant exchange rate facing Indonesian exporters, since an

> 11World Integrated Trade Solution (WITS).

12
Sweden’s real hourly wages and nominal hourly unit labor cost adjusted to the crisis more quickly than those of the majority of OECD countries. The growth rate of real hourly wages in Sweden decreased substantially with the start of the crisis in 2008, falling from 1.2 percent in 2007 to −1.6 percent in 2008 (Figure 3). Sweden and the United Kingdom had the biggest decreases in the OECD; Denmark and Finland had the smallest. Sweden also had a bigger decrease in the growth rate of the nominal unit labor cost than the majority of OECD countries, including Denmark and Finland. Although Sweden saw one of the biggest declines in the growth rate of labor productivity at the onset of the crisis, it quickly recovered, reaching the highest level in the OECD in 2010 following Denmark.

Owing to the rapid adjustments in wages and labor cost, Sweden had a smaller decline in the growth rate of full-time employment than the majority of OECD economies during the crisis period, despite having had one of the highest growth rates before the crisis. And along with Germany, it had the sixth largest recovery in the growth rate of employment in 2010 among 31 OECD high-income economies—though in both countries it remained below the pre-crisis and early-crisis rates (Figure 4).

Figure 4: Sweden saw a smaller decline in employment growth during the crisis than many other OECD countries—and among the largest recoveries <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>2007 2008 2009 2010<br>Annual gowth rate of full time employment, percent<br><!-- End of picture text --> Source: OECD (2014b).

Note: Countries and country groups are in descending order by the growth rate of employment in 2008. The Nordic average excludes Sweden.

9