rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7492

# **Abstract**

Using highly disaggregated firm-level customs transaction data for imports and exports in Peru over the 2000–2012 period, this paper explores the relationship between imports of intermediate inputs and firm export performance. The paper shows that greater use, variety, and quality of imported intermediate inputs is significantly correlated with higher exports, faster export growth, greater diversification of export markets, and higher quality exports (as measured by relative unit prices) at the firm level. This relationship is robust and persistent to controls for unobserved firm heterogeneity and year fixed effects. The use of imported inputs is also associated with higher productivity at the firm level. Considering the relationship between specific trade policy measures and the import performance of those exporters that are direct importers, the analysis shows that those exposed to higher tariffs and nontariff measures import less in total and exhibit lower import variety. The use of the advanced clearance procedure as the modality to clear customs for imports is favorable to the import performance of exporter-importers, in that the users of the modality import more and import a more diversified bundle of inputs than those that do not use it, even after controlling for firm size.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mpierola@worldbank.org, afernandes@worldbank.org, and tfarole@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# **2. Climate Change and Agricultural Crop Yields**

# _Selecting climate change scenarios_

General Circulation Models (GCM) produce a wide range of future climate change scenarios, especially when examined at the country-level (see Solomon et al. 2007). Apart from differences in the science of modeling global climate systems, there is also uncertainty about other key variables such as how the global economy will evolve in coming decades. To account for this, GCMs typically employ different “emission scenarios” based on assumptions about future populations, technological advances, and global agreements to reduce carbon emissions.

To capture a range of possible climate change realizations, we select four projections with different temperature and precipitation outcomes averaged over all land areas of the country. The scenarios employed are presented in Table 1. In addition to temperature and precipitation deviations, the table presents the Climate Moisture Index ( _CMI_ ) (Willmott and Feddema, 1992), which is an indicator of a region’s aridity, at the national level. The CMI depends on average annual precipitation ( _P_ ) and potential evapotranspiration ( _PET_ ). A climate is classified as semi-arid (semi-humid) and then arid (humid) as _PET_ increases (decreases) relative to precipitation. The CMI is defined as: A _CMI_ of -1 is very arid whereas a _CMI_ of +1 is very humid.

# [Insert Table 1]

The scenarios are labeled WET, DRY, COOL, and HOT. The COOL scenario is relative to the other scenarios, and projects a mean average temperature increase of 1.1 degrees Celsius by 2041-50 compared to 1.9 degrees Celsius in the HOT scenario. Three of the four scenarios project an increase in precipitation reflecting an analysis of climate futures for Tanzania conducted by the Tyndall Center (2010). While precipitation rises in three scenarios, the _CMI_ remains fairly constant in two of the three because rising temperatures increase _PET_ . It is of some interest to note that the WET scenario for Tanzania is the same scenario identified by the World Bank as the driest scenario globally out of all 56 possible scenarios considered for analysis (World Bank, 2010). This is a reminder that local or national conditions can differ drastically from broader global averages. Overall, the GCMs suggest that Tanzania’s climate will become warmer, although the extent of warming varies by GCM. In addition, the change in average precipitation may be positive or negative. In these senses, climate in Tanzania becomes more uncertain as a result of climate change.

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upper-middle-income countries, whose rate of participation approximately doubled between 2000 and 2015. Participation rates for lower-middle-income and low-income countries remained quite stable from 2000 to 2015. Differences between PISA cycle III and cycle IV in the participation rates for upper-middle-income countries may be partially explained by the effort to include more middle-income countries in PISA 2009 Plus, which added 10 middle-income countries for a PISA assessment in 2010.

<!-- Start of picture text --> Figure 1. Share of countries participating in PISA, by PISA cycle and<br>economic status<br>100<br>90<br>80<br>70<br>60<br>OECD<br>High income (includes OECD)<br>50<br>Upper middle‐income<br>40 Lower‐middle‐income<br>Low income<br>30<br>20<br>10<br>0<br>Cycle I Cycle II (2003 Cycle III Cycle IV Cycle V Cycle IV *includes PISA Plus<br>(2000/01)* (2006) (2009/10)* (2012) (2015)<br><!-- End of picture text --> _Participation by region._ The overall PISA participation rates of countries in Europe and Central Asia (58%) and in North American (61%), are higher than the participation rates of countries in other regions, with Sub-Saharan Africa recording the lowest participation rate (less than 0.5%). The inclusion of Central Asian countries in the ECA group hides the fact that the 8
the land administration is one of the more corrupt government agencies in Bangladesh, and the observed land ownership may partly be the outcome of corruption. We thus use other indicators of household heterogeneity such as the age of the household head and religion. Both of these characteristics are clearly exogeneous in the context of Bangladesh, as religion is not a choice (determined at birth) for most people, because conversion is rare. The e¤ects of rainfall on income may vary with the age of the household head, because a household with an older head is more likely to be in agricultural occupation and thus be more exposed to rainfall shocks. On the other hand, a household headed by younger individual will be better able to withstand a negative shock such as a ‡ood; a young individual has more energy, and is more likely to take advantage of temporary migration to a nearby town in response to a negative rainfall shock. Thus we would expect heavy rainfall to have stronger negative e¤ects on the households headed by older individuals. The heterogeneity with respect to religion may be due, for example, to di¤erences in social capital and strength of informal risk sharing. The minority groups usually cultivate a more cohesive social network, and thus are likely to have better informal risk-sharing. Also, for historical reasons, the minority groups such as Hindu’s in Bangladesh are more likely to be traders and artisans, and rely less on agriculture compared to Muslims.<sup>25</sup> However, an obvious objection to such interaction-based instruments is that age and religious a¢ liation may have a direct e¤ect on the propensity to pay bribes. We thus follow Carneiro et al. (forthcoming) who also use a similar interactions-based IV strategy, and control for the possible direct e¤ect of Muslim dummy and age of the household head (cubic polynomial terms) in the IV regressions.

# (5) Data

The main data used in this paper come from two sources: National Household Survey on Corruption (NHSC, 2010) conducted by Transparency International of Bangladesh (TIB) and Bandyopadhyay and Skou...as (2012) for rainfall data. As noted above, we also use HIES (2010) data for providing supplementary evidence on the validity of our identi...cation scheme. A brief discussion of the HIES (2010) data is provided in the online appendix to this paper.

## (5.1) NHSC (2010)

> 25 In our data set, Muslim households own more lands on average and also more likely to be farmers and unskilled laborers.

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the main outcome variable, the sum of the two outcome questions. On a scale of 1 to 8, tax morale appears highest in the “top-down accountability” condition at 5.30, followed by the bottom up participation condition at 5.23, and is lowest in the control condition at 5.07.

<!-- Start of picture text --> Index<br><!-- End of picture text --> <!-- Start of picture text --> Fine<br><!-- End of picture text --> <!-- Start of picture text --> Morale<br><!-- End of picture text --> <!-- Start of picture text --> Index Fine Morale<br>5.3 5.3 2.58 2.72<br>(21,613) (21,613) (21,613)<br>2.56 2.56 2.70<br>(19,457)<br>5.23<br>(19,455)<br>5.2 2.67<br>2.52 (19,455)<br>2.65<br>5.1 2.48<br>5.07(24,361) 2.46(24,362) 2.60 2.6(24,362)<br><!-- End of picture text --> Figure 2: Simple comparison of means, Combined index, tax fine and, Tax Morale (GSS) (95% CIs) This effect is sizable in magnitude, though not extremely large relative to the underlying variation in tax morale: in the top-down accountability condition, tax morale is 0.124 standard deviations higher than in the control group. Looking at the two component parts of the tax index, the rank order between conditions remains the same, though the difference between the two treatment conditions is a bit smaller on the tax fine attitude measure than on the GSS tax morale question.

It is difficult to directly compare these results to those in previous experimental studies on tax compliance, as many of those use binary behavioral indicators. However, these measures have been used in a number of observational studies. The effect sizes are somewhat larger than that observed for a 1 point increase in direct democratic rights on a 1-6 scale or a 1 point increase in trust in the legal system on a 1-5 scale (Torgler, 2005). In other words, these effect sizes are comparable to those effects seen for meaningful increases in institutional trust and involvement in observational studies.

# **Cross-country variation**

While we did not have ex ante hypotheses or strong priors about cross-country patterns of effects, it is also useful to look at estimates at the country level to make variation, as well as the overarching pattern, transparent and visible. We first show histograms (Figure 3) of country-level estimated effects for the two treatments. This shows that the while there is quite a bit of crosscountry variation in estimated ATEs, the mean effect estimate is not driven by extreme outlier countries. This is the case in particular with regard to the top-down treatment.

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also observable for the standard deviation of returns, which is highest for serial defaulters (in all eras), followed by that of “other” periphery sovereigns and that of UK/US bonds. The findings suggest that investors are compensated (primarily via rich coupons) for the incremental risk they take when holding bonds of volatile countries with a bad credit history.

Table 6: Sovereign bond returns by country

|Country|R|eal retu|rn|Geo|metric r|eturn|<sup>Exces</sup><br>return|<sup>s</sup><br> <br>SD of<br>real<br>returns|<br>Sharpe<br>ratio|<br>Skew.<br>of real<br>return|<br> <br> <br>Years<br>with<br>bond|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Full<br>sample|<br>1820-<br>1973|<br>1994-<br>2016|Full<br>sample|<br>1820-<br>1973|<br>1994-<br>2016|Full<br>sample|<br>Full<br>sample|<br>Full<br>sample|<br>Full<br>sampl|<br>returns<br>e|
|Cote d'Ivoire°|15.9||15.9|13||13|16.2|26.9|0.59|0.98|14|
|Ecuador|15.5|9.3|27.5|6.8|4.1|12.2|13.2|53.9|0.24|3.38|56|
|Switzerland*|14.8|14.8||14|14||9|15.2|0.54|2.27|10|
|Pakistan°|14.6||14.6|8.6||8.6|15.7|41.5|0.37|1.87|13|
|Estonia*|14|14||4.8|4.8||13.4|48.2|0.26|1.34|33|
|Nigeria°|13.5||13.5|11.9||11.9|12.6|20|0.64|1.13|17|
|Ukraine°|12.8||12.8|6.8||6.8|13.5|37.2|0.35|1.06|15|
|Iraq°|12.5||12.5|8.5||8.5|13.9|32.6|0.41|1.28|10|
|Finland<br>(interwar)*|11.9|11.9||9.1|9.1||9.7|26.9|0.38|1.48|39|
|Costa Rica|10.4|10.9|1.5|5.6|5.8|1.2|8.6|34.6|0.26|1.12|91|
|Serbia/Yugoslavia|10.2|10.6|7.5|5.1|5|5.2|9.7|33.3|0.28|0.8|70|
|Poland|9.9|11.3|7.3|3.8|2.2|7|9.6|37.8|0.25|1.57|66|
|Mexico|9.8|10.1|8.1||5.8|7.9|8.1|31.2|0.25|2.8|146|
|Germany*|9.2|9.2||4.7|4.7||8.2|37.2|0.21|3.18|65|
|France (interwar)*|9.1|9.1||7.9|7.9||8.1|16.1|0.52|0.01|31|
|Philippines°|9.1||9.1|8.7||8.7|9|10.1|0.91|0.18|20|
|Venezuela|8.9|7.1|14.6|6.3|4.7|11.3|6.5|24|0.28|0.41|90|
|El Salvador|8.7|9.2|6.5|5.1|5.1|5.5|7.7|29.8|0.24|2.09|70|
|Czechoslovakia*|8.7|8.7||4.9|4.9||8.4|26.1|0.31|0.33|44|
|Brazil|8.3|8|10.1||6.1|9|6.2|20|0.33|1.15|145|
|Dominican<br>Republic|8.3|8.1|8.9|3.7|2.9|6|6.9|31.6|0.22|0.9|56|
|Turkey|8.3|8.3|8.1|5.3|4.8|7.5|5.8|24.3|0.26|0.24|87|
|Colombia|8.3|8.3|8.1|4.6|4.1|7.7|6.4|28.7|0.24|1.02|126|
|Portugal*|8.3|8.3||6.3|6.3||5.4|20|0.32|0.24|114|
|Guatemala|8.1|8.4|3.5|4.8|4.9|3.4|5.1|28.1|0.19|1.57|77|
|Argentina|8|7.3|12.3||5.9|6.3|5.8|21.3|0.29|1.04|137|
|Greece*|7.8|7.8||4.3|4.3||5.6|28|0.21|0.91|128|
|Haiti*|7.8|7.8||6.5|6.5||7.2|16.6|0.45|0.24|22|
|Morocco|7.7|3.7|8.9|7.3|3.7|8.4|6.9|9.7|0.74|0.66|18|
|Zimbabwe*|7.6|7.6||7.2|7.2||4.5|9.5|0.54|0.66|17|
|Nicaragua*|7.6|7.6||6.7|6.7||4.4|13.9|0.37|0.04|38|
|Uruguay|7.5|7.7|5.8|5.7|6|3.7|6.2|18.9|0.34|0.2|114|

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Taken together, these studies indicate that changes in the gender gap may be generated by a third variable, such as health status, that may have a direct and independent effect on economic growth. Therefore, the estimated coefficient of gender inequality on economic growth is biased because it captures both its own effect and the effect of health status, with which it is correlated.

The cross-country literature shows that gender inequality is correlated with several other determinants of economic growth, including fertility, health, savings, trade liberalization, and “good institutions.”<sup>3</sup> In contrast with the reverse-causality problem discussed in section 3.1, in principle, the bias due to omitted variables can be eliminated by including all relevant variables in the analysis. In practice, however, this approach is unfeasible because standard cross-country data sets do not contain information on most of the relevant omitted variables. Moreover, because each country is an observation, adding several controls quickly exhausts the available degrees of freedom. Finally, to estimate the effect of gender inequality and its correlates separately, one must observe a sufficient amount of independent variation in the two variables. This method is not available for the most important omitted variables, namely, those that are strongly correlated with gender inequality.

Short of creating exogenous shocks to determinants of gender inequality or analyzing exogenous variation created by natural experiments, such as the spike in female labor force participation during WWII used by Fernandez et al. (2004), the estimated coefficient of gender inequality in cross-country regressions inevitably captures the effect of other determinants of economic growth. Therefore, its use as a policy parameter is limited because reducing inequality does not lead to the desired increase in economic growth if the other determinants are not changed at the same time.

# **_3.3. Mechanisms and External Validity_**

A crucial but often implicit assumption made by most cross-country studies is that the relationship between gender inequality and economic growth is the same across countries and across time periods. Thus, the variation in inequality and economic growth across all available countries and time periods is used to estimate one universal coefficient that is 11
inputs, they may have increased consumption. To test for an effect on household consumption, we construct a panel of mean per capita expenditure (Mpce), the standard measure of household consumption, using NSS micro-data as described in Section A.I and Appendix E. We estimate the impact of debt relief on consumption in Table VII, columns [9] to [12], and again find that the bailout had no measurable positive effect on household consumption.

The government believed that a recapitalization of banks would encourage new lending and that this, in turn, would stimulate new investment through the removal of disincentives for investment and the strengthening of households balance sheets. These positive effects, however, were not realized as lenders reallocated credit away from bailout districts driven, in part by the anticipation of borrower moral hazard as a result of the program.

# **6 Robustness Tests**

In this section, we present additional identification and robustness tests in two steps: we first report several tests of the difference-in-differences identification assumption, requiring that outcomes follow parallel trends before the program. Second, we check that our results are not confounded by a differential response to weather shocks in districts with different land distributions (e.g. a greater share of large landholdings).

The difference-in-differences strategy underlying our empirical approach relies on the assumption that outcomes for the treatment and control groups –in our case districts with different levels of bailout exposure– do not follow differential trends in the pre-program period and would have followed identical trends in the absence of the intervention. We now present three tests of this assumption.

We begin by presenting graphical tests of the parallel trends assumption for our main outcomes of interest. Figure 2 and Figure 3, plot lending and loan performance for high-bailout (above median) and low-bailout (below median) districts. The graphs show no indication 25
2007). On the other hand, several publications suggest that further exacerbation in the water situation may even open the door to new water allocation opportunities between these riparians (ESCAP 1997), and others (e.g., Dinar S., 2009 and the literature he cites) are more specific, suggesting that water scarcity is actually an impetus for cooperation, following a hill shaped relationship between scarcity and cooperation.

Several economic studies analyze, using a general framework, river sharing agreements with deterministic water flows (Ambec and Sprumont, 2002; Ambec and Ehlers 2008). The impact of different water availability levels on stability of cooperation is assessed, using different approaches. Beard and McDonald (2007) assess the consistency of water allocation agreements over time if negotiations are held periodically with known river flow prior to the negotiation. Janmatt and Ruijs (2007), in a stylized model of two regions, wet and arid, suggest that storage could mitigate water scarcity, if upstream and downstream riparian countries find a beneficial allocation to sustain it. They find that the collaboration potential is greater in arid than in wet regions, but that there is little scope for capturing the gains from basin level management if economic integration does not extend beyond water issues. Another work (Ansink and Ruijs, 2008) introduces the effects of climate change on both the efficiency and stability of water allocation agreements in international basins. Using a game theoretic framework it is shown that a decrease in mean flow of a river decreases the stability of an agreement while an increase in variance may have both positive and negative effects on treaty stability.

Others introduce water supply variability into their analysis of specific case studies. Abbink et al. (2005) apply an experimental economics framework to the case of the Syr Darya (Aral Sea Basin) conflict in order to evaluate various governance structures and allocation rules needed for enhanced cooperation among Kyrgyzstan, Uzbekistan, and Kazakhstan under several water supply regimes. The conclusion reached by Abbink et al. (2009) is that under the tested water availability values and the proposed payoff schemes, it is not likely that cooperation can be reached in that basin.

Existing studies either address the impact of water scarcity on treaty cooperation, or the effects of water variability in the context of a very specific case study. Studies of international water cooperation focus mainly on water scarcity as a trigger for either conflict or cooperation (e.g., Dinar S. et al. 2007; Hamner 2008; Dinar S. 2009; Tir and Ackerman, 2009; Hensel and 3