rafmacalaba/data-use-mentions-extended huggingface dataset:
ministers to the cabinet is associated with reductions ranging from 1 to 2.3 percentage points across these measures of governance. Looking at the control variables, two patterns stand out. GDP per capita has a consistently positive association with the outcome measures, and left leaders with worse governance scores.

Although the time series available for most measures of governance is short, which gives rise to Nickell bias in dynamic models with fixed effects, we checked whether our results are robust to including a lagged dependent variable. These supplementary results are reported in Table 2. In terms of our variable of interest, the basic pattern of results goes through. While the coefficients on the cabinet size variable are smaller, the estimates still achieve statistical significance at conventional levels in four of six regressions. Executive corruption as measured with the _V-Dem_ data is largely accounted for by the lagged dependent variable. In terms of control variables, one notable change is that the coefficients on the democracy measures are more precisely estimated. Democracy has a positive association with five of six governance measures.

10
**Figure 8:** Illustration of Counterfactual Experiment

<!-- Start of picture text --> (a) 1988–1996 period (b) 1996–2006 period<br>0.8 0.8<br>1996<br>0.75 1996 debt due to<br>0.7 decentralization<br>0.7 (18%)<br>2006 counterfactua<br>0.65<br>0.6<br>0.6<br>1996 counterfactual<br>0.5<br>debt due to 0.55<br>decentralization<br>(39%) 0.5<br>0.4 1988<br>2006<br>0.45<br>0.3 0.4<br>1986 1988 1990 1992 1994 1996 1998 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>Time Time<br>Change only (e,f,<br>) to 2006<br>) to 1996<br>Change only (e,f,<br>Change all parameters to<br>calibrated 1996 values<br>Change all parameters to<br>calibrated 2006 values<br>Total government debt Total government debt<br><!-- End of picture text --> Note: Counterfactual experiments change decentralization parameters ( _e_ , _f_ and _θ_ ) to the end year values, and keep all other parameters at their starting year values. The experiments illustrate the change in debt that is explained by decentralization.

**Table 5:** Counterfactual Experiment Effects of fiscal decentralization on debt levels

|||1988–19|96||1996–20|06|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(
. Speculative uranium resources, i.e. deposits that are likely to be discovered on the basis of the geological characteristics of already identi...ed resources, have risen to over 10.5 million tonnes (NEA, 2008).

Renewable technologies and nuclear power have signi...cant environmental bene...ts compared to fossil fuel based electricity generation. Under normal operating conditions, wind, solar, and nuclear plants produce almost none of the residual fossil fuel pollutants which include particulate matter, sulfur dioxide, nitrogen oxides, and a variety of heavy metals— mercury being the most prominent. In the case of nuclear power, small quantities of radioactive gases are emitted under controlled conditions imposed and supervised by regulatory authorities and pose no signi...cant threat to plant workers or surrounding populations. Moreover, numerous LCA cradle-to-grave studies point to a very substantial gap between coal, oil and gas at the high end and wind, hydro, solar, and nuclear at the low end, in terms of their CO2 emissions per unit of electricity generated–the average estimates being 1.2 kg CO2/kWh for coal, .4 kg CO2/kWh for gas, .06 kg CO2/kWh for solar PV, .011 kg CO2/kWh for wind (onshore), .008 kg CO2/kWh for nuclear, and .005 kg CO2/kWh for hydro (OECD/NEA, 2008).

# 5.2 Capacity to Deliver Net Excess Energy

There is substantial variance across the di¤erent electricity generation options in terms of their lifetime capacity to deliver net excess energy. With the exception of hydro, renewables 18
percent; reaching on average 27 percent of the goods value in the case of the US (Clark, Kozlova, and Schaur, 2016).

Several studies have formally quantified the effect of timeliness on trade volumes. Based on disaggregated import data for the US, Hummels (2001) finds that each day in transit reduces the probability that the US imports manufactured goods from a given country by 1.5 percent and is equivalent to an ad valorem tariff rate of 0.8 percent, a figure updated to range between 0.6 and 2.3 percent in Hummels and Schaur (2013). Using similar data, Clark, Kozlova, and Schaur (2016) show that unexpected delays in the arrival date of shipments at ports in the US, captured by the difference between the actual and expected dates of arrival, reduce imports by 1 to 2 percent. Time costs also impede exports both at the extensive and intensive margins. Using a sample of 98 countries, Djankov, Freund, and Pham (2010) argue that each additional day required to move products from the factory gate and onto the ship reduces the volume of exports by more than 1 percent, while Li and Wilson (2009) find that it decreases the probability to export and the share of exports in total sales in developing countries. Freund and Rocha (2011) convey a similar message in the case of African countries and find inland transit delays to matter more than those related to documentation processing and Customs clearance. Likewise, Volpe Martincus, Carballo, and Graziano (2015) show that a 10 percent increase in Customs-driven delays in the time to export leads to a 3.8 percent decline in firms’ exports in Uruguay, with additional negative consequences for export

> 1World Bank Group; IDE-JETRO; OECD; UIBE; World Trade Organization (2017), _Global Value Chain Development Report 2017: Measuring and Analyzing the Impact of GVCs on Economic Development._ Washington, DC: World Bank.

2
. Partly as a result, its eastern region (e.g., Beijing, Fujian, Guangdong, Shanghai, and Tianjin) has been growing faster than the central and western regions.

The coastal agglomerations (Bohai and Southeast regions) indeed outperform the rest of the country on nearly every investment climate measure, according to a comprehensive review of the investment climate in 120 cities in the PRC (World Bank 2006). Differences in city characteristics, such as per capita GDP, economic growth, and transport costs alone explain about 33% of the variation in firm productivity. The PRC’s wealthiest regions tend to attract more capital and FDI 4
Because it is not clear where in the distribution of scores a lender would set the acceptance threshold, we trace outcomes along the full range of thresholds. Figure 1 shows how the default rate varies with the fraction of borrowers accepted (where borrowers with lowest predicted default are accepted first).

The receiver operating characteristic curve (ROC) plots the true positive rate of a classifier against the false positive rate, tracing out performance as the acceptance rate is varied. Following the credit scoring literature, we report the area under this curve (AUC) to summarize performance across the range of possible acceptance thresholds.<sup>12</sup> A naïve classifier would generate an AUC of 0.5 and a perfect classifier would generate an AUC of 1.0. Figure 2 illustrates the ROC for the best benchmark model (stepwise logistic) and the most conservative model using indicators derived from phone data (random forest weekly ensemble).

Results for a variety of specifications are presented in Table 3, measuring performance with AUC. (Performance is assessed with alternate metrics in Table S3). Results are presented for the entire sample, and then for the subsamples that do, and do not, have credit bureau records. In this population with thin files, credit bureau information does not perform especially well in predicting repayment (AUC 0.51-0.57). For bureau indicators, the logistic model outperforms the random forest, suggesting that the underlying relationship between those indicators and repayment is relatively linear. In contrast, standard models built on phone indicators (CDR) are predictive, reaching AUCs of 0.71-0.77 when trained and tested on the same time period. Our more conservative CDR-W models achieve lower performance when trained and tested in the same time period (AUCs 0.62-0.63), but also outperform credit bureau models. The performance of our models is also in the range of a sample of published within-time AUC estimates for traditional credit scoring on traditional loans in developed settings (0.50-0.
)/2.

Sales values vary a lot across firms and outliers can be a much serious concern than product growth and this formula is robust to outliers. I also computed sales growth as log difference of sales levels which provided quite similar results.

The results are presented in Table 8. The table shows that unlike in Aghion et al. (2008) licensing reform significantly increases both sales and sales growth. The licensing reform increases sales by 3.4 percent. Although the estimation method and the controls used in this study differs from those used in Aghion et al. (2008), magnitude of the coefficient is quite close to 3.1 percent increase obtained in their study. However the coefficient in their estimation is not significant. Licensing reform has also a significant impact on sales growth by leading to a 5 percent increase. This value is close to the impact of the reform on product growth rate which could mean that the reform mainly affected product growth rate rather than the growth in revenues generated from existing products. To further support this hypothesis, I computed total unit sale values of every product and then computed the growth rate of this value. Replacing total sales growth with the growth of unit product sales yields an insignificant coefficient on licensing reform. This supports the argument that the reform mainly contributed to product creation rate rather than a growth in sales of each product.

# **V. Conclusion**

This study investigates the impact of regulatory environment on the success of firms’ innovative efforts. Using data from Indian manufacturing sector, it focuses on a particular reform on license requirements of firms. This reform liberalized product markets in introducing new products, increasing capacities, and 16
|Livestock|0.73|0.55|0.55|350|||||||||
|**Owners**|||||||||||||
|Financial assets|3.61|2.88|2.74|200|2.97|2.43|2.12|5767|2.59|2.18|1.33|81|
|Land|12.04|9.58|13.26|3298|10.08|5.92|13.43|10852|18.36|13.17|16.73|1977|
|Mobile phones|1.76|1.37|1.37|2967|1.07|0.92|0.63|8056|1.39|1.17|0.80|1792|
|Livestock|2.87|2.35|2.11|1720|||||||||

Notes **:** The tables describe the modules length for asset modules for each individual respondent.

Due to the disaggregated nature of the paradata, we are also able to further estimate how average total time spent in the household varies by the number of adults targeted for individual interview. Figure 1 provides a visual comparison of the average total time spent at household (for the administration of household and individual questionnaires) versus the average total duration of individual interview targets in each household. The estimates are presented in accordance with the number of individual interview targets and the average duration estimates for the land, labor, health and education modules are included as well. Total time spent at household increases with the number of individual interview targets. In Cambodia, interviewers spent on average 103 minutes in total for the administration of household and individual questionnaires when the household only had one individual interview target. Moving from one target to two, the average total
, 40, 153–187.

- Donohue, J. J. I., J. Grogger, and S. D. Levitt (2009): “The Impact of Legalized Abortion on Teen Childbearing,” _American Law and Economics Review_ , 11, 24–46.

- Donohue, J. J. I. and S. D. Levitt (2001): “The Impact of Legalized Abortion on Crime,” _Quarterly Journal of Economics_ , 141, 379–420.

- Drèze, J. and R. Khera (2000): “Crime, Gender, and Society in India: Insights from Homicide Data,” _Population and Development Review_ , 26, 335–352.

- Ebenstein, A. Y. and E. J. Sharygin (2009): “The Consequences of the Missing Girls of China,” _The World Bank Economic Review_ , 23, 399–425.

- Edlund, L. (1999): “Son Preference, Sex Ratios and Marriage Patterns,” _Journal of Political Economy_ , 107, 1275–1304.

- Edlund, L., H. Li, J. Yi, and J. Zhang (2007): “Sex Ratios and Crime: Evidence from China’s One Child Policy,” _IZA Discussion Paper 3214_ .

- Epner, J. E. G., H. S. Jonas, and D. L. Seckinger (1998): “Late-term Abortion,” _Journal of American Medical Association_ , 280, 724–729.

- Filmer, D., J. Friedman, and N. Schady (2009): “Development, Modernization, and Childbearing: The Role of Family Sex Composition,” _World Bank Economic Review_ , 23.

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- George, S. (2006): “Sex Ratio in India, Response to Jha, Prabhat, Rajesh Kumar
A3). This also suggests strong catch-up effects as the debt depreciates which we will turn towards in what follows.

The second aspect is that the link we find is not specific to the poverty measure we use. In the appendix we use the multi-dimensional poverty index developed in Alkire and Jahan (2018) to show that the strong link between debt burden and poverty also holds in that data (Appendix Table A4). We find extremely similar magnitudes to the results shown in Table 1. The multidimensional aspect of the data also allows to investigate the channels through which conflict debt might affect conflict. We find very little on the health dimension (nutrition and child mortality) but strong links to the education dimension and even stronger links to all standard of living categories. This is reasonable as it is less likely that, years after conflict ended, conflict history still affects harvests. But if it disrupts the economy and affects human capital accumulation then this will affect years of schooling and assets in the mid-term. We find that deprivation from public services like sanitation, electricity and drinking water increases with conflict burden which suggests that the state capacity link as a confounder could be relevant.

When interpreting Tables 1 and 2 it has to be kept in mind that the goal of our empirical exercise is not to show the dynamic response of poverty to innovations in conflict. Instead, we are interested to quantify broad trends at the country level as they are shown in Figures 1 and 2. What our empirical results show is that poverty trends change in line with a model in which conflict leaves a debt. We will bring this model to the cross-sectional data below and this is also where we see the strength of our statistical model – it provides a useful hypothesis for all levels of and for panel and cross-sectional data.

If we were to interpret the coefficient in Column (3) of Table 1 as causal – how economically significant is the relationship? The standard deviation of the debt variable is 0.6 which would imply that an increase of
include provisions that prioritize gender issues. Yet, it is worth mentioning that it is not the number of provisions or length of chapters including gender considerations that matter but their enforcement. Bahri (2021) shows how some RTAs such as Canada-Chile and Canada-Israel Agreements include whole chapters that address gender issues; however, there is a lack of legal obligations that ensure their proper implementation. Conversely, other RTAs such as the Stabilization and Association Agreement between the European Communities and the Republic of Montenegro, where gender provisions are included in the chapter on development and not standalone chapters for gender issues, are more efficient due to the existence of strong legal obligations that enforce the parties to respect the clauses and to stop any discrimination based on gender. At the internal level, and given the discrimination against women on the labor market, they might face more barriers when it comes access to finance, permits to start a business, tax policy, etc. This is why it is important to see how the effect of GVCs on women’s empowerment is moderated by the obstacles they might face.

Against this background, there are no previous empirical studies, to our knowledge, that tackle the impact of GVCs on women’s entrepreneurship in developing countries and especially the Middle East and North Africa (MENA) region, which is characterized by both low female labor force participation and low integration into GVCs. In addition, no empirical studies have been conducted to assess the impact of gender provisions on women’s engagement in international trade in the MENA region. Indeed, compared to the other regions, the MENA region is ranked the lowest in the world for attaining gender equality based on the World Economic Forum’s Global Gender Gap Report (2021). Moreover, based on the Women Peace and Security (WPS) Index, the MENA region’s performance was very poor as it comprises 12 of the 25 worst performing countries globally (Danon and Collin, 2021). Therefore, the contribution of this paper is twofold. First, it
Where {NEGATIVE}i is a vector of variables conveying negative information regarding borrower _I_ ; {POSITIVE}i is a vector of variables conveying positive information regarding borrower _i_ . Even though the datasets in the three PCRs are somewhat different, the variables are defined in a conceptually consistent manner to allow for comparability of the results. Table 2 describes the actual PCR data used in the regressions for each of the three countries.

The dependent variable is binary and takes a unit value if the borrower defaults, meaning a late payment of 90 days or more, and is zero otherwise. The last year of the data available is the default horizon and is used for defining the binary dependent variable. Data available prior to that, which goes back 18 months in Argentina, 12 month in Brasil, and up to 59 months in Mexico, is used to define explanatory variables and represents the credit history of a borrower with individual banks and the banking system.

Only Mexico provides straight-forward information on the number of days late for loans in arrears, so for Argentina and Brazil<sup>8</sup> borrower ratings were used as a proxy for delinquency status. Ratings are based on a set of criteria including the number of days pastdue. In Argentina default corresponds to a “3” rating, and in Brazil the rating “E” was used as the default threshold. For Argentina, a borrower was considered to be in default for our exercise if their average rating across banks in the system was “3” or worse at any time during the year-long default horizon. In the case of Brazil there are eight ratings categories; the borrower is considered in default if it had a rating “E” or worse with a given financial institution.

The negative variables then include information on past late payments and defaults while positive variables include a borrower’s (non-default) rating, aggregate exposure, the number of open credit lines, and availability of collateral. To ensure consistency across countries, we restrict the dataset to private
Policy Research Working Paper 6106

# **Abstract**

This paper examines how the applied multi-sector computable general equilibrium (CGE) literature has moved into quantication of the impacts of greater market access for services. This includes discussion of multi-sector linkages to the service sector, as well both measuring barriers to trade and investment (generally with a mix of firm surveys, price comparisons, and econometrics), and how changes in these barriers, however measured, have been implemented in the CGE literature. Three challenges are highlighted. The first is identification of how trade in services takes place and how market access is therefore affected by policy. The second is to find data sufficiently robust for modeling purposes. The third, linked to the data problem, is to quantify the barriers to be examined. Significant progress has been made in modeling foreign direct investment and linking this to productivity, which turns out to be important. The paper also provides an example of modeling productivity linkages to openness and domestic regulation, with an applied CGE model of Italy. This illustrates cross-sector linkages and the integration of economic data and policy measures to define service sector experiments. Priorities for future research include better modeling of market structure, the linkages between sectors and the complementarities between different modes of supplying services.

This paper is a product of the Poverty Reduction and Economic Management Network. 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 E.M.Christen@gmail.com; Joseph.Francois@gmail.com; or Bhoekman@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
place where he or she is surveyed. The DHS only has data on the date at which individuals _interviewed individually_ moved to their current place of residence. This information is not available for household members not eligible for an individual interview, and so it is not applicable to the sample used in the education analysis. In the next section, I explain how I can nonetheless check the robustness of my education findings based on DHS data to conflict-related migration.

The NLSS data on the individual’s district of origin before they last migrated is particularly useful in the marriage analysis, since in Nepal, as in most of South Asia, marriage is patrilocal. Marriage is therefore often accompanied by migration, not uncommonly to a different district, and so this may create both classical and non-classical measurement error in the district-level conflict measures used here. In particular, if when conflict was very intense, parents used marriage as a way of putting their daughters to safety by marrying them to men from more peaceful districts, this may create an artificially positive correlation between being married and being observed in a low-conflict intensity district. Therefore, when analyzing the impact of conflict on marriage, I present estimation results using data from both the DHS and the NLSS.<sup>1</sup> The data used to measure conflict intensity are taken from electronic files provided by the Informal Sector Service Center (INSEC), an independent, well-regarded, human rights NGO based in Kathmandu and with representatives in each of the 75 Nepalese districts, who monitor human rights violations. Data from INSEC has been extensively used in the media, international agencies and government reports, and in a number of academic studies, including Bohara et al. (2006) and Do and Iyer (2010). The INSEC data files contain the number of conflict-related deaths per month per district of Nepal between February 1996 and December 2006, as well as the total number of school destructions and abductions by Maoists at the district level, which are used to construct most measures of exposure to