rafmacalaba/data-use-mentions-extended huggingface dataset:
lower-skilled labor. Third, we provide novel evidence on how country-level characteristics shape the impact of conflict on firms. We document that economic, social, and political conditions can either amplify or attenuate the effects of conflict exposure. Furthermore, we show that the specific mechanisms through which conflict affects firm activity vary systematically with these characteristics, suggesting that policy interventions to address the effects of conflict need to be context-specific and tailored to the conditions in which firms operate.

The paper proceeds as follows. Section 2 describes the data. Section 3 outlines the empirical strategy. Section 4 presents the results. Section 5 concludes.

# **2 Data**

## **2.1 Firm data**

Our main data source is the World Bank Enterprise Survey (WBES), a global dataset providing firm-level information for the manufacturing, retail, and other service sectors across 148 countries.<sup>4</sup> Data are collected through face-to-face interviews using a standardized questionnaire and are designed to be representative at the country level. The survey targets privately owned firms with at least five employees operating in the formal (non-agricultural) sector.<sup>5</sup> Our data cover the years 2006-2019 (to exclude the COVID-19 pandemic period).

We use a confidential version of the WBES global dataset, which also provides firms’ geo-localization. This information allows us to match each firm with conflict and political violence events occurring in its geographical neighborhood and is therefore essential for constructing the firm-specific measure of conflict exposure used in our analysis.<sup>6</sup> Another important feature of the WBES is that—although designed as a repeated cross-section—it includes a sizable panel component for a subset of countries, with some firms interviewed in multiple waves. As discussed in Section 3, our estimation strategy relies on firms for which geo-localized data are available and that belong to this panel component. Restricting further to firms with non-missing sales data, our main estimating sample consists of 36,087 firm-year observations across
|6.61|561.60|
|12D06|1|1.16|Los Ríos|Costa|6.76|562.17|
|21D01|1|1.16|Sucumbíos|Amazonía|6.45|564.67|
|12D01|1|1.16|Los Ríos|Costa|6.95|568.00|
|19D02|1|1.16|Zamora<br>Chinchipe|Amazonía|6.84|572.80|
|09D06|1|1.16|Guayas|Costa|6.79|575.94|
|**Total**|86|99.97|||||

_Source:_ Author using _Ser Maestro_ 2016 and _Ser Bachiller_ 2017/2018 datasets.

18
Table B5: Rider audits: platform observations

|**Variable**|**Defnition**|
|---|---|
|Percent of passengers who are standing||
|High crowding||
|Diference in presence of male riders be-|Share of reserved space rider who are male - Share of reserved|
|tween spaces|space rider who are male|
|Share of reserved space rider who are male||
|Share of public space riders who are male||

Table B6: Administrative data

|**Variable**|**Defnition**|
|---|---|
|Load factor|Data from Supervia indicates the share of maximum allowed ca-<br>pacity between every two stations by month, hour of the day, line<br>and direction of travel.|
|Crimes against property rate at home sta-<br>tion<br>Crime rate at home station|Monthly data on crime and population from Instituto de Segu-<br>ran ̧ca P ́ublica do Rio de Janeiro, spatially matched to riders’<br>|
|Violent crime rate at home station|home station|

Table B7: Platform survey

|**Variable**|**Defnition**|
|---|---|
|Finished the platform survey|= 1 if respondent did not leave before the end of the interview|
|Number of platform where survey was con-<br>ducted||
|Responded to IAT|= 1 if respondent accepted to take the IAT|
|Was invited to take the IAT|= 1 if respondent was randomly selected to take the IAT<br>_Respondent profle_|
|Age|Median age in years of the respondent’s age category|
|Female family members usually choose re-<br>served space|= 1 if answer to “Which of the Supervia spaces do the women in<br>your
**Figure 5. Factors supporting LIC progression to MIC per capita incomes**

<!-- Start of picture text --> A. Low-income countries, 2001 B. Growth in 2001 LIC per capita C. Progression of 2001 LIC per<br>and 2020 incomes between 2001 and 2020 capita incomes<br>Number of countries Percent Rapid growing LICs<br>70 400 9.5 with low starting pointsContinued LICs 2001 LIC<br>Average threshold<br>60 New LICs 8.5 LICs turned MICs<br>300 Median<br>50<br>Other LICs turned<br>40 MICs 7.5 2020 LIC<br>30 Transition LICs 200 threshold<br>turned MICs<br>6.5<br>20 Low-income 100<br>10 countries<br>5.5<br>0 0 4.5 5.5 6.5<br>2001 2020 LICs turned MICs Continued LICs Log of 1999 GNI per capita, USD<br>Log of 2018 GNI per capita, USD<br><!-- End of picture text -->

## **D. Secondary school enrollment in LICs turned MICs**

## **E. Government effectiveness in F. Rule of law in LICs turned LICs turned MICs MICs**

<!-- Start of picture text --> Percent Index Index<br>90 Median Interquartile range 0.0 Median Interquartile range 0.0 Median Interquartile range<br>-0.3<br>-0.3<br>60<br>-0.6<br>-0.6<br>-0.9<br>30<br>-0.9<br>-1.2<br>0 -1.2 -1.5<br>5 years before Year turned 5 years after 5 years before Year turned 5 years after 5 years before Year turned
control. We reject the null hypothesis that jointly, the coefficients are equal to zero. As such, it is critical to control for baseline covariate differences in the estimation of effects below.<sup>13</sup> With respect to the household characteristics of S2 students (Panel B, Table 4), the average size of the household in the control group is 9.5; while the ratio of child to adult was over 1.5. 63% of students in the control group reported their parents being from the same district they currently live. On average, students in the control group were more likely to report that their parents were farmers than in the treatment group, with 63% of mothers and 47% of fathers, compared to 54% and 40% respectively in the treatment group. Students in the control group reported 61% of mothers and 84% of fathers as literate. The index of household services represents a simple additive composite of four variables: roof, toilet, drinking water and electricity. There was no significant difference in reports from the two groups, with an average of 2.17 on the index. Overall, two student attributes (mother and father education) were statistically different between treatment and control. We cannot reject the joint test that all these differences are equal to zero (Chi-square test of 14.033, with a probability of 0.172).

Despite the inability to reject the null hypothesis of these imbalances being equal to zero, the data show some differences in students and family characteristics between treatment and control schools at baseline. The key difference in baseline student characteristics appears to be that control schools have a higher proportion of repeating students than treatment schools – which creates an imbalance in average age of the student (and possibly gender). It is unclear the direction of the bias that these imbalances can induce for our estimators. On one hand, repeating and older students may perform better in test because either they already took the test or have
biases generally point in the direction of an overestimate of poverty in 1995, there are still a number of unknowns--particularly the small sample size and lack of region-specific price data--that make it impossible to draw firm conclusions about the direction of the change in poverty between 1993 and 1995.

The deficiencies of the 1993 and 1995 household surveys lead to a number of recommendations to improve the reliability of the conclusions. First, more attention needs to be paid to assuring the comparability of the surveys as well as to developing region-specific consumer price indicators if one of the purposes of the household surveys is to monitor the change in poverty over time. A particularly important issue for surveys of the priority-type format is the reliability and comparability over time of the method used to estimate food expenditures. A second lesson is that the ability to explain the trends observed in the poverty data depends to a large part on being able to understand the trends in the various components of household income. This is a weak point of many household surveys, especially household priority surveys. A third recommendation would be to pursue some of the qualitative analysis once the results of the quantitative surveys are available to explore whether the trends reported in the data make sense on the ground. A fourth recommendation would be to give some thought to sample size so that region-specific cell sizes for the major socioeconomic groups are sufficiently large to make meaningful comparisons possible. For example, given the difference in pricing policies applied to cocoa and coffee, it would be interesting in future studies to examine the evolution of poverty between these two groups of farmers. A fifth recommendation would be to examine how well the socioeconomic classification used in the 1993 and 1995 surveys correlates with the primary source of income of the household.33 A sixth recommendation would be to consider in greater detail whether the limited time frame of the priority survey will yield accurate estimates of expenditure given seasonality in expenditures. If a limited time frame is adopted, including questions on normative monthly food purchases would be a useful supplement to
climate they use the Standardized Precipitation and Evapotranspiration Index (SPEI) developed by VicenteSerrano et al. (2010). The SPEI factors in both precipitation and potential evapotranspiration to capture the ability of soil to retain water, and thus outperforms other indices in predicting crop yields. The SPEI is expressed in units of standard deviation from each grid-cell’s historical average (and thus has a mean of zero). HF measure yearly SPEI for each grid-cell as well as SPEI specific to the growing season of the main crop in a given grid-cell. Additionally, HF utilize data on conflict occurrence between 1997 and 2011 from the Armed Conflict Location and Event Dataset (ACLED), which records a wide range of conflict events such as protests, battles and rebel activities derived from war zone media reports, humanitarian agencies and research publications. As is standard in the conflict literature, HF code a grid-cell ( ) as a dummy equal to 1 if the grid-cell experienced any conflict event in a given year ( ). The data from HF on the occurrence of ii conflict and the yearly SPEI for the continent of Africa used by HF are presented in Figure A1 in the appendix. tt The analysis in section 5.1 also uses all time-invariant characteristics of grid-cells that HF also control for, taken directly from their data – these include cell-specific measures of elevation, roughness, area, presence of roads, distance to river, cell shared by multiple countries, border presence, presence of minerals, and ethnolinguistic fractionalization.

The next empirical approach presented in section 5.2 examines conflict breakout in West Africa more specifically, before and after the Arab Spring (circa 2011), covering the period between 1998 and 2017. This requires using data on conflict events directly provided by ACLED, since the HF analysis does not cover the post-2011 period. To focus the analysis on fragility arising from violent political clashes over control of irrigated territory, this section focuses on a narrower definition of conflict which captures only the occurrence of “battles” in ACLED, which is
the level of air pollution at ground level. Satellites produce images that can be used to create gridded data sets of aerosol optical depth (AOD) as well as individual aerosols that are comparable across the world and can be used to estimate levels of particulate matter. However, satellite data does not directly measure particulate matter at ground level and provides data at a lower temporal and spatial frequency. Third, low-cost monitors that citizens can use to track their own air pollution exposure can be deployed by governments to create a network of air pollution monitors at low-cost. However, low-cost monitors do not record pollution measurements at the same level of accuracy as regulatory-grade monitors. Because each of these data sources has advantages and disadvantages, evaluating their performance in measuring air pollution levels and in estimating the effects of policies to reduce air pollution is critical for researchers and policy makers. This is especially important for LMIC settings that can face additional challenges implementing these data sources, where there have been few studies comparing their performance, and where effective environmental policies are most needed.

Dakar, Senegal provides an ideal context for this study for three reasons. First, Dakar is a large and growing city in a lower middle-income country with large industrial plants and recurrent construction works. This implies that our results are informative for other fast-growing Sub-Saharan African cities that are similarly experiencing high levels of congestion and pollution. Second, in addition to the air pollution generated by congestion and economic activity, Dakar experiences Saharan desert storms, especially during the first few months of the year. These environmental conditions differ from those typical of the high-income contexts where air pollution data is usually calibrated and analyzed. Third, Dakar currently has seven regulatory-grade monitors, which allows for comparison and calibration. But in contrast to high-income settings, the density of the monitoring network is low, and the data available is sparse and of uncertain accuracy due to challenges in maintaining the monitors. While this amplifies the challenges in comparing data sources and calibrating the data from low-cost monitors, this reflects the realities of air
Policy Research Working Paper 8720

# **Abstract**

An earlier paper titled “Non-linear effects of tax changes on output: The role of the initial level of taxation,” estimated tax multipliers using (i) a novel dataset on value-added taxes for 51 countries (21 industrial and 30 developing) for the period 1970–2014, and (ii) the so-called narrative approach developed by Romer and Romer (2010) to properly identify exogenous tax changes. The main finding is that, in line with existing theoretical distortionary and disincentive-based arguments, the effect of tax changes on output is highly non-linear. The tax multiplier is essentially zero under relatively low/moderate initial tax rate levels and more negative as the initial tax rate and the size of the change in the tax rate increase. This companion paper first shows that these findings have important policy implications, given that the initial level of taxes varies greatly across countries and thus so will the potential output effect of changing tax rates. The paper then turns to some specific policy applications. It focuses on the relevance of the arguments for revenue mobilization in countries with low levels of provision of public goods and social and infrastructure gaps, as well as in commodity-dependent countries. The paper then considers some practical implications for the standard debt sustainability analysis. Lastly, it evaluates the implications of the findings for the Laffer curve.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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://www.worldbank.org/ research. The authors may be contacted at gvuletin@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
understand whether these results are driven by the women’s ownership and decision-making power over the land, we rerun the same analysis but by controlling for the number of parcels the female spouse is controlling and the number of parcels where she makes decisions on agricultural activities.<sup>24</sup> The results remain the same for extension outcomes while there are a few differences in the size and significant level of coefficients from the technology tack-up variables. The two main differences are: (1) we find a positive joint effect on the likelihood of the female spouse trying new technology introduced by the DA, and (2) the likelihood of the male spouse trying new technology on farming method is no longer significant when both spouses are targeted.

Since extension services and technology take-up are defined as the mechanisms to increase agricultural productivity (see section 6.1), we rerun the agricultural productivity outcome variables to see if there is any difference after controlling for land ownership and decisionmaking variables.

When both spouses are targeted, the effect on the size of cultivated land is no longer significant at the midline while the decline by the endline becomes statistically significant at 10%. In addition, the value of harvest at the midline is now statistically significant when both spouses are targeted. Finally, the joint effect of the interventions on the value of harvest is now significant both at the midline and endline.<sup>25</sup>

# **8 Conclusion**

We contribute to the literature by showing that whom you target in a married household for agricultural interventions matters for the type of outcomes achieved. We also show that the reason for these different outcomes is that whom you target also determines the channel through which the impact is generated. Our results indicate that to impact agricultural productivity, it is important to target both spouses, which translates into an increase in take-

> 24 Note that the sample size has declined from 2,428 to 2,355 due to missing values for ownership and decisionmaking variables.
22 Table 6: Summary statistics for pass-through coefficients for kerosene (percentage of countries in each category except for the median)

|**Parameter**(number of countries)|**Median**|**<0%**|**0–50%**|**50–75%**|**75–100%**|**100–150%**|**>150%**|
|---|---|---|---|---|---|---|---|
|**Income category**||||||||
|Low income (11)|49|18|36|18|9|18|0|
|Lower-middle income (19)|44|16|37|21|16|11|0|
|Upper-middle income (14)|81|14|14|14|21|36|0|
|**Developing countries only (excluding h**|**igh-income**|**countries**|**)**|||||
|Net oil importer in 2009 (32)|63|6|31|19|19|25|0|
|Net oil exporter in 2009 (12)|9|42|17|25|8|8|0|
|East Asia & Pacific (5)|63|0|40|20|20|20|0|
|Latin America & the Caribbean (11)|84|0|9|18|27|45|0|
|Middle East & North Africa (5)|14|20|60|20|0|0|0|
|South Asia (5)|44|0|80|0|20|0|0|
|Sub-Saharan Africa(17)
marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

38
.13|194|14.69|
|Less Developed|87|6.59|78|5.90|165|12.49|
|Low-Income|200|15.14|12|0.91|212|16.05|
|Total|880|66.62|441|33.38|1,321|100.00|

Notes: Authors’ elaboration on Eurostat data. Percentages are defined on the total number of regions in the sample. High-income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 90% of the sample average. Transition regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 75% of the sample average, but lower than the 90% of the sample average. Less developed regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 50% of the sample average, but lower than the 75% of the sample average. Low-income regions are those with an average yearly GDP per capita over the period 2003-2017 lower than the 50% of the sample average. High- (low-)growth regions are those recording a long-run growth rate of GDP per capita between the years 2003 and 2017 equal to or greater than (lower than) the 50% of the sample average.

> 19 Definition taken from “https://ec.europa.eu/regional_policy/en/policy/how/improving-investment/lagging_regions/”.

> 20 It is worth noting that Rodriguez-Pose and Ketterer (2020) have employed the official ‘catching up’ taxonomy proposed by the European Commission to analyse the relationship between institutional quality and economic growth at the NUTS-2 level.

> 21 With respect to the income level dimension, the choice of considering the average yearly GDP per capita evaluated over the period 2003-2017 rather than the 2013 figure, or that referring to the last available year of observation
# **5. Conclusion**

Using panel data of approximately 84 countries from 2006 to 2009, this paper is the first to show empirically that for the _average_ country, improvement in the official Doing Business Rankings is likely to increase FDI into a country. While improvements in some determinants of the Doing Business Ranking are indeed correlated with greater FDI inflows, it is perhaps improvements in the Doing Business Rankings of the average country that act as a strong signalling effect to investors. Nevertheless, there appears to be no evidence to suggest large improvements in Doing Business Rankings (i.e. ‘reform’ countries) attract significantly greater FDI inflows.

When focusing on developing countries in isolation, the relationship is insignificant. This result should be taken with caution given it refers to the average developing country across a four year time period. A more complete picture of the influence of Doing Business Rankings on FDI inflows may be available as more data becomes available.

As countries are not homogenous, it would be interesting to note how improvements in the Doing Business Rankings influence FDI inflows into countries of different economic sizes. For example, would improvements in the Doing Business Rankings for smaller economies such as those in the Pacific Island countries lead to greater FDI inflows or is this correlation stronger among larger economies such as India, Vietnam or Brazil? Similarly, analysis could be undertaken across geographical areas. Are investors more likely to favor African countries relative to Asian countries given similar changes in the Doing Business Rankings or would their focus largely be on Europe or the Americas? Again, as more data become available, researchers and policy makers should be able to investigate the answers to these questions. This may also encourage, or discourage, governments to improve their Doing Business Rankings.

Finally the relationship between improvements in the Doing Business Rankings and GDP growth using panel data could also be undertaken.

9