rafmacalaba/data-use-mentions-extended huggingface dataset:
Clements et al. 2015). Our findings suggest that more attention should be paid to the redistributive potential of tax design in developing countries. Second, we obtain our results by measuring informality from consumers’ side, while most empirical work measures informality at the firm or worker level (see review in La Porta and Shleifer, 2014). This leads to a more nuanced perspective on the welfare effects of informality. In particular, our findings caution that enforcement policies to reduce informality, which are often found to yield efficiency gains (Ulyssea, 2020), may also have distributional costs by increasing the burden of taxation on poorer households. Third, our classification of retailers as formal or informal overlaps with the ’traditional’ versus ’modern’ retail categories studied in the macro-development literature (Lagakos, 2016). By combining micro survey data from countries spanning a wide income range (from Burundi to Chile), we document stylized facts on consumption patterns across retailers and goods as households get richer, both within and across countries. Our database could be used in future research to test competing theories of development and global retail (Bronnenberg and Ellickson, 2015).

The rest of the paper is structured as follows. In the following sub-section, we summarize our contributions relative to existing literatures. Section 2 describes our data sources and Section 3 new facts on methodology. provides stylized consumption patterns across places of purchase and types of goods as households get richer within and between countries. Section 4 investigates the implications of these consumption patterns for tax progressivity. Section 5 develops a model to characterize optimal commodity tax policy with informal consumption. Section 6 calibrates the model using our data and investigates the impacts of consumption taxes on inequality. Section 7 concludes.

# **1.1 Related Literature**

Our paper makes two main contributions to the literature on tax policy in developing countries. First, we introduce differences in informal consumption along the income distribution as a novel channel through which consumption taxes are redistributive. This contrasts with the existing literature
KUR borrowers. Existing studies of the KUR program rely on internal program monitoring data, interviews with government officials, financial institutions, or small samples of KUR borrowers. Our findings on KUR’s large collateral requirements echo those in other studies (De Braw et al. 2020; Martokoesoemo et al., 2019; ILO 2019; OECD 2018; Dea 2019). We also confirm that KUR’s de facto collateral requirements pose a constraint for women’s access to the program (Martokoesoemo et al., 2019; Dea 2019; Farida 2015). Existing literature has cast doubt on KUR’s ability to reach first-time borrowers (Martokoesoemo et. Al. 2019; ILO 2019; OECD 2018). Nevertheless, using large-scale, nationally representative quantitative data, we find that the challenge is more nuanced. First-time borrowers are able to access KUR; however, the main challenge lies in the incentives to graduate from KUR to commercial lending.

> 3 The initial study design used genetic matching to identify potential borrowers in the program data who did not receive KUR. However, interviews revealed that almost all borrowers in the database had received KUR at some point, suggesting either a very low rejection rate or that the monitoring and evaluation data does not accurately capture individuals who apply for KUR loans and do not receive them.

> 4  Regulation Number 1 of 2023 of the Coordinating Minister of Economic Affairs, Concerning Guidelines for the Implementation of KUR. January 25, 2023. 5  KUR Administrative Data, provided by Coordinating Ministry of Economic Affairs (CMEA), March 2024.

**5**
Policy Research Working Paper 5692

# **Abstract**

In many emerging democracies women are less likely to vote than men and, when they do vote, are more likely to follow the wishes of household males. The authors assess the impact of a voter awareness campaign on female turnout and candidate choice. Geographic clusters within villages were randomly assigned to treatment or control, and within treated clusters, some households were left untreated. Compared with women in control clusters, both treated and untreated women in treated clusters are 12 percentage points more likely to vote, and are also more likely to exercise independence in candidate choice, indicating large spillovers. Data from polling stations suggest that treating 10 women increased turnout by about 9 votes, resulting in a cost per vote of US$ 2.3. Finally, a 10 percent increase in the share of treated women at the polling station led to a 6 percent decrease in the share of votes of the winning party.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group; and the Poverty Reduction and Equity Team, 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 xgine@worldbank.org and gmansuri@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
geophysical (earthquakes, tsunamis, volcanoes) disasters. Wars include internal (intra-state) and external (inter- and extra-state) armed conflicts that are identified using the Correlates of War (COW) and Peace Research Institute Oslo (PRIO) databases. Financial crises include banking, currency and debt crises and are based on the Laeven and Valencia (2018) database. For additional details on the definition of the events and data, we refer the reader to **Annex A** .

# **3.2 Methodology**

To assess the effects of adverse events on productivity, the local projection method (LPM) is used, with country productivity level, measured as output per worker, as the dependent variables (Jord`a et al., 2013; Jord`a, 2005).<sup>25</sup> The LPM approach provides an estimate of the response of labor productivity (and TFP) to adverse events over various horizons. It also helps to identify key transmission channels, assess how countries’ resilience to adverse events has changed over time and analyze the role of policies in mitigating their effects. The advantage of this approach is that it avoids the problem of dimensionality inherent in other approaches such as vector autoregressions.<sup>26</sup> However, it does not directly take into account the severity of the adverse event.

The dependent variable is the cumulative change in labor productivity, TFP or output levels between horizons _t −_ 1 and _t_ + _h_ , measured as difference in the natural logarithms _yt,j_ . The explanatory variables include the event dummy and controls. For a specific type of event, the explanatory variable of interest is an episode which equals 1 if the event occurred at least once in a particular country in a year and 0 otherwise. The baseline model is given by where _h_ = 0 _,_ 1 _,_ 2 _, · · ·_ is the horizon, _α_ ( _h_ ) _,j_ and _τ_ (
Table A4: Robustness to using unconditional employment in mining

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.:Growth inper capita i|ncome(1996-20|11)|||
|Homeland (=1)|1.428***|1.610***|0.971**|0.300|
||[0.530]|[0.530]|[0.467]|[0.966]|
|Share (Uncond.) Employed|-0.761*|-0.431|-1.241***|-0.499|
|mining1996|[0.445]|[0.495]|[0.471]|[0.456]|
|Homeland * Share (Uncond.)|2.298***|2.291***|2.274***|1.808***|
|Employed mining1996|[0.565]|[0.582]|[0.558]|[0.543]|
|Observations|9,619|9,619|9,619|9,593|
|R-squared|0.592|0.6|0.634|0.711|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is
initiative so that markets could distinguish between good borrowers and bad ones and doing so would facilitate capital market access to the former (but not to the latter)? In order to shed some light on such an important question, and provide some thoughts on how to design future debt relief initiatives, in the remaining of the paper we sketch a simple model that could help us to better understand the incentive problems associated with debt relief versus other more traditional forms of assistance.

# 4 An Asymmetric Information Model of Debt Relief, Aid and Private Lending

In this paper, we extend Sachs (1988) classical debt overhang model introducing uncertainty about (debtor) governments’ time preferences and thus their willingness to invest. More precisely, we

> 6 Country data on interest rates are weighted averages of the interest rates on new debt commitments taken from the GDF database.

> 7 Evidence of higher borrowing costs would emerge from excluding bank credit covered by guarantees of exportcredit agencies.

5
# **4. Empirical Results**

This section tests which regionally specific factors correlate with conflict as it is reported in the 2003 PODES data. We use the analytical framework as a basis for the selection of variables to be included in the model. The estimated coefficients should be viewed as measures of association, rather than causation. Almost any variable could be considered endogenous in the model as conflict affects almost all aspects of civic, political and economic life. In addition, respondent bias could cause apparent correlations between variables reporting conflict and right hand side variables drawn from the same data source.

We derived proxies for local poverty, inequality, diversity, economic shocks, land tenure, and community political, associational, and security arrangements from the PODES, 2000 Population Census and the 2000 SUSENAS household survey (see Table 7). In the case of the PODES, we were able to utilize these variables at the local level. We matched variables from the Population Census as the sub-district level. SUSENAS data on household consumptionrelated indicators were available at the district level.

A logit model is applied and estimated at the village level. Table 8 reports estimated coefficients of the models with the variable indicating whether the community experienced conflict in the past year the dependent variable.<sup>14</sup> In our estimates we distinguish between rural and urban areas, based on the assumption that conflict dynamics may be structurally different

> 14 The model controls for the fact that observations might not necessarily be independent within sub-districts.  Our variable was define as one whether or not the community reported casualties or material damage.  The results for the narrower definition of conflict were very similar.

22
the 1 percent confidence interval (see stability test graphs 9 and 10). This provides additional confidence to the predictive power of the model and robustness of related findings and analysis.

# **_Key Economic Findings of the Short Run Parsimonious Model—Closed Economy Formulation_**

The short run equation for the real money balances in the parsimonious indicates that short run movements of money demand varies positively with the lagged change in money and negatively with the error correction term. Accordingly, the demand for money in the current period influences the demand in the next period. The stability of the short run money demand requires a pulse dummy for the year 2008. This pulse dummy captures a 12 percent decline in the change in demand for real balances. As earlier discussed, this marks the year of the global financial crises. It is evident that the public lost trust in national currency during this year, influenced by the potential impact of the crises on the domestic economy.

It worth noting that the government launched a stimulus package that reached almost 5 percent of GDP to revitalize the economy; this, coupled with a hike in public spending and inflation rates. The money adjustment to a 1 percent deviation from disequilibrium is around 1.6 percent. This is a bit higher than the CBE’s 2010 study that read 1.3 percent for monthly data covering the years 1990–2010. Yet the later was under an open economy formulation.

The growth in GDP varies positively with the change in real money two periods ago, and negatively with the previous period’s interest rate. If the change in inflation rate is 5 percent, the change in output would drop by around 0.001. In case the interest rate is 10 percent, the change in real income will drop by 0.002. The change in real income would be 0.45 if the money grows by 1 percent.

The change in the current period’s’ inflation varies positively with the growth in real money balances two periods ago and the
.|..|..|..|..|..|..|..|-0.0535**|..|-0.0614**|
|_(% GDP, exports and imports)_||||||||||(0.0229)||(0.0237)|
|Trade to Developing Countries<br>|..|..|..|..|..|..|..|..|..|-0.0308<br>|..|-0.00329<br>|
|Outside the region_(% GDP)_||||||||||(0.0759)||(0.0919)|
|Intra-regional Trade|..|..|..|..|..|..|..|..|..|0.0333|..|0.0252|
|<br>_(% GDP, exports and imports)_||||||||||(0.0252)||(0.0259)|
|_International Finance_|||||||||||||
|Gross Capital Inflows|..|..|..|..|-0.0730|-0.0719|-0.0998|-0.0935|-0.0976|-0.103|..|..|
|_(% GDP)_<br>Financial Openness Index|-0591**|-0610**|-0419|-0556*|(0.0667)<br>|(0.0783)<br>|(0.0602)<br>|(0.0646)<br>|(0.0805)<br>
estimates of _Pr_ [ _Y > τ, district d_ ] for _d_ = _Cairo, Alexandria_ . We estimate these by: _Pr_ [ _Y > τ, district d_ ] = _Pr_ [ _Y > τ |district d_ ] _Pr_ [ _district d_ ], where _Pr_ [ _district d_ ] (the share of the urban population residing in district _d_ ) is obtained from the most recent population census and where _Pr_ [ _Y > τ |district d_ ] is estimated using Proposition 7. For comparison the latter is also estimated using data from the HIECS only. The two different estimators are denoted by _λ_<sup>ˆ</sup> _prop_ 7 and _λ_<sup>ˆ</sup> _svy_ , respectively. _Pr_ [ _Y > τ, district d_ ] and _Pr_ [ _district d_ ] are denoted by _P_ and _π_ , respectively, such that _P_<sup>ˆ</sup> _prop_ 7 = _πλ_<sup>ˆ</sup> _prop_ 7 and _P_<sup>ˆ</sup> _svy_ = _πλ_<sup>ˆ</sup> _svy_ (where we suppressed the subscript _d_ for ease of notation). The estimates are presented in Table 4.

|sub-group|_π_|ˆ_λprop_7|ˆ_λsvy_|ˆ_Pprop_7|ˆ_Psvy_|
|---|---|---|---|---|---|
|Cairo|0.251|0.116|0.101|0.029|0.025|
|Alexandria|0.130|0.079|0.048|0.010|0.006|
|Other urban|0.619|0.028|0.028|0.017|0.017|

Table 4: Estimates of _π_ , _λ_ and _P_ Notice that our estimate of _λ_ finds that the percentage of households residing in Cairo and Alexandria with incomes exceeding _τ_ is larger than what the HIECS alone would have us believe. This combined with the
draw on the Integrated Crisis Early Warning System (ICEWS) dataset to obtain geolocated data on conflict and political violence events in each country. This allows us to construct a firm-specific, time-varying measure of conflict exposure, which we use in a firm-level fixed-effects model to estimate the impact of conflict on several outcomes.

This study yields a number of empirical findings. We show that higher conflict exposure—as measured by the number of violent events occurring in the neighborhood around a firm—leads to a decline in both sales and spending on production inputs, indicating contradiction in output. These effects result in a null impact on profit for surviving firms. However, the estimated coefficient is consistently negative, and it becomes statistically significant in countries with particular characteristics, as detailed below We address several potential identification threats to the interpretation of our empirical results. First, we tackle the issue of reverse causality by showing that the local

> 1Political violence refers to the intentional use of power or force to achieve political objectives. It includes war and related violent conflicts, state violence, and similar acts carried out by organized groups WHO (2002). Throughout the paper, we use the terms exposure to political violence events and exposure to conflict-related events interchangeably.

1
stock prices. An increase of one standard deviation in exposure to the energy sector increases CDS spreads by 10.4 percent (column 3) and reduces stock prices by 3.2 percent (column 4) after the shock. One reason why banks with higher exposure to the energy sector were more affected by the shock is that their borrowers were in distress. The results in column 5 confirm this, as the delinquency rate after the shock increased substantially, given the ex-ante bank exposure. An increase of one standard deviation in exposure to the energy sector leads to an increase in delinquencies in the portfolios of banks by about 0.16 percentage point (roughly 8 percent).

# **4.2. Impact on Credit to Energy Borrowers**

The results suggest that banks with higher ex-ante exposure to the energy sector expanded their lending to the energy sector after the collapse of energy prices. In this section, we use loanlevel data on the universe of loans to energy sector borrowers to document how the credit terms of firms in the energy sector changed in response to the energy price shock.

Table 3, panel A, summarizes the results of our benchmark equation 2 on three credit outcomes: (i) total lending, (ii) lending for working capital, and (iii) lending for investment projects. All the regressions include _Bank*Firm_ and _Month_ fixed effects, and the regressions displayed in columns 2, 4, and 6 further include fixed effects at the _Firm*Month_ level. The inclusion of the latter limits our sample to firms that borrowed from more than one bank at a given point in time. However, this helps us isolate time-varying changes in the demand for credit of borrowers in the energy sector. This is important, as the decline in energy prices directly impacted producers’ revenues, forcing them to demand more external funds.

Columns 1 and 2 corroborate the earlier finding that banks with higher ex-ante exposure to the energy sector channeled more credit to the sector. Once we control for time-varying
# **2.3 Additional data**

In our heterogeneity analysis, we draw on a wide range of additional data sources to classify countries across several economic, institutional, and political dimensions. Detailed definitions and data sources are listed in Table C1.

**Income** Countries are grouped by their level of economic development, following the World Bank classification.

**Trade, agriculture, and natural resources shares in GDP** Countries are classified based on three indicators from the World Bank: i) merchandise trade as a share of GDP, capturing countries’ engagement in international trade; ii) the share of agriculture in GDP; and iii) the share of natural resources in GDP.

**Fragility** We use the World Bank classification to identify fragile countries—those experiencing deteriorating governance, prolonged political crises, post-conflict transitions, or gradual but still unstable reform processes. In our sample, 16% of countries fall into this category.

**Economic Complexity** Countries are categorized according to the complexity of their production structure using the Economic Complexity Index (ECI, Hidalgo and Hausmann, 2009). The ECI has been shown to explain income differences across countries and to predict future growth (see, for instance, Hausmann et al., 2014). **Drug producing** Major illicit drug-producing and/or drug-transit countries are identified based on the _2006 International Narcotics Control Strategy Report_ , published by the U.S. Bureau for International Narcotics and Law Enforcement Affairs. **Bureaucracy quality** The quality of a country’s bureaucracy is measured using data from the International Country Risk Guide (ICRG, Political Risk Services Group). This index captures the institutional strength and quality of the civil service, including its ability to function without political interference, ensure continuity in government policy, and effectively implement reforms.

**Corruption** Country-level corruption is measured using an index from the ICRG that captures perceptions of corruption within the political system. This includes the misuse of public
is shown in Table 1; the summary statistics for our key variables are in Table 2. Our final sample consists of up to approximately 65,000 firms covering 579 cities in 103 countries.<sup>13</sup>

> 11 Thus, wholly state-owned firms are not in the sample.

> 12 See http:\\www.enterprisesurveys.org for a more detailed description of the WBES. 13 For each dependent variable, the number of observations differs, and the figure of 65,000 is for the dummy variable of female head which has fewer missing observations. The 579 cities in 103 countries refers to the sample where the missing data issue does not arise.

12
sector and microenterprises and work in agricultural production and livestock activities. Women are heavily involved in crop production, provide more than 60 percent of labor in subsistence farming, and are mainly responsible for marketing fruits and vegetables for domestic consumption and account for 60-80 percent of domestic meat and milk trade, hides and skins, as well as petty trade (UNDP,

> 4 See Georgetown Institute for Women, Peace and Security/Peace Research Institute of Oslo “Women Peace and Security Index”. https://giwps.georgetown.edu/wp-content/uploads/2019/12/WPS-Index-2019-20-Report.pdf

8
In this study we focus on the effects that rainfall variability and weather shocks have on young children’s health, and more importantly on shedding light on the channels through which climate affects health. We focus on health outcomes, and particularly on children’s health status, due to data constraints. An alternative approach would consist in using income or consumption data. Unfortunately, there is no nationally representative survey in Nigeria with such data that could be linked to climate data at a fine geographical scale.<sup>1</sup> To carry out such analysis one would be forced to rely on the rather restrictive assumption that the same weather shock affects all households living in the same state. In contrast to the NLSS, the Demographic and Health Surveys (DHS) provide information on infant and child health status accompanied by geographic coordinates of the cluster in which they reside.

The link between weather shocks and child health has received little attention in the empirical literature, although in recent years there have been substantial advances in our understanding of the complex interrelationships involved. Most of the existing literature for developing countries has focused on a rather extreme health outcome, child mortality (for example Rose, 1999; Wang, Kanji and Bandyopadhyay, 2009; Pitt and Sigle, 1997; Burgess, Deschenes, Donaldson and Greenstone, 2010; Baird, Friedman and Smitz, 2010; Bhalotra, 2010; and Kudamatsu, Persson and Stromberg, 2011). However, little is known about the effect that weather variability has on those children that have survived; which are the majority. Fewer papers have analyzed the impact of weather variability on child morbidity (Wang, Kanji and Bandyopadhyay, 2009), or have used anthropometric measures as a proxy for children’s health status (Hoddinott and Kinsey, 2001; Wang, Kanji and Bandyopadhyay 2009; Bengtsson 2010; Portner 2010; and Skoufias and Vinha, 2012). We follow this last set of papers by using two rounds of the Nigeria DHS to assess the effect of rainfall shocks