rafmacalaba/data-use-mentions-extended huggingface dataset:
Section 5 provides the results of the interventions on the rate of formalization among informal firms, and Section 6 concludes with discussion of implications for both policy and further studies in this area.

# **2. Context and the Process of Formalizing**

Belo Horizonte is the capital city of the state of Minas Gerais in Brazil, and has a city population of almost 2.5 million, with 5.5 million in the official metropolitan area (the third largest in Brazil after São Paulo and Rio de Janeiro). A 2009 survey by the Brazilian statistical agency IBGE along with government records was used by SEBRAE, the government agency for supporting micro and small businesses, to estimate that Belo Horizonte has a total of 561,310 businesses, of which 402,744 were informal (72 percent).

# **2.1. Registering a Microenterprise in Belo Horizonte**

The Complementary Federal Law 123 defines micro-enterprises as firms with annual revenues up to R$ 360,000 (US$177,000)<sup>3</sup> , provided they are not the subsidiary of another firm. Microenterprises which meet several other conditions – the key ones being they do not have a foreign owner or partner, they are not in certain sectors like financial services, consulting, alcohol or tobacco, or transportation – are eligible to register their businesses formally under a national simplified taxation system called SIMPLES. The SIMPLES regime combines several ongoing tax and contribution payments into a single payment (including employee taxes and ICMS, the State sales tax), but does not simplify the registration process itself. In addition, at the time of beginning our study, enterprises with one or fewer employees and which had R$36,000 or under in annual revenues could instead register as individual microentrepreneurs (MEIs). This was changed after our intervention had begun, with the eligibility threshold being raised to R$60,000 after a law change in September 2011.

The State government created a unit called Minas Fácil
)|(0.094)|
|3 years|1,562|0.850|0.103***|1,279|0.056|-0.021*|-0.195*|
|post application|||(0.014)|||(0.012)|(0.114)|
|5 years|1,685|0.812|0.077***|1,106|0.083|-0.036**|-0.455**|
|post application|||(0.018)|||(0.015)|(0.211)|

_Notes:_ Columns (1) to (6) report impacts of several programs on self-employment and migration. The last column reports estimates from a two-stage least squares regression of the migration indicator on an indicator for self-employment instrumented by treatment status. Panel A uses the sample of participants to the Uganda YOP (Blattman et al., 2014, 2019). Each coefficient comes from a weighted least squares regression that controls for district fixed effects and a vector of baseline covariates. Regressions are weighted by inverse probabilities of attrition and selection into the endline tracking sample. Standard errors are clustered at the group level. Panel B uses data from the Uganda WINGS program (Blattman et al., 2016). Treatment effects are computed as the linear combination of the impact of an indicator for assignment to the general treatment group, and an indicator for assignment to the group dynamics training. Each regression controls for a vector of baseline covariates and strata fixed effects. Standard errors are clustered at the village level. Panel C uses data on the sample of participants to the Nairobi microfranchising interventions discussed in Brudevold-Newman et al. (2017). We further distinguish between franchise and grant treatment in sub-panel (a) and (b), respectively. Each regression controls for baseline covariates and fixed effects for randomization
Policy Research Working Paper 5582

# **Abstract**

Why do women engage in transactional sex? While much of the explanation is that sex-for-money pays more than other jobs, this paper uses a unique panel dataset constructed from 192 self-reported diaries of sex workers in Western Kenya to show that women who supply transactional sex develop relationships with regular clients, and that these clients send transfers in response to negative income shocks. Regular clients are the primary source of inter-person insurance that women receive, and women report in a separate survey that client transfers are an important reason that they participate in the market.

This paper is a product of the Human Development Sector Unit, Europe and Central Asia 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://econ.worldbank.org. The author may be contacted at eyeh@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
<!-- Start of picture text --> Correlation of Orig. Commitments w/ Quarterly Correlation of Disbursements w/ Quarterly<br>Real GDP (2000-2019) - Pre and Post Global Real GDP (2000-2019) - Pre and Post Global<br>Financial Crisis Financial Crisis<br>0.29 0.20<br>0.11<br>0.12 0.09 0.08 0.03 0.06 0.03 0.05<br>0.03 0.00<br>-0.02 -0.01 -0.05<br>-0.05 -0.05 -0.13<br>-0.14 -0.16<br>EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR SSA<br>Pre Crisis Post Crisis Pre Crisis Post Crisis<br><!-- End of picture text -->

# **4.3 Sectoral Comparison**<sup>**14**</sup>

At the sector level, commitments were consistently countercyclical, yet disbursements were consistently procyclical (Figure 3). This pattern could imply an “intent/implementation mismatch,” in which investments that are designed to be countercyclical become procyclical due to prolonged lags between commitment and disbursement. Disbursement procyclicality was also persistent, as all sectors displayed positive correlations between disbursements and economic activity in the recipient country both before and after the GFC. In the manufacturing, agribusiness, and services (MAS) and disruptive technology and funds (CDF) sectors, commitments turned countercyclical after the GFC, while commitments in the financial institutions group (FIG) remained countercyclical across the entire period. Infrastructure and natural resources (INR) sector commitments were almost acyclical in the post-GFC period.

**Figure 3: Sector-Level Correlation Results**

<!-- Start of picture text --> Correlation of Orig. Commitments w/ Correlation of Disbursements w/ Quarterly<br
the next formula: where _φcjs_ comes from the input‐output tables from the year 2010 reported at 2‐digit level in which the input shares are computed based on the total value‐added of inputs used, and where the services reform index refers to the country‐sector‐year specific regulatory indexes in services from the OECD’s NMR and the FDI indexes database. The fact that we chose our input‐output coefficients to be industry‐specific from these matrixes is because Arnold _et al_ . (2015) claim that deployment of inputs reliance measures at firm‐level may suffer from endogeneity issues in connection with the performance of the firm. Industry

> 15 On a more technical note, some regulatory variable indexes from the NMR or related to FDI do not show much variance over time and therefore regressions could pick up any variation of the input‐output coefficients for each country alone rather than the variation of input‐output coefficients interacted with the regulatory variable indexes. This provides us with an additional argument to use EU‐wide or one country‐specific (i.e. US) input‐output coefficients as explained in the main text of this section.

12
and the local DB line (hereafter the “DB gap”), so that a minimum income is guaranteed. However, very little is known about the performance of the program in reaching the poor, even though it is evidently one of the largest cash transfer programs in the developing world. On paper, the program eliminates poverty (at least by its own definition of who is poor). But how close does it come to this ideal in practice?

This paper offers the first systematic assessment of _Di Bao_ ’s performance, based on independently-collected household survey data. We use the program as a case study for addressing a number of long-standing concerns about how effective transfer programs are in reducing poverty in developing countries. We focus on two issues that have clouded inferences from past work. Firstly, the performance of a program such as _Di Bao_ will depend in part on <u>behavioral responses. Yet in assessing targeting performance and poverty impacts it is common</u> practice to simply deduct transfers received from post-transfer income to estimate pre-transfer income. Here there are concerns that recipients’ labor supply or private transfer receipts will fall in response to DB, such that the net income gains are lower than the actual money received. On paper, the design of DB implies high marginal tax rates, which suggests that there may be strong incentive effects, which could undermine the program’s effectiveness against poverty. The literature on the design of such programs suggests that the benefit withdrawal rate (BWR) — the amount by which the transfer payment falls for each extra unit of pre-transfer income — should be positive, but less than one. For programs aiming to reduce poverty a BWR around one half is consistent with evidence on the relevant income elasticity of labor supply (Kanbur et al., 1995). Taken literally, DB’s aim of exactly filling the poverty gaps implies a BWR that is too high. However, it should not be assumed that any program operates exactly the way it is designed. There
suggests that a handful of high values on aid share could be influential in producing its negative and significant coefficient. Accordingly, in equation 4 we re-run equation 1, but dropping the 29 observations in which a donor’s aid share exceeds one-third. The aid share coefficient remains negative and significant, and even increases in magnitude.

The majority of aid share values in the data are under 1%. Arguably donors have no leverage in these cases (Munro, 2005), or if they do, there is no real increase in leverage from providing (say) 0.8% instead of 0.2%. We therefore checked to ensure that our results were not sensitive to the possibly spurious variation among these small values. Specifically, we tested an alternative aid share variable in which all values greater than 0% but under 1% were set equal to 1%. Results are shown in equation 5. The coefficient and significance of the aid share variable are affected only trivially, compared to the base specification of equation 1.

The majority of tied aid values in the panel data are equal to one of the extreme points, 0 or 1. We are unable to use a tobit estimator, because fixed-effects tobit estimates are biased. The value added of tobit estimation is relatively small in any case, according to Angrist and Pischke (2009). However, we report results in equation 6 for the smaller sample of values strictly between 0 and 1. Aid share remains highly significant in this limited set of observations.

Amegashie et al. (2007) raise the possibility that the costs of tying aid must be balanced against losses from graft when aid is untied. Where corruption levels are high, tying aid does not necessarily reduces its development benefits. A testable implication is that the negative relationship between aid shares and aid tying should strengthen when the most corrupt recipient countries are dropped from the sample. Accordingly, in equation 1 of table 4 we retain only the less corrupt 50% of observations, namely those scoring
# 3. Data, model specification, and basic results

The sovereign debt data used in our analysis (spread-to-benchmark, bond S&P rating, tranche volume, years to maturity, jurisdiction of issue) were sourced from the Dealogic Bondware database. We consider all countries defined by the World Bank as low, lower-middle and upper-middle income, with the addition of the Republic of Korea and the high-income nations of Latin America, Africa and Eastern Europe, as long as they are not within the group of smaller economies viewed as tax havens. We work with a sample of 53 emerging market governments that issued dollar-denominated debt in these two jurisdictions between 1990 and 2015.<sup>8</sup> Following Ratha, De and Mohapatra (2011) we use a reversed rating scale (with 1 denoting the highest rating and 21 the lowest) to convert the letter S&P rating into a numeric grade (Table 4). Within the converted scale, an investment grade dummy identifies all bonds graded 10 or lesser. More series were added to control for the macroeconomic environment, sourced either from Bloomberg (the VIX CBOE expected volatility index and the bond-level collective action clause indicator) or from the World Bank World Development Indicators (annual GDP growth). For secondary market spreads data from the Bloomberg database are used.

The econometric model has spread to benchmark at launch as the dependent variable. The first set of explanatory variables are bond-specific ones, such as S&P rating, an investment grade dummy, the size of the initial tranche, the maturity, and a dummy identifying bonds issued under U.K. governing law. The next set of variables control for macroeconomic conditions, both global and domestic. We include here the VIX index of stock option implied volatility, and the one-year lagged GDP growth rate. Year dummies capture global macroeconomic conditions not reflected in these variables.<sup>9</sup> In addition, a set of regional dummies is included to capture the effect of long established geographic connections
<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Electrified PremisesToilet AccessWater Television Refrigerator ConditioningAir WheelerTwo Car Computer WashingMachine Pucca Wall Pucca Roof<br>Reweighted CPHS 2015 NFHS, 2015 Reported CPHS 2015<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Electrified PremisesToilet Water Access Television Refrigerator ConditioningAir Two Wheeler Car Computer WashingMachine Pucca house<br>Reweighted CPHS 2019 NFHS, 2019 Reported CPHS 2019<br><!-- End of picture text --> Figure 3: Access to services and asset ownership: NFHS and CPHS 2015 (panel (a); top), NFHS and CPHS 2019 (panel (b); bottom) Notes: Figure shows asset ownership shares and access to public services. Electrified households in CPHS are defined as those that pay non-zero amounts towards electricity; in NFHS these include households possessing an electrical connection. Toilet in premises in NFHS includes all households that do not have a toilet facility or conduct open defecation. Water in premises in NFHS includes those that have piped water in dwelling unit or use improved water sources. Pucca houses are those that have both pucca walls and pucca roofs. NFHS 2019 all-India estimates are produced by multiplying state-level ownership shares with estimated number of households reported in state-level fact sheets by DHS. Graphs highlighted with a red box denote indicators that were not included in the set of target variables for reweighting. All indicators in 2019 belong to this group.

Overall, reweighting has helped close the biases for these education variables that are observed in the CPHS when using the reported weights. Discrepancies in education 16
as follows: _Yisct_ denotes, for firm _i_ in sector _s_ and country _c_ , the reported percent change in sales, 30 days prior to the interview at time _t_ , relative to the same period in 2019. _Sizeict_ denotes the size of the firm (log of the number of employees); _mobilityct_ gauges the country-specific severity of the shock through the average mobility around transit stations 30 days prior to the interview as measured in the Google mobility reports (Google, 2021). The amenability of remote work at the 4-digit sector level is denoted by _remotes_ while _internetc_ denotes internet penetration at the country level.<sup>4</sup> We include fixed effects for 2-digit sectors _δs_ and country fixed effects _δc_ to correct for potentially omitted country and sector specific time-invariant covariates. We also include quarter fixed effects _δt_ (April-June 2020, July-September 2020, etc.) to capture the changing severity of the shock.

We estimate equation 1 by least squares for exporters and non-exporters, together and separately, to compare estimates of the coefficient _γ_ across samples. Since sampling weights are not consistently available across data sets, we weight the estimates using the inverse of the number of observations

> 4Both enter the regression as z-scores.

7
(1) Approximately 5% (risk model 1: arsenic, salinity water-storage depletion combined) to 24% (risk model 2: arsenic and salinity) land area in Bangladesh is exposed to extremely high to high risks of elevated arsenic, salinity and groundwater depletion hazards.

(2) An estimated 4.5% (risk model 1) to 17% (risk model 2) of total population (144 million) of Bangladesh are exposed to extremely high to high risks of elevated arsenic, salinity and groundwater depletion hazards; 2.2 million to 8.6 million of these vulnerable populations are poor.

(3) Nearly a million (risk model 1) to over 4 million (risk model 2) children, who are exposed to extremely high to high risks of multi-hazard groundwater risks are either underweight or stunted.

(4) An estimated 32 km<sup>3</sup> of groundwater is withdrawn annually in Bangladesh for irrigation (90%), and domestic and industrial (10%) use; this volume is equivalent to ~4% of global groundwater withdrawals. Furthermore, an estimated 2.3 million (risk model 1) to over 5.7 million (risk model 2) people, who depend on groundwater-fed irrigation are exposed to extremely high to high risks of multi-hazard groundwater risks.

Finally, we have updated the existing, rather speculative estimates of population exposure to arsenic or salinity contamination individually. Our new estimation using the geospatial risk mapping suggests that nearly 33 million people are exposed to >50 μg/L (Bangladesh drinkingwater standard) of arsenic concentrations, and nearly 26 million are exposed to considerably high (electrical conductivity value >1,500 μS/cm) salinity in groundwater.

# **Contributors**

MS conceived the idea of groundwater risk mapping at the national scape in Bangladesh. MS also designed overall study and prepared groundwater arsenic, salinity and storage datasets with technical support from AZ. GJ and SSH provided extensive inputs to the preparation of the manuscript
the following, with all subscripts now explicit for completeness: Above, _eijt_ is an error, for time _t_ , place _j_ , and sensor _i_ . _dt_ is a time fixed effect and _cj_ is a place fixed effect, both of which are systematically included in Chen and Nordhaus (2011) and Henderson et al. (2012). Our controls are distinct in two respects. First, our newer night lights sample contains observations from both the historical DMSP-OLS sensor from 2005-2013, and the more modern VIIRS sensor from 2014-2020. Thus, we include a sensor fixed effect, _bi_ . Second, we systematically include a country-specific time trend _aj · t_ unless otherwise noted. Thus, the specifications of primary interest to us involve off-trend fluctuations, unlike previous

> 10 We use the notation of Henderson et al. (2012) with minor changes. Chen and Nordhaus (2011) named the model (p. 8,590).

13
resemblance and the coexistence of residential and commercial tenants in the same building). As a result, non-residential areas are not excluded in the population prediction of several gridded population datasets. Consequently, certain pre-EAs that are located within non-residential areas exhibit positive population values.

It should be mentioned that when the pre-EAs were verified against high-resolution satellite imagery base maps from ESRI and Google, many of these validations were carried out by observation. Since the dates of the satellite imagery were not considered, these evaluations might not be impartial. Therefore, without a comprehensive ground check, these assumptions cannot be verified.

**Digitized Elements and Boundaries:** Digitalized elements, both natural and man-made, are essential for the automatic generation of the national sample frame. The method in this paper made use of OpenStreetMap’s (OSM) extensive digital line availability on roads, railways, and waterways. However, the pre-EAs that were created exceeded the specified thresholds, such as population and geographic area, because of the poor quality and incomplete spatial coverage of the existing digitized boundaries. The primary causes of this are (i) incomplete and (ii) disconnected lines. If any of the natural and artificial features still need to be fully digitized, more work must be done either manually or automatically. A line should never be left open, and it should always be connected to other features as much as feasible. This is because disconnected lines will not be polygonized during the polygonization process, which leads to the creation of large pre-EAs. In addition to spatial coverage, the quality of the OSM attribute data is critical for the automatic creation of the national sample frame. Several line features, including major roads, rivers, and other impediments, were designated as uncrossable to facilitate the collecting of ground data and increase efficiency. The sole source that can determine the kinds of features on the ground is the attribute information. An inaccurate feature class in the attribute table may result in the national sampling frame’s uncrossable features being set incorrectly.

The
cancellation rates could be underestimated if one-year operational projects were included, as these projects are likely too young to reveal inherent problems that could lead to distress or cancellation.<sup>6</sup> Comparing the percentage of projects with multilateral support that are canceled (6.1%) to the rate of cancellation of non-MLS projects (5.8%), the rates are not statistically different (see Table 1 and Figure 2).

Table 1. Summary statistics of project data set, PPPs from 1990-2010

|PPP Status|Multilate<br>With MLS|ral Support<br>Without MLS|Total|
|---|---|---|---|
|Canceled PPPs|24|149|173|
|Total operational or concluded|368|2,409|2,777|
|Total(canceled,operational,or concluded)|392|2,558|2,950|
|Cancellation rate|6.1%|5.8%||

Source: PPI Database, Feb 2016 Figure 2. Relative cancellation rates of MLS and non-MLS projects, 1990-2010 <!-- Start of picture text --> Cancellation rate<br>0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%<br>With MLS Without MLS<br><!-- End of picture text --> Source: PPI Database, Feb 2016 These observed patterns could lead one to conclude that the cancellation rate of PPP projects is not reduced with financing from multilateral organizations. However, the effect of ‘not having MLS’ is not directly observable in the group of PPPs that do not have MLS. To this end, a proper non-MLS comparison group (counterfactual) for projects _with_ MLS needs to be established.

To do this, a probabilistic model was first estimated to calculate the probability of receiving multilateral support for the full set of PPP projects. Second, a