rafmacalaba/datause-encoder-data huggingface dataset:
than in cash) starting in October 2015. To prepare for the implementation of the mandates, the financial inclusion law required banks to offer free bank accounts that fulfilled certain criteria (specified numbers of free transfers, withdrawal etc.).<sup>9</sup> Figure A.8 shows that the use of bank accounts and electronic payment technology in Uruguay increased significantly between 2011 and 2017, much more than in most other countries over the same period.

# **3.4 Data**

To study the effect of electronic payments on tax compliance, we merge multiple data sets. First, we use transaction-level card payment data, which contain the universe of transactions between 2007 and 2016. Credit and debit card companies send these data to the tax administration every month. The data contain the transaction date, transaction amount, VAT rebate amount, the tax ID of the firm, and a POS identifier. We can thus count the number of POS a firm uses. We collapse the data at the firm-month level.<sup>10</sup> While we refer to these data as the card payment data for simplicity, it is important to note that these data contain all electronic transactions (e.g. including transactions via apps such as PayPal, Square etc.).<sup>11</sup> We merge the card transaction data with monthly VAT returns, containing all line items from the tax return.<sup>12</sup> Our main outcome variables are output VAT (i.e. VAT on sales), input VAT (i.e. VAT paid on inputs and deducted from output VAT), and the net VAT liability (=max(output VAT - input VAT, 0)).

Information on firms’ sector of activity is obtained from the firm registry, which contains the six-digit CIIU industry code for all firms ( _Clasificaci ́on industrial internacional uniforme_ ). In the CIIU, the first two digits of the CIIU code capture the division. Division number 46 designates retail firms and division number 47 designates wholesale firms. The firm registry also documents in which one of Uruguay’s 19 departments the firm is located.

Finally, we have access to the list of firms that received the subsidy for POS rental, with the months during which the firm received the subsidy and the total subsidy amount each month.

> 9Having to offer the free bank accounts became a mandate for banks in October 2015. For wage earners and social benefit recipients who did not exercise the option to create a bank account by June 2016, the employer or social security agency had to choose a financial institution for the beneficiary by September 2016. It became mandatory for wages and pensions to be transferred into bank accounts from May 2017 onwards. In 2014, 43 percent of respondents in the World Bank Global Findex Survey indicated having used a debit or credit card in the previous year.

> 10A variable indicating the type of card transactions (debit or credit card) is available only since August 2014.

> 11We are also in the process of trying to obtain data on the number of credit and debit cards issued around the time of the reform from a large private bank.

> 12These data are also used and described in B ́ergolo et al. (2021) and Foremny et al. (2018).

10
in equation (1) capture time-invariant heterogeneities across countries, while the weekly time dummies capture aggregate trends in demand for services.

Our identifying variations come from within-country temporal dynamics in COVID-19 cases/ deaths. The capacity of health systems to respond to the pandemic, sectoral composition of economic activities, degree of urbanization and population density influence the spread of the pandemic. They are also likely correlated with the level of development of countries, hence demand for services. Such time-invariant differences across countries will be absorbed by country fixed effects. Heterogeneities in government responses to the pandemic present another challenge to our identification. These responses, which include mobility restrictions and business closures, are direct consequences of COVID-19 and hence are likely to amplify the impacts of the spread of the pandemic. To isolate the effects of government measures, we interact the spread of the pandemic with government responses, by generating dummy variables for the weeks during which government measures have been in place. As the spread of the pandemic has been swift, conditional on government responses, such temporal dynamics in COVID-19 cases/ deaths are likely to be exogenous. Thus, our coefficient of interest, 1, captures the causal impact of a unit increase in the number of cases. We also estimate the impacts of COVID-19 deaths by replacing ββ the number of confirmed cases in equation (1) by confirmed deaths.

The effects of COVID-19 cases and deaths are likely to be non-linear. The first 100 confirmed cases/deaths, for instance, are probably more impactful than the subsequent additional 100 cases due to behavioral heuristics. Thus, we consider slightly different measures of the spread of the pandemic by constructing "milestone" event indicator variables for the number of confirmed cases and deaths. For each country, three dummy variables are constructed for: (i) the first confirmed case/death, (ii) the first 10 confirmed cases/deaths, and (iii) the first 100 confirmed cases/deaths. We re-estimate equation (1) by replacing the number of confirmed cases and deaths by these "milestone" dummy variables, which also assume time-varying values.

A country's contribution to changes in global demand for services, as captured by the Google search index, depends on its population size and internet penetration. To adjust for such factors, we weight our regressions using countries’ population with internet access. To uncover potential heterogeneities in the impacts due to policy harmonization, geographic proximity, or similarity in level of development across regions, we estimate our model separately across continents. To probe the robustness of our results, we also estimate our models only for the 15
TRUST stands for Transparency, Risk management, Use of country systems, Strengthening capacities and Timely and predictable aid.

The focus on aid effectiveness at the political level articulated in the New Deal is supported by two indicators: the share of net ODA in Gross National Income (GNI) and net ODA per capita. The comparison between FCS and non-FCS below shows that both indicators are significantly higher for FCS.

**Figure 1: The Importance of Aid in FCS**

<!-- Start of picture text --> Net ODA per capita ($)<br>1000<br>: FCSs<br>: Non-FCSs<br>FCS Ave $197<br>(Median $77)<br>100 Non-FCS Ave<br>$101<br>(Median $50)<br>10<br>1<br>0.01 0.1 1 Non-FCS Ave 10 FCS Ave 100<br>5.3% 19.7%<br>(Median (Median<br>2.6%) 14.2%)<br>Net ODA % GNI<br>0.1<br><!-- End of picture text --> _Source: Author’s Calculation based on the World Bank database_ In Figure 1, the horizontal axis shows net ODA in GNI in log, and the vertical axis shows the net ODA per capita in US$. In order to avoid fluctuations across years, data in these indicators uses annual average between 2005 and 2009. Twenty-nine countries marked in red are FCS and 106 countries marked in blue are non-FCS.

The unweighted average net ODA as a share in GNI reaches almost 20 percent in FCS, which is more than three times as high as in non-FCS (5.5 percent). The unweighted net ODA per capita in FCS (US$197) is almost twice compared with that in non-FCS (US$101). Medians of these indicators show that while the gap between FCS and non-FCS is narrower on net ODA per capita, the gap is wider on net ODA as a share of GNI. These indicators show that aid effectiveness plays a relatively more significant role in FCS than in non-FCS.

4
**Table 20. Impact of China and India full liberalization (CIFTL scenario)**

|**Peanuts Trade**|1999/2000|New levels aft<br>2000/01|er reform<br>2001/02|1999/2000|baseline levels<br>2000/01|<br>2001/02|average change<br>for 3 years (%)|
|---|---|---|---|---|---|---|---|
|Net Exporters||||||||
|<br>Argentina|223|174|160|226|177|185|-6%|
|China<br>Gbi|586<br>8|531<br>11|589<br>16|540<br>8|450<br>11|525<br>15|13%<br>3%|
|ama<br>India|55|-38|0|100|100|125|-94%|
|Malawi|4|5|5|2|3|3|84%|
|Nigeria<br>|12|19|22|0|0|0|1776%|
|Senegal|-24|-19|-9|2|4|5|-708%|
|South Africa|24|19|37|20|16|35|14%|
|<br>USA|258|142|238|255|141|231|2%|
|Total Net Exports|1147|845|1058|1153|902|1124|-4%|
|Net Importers||||||||
|<br>Canada|112|103|106|116|107|110|-4%|
|European Union|448|433|453|457|441|463|-2%|
|Mexico|96|67|70|101|72|75|-6%|
|Rest of the World|302|232|369|290|272|415|-7%|
|Residual|189|10|61|189|10|61|0%|
|Total Net Imports|1147|845|1058|1153|902|1124|-4%|
|**Peanuts Price US Run. 40/50, CIF Rotterdam $/mt**|877|952|763|820|888|700|8%|
|**Peanut Meal Trade**||||||||
|<br>Net Exporters||||||||
|<br>Argentina|76|57|67|67|50|52|18%|
|China|114|122|126|9|15|25|759%|
|Gambia|7|12|11|5|10|10|22%|
|India|-309|-294|-210|10|20|100|-1690%|
|Malawi|1|0|0|0|0|0|46%|
|Nigeria<br>|26<br>|26<br>|34<br>|0<br>|0<br>|0<br>|2867%<br>|
|Senegal<br>South Africa|150<br>-3|165<br>3|155<br>2|130<br>-5|144<br>0|140<br>0|14%<br>139%|
|USA|37|33|35|6|5|5|563%|
|Rest of the World|108|108|80|8|14|-12|385%|
|Total Net Exports|205|233|300|230|258|320|-9%|
|Net Importers||||||||
|European Union|161|169|158|186|194|178|-13%|
|<br>Residual|44|64|142|44|64|142|0%|
|Total Net Imports|205|233|300|230|258|320|-9%|
|<br>**Meal Price 48/50% CIF Rotterdam $/mt**|144|159|147|122|134|125|18%|
|**Peanut Oil Trade**||||||||
|<br>Net Exporters||||||||
|<br>Argentina|51|45|53|46|41|42|16%|
|China|53|61|74|0|5|2|3354%|
|Gambia|5|6|6|0|0|0|567%|
|India|-240|-226|-269|0|0|0|-24481%|
|Malawi|2|2|2|0|0|0|185%|
|Nigeria|71|71|76|35|35|30|120%|
|Senegal|116|126|126|98|102|109|19%|
|South Africa|2|2|2|0|0|0|224%|
|<br>USA|26|-14|9|2|-30|-10|326%|
|Rest of the World|92|79|85|18|11|8|665%|
|Total Net Exports|180|151|166|199|164|181|-8%|
|Net Importers||||||||
|<br>European Union|131|97|105|150|110|120|-12%|
|Residual|49|54|61|49|54|61|0%|
|Total Net Imports|180|151|166|199|164|181|-8%|
|**Peanut Oil Price CIF Rotterdam $/mt**|924|857|844|744|685|659|26%|
|<br>**Welfare effects(million $)**|765|1013|815|-|-|-|864|

29
**Table 7:  FUA concentration anomaly trends: inside/outside test results (p(** γ1jI **) ≤ p(.05); FUAs with populations > 100,000)**

Conditions for Rejection of H0: sign(γ1jI) ≠ sign(γ1jO) or p(γ1jI) ≤ p(.05); p(γ1jO) > p(.05)

|Sign Reversal|Count|Percent|
|---|---|---|
|Yes|92|27.5|
|No|243|72.5|
|Total|335||
|If No Sign Reversal:|||
|Significance (95%)|||
|Yes [p(γ1jO) ≤ p(.05)]|107|44.0|
|No [p(γ1jO) > p(.05)]|136|56.0|
|Total|243||
|Atmospheric Circulation|||
|Hypothesis|||
|Accepted|107|31.9|
|Rejected|228|68.1|
|Total|335||

18
Policy Research Working Paper 8472

# **Abstract**

Small area poverty maps allow for the design of policies based on spatial differences in welfare. They are typically estimated based on a consumption survey reporting on poverty and a census providing the spatial disaggregation. This paper presents a new method which allows for the estimation of up-to-date small area poverty maps when only a dated census and a more recent survey are available and predictors and structural parameters are subject to drift over time, a situation commonly encountered in practice. Instead of using survey variables to explain consumption in the survey, the new approach uses variables constructed from the census. The proposed estimator has fewer data requirements and weaker assumptions than common small area poverty map estimators. Applications to simulated data and to poverty estimation in Brazil show an overall good performance.

This paper is a product of the Poverty and Equity Global Practice. 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 upape@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
have an average score of 3.1 on the index of executive constraints and an average degree of heterogeneity close to 55 out of 100. Countries with ongoing slumps score 1.54 in terms of executive constraints and have an average level of heterogeneity of about 77. The values refer to the smaller sample, but are not much different for the data underlying panel (b). This strongly suggests that it is the composition of the two groups that matters and not the fact of censoring. The whole point of duration analysis is that we can use censored observations without introducing bias. Note that if the slumps are defined in terms of GDP rather than GDP per capita, there are considerably fewer censored spells and our results hold in both samples without the censored data points.

– **Table 4** Robustness: heterogeneity, outliers and recurrent spells

||||_Current _|_modifcation_|||
|---|---|---|---|---|---|---|
||Country<br>RE<br>(1)|Without<br>Ex-Soviet<br>(2)|Without<br>Africa<br>(3)|Without<br>Censored<br>(4)|Single<br>Spells<br>(5)|Endogenous<br>Institutions<br>(6)|
||_P_|_anel (a) Stru_|_ctural break_|_s estimated _|_with size = _|_0.10_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.292***<br>(0.100)|-0.274***<br>(0.078)|-0.093*<br>(0.054)|-0.035<br>(0.051)|-0.256**<br>(0.105)|-0.213**<br>(0.087)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.019***<br>(0.007)|0.021***<br>(0.005)|0.007**<br>(0.004)|0.009**<br>(0.004)|0.020***<br>(0.006)|0.018***<br>(0.006)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.005**<br>(0.002)|-0.004***<br>(0.001)|-0.002*<br>(0.001)|-0.002<br>(0.001)|-0.005***<br>(0.002)|-0.005***<br>(0.002)|
|Region FE|YES|YES|YES|YES|YES|YES|
|Income Level FE|YES|NO|NO|NO|NO|NO|
|Exits|43|40|30|43|38|38|
|Spells|54|51|35|43|46|55|
||_P_|_anel (b) Stru_|_ctural break_|_s estimated _|_with size = _|_0.20_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.212***<br>(0.080)|-0.250***<br>(0.063)|-0.104**<br>(0.053)|-0.085*<br>(0.044)|-0.250***<br>(0.081)|-0.221***<br>(0.073)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.012**|0.014***|0.003|0.008**|0.012*|0.012**|
||(0.006)|(0.005)|(0.005)|(0.003)|(0.006)|(0.005)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.004**<br>(0.002)|-0.004**<br>(0.001)|-0.002<br>(0.002)|-0.002**<br>(0.001)|-0.004**<br>(0.002)|-0.005***<br>(0.002)|
|Region FE|YES|YES|YES|YES|YES|YES|
|Income Level FE|YES|NO|NO|NO|NO|NO|
|Exits|61|57|42|61|52|54|
|Spells|74|70|48|61|61|75|

_Notes_ : All models include the US real interest rate, the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for recurrent spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

Until now, we have treated spells recurring in the same country as interchangeable.

28
percent of those in group 1 remembered the _entreprenant_ program could be an argument for this last point (see section 4 and Table 5).

# **7. Conclusions and next steps**

These early findings show that the launch of the entreprenant status and assistance registering under it has resulted in an increase in formalization, with this increase greater when the status is coupled with access to business services and tax mediation. A key question is then whether it is worth it for these firms to formalize from either a firm viewpoint or a public policy viewpoint. This depends on the extent to which formalization enables firms to access finance, gain more customers, and grow, and the extent to which incorporating firms into the formal sector results in increased tax payments or other public policy benefits. Our ongoing impact evaluation will attempt to measure these channels.

# **References**

Alcãzar, L., Andrade, R. and Jaramillo. M. (2010): “Panel/tracer study on the impact of business facilitation processes on enterprises and identification of priorities for future business enabling environment projects in Lima, Peru – Report 5: impact evaluation after the third round.” Report to the International Finance Corporation. World Bank Group, Washington, DC.

Andrade, G. H., Bruhn, M., and McKenzie, D. (2013): “A helping hand or the long arm of the law” Experimental evidence on what governments can do to formalize firms”, _World Bank Economic Review_ , forthcoming.

Bruhn, M. (2013): “A Tale of Two Species: Revisiting the Effect of Registration Reform on Informal Business Owners in Mexico” _Journal of Development Economics_ 103: 275–83 Bruhn, M., McKenzie, D. (2009): “In Pursuit of Balance: Randomization in Practice in Development Field Experiments”, _American Economic Journal: Applied Economics_ , 1(4): 20032.

Bruhn, M., McKenzie, D. (2014): “Entry regulation and formalization of microenterprises in developing countries”, _World Bank Research Observer_ , 29(2): 186-201.

Campos, F., Goldstein, M., and McKenzie, D., (2015): "Short-term impacts of formalization assistance and a bank information session on business registration and access to finance in Malawi," _Policy Research Working Paper Series_ 7183, The World Bank.

De Giorgi, G., and R. Rahman. (2013): “SME’s Registration: Evidence from an RCT in Bangladesh.” Stanford University, Stanford, CA, and World Bank, Washington, DC.

24
igation policies (Peng et al., 2021) and differences in climate adaptation can create winners and losers.

The rest of the paper is structured as follows. Section 2 provides the conceptual framework for our study, Section 3 describes the data in detail, and Section 4 lays out our empirical strategy. Section 5 reports the effects of heat on arrests. Section 6 presents estimates for how the probability of arrest will change due to climate change and discusses the role of adaptation in reducing these effects. Section 7 concludes.

# **2 Conceptual Framework**

There is a robust literature both in and outside of economics on the adverse relationship between heat and crime. Hotter days have been shown to increase crime across a range of settings in the United States (Ranson, 2014; Heilmann and Kahn, 2019; Harries et al., 1984; Anderson, 1989; Anderson et al., 2000; Anderson, 2001). Other work has extended these findings to middle (Bruederle et al., 2017; Garg et al., 2020) and low income settings (Blakeslee et al., 2018). In much of this work, heat appears to increase non-property crimes more than property-focused crimes.

Different hypotheses have been advanced to explain this well-documented empirical relationship. Broadly, these can be classified as pointing to a “rational” economic channel for the impact of heat in the mold of Becker (1968) or towards an explanation that focuses on the role that heat plays in reducing psychological control. Arguments in favor of the former mechanism situate the decision to commit a crime in an expected utility framework and focus on the role that heat may play in changing either the costs or benefits of crime. For example, heat may change the effort that police exert in pursuing criminal complaints by making effort more costly (Obradovich et al., 2018) and so reduce the expected cost of committing a crime by reducing the likelihood of being caught. Heilmann and Kahn (2019) uses data on criminal and police activity in Los Angeles to test this hypothesis and finds 8
Table 8: Results with national economic condition indicators as control variables

|Dependent Variable|Employe|d with social se|curitycoverage||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|-0.386***|-0.0323***|-0.0907***|-0.00185|
|1.pot<br>exp#c.indepvar|-0.152*|0.0388***|0.0449*|-0.00530*|
|2.pot<br>exp#c.indepvar|-0.156**|0.0142|0.0158|-0.00580***|
|3.pot<br>exp#c.indepvar|-0.137|0.0159|0.0215|-0.00976**|
|4.pot<br>exp#c.indepvar|-0.0324|0.00392|0.0237**|-0.00420*|
|5.pot<br>exp#c.indepvar|0.204*|0.00256|0.0326***|-0.00148|
|6.pot<br>exp#c.indepvar|0.268**|-0.00287|0.0249**|0.00210|
|7.pot<br>exp#c.indepvar|0.133|0.00232|0.0193**|0.00380|
|8.pot<br>exp#c.indepvar|-0.194|-0.0135|-0.00925|-0.000245|
|9.pot<br>exp#c.indepvar|-0.240**|-0.0222*|-0.0296**|-0.00347|
|10.pot<br>~~e~~xp#c.indepvar|-0.278**|-0.0475***|-0.0420***|-0.00839***|
|indepvar|-0.00162|-0.0108|-0.0359*|-0.000769|
|cohort|-0.0129***|-0.00853***|-0.0116***|-0.0106***|
|# obs|14,311|14,311|14,311|14,311|
|R-squared|0.819|0.819|0.819|0.818|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: The dummy variable of being employed with social security coverage or not is regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation. (ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

20
and borrowers have to deal with an ineffective collateral system for movable and immovable assets. Land titling is hampered by poor and unreliable records, high fees, and fraud. The movable collateral regime is effectively available only for limited liability companies, as these are the only ones that can register charges with the Registry of Joint Stock Companies (Meagher, 1998). This effectively excludes most of the small and micro enterprises from using such assets as collateral. These problems have increased reliance of lenders on personal guarantees from company directors. The bankruptcy legislation is rarely used in practice as a tool to resolve corporate distress, with less than half of the cases in the two bankruptcy courts actually being declared bankrupt at the end of the process (ADB, 2004). This is partly due to the stigma attached to bankruptcy and possible criminal prosecution of bankrupt debtors (Sobhan and Werner, 2003). While some progress has been made with the recent introduction of the money loan courts that allow a faster and less bureaucratic enforcement of claims, it is still too early to say whether this reform has significantly impacted the availability and cost of loans. Further, appeals to the high court still pose a major bottleneck to the quick resolution of claim resolution as they are to wait in queue with other cases (Sobhan and Werner, 2003).

The deficiencies in the contractual framework are reflected in cross-country benchmarking. Bangladesh is ranked 65<sup>th</sup> in the Ease of Doing Business Indicator (out of 155), a rank that is driven by deficiencies in the conditions for registering property where Bangladesh ranked 151<sup>st</sup> . Bangladesh is ranked 75<sup>th</sup> in enforcing contracts and 77<sup>th</sup> in closing a business.

# **4.3. Informational framework**

As financial markets and institutions arise due to information asymmetries that prevent direct interaction between multiple savers and investors, effective financial intermediation 16