rafmacalaba/datause-encoder-data 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, which omits informal consumption and concludes that consumption taxes have, at best, limited redistributive impacts in developing countries (for recent studies, see Harris et al., 2018 and Lustig, 2018).<sup>3</sup> Our approach is thus related to recent the redistributive of differences in papers investigating implications consumption patterns across the income distribution, though these studies are in rich

> 3Two exceptions are Mu ̃noz and Cho (2003) and Jenkins et al. (2006) who use retailer information to classify expenditures as formal or informal in respectively the Dominican Republic and Ethiopia.

4
# **Figure  5.   Demand for Representative Business Service** **_s_ (Dixit-Stiglitz) Sectors in Regional Market (RM) m**

<!-- Start of picture text --> Business Service s<br>CES σ =1, 5<br>Rest of World<br>Own Regional Market Services<br>Cross Border Services<br>CES<br>σ =3 = esub<br>Multinational Service Russian Service<br>Providers in RM m Providers in RM m<br>CES σ =3 = esub CES σ =3 = esub<br>Multinational firms in RMm Russian firms from RMm<br><!-- End of picture text --> 68
alternative I-sets in order to gauge the robustness of the results to measures of political organization.

It is instructive to examine some features of PAC spending data, and assess whether they are in line with the Grossman-Helpman idea of money buying policy (as different from money buying mere access to policymakers).<sup>36</sup> Farm PACs contributed between $5.5 million and $7 million during each of the five election cycles. Among farm products, the most politically active were sugar PACs, dairy PACs and ranch PACs. Together these three PACS accounted for about 75% of total farm PAC contributions.<sup>37</sup> Over 200 PACs were politically active during this period. Cotton, dairy and wheat had the highest degree of PAC concentration, and represented by the equivalent of three or four equal sized PACs. Ranch, sugar, and fruits and vegetables were represented by the equivalent of eight to ten equal-sized PACs.<sup>38</sup> PAC money is clearly influential in agriculture. A study of the 1985 and 1990 voting during sugar legislations by Brooks, Cameron and Carter finds that in the House and Senate both votes were responsive to sugar PAC contributions.<sup>39</sup> Further, the value of sugar production in their constituency also determined how congresspersons voted. Sugar lobbies also targeted candidates that were likely to advance pro-sugar policies.

Two-thirds of agricultural PAC money went to House candidates and one-third to Senate candidates. Figure 1 indicates the top 20 recipients in the House of agriculture PAC contributions during the 1993-94 election cycle.<sup>40</sup> During this cycle, 13 of the top 20 House recipients of 36 Gawande (2006) details the structure of lobbying in agriculture over the 1990-2000 period in the United States.

37 Food manufacturing PACs, forestry and nursery PACs, agriculture service PACs and agriculture distribution PACs contributed more than did farm PACs.

38 Based on Herfindahl indices computed by the authors.

39 Brooks, Cameron and Carter (1998). Lopez (2001) also finds that PAC contributions influenced agricultural policies in other sectors.

40 The PAC data are in three relational data files: candidate information files (CN), PAC committee information files (CM), and files containing transactions between PACs and candidates (PAS). For each election cycle, aggregate contributions by each PAC to every candidate were computed from the PAS 16
The second component consisted of education grants paid to mothers conditional on their school-age children attending school on a regular basis. Specifically, mothers would receive transfers every two months if their children were enrolled in grades 3-9 and attended 85% or more days of school. The education grants were the largest component of the program and will be the part of the program we focus on. For example, the nutritional component (in 1998) was 100 pesos per month (or around $10 USD) which corresponded to around 8% of the beneficiaries income, while the average education grant per household with children was around 348 pesos per month (Attanasio et al, 2011). See Table I for a summary of the Progresa grant schedules.

Progresa was first targeted at the locality level (targeted localities had both a high marginality index and adequate health and schooling infrastructure). Within each locality, individual households were targeted via proxy-means testing and then split into two groups: poor and non-poor. Poor households were eligible for Progresa transfers while non-poor households were ineligible.<sup>17</sup> The program was initially implemented as follows: 506 of the targeted localities were randomly chosen across 7 states, and of these, 320 were randomly chosen to be offered the transfers immediately, starting May 1998. We refer to these 320 localities as the treated localities. The other 186 localities, the control localities, started the program 2 years later. Thus, eligible households in the treated localities started receiving transfers in May 1998, while eligible households in control localities did not start receiving transfers until May 2000.

Prior to the start of the program, a comprehensive survey was carried out in September 1997, and then supplemented with an additional survey in March 1998. These two surveys constitute the baseline survey. We will use these two surveys along with two surveys taken in November 1998 and November 1999 for our analysis. These surveys cover all households in the evaluation sample (i.e., the 506 localities) and contain extensive household information as well as information on each child including age, gender, education, labor supply, earnings, and school enrollment. Importantly, we know which households are considered eligible for the Progresa grants in both treated and control localities. These surveys cover approximately 24,000 households over 1997, 1998, and 1999.

17. See Skoufias et al (1999) for more information on targeting of localities and households.

22
# **Figure 5**

Marginal Effect of Enablers Data Economy by Intensity of COVID <!-- Start of picture text --> 2 3 4 5 6 7 8 9 10 11 12 131415<br>COVID Growth Shock (2020)<br>2<br>1<br>0<br>-1<br>Change in Digital Payments from<br>Change in Enablers Data Economy<br>-2<br><!-- End of picture text --> Notes. Figure derived from Table 5, model 5. Horizontal scale is logarithmic. The line reflects average marginal effects, the whisker endpoints mark the 90% confidence intervals.

44
middle-income countries, only 1 percent to mainly SSA low-income countries and the reminder 3 percent to advanced economies in the Middle East and in East Asia (Figure 2).

This paper uses data from a survey of BRI-related investment that has been commissioned by the World Bank in 2018 and compiled by WIND, a Chinese consultancy firm. The WIND database covers China’s construction contracts and other investment projects in non-financial sectors in 50 countries that have been identified in the news as being BRI related. For each project, the database reports their status: completed, under construction, or planned for the years 2013 to 2018. Planned projects are all officially confirmed. For the purpose of this analysis, we only include projects that are planned or under construction from 2016 to 2018. These data are complemented with investment information compiled by the World Bank’s country economists for Tajikistan, Georgia and Djibouti. While the BRI has been announced in 2013, actual public and publicly guaranteed debt data used in this paper are available until 2016 and are therefore unlikely to include BRI-financing for non-completed projects identified from 2016 to 2018.

Figure 2. BRI Recipient Countries <!-- Start of picture text --> By number<br>Region Income Lending terms<br>Sub- High<br>Saharan income, Low<br>Africa, 2 2 income,<br>Asia, 8South & Pacific, East Asia 10 middle Upper 6 IDA, Blend, 6<br>Middle income, 14<br>East & 21<br>North Lower<br>Africa, 8 Europe & Asia, 22Central income, middle 21 IBRD, 30<br>By investment amount<br>SSA High Low<br>2% income income<br>3% 1%<br>SAR IDA Blend<br>21% Upper 16% 16%<br>EAP middle<br>MNA 34% incom<br>9% e Lower<br>46%<br>middle<br>ECA incom IBRD<br>34% e 68%<br>50%<br><!-- End of picture text --> Source: WIND database.

The WIND database is the most comprehensive and up-to-date database on Chinese BRI-related outside investment. The American Enterprise Institute (AEI) compiles the China Global Investment Tracker (CGIT),<sup>8</sup> which tracks both overseas investment and construction contracts and identifies BRI-related projects (See Annex II for key statistics comparing the CGIT and the World Bank database). However, the AEI database includes fewer countries (38 vs 44 BRI countries with identified investment), it does not include data for

> 8 <u>http://www.aei.org/china-global-investment-tracker</u>

6
denotes the interest rate, _GDPt_ denotes gross domestic product (GDP), and _MUVt_ repre- _i_ sent a price index of manufacturing exports. The _β_ s are parameters to be estimated, and _εt_ is the error term.

Although some of the drivers often cited as key in explaining post-2004 price movements do not appear explicitly in the model, they are accounted for indirectly by the _S/U_ ratio. The diversion of food commodities to the production of biofuels increases consumption and reduces stocks, therefore leading to a lower _S/U_ ratio. Similarly, to the extent that weather patterns affect global production, they are accounted for by the _S/U_ ratio. Furthermore, the _S/U_ ratio captures the income effect because higher (lower) income leads to higher (lower) food consumption, with reverse impacts on the _S/U_ ratio. Investment fund activity is likely to affect price variability rather than long-term trends; therefore, its exclusion is unlikely to alter the results. The most notable exclusion is trade policies, which remained stable at a global level during the recent boom period, except for interventions with impacts within a specific year that targeted a few commodities (mostly rice and less so wheat).

The interpretation and signs of most of the parameters are straightforward. The _S/U_ ratio is expected to be negative because a low _S/U_ ratio (associated with scarcity) leads to high prices and vice-versa. To account for likely simultaneity bias between stocks and prices, the _S/U_ ratio enters the regression in lagged form. The price of crude oil should have a positive effect on the prices of food commodities because it is a key factor of production (Baffes 2007). The depreciation of the U.S. dollar—the currency of choice for most international commodity transactions—strengthens demand and limits supply from non-U.S. dollar commodity consumers and producers, thus increasing prices. In contrast, the effect of interest rates is ambiguous. High interest rates can be associated with lower commodity prices because they dampen current commodity demand and may change expectations about future economic activity because of lower investment; however, they may be associated with higher prices because high interest rates increase the required rate of return on storage (Newbery and Stiglitz 1989). Income growth (proxied by GDP of low- and middle-income countries, where most of the commodity demand growth is taking place) is expected to lead to higher prices. Last, because of the long period under consideration, the MUV was also included in the model, but it was treated as an explanatory variable (instead of adjusting prices) to relax the homogeneity restriction and obtain a direct estimate of the effect of manufacturing prices (Houthakker 1975).

# Data

World prices were taken from the World Bank’s database and represent annual (calendar) averages, expressed in U.S. dollar per metric ton (mt), except crude oil which is expressed in US dollars per barrel. The description of commodity prices is as follows: — 4 —
the student and teacher surveys using the unique population registry code (CURP).

Descriptive statistics of baseline characteristics are presented in Table 1 for the double grant experiment and Table 2 for the information experiment. Each table is organized as a balance test of school-level variables in Panel A and a balance test of student-level variables in Panel B.

In experiment 1, both general schools (which provide all instruction in Spanish) as well as indigenous schools (which provide instruction in both indigenous languages and Spanish) participated in the study. While indigenous schools are 10.4% more likely to be found in the treatment group, we find no systematic difference between treatment and control schools in other school- or studentlevel variables. We conduct a joint F-test of the null hypothesis that there are no mean difference between treatment and control groups across all variables in each panel. The _p_ -values suggests that our randomization provided balanced treatment and control groups at both the school level (our level of randomization) and student level.

The summary statistics in Table 1 highlight two important features of our study context. First, parents have low levels of education. Of the parent association presidents, 77.6% in control schools and 81.6% in treatment schools reported primary (grades 1-6 in Mexico) as their highest level of education completed. Second, teachers are substantially more educated than parents. On average, 76.6% of teachers in control schools and 75.4% of teachers in treatment schools completed either a teaching college degree or university degree.

Panel B of Table 1 shows the characteristics of students participating in the double grant experiment. Students in grades 3, 4 and 5 are surveyed, and nearly half of the study sample is female. The survey includes a number of questions about household assets, which are used to construct a wealth index using Principal Components Analysis (PCA). We further normalize the wealth index to be mean 0 with standard deviation 1 using the control group at baseline. Language and math test scores are from the national standardized exams (ENLACE). The test ranges from 200 to 800 points, with a national average of 500 and a standard deviation of 100. On average, students in experiment 1 score below the national average, with mean test scores ranging from 437 to 450. In our analyses below, we standardized the test scores in Spanish and Math for each grade to be mean 0 with standard deviation 1 using the control group scores at baseline.

In the information experiment (Table 2), only general schools participated in the study. Overall, we do not find significant differences between treatment and control schools in school-level variables.

8
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because of business or personal connections.

To examine whether households change their views and attitudes toward women after following violent events in the media, we compare the effects of distant Boko Haram and Fulani attacks among clusters that have more access to information (e.g., through TV news coverage) with the effects among clusters that have limited access to information. We interact the indicators of violent attacks with an indicator of an above-median share of households with access to a TV in the cluster, i.e., we use a dummy variable that indicates whether that share is above the median (43 percent) and interact this indicator with the continuous variable of violent events. We leverage established literature suggesting that events that are relatable to individuals (e.g., because they are familiar with the place or have business or personal connections with it) have a greater impact (Alsan and Wanamaker, 2017; Tabellini, 2008). Similarly, many individuals use local television news as a relevant source of information and the propensity of reporting about a violent event is greater in the television station of the affected locality.

The results are reported in Table 9. We do not observe significant differences in the effects of distant Boko Haram attacks between villages with high TV coverage and villages with low TV coverage, except for attitudes toward domestic violence (Column VII in Panel A of the table). As to those attitudes, in villages with higher TV coverage, we observe a significantly larger decrease in the likelihood that women report that domestic violence is acceptable. Importantly, we observe the same pattern for Fulani attacks (Column VII in Panel B). This is the same outcome variable for which we are unable to explain the effects of distant Boko Haram attacks among women through the IDP channel (see Section 6.1). This suggests that the change in attitudes of women toward violence is partly explained by access to information.

# **7 Conclusion**

In recent years, many countries have witnessed an increase in violent conflict (World Bank, 2011), including conflict related to religious extremism. Jihadist extremist groups have 36