rafmacalaba/datause-encoder-data huggingface dataset:
especially in Africa (Aker et al., 2013; Jack and Suri, 2015). The reduction of such costs and the ease of money transfer via mobile money improves the liquidity of the firm as cash flows through the firm at a faster rate (Bangens and Soderberg, 2011). There is an established literature that has identified the positive effect of improved cash flows on investment (Kadapakkam et al., 1998) and one can hypothesize that sufficient increases in cash flows induced by mobile money may lead to the same outcome of increased investment. Furthermore the reallocation of time away from dealing with financial transactions, including managerial time, to more productive activities can lead to increasing profitability (Frederick, 2014), potentially leading the firm to pursue growth opportunities through investment.

Beyond reduced transaction costs, there are other channels that have been empirically uncovered by which mobile money use could indirectly lead to increased investment. Using a dynamic general equilibrium model with heterogeneous entrepreneurs, imperfect credit markets, and the risk of theft, Beck et al. (2015) find that mobile money use increases the use of trade credit which in turn improves firm performance. The wider literature has documented the role of trade credit, via reputation effects, in increasing access to external sources of financing such as bank financing (Alphonse et al., 2006; Buckart and Ellingsen, 2004). Increases in access to finance, in turn, can facilitate investment (Rajan and Zingales, 1998; Levine et al., 2000; Demirguc-Kunt and Maksimovic, 1998; Cull and Xu, 2005). Finally, the use of transaction, saving, and financial operations data from the digital financial services platform allows to generate credit scores and evaluate and price credit risk. This can help to overcome the so-called collateral technology hurdle, which has hindered the development of credit markets in Africa (Ndung’u, Morales, and Ndirangu. 2016).

# **3. Empirical Strategy**

# **Data**

The data source used in this paper consists of firm-level surveys for Kenya, Tanzania, and Uganda conducted by the World Bank’s Enterprise Surveys with most of the information referencing fiscal year 2012.<sup>4</sup> The sample used for analysis covers a total of 1,228 manufacturing and services firms

> 4 The same set of questions on the use of mobile money was included in the Enterprise Survey conducted in Zambia as well. The data for Zambia, however, show a very low rate of adoption with only 25 firms out of a total sample of 720 using mobile money. Given the small sample of mobile money users, Zambia is not included in the analysis.

6
We combine the estimates from the regressions with the results of the GIS analysis to quantify the potential trade effects of the BRI. An upper‐bound estimate is that total BRI trade increases by 4.1 percent. This assumes that trade in all products can switch transportation modes relatively easily to take advantage of the improved transport links. A lower‐bound estimate, which assumes that products cannot switch transportation mode, is that trade increases by 2.5 percent. The trade effects of the BRI present a large variation across individual countries and across sectors. Largest improvements in trade are predicted for countries in Sub‐Saharan Africa, Central and Western Asia and for products that use time sensitive inputs such as chemicals. Complementing BRI infrastructure improvements with trade reforms such as deepening trade agreements or better market access would boost trade by respectively, 7.9 and 12.9 percent.

While there is much debate surrounding the Belt and Road Initiative, little rigorous economic analysis on its effects has been done so far. Exceptions include Villafuerte, Coron and Zhuang (2016) and Zhai (2018), who use a Computable General Equilibrium (CGE) model to assess the impact of the BRI on trade and economic growth.<sup>4</sup> Differently from the CGE approach, our paper builds on a number of recent econometric studies. First, there is a recent literature that aims at assessing the impact that reduction in time to trade, due to trade facilitation reforms or infrastructure improvements, has on trade flows (e.g. Djankov, Freund and Pham, 2010; Hummels and Schaur, 2013; and Baniya, 2017). Second, several studies analyze the trade effects of infrastructure projects (Donaldson, 2013; Duranton, Morrow and Turner, 2012; Alder, 2015). Finally, a subset of this literature also relies on georeferenced data and GIS analysis to assess the trade and spatial effects of transport infrastructure (e.g. Roberts, Deichmann, Fingleton and Shi, 2010; Roberts, Deichmann, Fingleton and Shi, 2010).

The rest of the paper is organized as follows. Section 2 presents the GIS analysis. The econometric strategy and results are discussed in Section 3. Section 4 outlines the trade impact of the BRI. Concluding remarks follow.

> 4 Related work by de Soyres et al., Mulabdic and Ruta (2018) and Maliszewska and van der Mensbrugghe (2018) uses the data on trade costs from de Soyres et al. (2018) in a structural and CGE trade model respectively.

4
Note that while Predictions 1 to 3 follow from the overall decline in the electoral asymmetry term _x_ following the implementation of the DPEP, Predictions 4 and 5 more specifically point to the mechanisms behind such a decline. In Section 6, we discuss our empirical results, which we present in the order in which the model predictions are introduced above. In Section 7, we further explore the mechanisms through which the DPEP impacted political selection.

# **4 Data**

Our data on DPEP implementation comes from Government of India reports, supplemented with information from other studies on the program; e.g., Azam and Saing (2017); Khanna (2023); Pandey (2000); Sunder (2020). We combine this data with electoral constituency (or seat) level data on state election outcomes from the Socioeconomic High-resolution RuralUrban Geographic data set for India (SHRUG) created and made available for public use by the Development Data Lab (Asher and Novosad, 2017). The SHRUG data set contains detailed information on several economic, environmental, and political aggregates at a local geographic level, including constituency-level state election information relevant for our analysis (the number of contesting candidates in each seat, candidates’ gender and political affiliations, and vote shares for each candidate). We make use of this electoral data for all state elections that took place in India during the period 1974-2018. We also utilize satellite data on district-level night-time luminosity from SHRUG (Asher et al., 2021) to estimate the impact of the DPEP on economic growth, for which night-time lights data has been used as a proxy measure in previous studies (Henderson et al., 2012; Prakash et al., 2019; Storeygard, 2016).

To measure heterogeneity in the effectiveness of DPEP implementation in improving school infrastructure and children’s engagement with primary education, we use data from India’s District Information System for Education (DISE) from the year 2004-05 (the first post-DPEP year for which the data are available). The DISE data are collected annually, and contain detailed information from all districts in India on school construction and infrastructure availability as well as on measures of student participation and the number of students appearing for exams. We use this data to construct an index of district-level educational performance that incorporates infrastructural inputs and student engagement measures, and investigate whether more female MLAs are elected in districts where the DPEP increased educational performance more effectively.

In addition, we utilize original candidate affidavits data provided by the Association for Democratic Reforms to estimate whether the DPEP had any impact on the composition of the pool of candidates. The data contains candidate declarations of their characteristics 12
to the formation of isolated pockets of subsistence. These autarkic sub-economies are usually characterized by extreme poverty (Cadot, Dutoit and de Melo 2005; Cadot, Dutoit and Olarreaga 2005). Such trends have been observed in Kazakhstan, where Raballand et al. (2005) documented the evolution of traffic density in the Kyzyl-Orda region. The region has the lowest road density in Kazakhstan (11.4 km per 1,000 km2), mainly due to the lack of maintenance to rural roads. The trucking fleet worsened and shrunk from 2,456 units in 1990 to 601 in 2000 (Satubalding and Zhandaulet, 2002; pp.67-68).

Trade costs not only depend on the quality of the “hard” infrastructure (roads, railroads, airports, etc.) but also on the “soft” aspects of the transit sector’s business environment. A recent report by the Asian Development Bank (2006) on regional cooperation in trade, transport and transit, outlines the results of a “logistics friendliness” survey (based on data provided by approximately ten companies involved in international transport and trade in Central Asia). Central Asian countries were surveyed during late 2004-early 2005 with the aim of assessing the affordability, speed and reliability of their logistics’ services. According to the survey results, Central Asian republics rank lower than Russia in all three dimensions. Kazakhstan and the Kyrgyz Republic rank best in the region, whereas Tajikistan ranks worst.<sup>8</sup> Moreover, reliability consistently appears as the most troublesome issue, suggesting the presence of severe managerial problems that could potentially be tackled through capacity-building programs.<sup>9</sup>

# **_Network structure_**

Finally, Central Asia has inherited a network structure that was designed for the needs of the Soviet Union, but is not necessarily optimal once market forces have realigned trade flows according to the principle of comparative advantage. The problem is inherent in any geographical area undergoing economic change and industrial relocation. For former Sovietbloc countries, however, it has been all the more severe as trade flows and industrial location were the result of central planning, which involved highly distorted prices (also for transport services) and paid scant attention to economic efficiency. Moreover, independence

> 8 These results are broadly consistent with the results of a similar survey reported in Ojala, Naula and Queiroz (2004).

> 9 Given the lack of available data on road and rails maintenance for such a database, we use the indicator from Limao and Venables (2001). It is commonly used since in the literature to assess the infrastructure level in international trade studies, even if it focuses on quantitative aspects of infrastructre rather than on qualitative ones.

8
**<u>Table 2: Comparison of the Gender Gaps in Different Sectors Between 2014 and 2019</u>**

||Gap in 2014|Gap in 2019|Significance of 2014 vs. 2019 Gap|
|---|---|---|---|
|LFP|0.12**|0.10**||
|By Age||||
|Age 15-29|0.11**|0.09**||
|Age 30-64|0.13**|0.11**|**|
|By Urbanicity||||
|Urban|0.12**|0.20**|**|
|Rural|0.12**|0.07**|*|
|By Region||||
|North|0.23**|0.07**|**|
|Central|0.07**|0.16**|**|
|South|0.03**|0.06**|**|
|West|0.14**|0.12**||
|Monthly Wages|68%**|82%**|**|
|By Age||||
|Age 15-29|54%**|76%**|**|
|Age 30-64|82%**|88%**|**|
|By Urbanicity||||
|Urban|64%**|49%**|**|
|Rural|58%**|97%**|**|
|By Region||||
|North|68%**|94%**|**|
|Central|73%**|62%**|**|
|South|36%**|87%**|**|
|West|75%**|83%**|*|
|By Age||||
|Business Revenues|62%**|61%**||
|By Age||||
|Age 15-29|37%**|65%**||
|Age 30-64|69%**|59%**||
|By Urbanicity||||
|Urban|49%**|50%**||
|Rural|62%**|66%**||
|By Region||||
|North|11%|74%**|**|
|Central|56%**|39%||
|South|60%**|71%**||
|West|84%**|62%**|**|
|Harvest Value|56%**|61%**|**|
|By Age||||
|Age 15-29|48%**|58%**|*|

35
**Table 1. Preferences for Redistribution and the "Socialist Legacy": Summary of Selected Studies**

|Study|Data<sup>1</sup>|Year|Sample|Socialist<br>Legacy<sup>2</sup>|
|---|---|---|---|---|
|Andreβ and Heien (2001)|ISSP|1992|Germany (East and West), Norway, USA|Yes|
|Alesina & Fuchs-Schündeln (2006)|GSOEP|1997, 2002|Germany (East and West)|Yes|
|Austen (2002)|ISSP|1987, 1992|Hungary, Poland and high-income (OECD)<br>countries|Yes|
|Blanchflower & Freeman (1997)|ISSP|1987, 1992|Bulgaria, Czech Republic, Hungary, Poland,<br>Russia and Slovenia and high-income (OECD)<br>countries including Germany (East and West)|Yes|
|Boeri and others (2001)|Own|2000|Germany (East and West), France, Italy and<br>Spain|Yes|
|Corneo (2001)|ISSP|1992|Germany (East and West), United States|Yes|
|Corneo and Gruner (2002)|ISSP|1992|Bulgaria, Czechoslovakia, Hungary, Poland,<br>Russia and high-income (OECD) countries<br>including Germany (East and West)|Yes|
|Delhey (1999)|ISSP|1992|Bulgaria, Czech Republic, Hungary, Poland,<br>Russia and Slovenia and high-income (OECD)<br>countries including Germany (East and West)|Yes|
|Heien (2000)|ISSP|1985, 1990,<br>1996|Bulgaria, Hungary and high-income (OECD)<br>countries including Germany (East and West)|Mixed|
|Lipsmeyer and Nordstrom (2003)|ISSP|1996|Bulgaria, Czech Republic, Hungary, Latvia,<br>Poland, Russia and Slovenia and high-income<br>(OECD) countries|Mixed|
|Mason (1995)|ISJP|1991|Bulgaria, Czechoslovakia, Estonia, Hungary,<br>Poland, Russia, Slovenia|Yes|
|Redmond and others (2002)|ISSP|1999|Bulgaria, Czech Republic, Hungary, Latvia,<br>Poland, Russia, Slovakia, and Slovenia and high-<br>income (OECD) countries including Germany<br>(East and West)|Yes|
|Shiller, and others (1991)|Own|1990|U.S. and Russia|No|
|Suhrcke (2001)|ISSP|1999|Bulgaria, Czech Republic, Hungary, Latvia,<br>Poland, Russia, Slovakia, and Slovenia and high-<br>income (OECD) countries including Germany<br>(East and West)|Yes|
|Verwiebe and Wegener (2000)|ISJP|1991, 1996|Bulgaria, Czech Republic, Hungary, Russia and<br>Western Germany|No|
|Wong (2004)|WVS|1990|Bulgaria, Czech Republic, Estonia, Hungary,<br>Latvia, Lithuania, Poland, Romania, Russia,<br>Slovenia other countries including China and<br>Germany (East and West)|No|

1GSOEP: German Socioeconomic Panel; ISJP: International Social Justice Program; ISSP: International Social Survey Programme; WVS: World Values Survey; and own survey.

2The papers covered here do not necessarily explicitly test for the existence of a socialist legacy. The summary information is based on our judgement of whether the main empirical results suggest the existence of such a legacy.

3
There are numerous socioeconomic, engineering, and political challenges that contribute to the low rate of electrification in Sub-Saharan Africa. As with any modeling exercise, our analysis omits many of these factors. Instead, we focus on a previously little-remarked problem: the role of regulated tari↵s set below cost-recovery levels and the disincentive these create for distribution utilities to connect new customers.<sup>7</sup> We believe that a narrow focus on “high connection charges” may lead to a misdiagnosis of the problem. If connection charges are high because distribution utilities wish to discourage unprofitable connections, then providing connection subsidies may result in the utilities using non-price barriers to connection instead. These might include, for example, reducing the reliability of electricity supply, or increasing the wait-time for a connection.

For the remainder of the paper, Section 2 provides descriptive evidence on the relationship between connection charges and electricity prices in Sub-Saharan Africa. Section 3 sets out our model of households and firms. Section 4 describes the data used for the analysis, and Section 5 presents our empirical findings. Section 6 discusses the implications of our results.

# **2 Descriptive evidence**

For many electricity distributors in Sub-Saharan Africa, adding a residential customer is unprofitable. The wholesale cost of electricity is high, the regulated retail tari↵s are low, and the typical consumption of residential users is low. High access charges may be an optimal way for distribution utilities to make up the losses from connecting new customers.

The wholesale cost of generating electricity in Sub-Saharan Africa is high compared to many other countries. Fossil fuels, especially coal, are the predominant form of generation: in 2015 they comprised 74 percent of total generation in the region, with most of the remainder (21 percent) provided by hydroelectricity.<sup>8</sup> Insufficient capacity means that expensive diesel generation is often relied upon to satisfy demand. Inadequate transmission capacity limits the market size and restricts the potential for economies of scale in generation.

Most electricity in the region is generated and sold by vertically-integrated firms, with little transparency on wholesale costs. One exception is the Southern Africa Power Pool (SAPP), which coordinates electricity trade between 12 countries in southern Africa. The

> 7This analysis has parallels with the setting described by McRae (2015). In Colombia, the regulator sets high subsidy reimbursement rates for informal settlements, creating a disincentive for distribution utilities to upgrade the quality of the infrastructure serving these areas. This result holds even if the upgrade could convert non-paying households to paying customers.

> 8Data from the BP Statistical Review of World Energy June 2016. Statistics are for all of Africa excluding the Arab Republic of Egypt and Algeria.

6
suggest a high degree of association: If the top 1% of earners had randomly been assigned capital incomes, 20% of them would be among the richest quintile of capitalists, compared to the observed 80%.

We test for rst-order dominance between 1966, 1985 and 2006, capturing the two 20year periods that we have just described.<sup>25</sup> The dierences between the survival association matrices is shown in Table 2, where we have highlighted the negative cells. We do not nd dominance between 1985 and 1966, since there are both positive and negative dierences between the two survival association matrices (panel a). At the top of the distribution, 1966 appears to dominate 1985, suggesting a fall in the association, consistent with what we observed above. The period between 1985 and 2006 presents a stark contrast of increasing association (panel b); the survival association matrix in 2006 lies everywhere above the 1985 matrix. In other words, over the same period as the top 1% income share doubled, tax units increasingly occupied similar positions in terms of earnings and capital income. Viewed over the entire period from 1966 to 2006 (panel c), the results also point towards increasing association, although the dierences are smaller and there are some small negative values (all less than 0.05 in absolute value). These dominance results are robust to using alternative income denitions (see Appendix A.5).

> 25We have chosen 1985 instead of 1986, because 1986 may be aected by the anticipation of TRA86, which was announced in 1986 and became eective in 1987. However, the results are robust to choosing 1986.

16
which is a proxy for the location of training. The issue is whether a Jamaican doctor who migrated to the USA as a child with his family, and trained in the USA, can be considered a real ‘loss’ for Jamaica. Beine et al. (2007) construct the database for OECD countries and show that the initial ‘brain-drain’ levels decline considerably for many countries as large numbers of their migrants obtained their tertiary education at the destination and did not necessarily impose a fiscal and productivity loss on the birth countries.

The next extension was the addition of non-OECD destinations. OECD’s DIOC-e database added over 30 destination countries to the original OECD-centric data set, again using census-style sources. Artuc et al. (forthcoming) increase the number of countries to 63 in 1990 and 100 in 2000. Their main innovation is to use a gravity-type estimation model to predict the data – by gender and skill level – for the missing destination countries. The result is a truly global overview of human capital mobility. They estimate that there were slightly over 110 million working-age migrants between the ages of 15 and 64, with around half in OECD countries, half female and a quarter tertiary educated. They establish striking patterns. For example, over 70 percent of all tertiary educated migrants in the world end up in just four countries – the USA, the UK, Canada and Australia. The constructed database enables researchers to perform many analyses, including the paper by Docquier et al. (forthcoming), which evaluates the impact of both immigration and emigration in OECD labor markets.

One of the most interesting ideas to emerge in this literature is the possibility of ‘brain gain’. The main premise is that higher expected returns to human capital in destination countries will induce people to invest more in their human capital. If some of 35
|Section E.4|Domestic Coffee Market|
|---|---|
|Section E.5|Estimated Marketing Margins|
|Section F|Other Coffee-Related Institutions|
|Section F.1|Government|
|Section F.2|International Development Agencies and NGOs|
|Section G|Competitiveness of the Dominican Coffee Sector|
|CHAPTER IV|OVERVIEW of DOMINICAN COCOA SECTOR|
|Section A|Trends in Cocoa Production and Export|
|Section B|Geography and Ecology of Production|
|Section C|Quantities Produced and Exported|
|Section D|Structure of Production|
|Section E|Structure of Post-Harvest Activities, Marketing, and Exports|
|Section E.1|Cocoa Intermediaries|
|Section E.2|Cocoa Exporters|
|Section E.3|Domestic Cocoa Market|
|Section E.4|Estimated Marketing Margins|
|Section F|Other Cocoa-Related Institutions|
|Section F.1|Government|
|Section F.2|International Development Agencies and NGOs|
|Section G|Competitiveness of the Dominican Cocoa Sector|
|CHAPTER V|CHALLENGES to the DOMINICAN COFFEE and COCOA SECTORS|
|Section A|Overview of Strategies for the Coffee Sectors of Central America and Caribbean|
|Section B|Challenges Strategies for the Coffee Sectors of Dominican Republic|
|Section B.1|Macro and Agricultural Sector Policies|
|Section B.2|Production and Marketing Issues|
|Section B.3|Land, Labor and Finance Issues|
|Section C|Commodity Price and Weather Risks|
|Section D|Environmental Issues|
|Section E|Conclusion: Towards a Strategy for the Coffee and Cocoa Sectors|

REFERENCES ANNEX World Bank’s Cocoa and Coffee Project 1982-1989 MAP Agricultural Regions of Dominican Republic 4