rafmacalaba/datause-encoder-data huggingface dataset:
Barboni, Georgia and Parul Agarwal (2018) “Knowing what’s good for you: Can a repayment flexibility option in microfinance contracts improve repayment rates and business outcomes?”, working paper, Warwick Business School.

Battaglia, Marianna, Selim Gulesci and Andreas Madestam (2019) “Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts”, working paper, Bocconi University.

Björkegren, Daniel and Darren Grissen (2018) “Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment”, _World Bank Economic Review_ , forthcoming.

Bursztyn, Leonardo Stefano Fiorin, Daniel Gottlieb, and Martin Kanz (2018) “Moral Incentives in Credit Card Debt Repayment: Evidence from a Field Experiment”, _Journal of Political Economy,_ forthcoming.

Chowdhry, 2010 De Mel, Suresh, David McKenzie, and Christopher Woodruff (2008) “Returns to capital in microenterprises: Evidence from a field experiment”, _The Quarterly Journal of Economics_ 123(4): 1329-1372.

Fafchamps, Marcel, David McKenzie, Simon Quinn and Christopher Woodruff (2014) “Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana”, _Journal of Development Economics_ 106: 211-226 Field, Erica, Rohini Pande, John Papp and Natalia Rigol (2013) “Does the Classic Microfinance Model Discourage Entrepreneurship Among the Poor? Experimental Evidence from India”, _American Economic Review_ 103(6): 2196-2226 Fischer, Gregory (2013) “Contract Structure, Risk Sharing and Investment Choice”, _Econometrica_ 81(3): 883-939.

Gompers, Paul, 1995. “Optimal Investment, Monitoring, and the Staging of Venture Capital”, _Journal of Finance_ 50(5), pp. 1461-1489.

Hall Robert, and Susan Woodward (2010) “The burden of the non-diversifiable risk of entrepreneurship”, _American Economic Review_ 100(3):1163–1194.

Hussam, Reshmaan, Natalia Rigol and Benjamin Roth (2017) “Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design In The Field”, Mimeo. Harvard.

Jensen, Richard, and Marie Thursby, 2001. “Proofs and Prototypes for Sale: The Licensing of University Inventions”, _American Economic Review_ , 91(1), pp. 240-259.

Kelley, Erin, Gregory Lane and David Schönholzer (2018) “The Impact of Monitoring Technologies on Contracts and Employee Behavior: Experimental Evidence from Kenya’s Transit Industry”, Mimeo. Berkeley.

Kerr, William and Ramana Nanda (2011) “Financing New Ventures”, _Harvard Business School_ Case Study 9-811-093.

23
<!-- Start of picture text --> Figure 1: Confidence Sets for the Coefficient on Inequality (Horizontal Axis)<br>and Redistribution (Vertical Axis) in the Levels Equation ‐‐<br>Ostry, Berg and Tsangarides (2014) Benchmark Specification<br>Panel 1: Based on Wald Statistic Panel 2: Based on AR‐Statistic<br>WALD AR<br>95% Confidence set 95% Confidence set<br>-2 -1 beta: xlev20 1 2 -2 -1 beta: xlev20 1 2<br>Panel 3: Based on KJ Statistic Panel 4: Based on CLR Statistic<br>KJ CLR<br>95% Confidence set 95% Confidence set<br>-2 -1 0 1 2 -2 -1 0 1 2<br>beta: xlev2 beta: xlev2<br>Notes: This figure reports confidence sets for the coefficient on the net gini (horizontal axis) and redistribution (vertical<br>axis). The dark‐shaded region corresponds to combinations of hypothesized values for these two parameters that jointly are<br>not rejected by the Wald, AR, KJ and CLR tests.<br>2 2<br>1 1<br>0 0<br>beta: xlev3 beta: xlev3<br>-1 -1<br>-2 -2<br>2 2<br>1 1<br>0 0<br>beta: xlev3 beta: xlev3<br>-1 -1<br>-2 -2<br><!-- End of picture text --> 28
interactions with the main factors associated with the political opportunities (i.e. democracy and political terror) and the resource mobilization approach (i.e. mobile phones and urbanization) to the model.

_Table 4 The role of context: Models including the interaction between suffering and struggling and political opportunities and mobilization variables_

||(1)<br>High<br>income<br>interaction|(2)<br> <br>Polity<br>score<br>interaction|(3)<br>Political<br>terror<br>interaction|(4)<br>Mobile<br>interaction|(5)<br> <br>Urbanization<br>interaction|
|---|---|---|---|---|---|
|Suffering (%)|0.022**|0.029***|0.008|0.030**|0.019|
||(0.010)|(0.009)|(0.026)|(0.013)|(0.028)|
|Suffering x High Income|0.014<br>(0.028)|||||
|Suffering x Polity score||0.001<br>(0.001)||||
|Suffering x Political terror|||0.008<br>(0.008)|||
|Suffering x Mobile phones||||0.000<br>(0.000)||
|Suffering x Urbanization|||||0.000<br>(0.000)|
|Struggling (%)|-0.002|0.016*|0.024*|0.019|<br>-0.014|
||(0.010)|(0.008)|(0.013)|(0.014)|(0.027)|
|Struggling x High income|<br>0.039***<br>(0.014)|||||
|Struggling x Polity index||-0.000<br>(0.001)||||
|Struggling x Political terror|||-0.002<br>(0.005)|||
|Struggling x Mobile phones||||-0.000<br>(0.000)||
|Struggling x Urbanization|||||0.000<br>(0.000)|
|Constant|-1.876|-2.419|-2.096|-2.585|<br>-0.148|
||(4.241)|(4.370)|(4.376)|(4.587)|(4.982)|
|Observations|882|882|882|882|882|
|Number of countries|119|119|119|119|119|
|Country FE|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes|Yes|
|Control Var|Yes|Yes|Yes|Yes|Yes|
|R^2 overall|0.0720|0.0935|0.0781|0.0981|0.0852|
|R^2 within|0.289|0.279|0.281|0.279|0.281|
|R^2between|0.0562|0.0563|0.0439|0.0604|0.0502|

Note: R-squared refers to pseudo R-squared for the logit models in Column 3-4. Estimates are obtained with an OLS model. Cluster robust standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05, ꝉ p<0.10 First, we test whether the effect of subjective well-being on nonviolent uprisings depends on the level of democracy. Whereas in democracies dissatisfaction can be raised at the 26
<!-- Start of picture text --> Lon niceRM NOS a A Z f<br>4 : 2 H GENTRAL AFRICAN REPUBLIC | SOUTH SUDAN = S ETHIOPIA Ps ,<br>= Thef ‘ CAMEROON |, fe7 Py in a ae‘ ee—_~ amcF, y y<br>EQUA ate \ SOMALIA _A<br>SAO TOME AND——Ee a }poe UGANDA : 4 KENYA | y y<br>~<br>GABON = "|CONGO | \ a<br>ai,RUM ;<br>i<br>:<br>\<br>a<br>ANS | B 7<br>Sea<br>aie ay =vLZoy /e MAYONTE(Fy +<br>.<br>: in N J os<br>eS - 2" &<br>“ai =<br>egies" yi<br>ay<br>) A<br>—| -=— LYy<br>A =A Se 5<br>Road Traffic (Avg Annual Daily traffic)<br>— — |= = m=<br>Unsnown < 100 100 = 200= = 000 000<br>>) 4000<br><!-- End of picture text -->

<!-- Start of picture text --> Road Traffic (Avg Annual Daily traffic)<br>— — |= = m=<br>Unsnown < 100 100 = 200= = 000 000<br>>) 4000<br><!-- End of picture text -->
FIGURE 3: REPRESENTATION OF BRAZILIAN POLICIES <!-- Start of picture text --> Brazil sugarcane Ethanol market World market for sugar<br>market efor sugarcane in suarcane w<br>ddp Ss dd&e eQuies<br>qj q2 t~~~~ q3 s-__{ I f (q3.q2) XL2-)<br>ql' q2' q3' (q3v-q2') (q2'-ql<br><!-- End of picture text -->

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with most people living from subsistence farming supplemented by small-scale incomegenerating opportunities that are typically more available to men than they are to women. The country is poor even by African standards: the GNI per capita (PPP, current international $) is $750 in 2007, compared to an average of $1,870 for Sub-Saharan Africa (World Bank, 2008).<sup>7</sup> Malawi also has the eighth-highest HIV prevalence in the world with 14 percent of the adult population infected (UNAIDS, 2007).<sup>8</sup> The gender gap in HIV prevalence among young adults, aged 15-24, is startling: prevalence was more than _four_ times higher for females than males in 2004.

Primary schools in Malawi are free through Standard 8, and upon completing this grade, students take the Primary School Leaving Certificate (PSLC) exam. Access to secondary schools is rationed, meaning that only those who obtain sufficiently high scores on the PSLC will gain access, and only if they can pay the tuition fees. Secondary students sit one exam after two years (the Junior Certificate Exam, or JCE) and a final exam on completion of secondary school (the Malawi Schools Certificate Exam, or MSCE). Each of these exams is nationally recognized and considered an important qualification on the labor market.

The CCT intervention that is the subject of this paper takes place in one district of Malawi, which both reduces project costs (lower fixed costs of office infrastructure and transport) and increases data quality through more careful supervision. Zomba district in the Southern region of Malawi was chosen as the site for this study for several reasons. First, it has a large enough population within a small enough geographic area rendering

> 7 Using the Atlas method, The GNI per capita (in current US$) in Malawi is 250 in 1997, compared with 952 in Sub-Saharan Africa as a whole.

> 8 The UNAIDS HIV estimate of 14.1 percent is close to the Demographic and Health Survey 2004 (NSO, 2005) estimate of 12.7 percent (National Statistical Office and ORC Macro, 2005).

8
Table E.7: Program impacts on occupational choice

||||||ITT regre|ssion (N=947|)||
|---|---|---|---|---|---|---|---|---|
||||Ther|apy only|Ca|sh only||Both|
|Outcome|Round|Control|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||mean|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Average hours/week of work, past 2 weeks|2–5w|36.773|1.044|[2.879]|7.439|[2.907]**|1.787|[2.966]|
||12–13m|34.273|1.030|[2.550]|0.681|[2.525]|-1.416|[2.493]|
|Non-agricultural high-skill work|2–5w|1.717|0.028|[.962]|0.512|[.894]|0.431|[1.006]|
||12–13m|3.903|-1.539|[1.080]|-1.046|[1.069]|-0.853|[1.145]|
|Non-agricultural low-skill business|2–5w|8.904|1.607|[1.991]|10.956|[2.339]***|6.214|[2.178]***|
||12–13m|8.880|3.465|[1.683]**|1.859|[1.695]|2.960|[1.728]*|
|Non-agricultural low-skill wage labor|2–5w|16.011|1.537|[2.172]|-2.652|[2.090]|-0.627|[2.200]|
||12–13m|15.804|-0.524|[2.242]|-1.684|[2.107]|-2.902|[2.067]|
|Agricultural work|2–5w|0.700|-0.576|[.401]|-0.501|[.459]|-0.627|[.411]|
||12–13m|0.569|-0.293|[.280]|0.060|[.390]|-0.325|[.319]|
|Illicit work|2–5w|9.281|-3.339|[1.608]**|-2.793|[1.599]*|-3.486|[1.685]**|
||12–13m|6.007|0.029|[1.522]|1.590|[1.491]|-0.995|[1.345]|

_Notes:_ The table reports intent to treat estimates of the effect of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. The income summary index is the standardized mean of three composite outcomes (themselves first standardized). Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1 xlvii
Table E.2. Women’s Marriage Age (Dropping Earthquake Districts) Post the Gujarat Riots of 2002

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|
|---|---|---|
|Post X Riot|-0.279<sup>_∗∗∗_</sup>|0.061<sup>_∗∗∗_</sup>|
||(0.102)|(0.016)|
|Observations|93826|95530|
|R-squared|0.219|0.151|
|Control Average|18.63|0.388|
|District Fixed Efects|Yes|Yes|
|Year Fixed Efects|Yes|Yes|
|Controls|Yes|Yes|

_Note_ : This table reports coefficients from specification 1 only including Gujarat and its neighboring states. Standard errors are clustered at the state-year level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male houshold head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Table E.3. Effect of Political Power Change on Women’s Marriage Age

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|
|---|---|---|
|Post 1995 X Gujarat|0.180<sup>_∗_</sup><br>(0.107)|0.013<br>(0.017)|
|Observations|42046|43237|
|R-squared|0.152|0.102|
|Control Average|16.96|0.560|
|District Fixed Efects|Yes|Yes|
|Year Fixed Efects|Yes|Yes|
|Controls|Yes|Yes|

_Note_ : This table reports coefficients from specification 1 only including Gujarat and its neighboring states. Standard errors are clustered at the state-year level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

42
given that assessments are done at the national level for some countries, and at the sub-national level for others (e.g. Afghanistan as opposed to Albania). Finally, although the quality of PEFA assessments has improved over time, only 18% of draft reports submitted in FY 2015 were awarded a PEFA CHECK, a non-mandatory yet indicative quality endorsement of the assessment requiring it to undergo a multi-step peer review process.

This is the first study to combine the World Bank’s Benchmarking Public Procurement database and firmlevel data from the World Bank Enterprise Surveys. We find that better quality of public procurement systems is correlated with positive firm engagement, infrastructure, innovation, and internet connectivity outcomes. Better quality of public procurement systems is also related to lower levels of corruption in the private sector. The rest of the paper is organized as follows. Section 2 lays out the conceptual considerations and section 3 describes the data. Section 4 provides the empirical estimation strategy, while section 5 presents the results and section 6 concludes.

# **2. Conceptual Considerations**

In this section, we describe the number of ways the quality of public procurement can influence the five outcomes of interest: firm participation, infrastructure, corruption, innovation, and online connectivity.

# _Firm participation_

The goal of good procurement systems is to encourage competition between firms bidding for contracts. Transparency and accessibility can lead to greater firm participation in the bidding process. Transparency and accessibility will inform a larger number of firms of procurement opportunities, and also encourage productive firms to participate given the trust generated from an open process. Greater participation of firms has several benefits, both pecuniary and ones related to the well-functioning of the procurement system. Non-competitive procedures in the procurement process can lead to increases in costs by 30 percent or more (Hoekman, 1998). But cost is not the only factor of concern. Better value for money and improved quality 4
|8|20.5|11.0|9.5|
|---|---|---|---|
|9|19.5|10.0|9.5|
|10|19.2|8.8|10.4|
|**Overall**|**22.6**|**11.7**|**10.9**|

1 The difference in all income deciles is statistically significant.

# **5.2.2 Distance to an urban area makes a difference**

It is not just the inherent classification of each district as rural or urban, but actual distance to an urban center that also affects access to international markets in terms of tariffs faced. Households in a state capital or recognized city or urban area<sup>10</sup> face the lowest tariffs in international markets at 9.6% (more than 10% of respondents), while those living within one to fifty kilometers from the closest urban center faced an average tariff of 14.3% (averages indicated by letters f and e, respectively in Table 3). The average tariff for the center-dwellers is 14.8 percentage points lower than the 24.4% tariff faced by those who are farther than 600 km from the city (less than 2% of respondents). Except for those living the farthest at more than 600 kilometers, any other distance from the nearest urban center does not make any significant difference in tariffs faced (averages with overlapping letters b to d in Table 3). The results also affirm that the poor face higher tariffs since, as shown in Table 3, incomes are lower for those who live far from urban conglomerates.

Table 3. Average income and tariff faced by distance to the nearest urban area

|Distance<br>(km) to<br>nearest<br>urban area|Average<br>Income<br>(Rupees)|Number of<br>respondents|Average<br>Tariff (%)|Duncan's<br>test<br>outcome*|
|---|---|---|---|---|
|GT 600|1,002|428|24.4|a|
|401‐600|1,116|1,883|17.3|b c|
|301‐400|1,199|3,509|16.3|d c|
|201‐300|1,235|4,992|15.7|d|
|151‐200|1,406|2,806|17.5|b|
|101‐150|1,429|3,215|17.5|b|
|51‐100|1,415|2,444|17.5|b|
|1‐50|1,881|1,685|14.3|e|
|0(Center)|2,185|2,610|9.6|f|

> 10 For example, Bagalkot in the state of Karnataka is 518 km to Bangalore, which is the state capital.  However, Bagalkot is only 406 km to Pune, a prominent urban area in the state of Maharashtra; this is therefore the closest distance to an urban area, which is used in the analysis. This measure is taken from Das et al. (2015).

11
Table A.10: Performance across cases and responses (percentage points)

||Without c|ovariates||With c|ovariates||
|---|---|---|---|---|---|---|
||(1)<br>Across cases|(2)<br>Across<br>responses|(3)<br>Across cases|(4)<br>Across<br>responses|(5)<br>Across cases|(6)<br>Across<br>responses|
|Reward|4.32***|2.04***|0.28***|0.22***|4.46***|1.98***|
||(0.97)|(0.45)|(0.06)|(0.05)|(0.96)|(0.45)|
|Penalty|3.36***|1.88***|0.22***|0.21***|3.41***|1.78***|
||(1.00)|(0.45)|(0.07)|(0.05)|(1.00)|(0.44)|
|Male|||||0.20|-1.35**|
||||||(1.26)|(0.58)|
|Age _≥_median|||||-1.82|-0.87|
||||||(1.18)|(0.54)|
|Doctor or nurse|||||0.42|2.45***|
||||||(1.83)|(0.79)|
|Years since qualifed _≥_median|||||-0.97|-0.61|
||||||(1.15)|(0.51)|
|Knowledge _≥_median|||||2.23|1.49**|
||||||(1.58)|(0.64)|
|Constant (%)|53.21***|56.46***|-0.17***|-0.14***|53.35***|56.60***|
||(0.58)|(0.26)|(0.04)|(0.03)|(1.21)|(0.55)|
|Facility fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|p-values from tests of coefcients|||||||
|Penalty v Reward|0.292|0.700|0.292|0.700|0.253|0.637|
|N respondents|1,363|1,363|1,363|1,363|1,363|1,363|
|R-squared (overall)|0.018|0.015|0.018|0.015|0.040|0.049|

* p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. OLS models; s.e. clustered at worker level. Omitted category: 15+ years since certification.

56
# **1. Introduction**

The decline in transportation and communications costs and the reduction in policy induced barriers have led to a rapid increase in the flow of goods, capital, people and knowledge across international boundaries. There exists an extensive literature exploring the effects of these forces, but much less attention has been paid to more subtle linkages and feedback mechanisms which are shaping the global economy. One of such linkages is the influence of migration on foreign direct investment (FDI), which is the focus of this study.

While a growing literature has documented a positive association between the presence of ethnic networks and international trade, the link between migration and FDI remains relatively unexplored. The main premise of the existing literature is that international transactions are plagued with informal trade barriers, in addition to formal trade barriers such as transportation costs and tariffs. Among these barriers are the difficulties associated with provision of information on many issues, including potential market opportunities, and with enforcing contracts across national boundaries. As shown by Gould (1994), Head and Ries (1998), Rauch and Trindade (2002) and Combes et al. (2003), the presence of people with the same ethnic background on both sides of a border may alleviate these problems. Their language skills and familiarity with a foreign country can significantly lower communication costs. They possess valuable information about the market structure, consumer preferences, business ethics and commercial codes in both economies. They decrease the costs of negotiating and enforcing a contract through their social links, networking skills and knowledge of the local legal regime. In short, business and social networks that span national borders can help to overcome many contractual and informational barriers and carry out mutually beneficial international transactions.<sup>1</sup> Foreign direct investment activities may face even larger information asymmetries than international trade transactions. Direct investment generally requires long-term focus and interactions with diverse group of economic agents from suppliers and workers to government

> 1 Gould (1994) finds that both US exports and imports are positively correlated with the stock of migrants from the partner country present in the US. Head and Ries (1998) reach a similar conclusion when examining Canadian data, as do Combes et al. (2003) using information on intra-regional economic activity in France. Rauch and Trindade (2002) distinguish between the effect of networks as a conduit of information and a contract enforcement mechanism. They show that the presence of ethnic Chinese networks matters more for trade in differentiated products than for trade in homogenous commodities. Given that it is harder to assess the attributes of differentiated products, these findings suggests that in addition to serving as an information channel, ethnic networks may provide implicit contract guarantees and deter opportunistic behavior among its members. See a survey of the literature by Rauch (2001) for more details.

1
Gregorio Impavido, Alberto R. Musalem, and Dimitri Vittas capital they can draw upon. The lack of resources limits the scope for the development of the financial infrastructure like payment systems, organized markets, supervision, and regulation; sunk costs are simply too high for poorer countries. As a consequence, small financial markets are generally shallow, incomplete, poorly regulated, illiquid, prone to lack of competition and concentration in the provision of services, inefficient, and characterized by relatively high transaction costs. Small economies are more volatile: their median standard deviation of private consumption, terms of trade, inflation, GDP growth, and capital flows per GDP is between 1.5 and 4 times higher than in larger countries (Bossone _et al._ 2001). All this, in turn, impairs investment and hence, growth.

**Table 2: Correlation matrix**

||M2|GDP|PerCGDapita|Population|Openness|M2/GDP|
|---|---|---|---|---|---|---|
|M2<br>p-value|1.0000||||||
|GDP<br>p-value|0.9404<br>[0.0000]|1.0000|||||
|PercapitaGDP<br>p-value|0.4992<br>[0.0000]|0.4651<br>[0.0000]|1.0000||||
|Population<br>p-value|0.3001<br>[0.0001]|0.2664<br>[0.0012]|-0.0099<br>[0.9058]|1.0000|||
|Openness<br>p-value|-0.1405<br>[0.1165]|-0.1919<br>[0.0313]|0.1049<br>10.2424]|-0.2098<br>[0.0105)|1.0000||
|M2/GDP<br>|0.2719<br>|0.1746<br>|0.5516<br>|0.2025<br>|0.3671<br>|1.0000|
|p-value|[0.0009]|[0.0356]|[0.0000]|[0.0139]|[0.0000]||

Notes: The correlation coefficient was calculated over the pair-wise available observations within a sample of 158 countries.

Source: WBI and authors' calculations.

Despite the risks associated with openness, it is widely recognized that as trade openness can compensate for the underdevelopment of the real sector, financial liberalization allows to achieve the economies of scale and scope otherwise forbidden. This involves allowing foreign ownership of intermediaries, cross border financial transactions, participation in regional markets, the creation of regional infrastructure like regional organized markets, or regulatory and supervisory agencies, having sound and credible macroeconomic policies, opening of the capital accounts, and establishing exchange rate regimes that facilitate international integration. In a sense, small financial systems could integrate with larger systems as regions integrate within country.

In the next sections we discuss the financial preconditions for the successful development of contractual savings in small financial systems, the policy options available to policymakers for their development, and the likely externalities for the rest of the financial sector.