rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

The literature on rural roads and economic development has emphasized impacts on transport costs and prices, and the consequent welfare impacts. For example, rural roads may allow farmers in remote (and often poor) rural areas to get higher prices for their output, and/or reduce the prices they face for inputs and consumer goods.<sup>1</sup> However, this way of thinking about the impacts of rural roads says little or nothing about how rural roads might also influence the geography of economic activity and, in particular, what role road improvements might play in local market and market-related institutional development. Initial conditions in remote poor areas are often characterized by highly geographically incomplete and non-existent markets. The goods concerned are simply not available in these areas, given high transport costs. Advocates of rural road projects often point to their potential benefits in stimulating market development (Gannon and Liu, 1997). Rigorous evidence appears to be non-existent.

Here it can be important to distinguish two ways in which access to markets can improve due to better transport infrastructure. One is through reduced travel costs to <u>existing</u> markets and institutions. The other is through the induced relocation of markets and institutions. The rural road impacts literature appears to have the first in mind, at least implicitly. But markets are mobile ─ not least so in developing countries. One response to road improvements could be the development of local markets defined as fixed places where villagers and outsiders gather at set times to buy and sell goods.

Why should we care about whether the residents of a poor area have goods commercially transported to their community and available in a local market, rather than

> 1  For example see Gannon and Liu (1997); Khandker, Bakht and Koolwal (2008); Escobal and Ponce (2004); Dercon, Gilligan, Hoddinott and Woldehanna (2006); and Lokshin and Yemtsov (2005).

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in high-income countries, with Card et al. (2018) finding from a combination of experimental and non-experimental studies that training averages a 2.0 percentage point impact on employment in the first year after training, and 6.6 percentage points one to two years later.<sup>6</sup> These effects appear particularly muted for at-scale programs operated by governments. Figure 3 shows evidence from five experimental evaluations of government vocational training programs that trained at least 5,000 people in a year. In this figure and the subsequent ones, we show both the point estimate and a 95 percent confidence interval from the evaluation. For example, the figure shows that vocational training in Türkiye resulted in an estimated 2.0 percentage point increase in employment, with a 95 percent confidence interval of -0.5 to 4.4 percentage points. With the exception of the impact for women of the Colombian Jóvenes en Acción program (Attanasio et al, 2011), the estimated effect on employment is 2 percentage points or lower in these programs, half that of the meta-analysis impact across all pilot, nongovernment organizations, and government programs of 4.0 percentage points reported by Agarwal and Mani (2023). There is more variability in the impact on earnings, but five of the seven reported estimates are also below the meta-analysis average impact of 8.2 percent.

> 6 Card et al. (2018) use a sample that combines developed and developing countries, with 64% of the training estimates coming from high-income countries. They find few significant differences in impact by country region, except for slightly higher estimates from non-experimental evaluations in Germanic countries.

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**Table 3. Comparing the actual destination to alternative destinations**

|All figures are relative to the district of origin|Actual|Mean in|Diff. in mean|
|---|---|---|---|
||Destination|Alt. Destin.|t-stat|
|**Income and consumption**||||
|Average income (log)|0.195|-0.037|-61.840|
|Differential in log income due to education and high caste|-0.035|0.007|9.031|
|Average consumption expenditures (log)|0.075|-0.046|-33.561|
|Differential in log consumption due to education and high caste|0.020|0.018|-0.832|
|Relative log income|-0.003|0.001|8.915|
|Relative log consumption|0.002|0.002|-1.001|
|**Subjective consumption adequacy**||||
|Average consumption adequacy index: total income|0.054|-0.008|-35.523|
|Differential due to education and caste: total income|0.002|-0.016|-8.508|
|Average consumption adequacy index: food|0.094|-0.010|-44.127|
|Differential due to education and caste: food|-0.008|0.014|7.844|
|Average consumption adequacy index: clothing|0.076|-0.019|-42.983|
|Differential due to education and caste: clothing|-0.002|-0.019|-7.700|
|Average consumption adequacy index: housing|0.070|-0.028|-47.457|
|Differential due to education and caste: housing|0.002|-0.004|-2.195|
|Average consumption adequacy index: health care|0.081|-0.022|-46.605|
|Differential due to education and caste: health care|-0.010|-0.009|0.255|
|Average consumption adequacy index: children schooling|0.093|-0.011|-45.711|
|Differential due to education and caste: children schooling|-0.003|-0.022|-6.701|
|**Prices and amenities**||||
|Log of rice price|-0.089|0.021|47.802|
|Housing price premium (log)|0.377|0.210|-12.221|
|Time travel to nearest paved road|-0.746|0.103|79.767|
|<br>Time travel to nearest bank|-0.373|0.091|71.345|
|**Population and distance**||||
|Population density|0.281|-0.033|-86.131|
|<br>Log(population)|0.330|-0.207|-74.129|
|<br>Elevation in meters|-0.317|0.166|57.156|
|Ethnic/caste similarity index|-0.042|-0.060|-13.664|
|Language similarity index|-0.123|-0.101|7.427|
|Religion similarity index|0.008|-0.017|-13.816|
|Distancein '000Km|0.261|0.281|13.822|
best terminology and how to minimize translation problems. Training materials and the training of interviewers was done by the GSS. The instruments were piloted and revised and the actual survey took place from October –December 2006.

# **3.  A Description of the Data**

The financial experiment is the most useful if its results can be extrapolated to the entire population and not just those households included in the survey. Every effort was made to ensure that this would be the case. The GLSS 5 is based on a probability sample and we used a randomly selected sub-sample of these households which should be adequate. However, since the FSS is, essentially a panel, it is important to determine whether or not there is panel attrition of significance. And, given that in the final stage, households were randomly assigned to one of three groups with each being administered a different questionnaire (and treatments), there is also potential for problems to have arisen. In principal we expect the basic characteristics of households in the FSS to match those in the GLSS 5 and for there to be no significant differences among the three groups of households administered the different FSS questionnaires. This section reviews the resulting data to identify issues that arose in the practice of the survey’s implementation.

As shown in column 1 of Table 3, more households in Ghana as a whole live in rural areas than urban, and over 65 percent of households have members working in agriculture. Less than one-fifth of households have members working as employees of firms. The population is young with the average age being only 20.

Household size is slightly more than 4 persons. Surprisingly perhaps, given the large agrarian base and rural population, most households are composed of nuclear 14
Our results for the share of Italian, Spanish and U.S. banks local claims indicate that this ratio is negatively impacted by restrictions on banks’ freedom to operate in the local market. On the other hand, larger economies of scale and business opportunities lead to more local foreign claims. Other factors such as entry bank requirements, startup and informational costs are also significant, but their impact is less consistent across international banks and also varies depending on the specifications.

Regarding the implications of the mix of foreign bank claims for the stability of foreign financing, we find that countries with a higher share of local foreign claims observe lower total foreign claims volatility. This result, which is robust to controlling for a number of other factors that might affect foreign claims volatility, helps us confirm in a systematic manner some of the descriptive evidence offered by other studies favoring local foreign claims to cross-border bank financing.

The rest of the paper is organized as follows. Section II discusses the data on Italian, Spanish, and U.S. foreign claims. Section III presents the empirical methodology pursued. Section IV discusses the empirical results. Section V concludes.

# **II- The data on foreign claims**

Perhaps one of the reasons why not much research exists on the mix of foreign claims extended by international banks across countries has to do with the fact that the data are not readily available. The main source of international banking data is the BIS. The BIS’ _Consolidated Banking Statistics_ contain information on the foreign claims extended by international banks from more than 20, primarily OECD, countries (referred to as BIS-reporting countries). The data reported by the BIS aggregates the information provided by individual banks in each BIS reporting country, so it is really country level as opposed to bank level data.

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10 studies of growth, and also by Panizza (1999).<sup>10</sup> For Russian regions this measure has not been readily available and was constructed using the data from the 1989 census.<sup>11</sup> Urbanization [URBAN] is conventionally measured by the percentage of the urban population.

The most challenging task is to construct a region-specific measure of democracy [DEM]. Most cross-country studies (including Panizza, 1999) use the Gastil (1990) index of democracy and political rights or similar survey-based indices. Unfortunately such indices are unavailable for Russian regions. Instead, two proxies for the regional level of democracy are considered.

The first proxy is based on electoral statistics and equals the spread between the percentage of votes for the winner and for the runner-up in the first round of the elections of regional executive.<sup>12</sup> Certainly, tense electoral competition is neither a necessary nor sufficient condition for developed democracy; however, on average one would expect a high positive correlation between the competitiveness of elections and the quality of democratic institutions. For the sample of 38 presidential democracies the correlation between the percentage of votes that the head of state got in the first round of the last elections prior to 2000 and the Gastil index of democracy in 2000 is 0.53.<sup>13</sup> The second proxy is based on the only currently available survey-based study of the institutional environment in Russian regions carried out by an independent international organization, namely the corruption perception index compiled by Transparency International for 40 Russian regions in 2001-02. Once again, the perception of corruption and the perception of

> 10 Earlier studies (e.g., Oates, 1972) distinguished between heterogeneous and homogeneous states by using a dummy variable.

> 11 Ethnic fractionalization may have plausibly increased during the 1990s as a result of significant external migration and various shocks. However, we did not have access to the data from the latest (2002) Russian census. 12 Electoral rules vary across regions. In some regions a simple majority is not sufficient for victory and unless one candidate gets more than 50 percent of votes a second round of elections is called between the two candidates who obtained most votes in the first round. Hence, only the first round spreads are comparable across regions.

> 13 Authors’ calculations based on the World Bank Database of Political Indicators. Higher values of Gastil Index correspond to lower levels of democracy and political freedom. Unfortunately the data on the runner-up votes are not readily available for a broad sample of countries, leaving the above comparison approximate.
is endogenously determined. Families are prone to support child-labor bans in situations where children compete with adult workers in the labor market and children’s incomes are not a large share of the family income. Otherwise, families would be against childlabor bans. Fertility decisions lock families into the child-labor equilibrium and childlabor bans can be triggered if some exogenous change induces parents to have smaller families; such as skill-biased technology changes.<sup>6</sup> They predict that even in countries that initially have strong opposition to child-labor restrictions, the support for childlabor bans may increase over time once the ban is in place. This happens if the cost of schooling is sufficiently low and the value of child work (opportunity cost) is not too high. If these two conditions are not met, families and children themselves would be worse off.

The evidence of consequences of child labor has increased in the last 15 years or so. Studies have found that child labor can harm individuals on different dimensions. Tyler (2003) used U.S. data from the 1980s and found that working while studying is detrimental to learning among high-school students. Bezerra et al. (2009) and Emerson, Ponczek, and Souza (2016) reached similar conclusions for Brazil.

Using data from Brazil, Emerson and Souza (2011) show that very early entry into the labor market harms individuals’ outcomes in adult life, over and above the effect on schooling. However, this negative effect is reversed when entry into the labor market occurs after age 14.

Also for Brazil, Lee and Orazem (2010) found that a simultaneous effect of an early entrance into the labor force and a premature school dropout resulted in worse health outcomes.<sup>7</sup> Beegle et al. (2009) investigated the medium-term consequences of child labor on schooling, the labor-market, and health outcomes in rural Vietnam and found child labor had a negative effect on school attendance and educational attainment but a positive effect on labor-market outcomes such as employability in paid work and earnings. They argue, that for some individuals, the returns on experience seem to overcome the returns on education.<sup>8</sup> This result is consistent with some theoretical

> 6 In our case, even if the change in law is endogenous, we believe our results are still valid since we compare families at the margins of the cut-off at the timing of the birth realizations. 7 Such as higher probability of back problems, arthritis, and reduced stamina.

> 8 Beegle et al. (2009) also found no impact on health outcomes.

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Policy Research Working Paper 8783

# **Abstract**

An expanding body of literature has shown that better management practices can offer significant boosts to firms’ productivity; this research illustrates that firms in South America are no exception. Using recent Enterprise Survey data from seven countries in South America (Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru, and Uruguay), the paper explores the various dimensions and drivers of management practices and analyzes how they are related to productivity. This is an important topic to investigate, given the lagging levels of productivity growth in the region. If management practices can boost firms’ productivity, this may be a cost-effective way to accelerate economic growth. The results show that improved management practices are associated with higher levels of productivity in all countries, and it is the impact of improved management specifically in larger firms that is driving the overall results. Indeed, in some countries, specifically Argentina, Paraguay, and Peru, it is only among larger firms that there is a positive link between management practices and productivity.

This paper is a product of the Global Indicators Group, Development Economics. 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 mhyland@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
Figure 25: National Level Sanitation Deprivation <!-- Start of picture text --> AGGREGATE SANITATION DEPRIVATION -GHS AND MICS<br>child child north child south child rural child urban<br>W A VE 1 W A VE 2 W A VE 3 MIC S 2016/2017 W A VE 4<br>24% 23% 27% 30% 28% 28% 28% 35% 24% 24% 24% 29% 24% 24% 24% 32% 24% 22% 27% 30%<br>14% 14% 14%<br>6% 6%<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Estimations are based on children age 0-17.

Unlike in other deprivation dimensions the North-East and North-West do not stand out in terms of severe sanitation deprivation, which is in line with findings of the World Bank’s WASH report (WB, 2017). Instead these face lower levels of deprivation. Children in the North-Central and South-West are facing the highest shares of severe sanitation deprivation. These are according to both surveys and in both sectors, much higher than in all other regions without a clear change over time. One exception is the urban South-West in wave 4. Figure 25 suggests rather a temporary increase in rural sanitation deprivation in the second wave, with the estimates based on GHS wave 3 falling back the levels in GHS wave 1.

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**<u>Table 3: TIAA Portfolio Backing Fixed Annuities, 2003</u>**

|Assets:|Percent of Total Portfolio|
|---|---|
|Corporate Bonds (public issues)|24|
|Corporate Bonds (private placements)|15|
|Mortgage/Asset-Backed Securities|33|
|Commercial Mortgages|16|
|Real Estate|4|
|Equity|4|
|Other Assets|6|
|Total|US$150 billions|

Source: TIAA-CREF Providers’ costs include the commissions paid to annuity brokers and operating costs. As shown in Figure 3, commissions averaged 3 percent of the premium in the early 1990s, increased continuously to almost 6 percent at the end of the decade, and then decreased sharply to levels around 2 percent. The increase in the 1990s reflected the practice of charging higher commissions and providing an informal cash rebate to the retiring worker. This rebate proved a popular marketing device but was also illegal and prompted a reaction from policy-makers, which at the end of 2000 submitted a new Pensions Law to Congress. The draft Law proposed a cap on broker’s commissions and a new electronic quotation system. Although the new Pensions Law was only passed in 2004, the threat of the Law and political pressure induced a change in behavior, as indicated by the sharp decline in commissions.<sup>4</sup> The commission cost adds about 30 basis points to the annuity rate, as shown by the adjusted annuity rate line in Figure 2, reducing the intermediation spread commensurately. In addition, providers also need to cover their operating costs, which have averaged 1.5 percent of assets. This implies thin net spreads, possibly negative for some providers. The question arises as to why profit-maximizing companies have issued annuities with such thin margins. It is possible that insurance companies have priced their annuities in the expectation of a future increase in interest rates, as well as lower operating costs due to increasing scale. There is also a possibility that some companies have adopted aggressive pricing strategies in order to drive competitors out of the market and gain market share. Industry participants and regulators acknowledge that intermediation margins have been thin and returns on equity low, and that is probably the reason why some life insurance companies have decided to exit the annuities market in recent years.<sup>5</sup>

> 4Walker (2005) examines the relationship between the annuity rate and the risk-free rate and concludes that the threat of the new Pension Law produced a change in behavior.

> 5 The average return on equity of insurance companies in the 1994-2004 period was only 4 percent, compared to 25 percent in the case of AFPs.
The potential of female workers and entrepreneurs is underused globally. In 2019, working-age women were 26 percentage points less likely to work (44.7% versus 70.4%) and, when they work, they earn significantly less than men with similar skills and employment status (ILO 2021). McKinsey (2015) estimates that the world GDP would be approximately USD 28 trillion higher (26%) if women were playing an identical role in labor markets as men.

The gender gaps in the labor market cannot be explained solely by financial and human capital constraints (Duflo 2012; Jayachandran 2021). Indeed, impact evaluations of cash grants and training programs aimed at stimulating entrepreneurship tend to find little effect on income and business outcomes of female recipients, but sizeable positive effects on male counterparts (De Mel et al. 2008; Klinger and Sch ̈undeln 2011; Fafchamps et al. 2014; Berge et al. 2015; Bernhardt et al. 2019; Gin ́e and Mansuri 2021). Recent research confirms that women face extra gender-specific constraints, including higher risk of expropriation by other household members and peers (De Mel et al. 2009; Jakiela and Ozier 2016; Bernhardt et al. 2019; Riley 2020), limited control over their bodies and over fertility choices (Bandiera et al. 2020; Carneiro et al. 2021), less-developed networks (Field et al. 2016), lower aspirations (Aloud et al. 2020), paucity of role models (Lafortune et al. 2018), and gender-related norms and beliefs stipulating the roles that women should and should not play (Field et al. 2010; Alesina et al. 2013; Bertrand et al. 2015; Bursztyn et al. 2020; Delecourt and Fitzpatrick 2021).

We report the results of a randomized controlled trial (RCT) of a multi-faceted program, which aimed to address not only women’s financial and human capital constraints but also some of the gender-specific constraints. The RCT was conducted in Tunisia, where gender gaps in the labor market are massive (ILO 2021). The RCT was targeted at women and had two treatment arms. First, 1,000 women received an unconditional cash grant worth USD 768 in PPP terms (TND 634).<sup>1</sup> This amount is large, about four times the median monthly income of the respondents at baseline. These women also received a one-day financial training, which covered themes such as money management,

> 1This paper uses the PPP conversion factor and the official exchange rate estimated by the World Bank.

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