rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5.  Economic Growth and Infrastructure –Joint Effects**

_Sample: 78 countries, 1961-2005 (5-year period observations) Estimation Method: System GMM_

||**Dependent V**|**ariable:**|
|---|---|---|
||**GDP per capi**<br>|**ta Growth**<br>|
||**[1]**|**[2]**|
|_Infrastructure Variables:_<br>|||
|Electricity Index<sup>1</sup>|0.749 ***|0.975 ***|
||[5.353]|[5.292]|
|Transportation Index<sup>2</sup>|1.093 ***||
||[3.102]||
|Telecommunication Index<sup>3</sup>|1.097 ***||
||[4.754]||
|Transportation & Telecommunication Index<sup>4</sup>||2.135 ***<br>[5.637]|
|_Control Variables_|||
|Initial GDP per capita|-2.452 ***|-2.814 ***|
|in logs|[-8.264]|[-8.092]|
|Education|0.604 *|0.668 *|
|secondary school enrollment rate, in logs|[1.749]|[1.925]|
|Financial Depth|0.859 ***|0.849 ***|
|private credit/GDP, in logs|[5.486]|[5.494]|
|Crisis Volatility|-1.679 ***|-1.627 ***|
|std dev of GDP per capita growth<sup>5</sup>|[-18.420]|[-13.560]|
|Government Burden|-0.530|-0.413|
|government expenditure/GDP, in logs|[-1.390]|[-0.864]|
|Inflation|-2.918 ***|-2.113 ***|
|1+Growth rate of CPI, in logs|[-6.696]|[-3.781]|
|Trade Openness|1.881 ***|2.265 ***|
|(exports+imports)/GDP, in logs|[5.164]|[5.657]|
|Growth rate of Terms of Trade|0.060 ***|0.060 ***|
|log differences of terms of trade index|[6.785]|[6.424]|
|Constant|26.779 ***|23.764 ***|
||[7.257]|[5.336]|
|Observations|522|522|
|Number of Countries|78|78|
|Number of Instruments|70|64|
|Arellano-Bond test for AR(1) in first differences|0.000|0.000|
|Arellano-Bond test for AR(2) in first differences|0.170|0.107|
|Hansen test of overidentifying restrictions|0.164|0.340|

_Numbers in brackets are the corresponding t-statistics._ _* significant at 10%; ** significant at 5%; *** significant at 1% Period fixed effects were included (coefficients not reported)._ 1 First principal component of two indicators: power loss (as % of electricity output) & electricity generating capacity (in MW per 1000 workers, in logs).

2 First principal component of two indicators: share of paved roads in the overall road network & length of roads (in km, sqrt of per 1000 workers*surface area, in logs).

3 First principal component of two indicators: main telephone lines per 1000 workers, in logs & main telephone lines per 1000 workers, in logs.

4 First principal component of two indicators: Transportation index & Telecommunication index.

5 Crisis volatility is the portion of the standard deviation of GDP per capita growth that corresponds to downward deviations below the world-wide 1-std-dev threshold.

30
Table 5 complements the analysis by presenting the poverty rate by age groups. The analysis is undertaken at the individual level, but we consider the per capita familiar income for poverty calculation purposes. In line with previous results, we find that elderly members benefit the most from the fiscal system, with a 15.3-percentage point reduction in the poverty rates, and are the only ones for which poverty rates decrease from pre- to post-fiscal income aggregate. Meanwhile, children are the most negatively affected group, with a 3.1-percentage points increase in poverty rate. They are followed by aged 16-17 (2.6 p.p.), and the non-elderly adults (1.2 p.p.).

**Table 5. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately for individuals aged 0‐15, 16‐17, 18‐59 and 60 or more**

|**Age group**|**Proportion of**<br>**individuals**<br>**(%)**|**Pre-fiscal**<br>**poverty rate**|**Post-fiscal**<br>**poverty**<br>**rate**|**Change in**<br>**poverty (in**<br>**p.p.)**|
|---|---|---|---|---|
|Individuals aged 0-15|22.6|46.0%|49.1%|3.1|
|Individuals aged 16-17|3.0|43.4%|46.0%|2.6|
|Individuals aged 18-59|60.6|28.2%|29.4%|1.2|
|Individuals aged 60 or more|13.9|28.6%|13.3%|-15.3|

Source: authors’ estimations based on PNAD-C 2019. Notes: Pre-fiscal income considers the household-level market income plus pensions, while the post-fiscal income considers the household-level consumable income. The proportion of individuals indicates the number of individuals in each group as a share of the total number of individuals in PNAD-C.

To understand the effects of headship structure, Table 6 presents the poverty rates for three related dimensions. In panel A, we show the categorization in terms of whether the household is single-headed or two-headed. In panel B, in turn, we split households into those headed by a female and those headed by a male and, finally, in panel C we show the combination of these two criteria.

Table 6 indicates that the change in poverty rates is larger in absolute terms for (i) single-headed households, and (ii) those headed by men. Poverty changes for each group are small for many groups. Individuals living in a household headed by a single male show the largest drop in poverty (-4.8 p.p.).

33
# **3.3 Private Transfers of Cash in the Form of Remittances**

Remittance flows to developing countries totaled $429 billion in 2016, exceeding the total of all international aid flows (World Bank, 2017). Just as with public transfers, there is often expressed fear that these private transfers or remittances will “make people lazy” (e.g. GMA News 2007). In response to this fear, there is a relatively large literature that attempts to measure the impact of remittances on labor outcomes. However, unlike studies of public transfers that randomly allocate transfers to some households and not others, no study has randomized private transfers to measure their response. Instead, one needs to compare the behavior of households that receive remittances to those that do not, raising concerns about the quality of much of this evidence. We therefore restrict our discussion to those studies we consider the most reliable.

One type of study considers remittances as largely unconditional cash transfers, examining what happens when a household receives a shock to the amount of remittances they receive. The clearest example is Yang (2008), who examines Filipino households with migrants abroad, where exchange rate shocks during the Asian financial crisis meant that households with migrants in some destinations suddenly experienced a large increase in remittances compared to households with migrants in destinations where the local currency did not appreciate against the Philippine peso. He finds that households receiving more favorable shocks keep children in school longer and reduce child labor hours. This may be a pure income effect, or it could also reflect a liquidity constraint effect that prevented households from investing as much as they wanted in schooling. The impact on total adult labor hours is positive, but not statistically significant. Adults increase the number of hours they spend in self-employment, and households are more likely to start new entrepreneurial activities. This leads to a positive, but not statistically significant, change in selfemployment income, and negative, but not statistically significant, change in wage income. The impact for adults appears to be through the self-employment liquidity effect – a channel also emphasized by Woodruff and Zenteno (2007) for Mexican migrants.

A second type of study considers remittances as conditional cash transfers, where the condition to receive remittances is that the household has to send a member abroad to migrate. As a result, what can be measured is the overall impact of migration, which captures both the direct effect of higher remittances, and the indirect effects that arise from the absence of a household 12
commercially funded (profit oriented) MFIs tend to be supervised more often than noncommercially funded ones, there is still variation in supervision within each group. This is the variation we exploit in the paper: we compare the supervised with the unsupervised MFIs in each sub-group (commercially oriented and non-commercially oriented). We argue that this is a fair test because MFIs in the commercially funded group are facing similar incentives to make profits. Similarly, the MFIs that are not commercially funded face weaker incentives to be profitable, so comparing the supervised and unsupervised within that group is also a fair test.

The rest of the paper is organized as follows. In section two we describe our data and present the relationships between MFI characteristics (e.g., size, lending methodology), MFI performance (profitability and outreach), and our regulatory variables. Those relationships help to form the profiles for regulated versus un-regulated MFIs. Because the profiles in section 2 indicate strongly that the assignment of regulation and supervision to MFIs is non-random, we discuss estimation techniques that can account for selection effects in section 3. We present regression results in section 4, robustness checks based on split-sample tests in section 5, and offer concluding remarks in section 6.

# **II. Data**

The analysis relies on data from 346 microfinance institutions (MFIs) in 67 developing countries that were collected by the Microfinance Information eXchange (or the MIX), a not-for-profit private organization that aims to promote information exchange in the microfinance industry.<sup>1</sup> There are 540 observations in our database because some MFIs report information for multiple years. In the regressions that follow, we use only the most recent observation from each MFI because the error terms from observations from the same MFI are likely to be correlated, leading to an artificial reduction in the standard errors of the estimated coefficients. Qualitative results are similar when we include all

> 1 This is a substantial increase over the MIX database used in Cull, Demirgüç-Kunt, and Morduch (2007), which contained information from 124 MFIs in 49 countries. That data set was a variant of the so-called MBB 9 database. In this paper, we use a variant of the MBB 10 database.

6
<!-- Start of picture text --> Fleet Capacity Fleet Count<br>6005<br>_ 6004<br>i Fleet Type<br>a =—® Containership<br>=<br>xe 4004 —* Dry Bulk<br>o<br>a<br>=<br>1995 2000 2005 2070 2015 20201995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
|Vassilikos Pendaskinos Irrigation Project||
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|Vassilikos Pendaskinos Irrigation Project||
|Vassilikos Pendaskinos Irrigation Project||
|Fruit and Vegetable Project||
|Fruit and Vegetable Project||
|Fruit and Vegetable Project||
|Fruit and Vegetable Project||
|Fruit and Vegetable Project||
|HighwayProject(03)||
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|Khrysokhou Watershed Irrigation Project||
|Khrysokhou Watershed Irrigation Project||
|Khrysokhou Watershed Irrigation Project||
|Khrysokhou Watershed Irrigation Project||
|Khrysokhou Watershed Irrigation Project||
|Khrysokhou Watershed Irrigation Project||
|EnergyPlanningand Conservation Project<br>||
|EnergyPlanningand Conservation Project||
|EnergyPlanningand Conservation Project||
|EnergyPlanningand Conservation Project||
|EnergyPlanningand Conservation Project||
|Southern Conveyor Project||
|Southern Conveyor Project||
|Southern Conveyor Project||
|Southern Conveyor Project||
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|Limassol Sewerage and Drainage Project||
|Limassol Sewerage and Drainage Project||
|Limassol Sewerage and Drainage Project||
|Limassol Sewerage and Drainage Project||
|Limassol Sewerage and Drainage Project||
|Agricultural Research and Extension Project||
|Agricultural Research and Extension Project||
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|Southern Conveyor Project(02)|Joma Mohamad Mohamadi|
|Southern Conveyor Project(02)|Joma Mohamad Mohamadi|
|Southern Conveyor Project(02)|Joma Mohamad Mohamadi|
|Southern Conveyor Project(02)|Joma Mohamad Mohamadi|
<!-- Start of picture text --> Table 2c: Tanzania<br><!-- End of picture text --> <!-- Start of picture text --> Firm Count by Industry<br>Number of Firms Number of Firms<br>2008 2013<br>Garments 1 Precision Instr. 1<br>Elec. Machinery 1 Leather 2<br>Vehicles 1 Oth. Transport 2<br>Leather 3 Tobacco 4<br>Oth. Transport 3 Garments 4<br>Tobacco 4 Elec. Machinery 7<br>Media Equip. 5 Basic Metal 14<br>Precision Instr.Paper 57 PetroleumPaper 1415<br>Basic MetalPetroleum 77 Media Equip. 21<br>Rubber & PlasticsMachinery 1315 ChemicalsMachinery 3233<br>Wood, not furn 17 Rubber & Plastics 43<br>Non-metallic metal 26 Textiles 45<br>Chemicals 30 Printing 46<br>Fab. metal 31 Fab. metal 54<br>Textiles 40 Wood, not furn 68<br>Printing 51 Furniture 76<br>Furniture 177 Non-metallic metal 88<br>Food & Bev 236 Food & Bev 430<br>0 100 200 300 400 500 0 100 200 300 400 500<br>Graphs by year Graphs by year<br><!-- End of picture text --> Table 3c: Tanzania Firm Shares by Industry <!-- Start of picture text --> Share of Firms Share of Firms<br>2008 2013<br>Garments 0.1 Precision Instr. 0.1<br>Elec. Machinery 0.1 Leather 0.2<br>Vehicles 0.1 Oth. Transport 0.2<br>Leather 0.4 Tobacco 0.4<br>Oth. Transport 0.4 Garments 0.4<br>Tobacco 0.6 Elec. Machinery 0.7<br>Media Equip. 0.7 Basic Metal 1.4<br>Precision Instr.Paper 0.71.0 PetroleumPaper 1.41.5<br>Basic MetalPetroleum 1.01.0 Media Equip. 2.1<br>Chemicals 3.2<br>Rubber & PlasticsMachinery 1.92.2 Machinery 3.3<br>Wood, not furn 2.5 Rubber & Plastics 4.3<br>Non-metallic metal 3.8 Textiles 4.5<br>Chemicals 4.4 Printing 4.7<br>Fab. metal 4.6 Fab. metal 5.4<br>Textiles 5.9 Wood, not furn 6.8<br>Printing 7.5 Furniture 7.6<br>Furniture 26.0 Non-metallic metal 8.8<br>Food & Bev 34.7 Food & Bev 43.1<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>Graphs by year Graphs by year<br><!-- End of picture text --> 41
Policy ReseaRch WoRking PaPeR 4705

# **Abstract**

Using 2005 firm level data for 26 countries in Eastern and Central Europe, this paper estimates performance gaps between male and female-owned businesses, while controlling for location by industry and country. The findings show that female entrepreneurs have a significantly smaller scale of operations (as measured by sales revenues) and are less efficient in terms of total factor productivity, although the difference is small. However, women entrepreneurs generate the same amount of profit per unit of revenue as men. Although both male and female entrepreneurs in the region are suboptimally small, women’s returns to scale are significantly larger than men’s, implying that women would gain more from increasing their scale. The authors argue that the main reasons for the sub-optimal size of female-owned firms are that they are both capital constrained and concentrated in industries with small firms.

This paper—a product of the Gender and Development Group, Poverty Reduction and Economic Management Network (PREM)—is part of a larger effort in the PREM Network to explore the determinants of growth and poverty reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ssabarwal@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
# <u>2.3 Selection of schools and program participants</u>

In fall 2001, ICS selected 12 primary schools in Western Kenya to participate in the CSP. The 12 primary schools are all in Busia district, the westernmost district of Kenya’s Western province. Busia district borders Uganda and is located just north of Lake Victoria. Through a qualitative process of local consultation, the NGO identified schools of particular vulnerability within the area.

In January 2002, ICS organized a census of children in standards one through four of the 12 selected schools. Based on that census, ICS selected all children who had experienced one or both parent deaths (i.e., orphans) to automatically receive sponsorships.<sup>5</sup> Therefore out of the 876 sponsorships ICS had to allocate, 309 were assigned to orphans after the initial census. ICS then used a lottery to randomly select the remaining beneficiaries. All the remaining (i.e., non-orphan) students were automatically enrolled in the lottery. Since there were only a fixed number of sponsorships available, ICS determined that a lottery would be the fairest way to distribute the remaining sponsorships among equally needy children. The result was that 550 non-orphans were randomly chosen for sponsorship and 602 nonorphans were not chosen for sponsorship. Since sponsorships were not randomly allocated to the orphans, the effects of those sponsorships are not included in this analysis. Thus, the 550 non-orphans chosen were the group intended for treatment; and the 602 not chosen were intended as a comparison group. This is summarized in Figure 1 (Stage 1).

Next, a field representative from ICS went to the 12 schools to enroll those children selected for sponsorship into the program. For enrollment in the program, a child had to be present for a photograph to be taken and a small information card to be filled in, which would then be sent to the sponsor (with some basic information about the child, such as her preferred pastimes). ICS made efforts to ensure that

> 5 Children are referred to as “orphans” if they have either experienced one parent death or both parent deaths.

8
# Enforceability of Labor Law: Evidence from a Labor Court in Mexico<sup>∗</sup>

David S. Kaplan<sup>†</sup> Enterprise Analysis Unit The World Bank Joyce Sadka<sup>‡</sup> Centro de Investigación Económica Department of Economics Instituto Tecnológico Autónomo de México December 2007

> ∗We gratefully acknowledge helpful comments from Jennifer Reinganum, Simeon Djankov, Lior Ziv, and from seminar participants at ITAM, Macalester College, and the World Bank.

> †MailStop F4P-400, 1818 H Street, NW, Washington, DC 20433. Email: dkaplan@worldbank.org.

> ‡Camino a Sta. Teresa #930. México, D.F., C.P. 10700. Mexico. Email: jsadka@itam.mx.
Tonga, Samoa or Fiji which could then be withdrawn as cash, again at very low transaction costs. To date this new method seems to have very low take-up.

One reason that convenience may win out is that the amounts saved through better financial literacy may just be too trivial to warrant action. This is especially the case for the East Asians (who tend not to remit that much, and when they do, remit large amounts), and the Sri Lankans (who were using cheap methods to start with). But even for the Pacific Island sample, the savings from switching to one of these cheapest methods might amount to NZ$6-12 per $200 transaction, which may only merit the costs of learning a new method and trying it out if people remit very frequently. Given that the baseline survey revealed an average frequency of remitting for the Pacific Island sample of five times per year, the annual saving from switching to the cheapest methods is just NZ$30-60. The scope for changes in ultimate outcomes may be greater for financial literacy transactions which focus more on savings and budgeting behavior, or those which allow people at risk of obtaining expensive credit to avoid very expensive loans (e.g. Bertrand and Morse, 2011).

# **5. Conclusions**

Our results show that simple financial education training for migrants can change their knowledge about the costs of remitting and lead them to look around more at prices. There is also some suggestive evidence from the Pacific Island sample that coupling this with information on different sources of credit may help migrants to avoid some of the most expensive forms of credit. Nevertheless, we find no big changes in ultimate outcomes – migrants avoid switching to more expensive or less transparent remittance channels, but do not change the amount or frequency of remitting.

Thus despite simply informing remitters about remittance costs being a relatively cheap and uncontroversial intervention, it will not necessarily lower average costs from remitters switching to cheaper methods. Instead governments targeting reduced average money transfer costs may need to address other barriers, which may include excessive regulation and exclusive arrangements made by state-owned entities that deter new entry into remittance corridors, and barriers to access of financial access on the receiving country side. It is also possible that the mere process of providing transparent information on the costs of remitting by different methods 20
which found that certain socio-demographic characteristics such as education, gender, and income level had a role to play in determining the level of financial inclusion of Sri Lankans. Fintech can also play a role in improving financial inclusion for rural communities, as seen in many parts of the world. A study by Colombage (2010) explored the relevance of fintech in the Sri Lankan context.

However, none of the aforementioned studies specifically focused on the implications of financial inclusion in contributing to inclusive growth in Sri Lanka. Analysis of the status of financial inclusion with a view towards creating a more level playing field and increasing opportunities for productive engagement needs to consider socio-demographic factors as well. This paper provides a comparative analysis of the state of financial inclusion in Sri Lanka, taking into consideration socio-demographic factors. It analyzes the determinants of financial inclusion using data from the 2018 National Financial Inclusion Survey for Sri Lanka. It concludes by highlighting some implications for financial inclusion and inclusive growth in Sri Lanka.

# **1.1. A Brief History of Financial Inclusion Initiatives in Sri Lanka**

The arrival of the British in 1828, and the establishment of the commercial plantation sector, saw the start of formal financial services in Sri Lanka (Seelanantha and Wickremasinghe, 2009). A parallel informal financial sector also emerged, composed primarily of moneylending and pawnbroking. As pawnbroking became popular, laws were enacted to protect customers<sup>6</sup> and implement anti-usury policies (Karunagoda, 2007). The 20<sup>th</sup> century saw the rise of the cooperative movement as an organized mechanism to serve the farming community’s financing needs. Sri Lanka’s central banking system was formally established in 1950 following independence from the British, where the Central Bank of Sri Lanka’s (CBSL) primary objective initially was on monetary policy.<sup>7</sup> In later years, the CBSL formally took on the role of managing and deploying development finance lending programs on behalf of the government under a dedicated regional development department.

> 6 The first Ordinance for Pawnbroking was enacted in 1942.

> 7 https://www.cbsl.gov.lk/en/about/about-the-bank/bank-history

5