rafmacalaba/data-use-mentions-extended huggingface dataset:
build confidence. Spatial and temporal inconsistencies can be checked, and in rare cases overlapping surveys can be crosschecked. As a result, well-established survey programs such as the Demographic and Health Surveys and Living Standards Measurement Study are often treated as close to the ground truth. Validation is more difficult for big data research in general and for CDR models in particular. These data are not specifically collected for socioeconomic analysis, so inspection may not be useful and pre-testing is often not possible. Moreover, CDR models are usually designed to interpolate between conventional survey models in either time or space, so direct validation against a survey is not possible.

> 14 See <u>http://www.worldpop.org.uk/</u>

> 15 These maps are built by combining census data with physical covariates, such as climate, elevation, slope and water bodies, and human covariates, such as urban sprawl, based on satellite imagery, known settlements, roads, and points of interest under a Bayesian framework.

> 16 The World Bank Innovation Lab is presently sponsoring additional CDR analysis work with Flowminder/Worldpop in Haiti, which is expected to provide deeper insight into their methods.

> 17 Letouzé and Vinck (2015).

**18**
social benefits, child allowance and financial social assistance have the biggest inequalityreducing and poverty-reducing effects. These programs are well targeted to the poor. Almost 93 percent of the financial social assistance and 90 percent of the child allowance is concentrated at the bottom 40 percent of the distribution of market income (including pensions as deferred income). Half of all the spending on parental allowance goes to bottom 40 percent of the population in terms of market income (Figure 5).<sup>17</sup> However, the marginal effects of social benefits on poverty and inequality-reducing are constrained by the relatively small coverage of the most targeted transfers. The marginal contributions of all direct transfers (excluding contributory pensions) are to reduce the Gini by 0.0139 points and the poverty headcount by 0.0216.

It should be noted that the different benefit programs may have different intentions. Not all programs necessarily have a redistributive or poverty-reducing objective, for example, sick leave and wage compensation during maternity leave. Almost 40 percent of the wage compensation during maternity leave goes to top 40 percent of the market income distribution.

**Figure 5. Concentration of social benefits (by market income plus pensions quintiles)**

|0%|20%<br>40%|60%|80%<br>100%|
|---|---|---|---|
|Birth grant<br>Child allowance<br>Monetary social assistance|||Poorest<br>2|
|Sick leave|||3|
|Unemployment benefit<br>Care allowances|||4<br>Richest|
|Wage compensation pregnancy/maternity leave||||
|All direct transfers||||

Source: Own estimations using HBS and SILC data Education and health in-kind benefits have inequality-reducing effects. As noted earlier, we assume that the benefit received by the individual is equal to the amount spent per capita. Figure 6 shows that overall education spending is
the two follow-up surveys illustrate this. Midline data show that shortly after the revolution in 2011, many believed that the revolution improved their chances to access a wage job or realize their own project. However, this sense of optimism was not as strong at endline in 2014, with fewer people feeling optimistic about their future prospects with respect to labor market outcomes three years later.<sup>9</sup>

# **4. Empirical identification strategy**

# _Intent-to-Treat estimates_

Identification of the impacts of entrepreneurship education relies on the randomized assignment of applicants to the entrepreneurship track. We focus on intent-to-treat (ITT) estimates, measuring the impact of offering business training and coaching independently of actual take-up. We estimate the following individual-level intent-to-treat regression: where Y� is the outcome of interest for student i at follow-up (separately for midline and endline), Ti is a binary variable for being randomly assigned to the treatment group, Xi is a set of control variables, πis are fixed effects for each randomization strata (by gender and study subject) and εi is a mean-zero error term.<sup>10</sup> We present results for a specification where Xi includes the few baseline variables in Table 1 that are imbalanced between the treatment and control groups.<sup>11</sup> The sample includes 1,452 individuals tracked at follow-up. We calculate robust standard errors clustered by gender and study subject.<sup>12</sup>

# _Heterogeneous treatment effects_

To determine whether the entrepreneurship track has heterogenous effects between subgroups of applicants, we conduct heterogeneity analysis by estimating the following specification:

> 9 There are no differences between the treatment and control groups in perceptions of labor-market opportunities.

> 10 We include a binary variable for each randomization strata to increase power (Bruhn and McKenzie, 2009). 11 The footnote of Table 2 provides the full list of control variables. Given there was some attrition in the baseline phone survey, in this specification we impute the sample mean to missing
Almost half (48%) of loans extended in 2009 charged an annual rate of 6.0%, and a further third charged rates of 3, 5, 7, or 8 percent, as Table 7 shows; we model these differences more formally in section 7 below.

|**Table 7. Breakdown o**|**f VF loan**|**s by interest rate**|**charged**||||||
|---|---|---|---|---|---|---|---|---|
|Interest rate<br><3|3|4<br>5|6|7|8|9-11|12|>12|
|% breakdown<br>6.1|6.2|3.6<br>13.5|47.6|7.0|7.4|4.1|4.1|0.4|
|Note: Mean interest rate: 6.0%<br>Source: Thailand Socio-Econom|p.a.; standa<br>ic Survey 20|rd deviation of interest ra<br>09|te, 2.3%.||||||

The amounts lent vary somewhat from one loan to the next; formally, the borrower requests an amount, and although there are informal norms, there is no set amount. The mean amount borrowed in 2009 was 15,790 baht, equivalent to about 8% of income, and 78% of VF loans in 2009 were between 10,000 and 20,000 baht, as Table 8 shows. In 71% of all village funds, the maximum loan is 20,000 baht; in 10% of cases the

> 2 The average rural wage is about 300 baht per person per day.  We define volunteer time as any
and implemented—that is, the local initial conditions and country characteristics—and the design characteristics of the project itself.

For example, the geographic scope of a project may have a significant impact on the realization of WEBs. On the one hand, projects with a larger expanse (national or transnational) may create a strategic connection with extra catalytic effects on the economy. On the other hand, the inherent difficulty in implementing such large and complex projects may significantly delay or even taper the ultimate impact on the economy. Similarly, as compared with projects on a flatter terrain, projects on more rugged terrains may preclude the local communities from benefiting from the strategic connectivity created by the project if suitable first and last mile connectivity is not provided. However, if the level of initial connectivity in more rugged terrains is lower, then the benefits from the development of a strategic transport corridor including secondary connectivity (roads) may be higher, compared with the same corridor being developed on a flatter terrain that already has a higher level of initial connectivity. Such questions should be assessed empirically to understand the key conditional relationships for successful project design.

The paper assesses whether transport corridors appear to spur higher economic activity around them, considering the following questions:

- Are the corridors initiated in countries that are richer, bigger, and easier to connect (initial conditions)?

> 15 Comparable nightlights data are available between 1992 and 2012 (inclusive), which limits the number of projects in the sample. Three of the projects were appraised in 1991. For these projects, nightlights data from 1991 were used. In addition, four projects closed in 2012. For these, 2012 data for nightlights were used.

11
<br>10%<br>0%<br>Direct - Skilled Direct - Unskilled Indirect - Skilled Indirect - Unskilled<br><!-- End of picture text --> Source: Own calculations based on Lesotho’s Social-Accounting Matrix and LACEX database<sup>6</sup>

> 6 The Labor Content of Exports (or LACEX) database <mark>was developed by Cali et al. (2016) on the basis of a panel of global inputoutput tables and exports from the Global Trade Analysis Project (GTAP) and employment data from the ILO. The database measures the contribution of labor to a given country</mark> <mark>`’` s exports measured as employees</mark> <mark>`’` compensation or wages (LACEX).</mark>

7
# _Education_

We also expect education to be positively correlated with higher urbanization, as returns to education tend to be higher in urban areas. Education itself may have an aspect of "urban bias": rural students do not necessarily learn agricultural skills, and may even be educated in such a way as to be averse to farming (Gugler and Flanagan, 1978). Using education data from the Barro and Lee data set (Barro and Lee, 1996), we find in regression 5 that the higher the proportion of the population with no education, the lower the level of urbanization. This effect is non-linear however, as evidenced by the fact that the square of NOEDUC is positive and significant. The interpretation is simply that among countries with many uneducated people, a very small improvement in education is associated with much higher urbanization. Conversely, among countries with relatively few uneducated people, improvements in education will basically not affect urbanization.

Other education variables are available from Barro and Lee, enabling us to distinguish among different levels of educational achievements. However, they tend to be very collinear. We therefore used only secondary education (SECONDARY), which yielded the best fit. It is positive and significant, suggesting that a typical country can expect to be 12% more urbanized given its level of income and income structure than a country whose adult population has half the average years of secondary education.

# _Urban bias_

No good measure of urban bias is readily available. In particular, it is impossible to measure whether public investment and spending is biased towards urban areas, due to a lack of data and the difficulty of assessing which populations benefit from which public investments. Two alternative measures can be used as proxies. One is the difference between the domestic producer price of agricultural products and their international market prices. These are available through the Food and Agriculture Organization of the UN for a number of basic products, notably cereals. If this functions as a measure of urban bias, we expect a negative coefficient. But regression 6
to reduce disparities and mismatch.

Substantial changes in labor market policies and institutions have been introduced in Latvia in recent years. Although these reforms have yielded good macroeconomic results and played an important role in stimulating job creation in services and export sectors, they have had limited success thus far in fighting unemployment. Using data from labor force surveys, this study has attempted a more precise understanding of the country’s labor force and its dynamics. These analyses should provide useful insights to policymakers for improving the functioning of Latvia’s labor market. This is particularly important given Latvia’s medium-term plans to adopt the Euro and to join the EU Pact on Stability and Growth, which will eventually leave labor market as the only effective economic tool to adjust to shocks.

# **REFERENCES**

Barrett, Alan, John FitzGerald and Brian Nolan (2000), Earnings Inequality, Returns to Education and Immigration into Ireland, IZA Discussion Paper No. 167.

Blanchflower, David G. and Andrew J. Oswald (1994). The wage curve. MIT press.

Brainerd, E. (2000), Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union, Industrial and Labour Relations Review, Vol.54, No.1, pp.138-162.

Brücker, Herbert and Parvati Trübswetter (2003), “Are East-West Migrants in Germany Favourably Self-Selected?” Mimeo, Berlin: _German Institute for Economic Research (DIW Berlin)._ Central Staistical Bureau of Latvia (2003a), _Latvia’s Regions in Figures._ Riga.

45
# **Identifying the Poor – Accounting for Household Economies of Scale in Global Poverty Estimates**

Dean Jolliffe and Samuel Kofi Tetteh Baah* _JEL codes_ : I32, O10, O20 _Keywords_ : global poverty, household economies of scale, sustainable development goals

***** Jolliffe, the corresponding author (djolliffe@worldbank.org) is a lead economist at the World Bank. He also holds positions as an adjunct lecturer at the Johns Hopkins University School of Advanced International Studies (SAIS), Research Fellow with the Institute for the Study of Labor (IZA) in Bonn, and as a Fellow of the Global Labor Organization (GLO). Tetteh Baah (stettehbaah@worldbank.org) is an economist in the Global Poverty and Inequality Data Team of the World Bank. The authors wish to thank Andres Castaneda Aguilar, Charles Yuji Horioka, Daniel Mahler, Martha Mendoza, Marta Schoch, Umar Serajuddin, Nishant Yonzan, and participants of the 37th IARIW General Conference (Luxembourg, 2022) for feedback on initial drafts of this work. The authors gratefully acknowledge financial support from the Republic of Korea, through the Korea Development Institute School of Public Policy and Management (KDIS) and its Knowledge Partnership with the World Bank; the Knowledge for Change Program (KCP); and the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Other social assistance|61,706|||||
|Housing subsidy|50,733|87,725|0.58|62,757|0.81|
|**Taxes and Social Insurance contributions**||||||
|Social insurance contributions|4,426,981|||6,304,944||
|Personal income tax|2,105,632|||3,289,536||
|Value added tax|3,631,000|||4,842,928||
|Excise duties|939,044|||1,370,640||

Notes:

* The original RLMS-HSE variable includes all child benefits received up to age of 1.5 years.

** The original RLMS-HSE variable includes all child benefits received by children aged 1.5+ years.

*** Only monthly cash payment is simulated; the original RLMS-HSE variable includes categorical benefits for transportation, medicines and recreation.

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data Income distribution estimates are shown in Table A-9. These are computed for individuals according to their per-capita household disposable income. Both average earnings and per-capita household 42
labor force, but the development of labor-intensive small and medium-sized enterprises is repressed because they do not have access to financial services. As their growth is repressed, fewer job opportunities will be available, dampening wages in China. For poor people, the only source of income, other than transfers, is their own labor. The financial structure artificially reduces these job opportunities as well as wages, lowering the income of low-income people.

**Figure 49: Total Financial Assets of China**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010<br>Total China less Big 4 Big 4<br>Current RMB, billions<br><!-- End of picture text --> <!-- Start of picture text --> Source: Bankscope.<br><!-- End of picture text -->

**Table 1: Chinese Interest Rates on Debt**

||2001|2002|2003|2004|2005|
|---|---|---|---|---|---|
|State Owned Enterprises|2.46|2.23|2.67|2.86|2.61|
|Private Enterprises|4.84|4.64|4.61|3.81|4.57|
|Difference|-2.38|-2.41|-1.94|-0.95|-1.96|

Source: Ferri and Liu (2009).

103. The World Bank Enterprise Survey conducted in 2004 provides further evidence for the concentration of lending. While among large firms, 65 percent had a loan from a bank or financial institution, only 32 percent of medium-sized
**Timeline and experimental design** We present the timeline of key activities associated with the IAPP project and the experiment in Figure 1. As the pulses are particularly suited for cultivation in the dry season, which starts in roughly November in Barisal, initial meetings with farmer groups were implemented a few months prior in July 2012. These initial farmer group meetings were used to collect our sampling frame for our baseline survey, which we describe in additional detail, along with other aspects of data collection, in Section 3.3. The baseline survey covered, among other things, agricultural production during the November 2011—March 2012 dry season, which we refer to as the “Pre-demo year”.

Figure 1: Timeline <!-- Start of picture text --> One year Two-years<br>Pre-demo Demo post-demo post-demo<br>Baseline survey First midline survey Second midline survey Endline survey<br>2011 2012 2013 2014 2015<br>Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3<br>Farmer Crop Adoption season<br>selection demos<br><!-- End of picture text --> _Notes:_ Dark gray solid lines indicate the boro season each year, denoted _Pre-demo_ , _Demo_ , _One year post-demo_ and _Two years post-demo_ , respectively. Survey periods are highlighted in black. Light gray brackets denote project interventions.

After initial farmer group meetings, we randomly assigned one fourth of the 110 villages in our study sample into one of three treatment arms plus a control group. Figure 2 summarizes the experimental design and sample sizes under each arm. We describe the activities implemented under each of the four experimental arms below.

**Control** In contrast to our three treatment arms, no demonstration resources were provided through farmer groups in control villages. However, as noted in Section 2, a number of activities associated with the promotion of the new seeds were implemented in control villages. Extension agents participate in regular meetings with the farmers
the following is more likely to be your decision? (a) I would take the risk and hope there is no drought; (b) I would sell my car and buy an irrigation system.” I distinguish in the empirical analysis between these two groups, where group (a) captures those who are not averse to idiosyncratic shocks, and group (b) captures those who are risk averse.

The literature on inequality of opportunity generally distinguishes between inequality due to unequal opportunities, and inequality due to effort (see Ferreira et al. 2008, 2010 and references above). However, as noted by Lefranc et al. (2009), luck is also an important factor, but generally gets bundled together with effort. In the LiTS survey it is possible to account for the influence of luck by conditioning on the 14
of bank size. To the extent that larger banks can take advantage of economies of scale, we would expect market share to be negatively related to spreads.<sup>13</sup> We control for bank orientation using _interest income_ , the ratio of total interest income to operating income, and _intermediation_ , the ratio of net loans and advances to total liabilities. Using bank-level data across countries, Laeven and Levine (2007) demonstrate that specialized loan-making banks have different performance characteristics than specialized investment banks, and that loan-making banks tend to have a higher share of interest income. We expect competitive pressure in the lending market to be better reflected in the banks specialized in that area, and thus we expect a negative association between _interest income_ and spreads. Similarly, we expect those banks that lend a relatively high share of their available liabilities to be most responsive to the same competitive pressures, and thus a negative relation between _intermediation_ and spreads.

Following the literature, in some specifications we include a control for banking sector structure and three macroeconomic control variables. _Herfindahl_ is a standard index of sector concentration, which we calculated based on bank shares of total deposits.<sup>14</sup> If deposits are concentrated in the hands of a few banks, those banks might be able to drive up lending rates, as they control the supply of funds. We would therefore expect a positive relation between concentration and spreads. The macroeconomic controls are the _T-bill rate_ , _inflation,_ and _real growth_ . The _T-bill rate_ is the rate of interest on short-term treasury bills, which is included as a proxy for the marginal cost of funds faced by banks. _Inflation_ is included because price shocks might not be passed through equally to the nominal lending and borrowing rates, and thus these differential effects would be reflected in the spread. _Real growth_ is included to capture business cycle effects that are reflected in spreads. As an economy slumps and growth slows, borrowers become less creditworthy, and thus banks must charge higher lending rates
# 1 <u>Introduction</u>

**This case study examines Vietnam’s power sector development and reform experience from the 1990s to 2018.** It explores links between country context, power sector institutions and market reform attempts, and dimensions of power sector performance. The case study is structured in three parts (after this introduction). Part 2 describes how power sector institutions have developed over time in context of the 1990s global model of market-oriented sector reforms. Part 3 focuses on sector performance in terms of four issues: security of supply; user access and affordability; supply-side efficiency and financial viability; and regulation of tariffs and other selected elements. Part 4 synthesizes the findings and reflects on lessons from Vietnam that may be relevant to other countries.

**The case study employs a mixed methodology with inputs from multiple sources** . It has involved a review of literature, collection of data from official sources, analysis of utility financial accounts for the period 2013 to 2017, comparison of several dozen indicators against good practice benchmarks, and 19 interviews with 25 actors including current or former government officials as well as academics and civil society representatives in Vietnam.

**Vietnam has seen significant changes to its market structure, gradually moving from a vertically integrated structure to a more competitive power market.** In less than two decades, the country’s power sector evolved from fragmented companies with high technical and financial losses to an integrated power system, and then to an unbundled group of stated-owned corporations with sizeable participation of domestic and international private sector actors in power generation. Vietnam has achieved rapid electrification and averted major crises of supply, thanks to technically-sound power system planning with political leadership and follow-through by national institutions. A highly dedicated and trained workforce, and a sociopolitical emphasis on ensuring consensus among key actors in the power sector at each reform step, have contributed to continuous implementation of the power sector reform agenda, albeit at a relatively slow rate of institutional reform.

# 2 Vietnam’s power