rafmacalaba/datause-encoder-data huggingface dataset:
Bangladesh under an official ordinance in 1983, which in turn inspired the first global Microcredit Summit Campaign, launched in February 1997 at a summit in Washington, DC, attended by over 2,900 delegates from 137 countries. At that point, just 13 million microfinance customers were counted globally. The summit featured the start of a nine-year campaign to reach “100 million of the world’s poorest families” by 2005. In line with Yunus’s emphases, the focus was on women especially, and explicitly on “credit for self-employment and other financial and business services.” The 1997 summit has been followed by 17 annual summits.<sup>4</sup> Microfinance grew fast. Figure 1 is adapted from the Microcredit Summit Campaign’s _State of the Summit Report 2015_ (the most recent at the time we are writing). It shows the impressive success of microcredit in reaching global scale. In 1997, of the 13 million customers counted worldwide, 8 million were among the poorest when entering (either living on income below international poverty lines or living on income that placed them in the bottom half of their country’s poor population).<sup>5</sup> By 2013, the total had hit 211 million, with 114 million among the “poorest.”<sup>6</sup> distinction to signify a pro-poor orientation. Other early providers of microcredit include Bank Rakyat Indonesia and Accion International, but it was Grameen Bank that created a focal point and developed the most widely replicated model.

4 See details on the Microcredit Summit website. http://www.microcreditsummit.org/about-thecampaign2.html.

5 The numbers are self-reported and, while there are attempts at outside verification, few if any of the statistics on the “poorest” are collected through careful household surveys on income and consumption. See Bauchet and Morduch (2010) for a discussion of the survey and its limits. 6 The social orientation of the Microcredit Summit means that their leadership’s choice of “headline” numbers deliberately focused on people, rather than the size of loan portfolios or the growth of lenders’ assets. By counting poor customers and building poverty targets explicitly into goals, the social dimension is reinforced, an idea that lines up with Muhammad Yunus’s aim to be a “banker to the poor” and not merely a banker.

6
Changes in employment structure are important in at least two ways. First, they tend to have far reaching labor market ramifications. Reallocation of labor across industries and sectors entails also reallocation across occupations and regions and as such is likely to give rise to structural mismatches. Jobs that are created in expanding industries usually require different skills and are located in different regions than jobs that are destroyed in declining industries. Particularly, the shift from industry to market services has been associated with a shift from bluecollar jobs to white-collar jobs requiring different skills. This imposes transition costs (in terms of time and effort) on workers who move from old to new jobs. Moreover, massive job and worker reallocation contributes to frictional and structural unemployment (when the skill and vocational structure of job openings differs from that of the unemployment). Accordingly it partially accounts for he high unemployment rates associated with the economic transition.

Second, the employment structure is an indicator of the level of economic development as well as the progress of the transition. In the particular case of transition economies, the share of market services, which was abnormally small before the transition, is a measure of successful labor reallocation. Figure 6 provides a ranking of ECA countries on this scale, indicating the progress of the transition toward a mature market economy.

# **Figure 6 Market services account for a much larger share of employment in European transition economies than in the CIS.**

**Market Services as a Percentage of Total Employment, 2002**

<!-- Start of picture text --> 40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>EstoniaHungaryCroatiaCzech R.LatviaSloveniaSlovakia _CEEPolandBulgariaLithuaniaKazahstanRussiaUkraineAzerbaijan _BRUGeorgia_CIS-7 + KRomaniaMoldovaBelarusKyrgyzstanUzbekistanTajikistan<br>percent<br><!-- End of picture text --> Source: ILO LABORSTA database (LFS data) The employment share of market services is the highest in fast reforming and most advanced transition economies, such as Estonia, Hungary, and the Czech Republic (above 30 percent), and is the lowest in slow reforming and less advanced economies, such as Belarus and most of the low-income CIS countries (below 20 percent).

18
Each of the national poverty lines in this sample is designed to attain recommended food-energy requirements with (socially-specific) allowances for basic nonfood needs. The median of these national lines is $2.00 per day at 2005 purchasing power parity (PPP) using the PPPs for household consumption from the 2005 International Comparison Program (World Bank, 2008; <u>www.worldbank.org/data/icp). As we will see, this is only slightly below the median for the</u> population of the developing world as a whole.

By definition, half the developing countries in the Ravallion et al. sample have national poverty lines below $2 a day.<sup>9</sup> For example, the official poverty lines in both China and India are only about half this figure, so there are many people who are not poor by official standards in both countries who are not yet “middle class” by my definition. Nonetheless, there can be no doubt that $2 a day is a frugal lower bound even amongst developing countries. There is a marked economic gradient in national poverty lines, reflecting more generous (food and nonfood) allowances for “basic needs” as countries develop (Ravallion et al., 2008). The highest line in the Ravallion et al. sample of developing countries is $9 a day (for Uruguay). For some ’ purposes, I will also focus on what might be termed the developing world <u>s upper middle class,</u> for which the lower bound is $9 a day.

As an upper bound for defining the developing world’s middle class, I will use the aforementioned US poverty line of $13 a day at 2005 PPP. This is a long way from the reach of those who are poor by developing-world standards. At an income growth rate of (say) 5% per year it would take almost 40 years to bring someone living on $2 a day up to the US poverty line; even at a growth rate of 10% it would take 20 years. Clearly $2-$13 is a wide interval.

I will not say that someone has entered the Western middle class until the person has reached the US poverty line, which accords closely with the lower bound of the MilanovicYitzhaki definition.

When talking about a growing economy, finding more density toward the middle could simply be due to that growth. It is also of interest to isolate the extent of the middle-income bulge that is attributable to changing distribution. There are a number of ways one might do this. The literature on the rise of the middle class in the US has often used a standard inequality measure. However, such a measure need not reflect well the extent to which density has shifted

> 9  For measuring extreme poverty, Ravallion et al. (2008) recommend a lower line of $1.25 a day. This is the average poverty line of the 15 poorest countries in the world.

5
# 5 INSTRUMENTS IN DEVELOPING COUNTRY FLOWS

Information about the instruments used in financial flows in developing countries is sketchy. For only a few countries are reasonably complete capital finance accounts available and even these are not prepared on a strictly comparable basis. Table 8 presents composite figures for eight countries in respect of which data are available. For each country, an average of three consecutive years was taken49.

There are quite wide variations between the data for individual countries from which the composite is formed. Tnis may be partly due to differences in the methodology of data collection, but is also undoubtedly also attributable 50 to real differences in economic structures. Accordingly, the composite should not be seen as a norm to which most countries approximate, but more as an approximate average of a fairly disparate group of country structures.

The main features of the business and household sector flow use of financial instruments are displayed sch3matically in figure 9. This shows th6 use of different types of financial instruments in household and business sector flows. The width of the arrows represents the relative magnitude of flows, based on the six-country composite of Table 8. Thus for example households 49 Except Portugal: two years. Data for a composite of four industrial countries are shown in Appendix Table 4.

50 The cases of Cameroon and Yugoslavia can be mentioned. In Cameroon there is a large informal financial sector, more developed, perhaps than in any other country. The 1984 study from which our data are drawn attempted to estimate the size of the informal intermediated financial transactions. While this was deduced as a residual, and may not be very trustworthy, it is interesting to observe that savings placed with the informal sector were estimated to account for about 50 per cent. of net financial savings of the household sector in Cameroon during 1983-84. The researchers estimated that this provided over 10 per cent of the corporate sector's use of funds, and over 25 per cent of their financial deficit. In Yugoslavia, the scale of trade credit seems to be much higher than in other countries, possibly reflecting the socialist organization of production. Furthermore the widespread holding of foreign-currency denominated deposits has had a significant effect on the structure of flows in Yugoslavia.

51
Policy ReseaRch WoRking PaPeR 4935

# **Abstract**

The authors examine the factors affecting the transition to self-employment in Bosnia and Herzegovina, using the World Bank Living Standard Measurement Survey panel household survey for the years 2001–2004. In the beginning of the sample, the country changed its legal framework, with the primary aim to promote labor market flexibility and to encourage entrepreneurial activity. The analysis identifies individuals that switched to self-employment (employers and own account) during the sample period and the viability of this transition, in terms of business survival for more than one year.

The results suggest an important role for financing constraints. Specifically, wealthier households are more likely to become entrepreneurs and survive in selfemployment. After controlling for household wealth, having an existing bank relationship increases the likelihood of starting a business with hired employees and increases the chances of survival for the new entrepreneur. By contrast, overseas—and in some cases domestic— remittances decrease the likelihood of becoming an entrepreneur.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand and promote entrepreneurship. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at _ademirguckunt@worldbank.org and lklapper@ 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
As a result of these policies, selected enterprises in 57 targeted, state-owned industrial groups received preferential treatment, including the allocation of a greater share of state assets within their respective groups, and targeted investment to improve R&D capabilities via a closer relationship with state research institutions. Many empirical studies have investigated whether these policies have contributed to the decline of energy intensity in China. Although Fan, Liao, and Wei (2007) point out that China’s energy intensity fell faster prior to market reforms initiated in 1992, they find that the own-price elasticity for energy was positive prior to 1992 and negative afterwards, providing further support that reforms are providing the necessary incentives for firms to reduce energy use in response to higher energy prices. Fisher-Vanden (2009) also argues that China’s transition to a market economy has induced a large decline in energy intensity. Using provincial panel data, He and Wang (2007) find that economic transition—including market liberalization, decentralization, and globalization—contributed to provincial energy efficiency improvements between the years 1996 and 2007. Lastly, FisherVanden, Jefferson, Liu, and Tao (2004), the only study using firm-level data, find that sectoral shift (i.e., structural change) can explain almost 50% of the decline in total energy intensity over the period 1997-1999.

Technological change, including subsector productivity changes and R&D input, has been shown to be the most effective factor driving China’s decline in energy intensity after 1979. For example, shifting from vertical shaft kilns to more efficient rotary kilns accounted for 21% of the total reduction in CO2 emissions in the cement industry in 2008 (Rock, 2011). Garbaccio, Ho, and Jorgenson (1999), in their decomposition analysis using input-output tables from 1987 and 1992, find sectoral technological change to be the largest factor explaining the decline in Chinese 3
Table E6: Test Scores, Stayers Only

|||Schoo|l level||Child level|
|---|---|---|---|---|---|
||(1)<br>Math|(2)<br>Eng|(3)<br>Urdu|(4)<br>Avg|(5)<br>Avg|
|High|0.150|0.191*|0.120|0.132*|0.235**|
||(0.093)|(0.098)|(0.085)|(0.077)|(0.094)|
|Low Treated|-0.114|0.054|-0.090|-0.034|0.095|
||(0.115)|(0.111)|(0.111)|(0.089)|(0.108)|
|Low Untreated|0.031|0.055|0.015|0.016|0.002|
||(0.077)|(0.084)|(0.071)|(0.063)|(0.083)|
|Baseline Score|0.279**|0.429***|0.365***|0.337***|0.637***|
||(0.135)|(0.118)|(0.109)|(0.098)|(0.049)|
|R-Squared|0.17|0.13|0.15|0.17|0.21|
|Observations|720|720|720|720|11,676|
|# Schools (Rounds)|720 (1)|720 (1)|720 (1)|720 (1)|711 (1)|
|Mean Depvar|-0.21|-0.21|-0.21|-0.21|-0.18|
|Test pval (H=0)|0.11|0.05|0.16|0.09|0.01|
|Test pval (_L_<sup>_t_</sup>= 0)|0.32|0.62|0.42|0.71|0.38|
|Test pval (_L_<sup>_t_</sup>=H)|0.02|0.21|0.06|0.06|0.19|
|Notes: * p<0.10, **|p<0.05, ***|p<0.001||||

a) This table examines whether our school test score results are driven by compositional changes. As before, school test scores are generated by averaging child average (across all subjects) test scores for a given school. We repeat all of the regressions in Table 4, but only include all children who report being at the same school for at least 1.5 years.

b) Regressions are weighted to adjust for sampling and include strata fixed effects, with standard errors clustered at village level. We include a dummy variable for the untested sample at baseline across all columns and replace the baseline score with a constant. Since the testing sample was chosen randomly at baseline, this procedure allows us to control for baseline test scores wherever available. The number of observations and the unique number of schools are the same since test scores are only collected once after treatment. The number of schools is lower than the full sample due to attrition and zero enrollment in some schools in the tested grades. The mean of the dependent variable is the test score for those tested at random at baseline.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

43
|<br>Dependent variable:_Employment_<br>_Growth_ (logdifference, %, annual)|**Table 4: Base**<br>**(1)**|**Regression Re**<br>**(2)**|**sults**<br>**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|Informal Competition (Country|-5.786*|-6.954**|-6.490**|-4.941**|-5.692**|
|level cell average)|(3.370)|(2.782)|(2.640)|(2.245)|(2.250)|
|Initial Employment (logs)|-3.702***|-3.860***|-3.798***|-4.088***|-4.130***|
||(0.712)|(0.626)|(0.560)|(0.552)|(0.564)|
|GDP per capita (lagged, logs)||-1.513**|-1.423**|-1.737***|-1.460***|
|||(0.625)|(0.563)|(0.486)|(0.474)|
|Age of Firm (logs)|||-3.440***|-3.422***|-3.406***|
||||(1.047)|(1.035)|(1.026)|
|Manager Experience (logs)|||0.025|0.007|0.082|
||||(0.359)|(0.349)|(0.359)|
|Exports (proportion of sales)|||2.220**|2.132*|2.213**|
||||(1.106)|(1.096)|(1.102)|
|Female Top Manger Y:1 N:0|||-1.418|-1.392|-1.388|
||||(1.602)|(1.542)|(1.556)|
|Multi-establishment firm Y:1 N:0|||3.643***|3.398***|3.424***|
||||(0.952)|(0.920)|(0.944)|
|Crime Losses Y:1 N:0||||0.120|0.088|
|||||(1.172)|(1.159)|
|Courts Obstacle Y:1 N:0||||-0.454|-0.392|
|||||(0.610)|(0.634)|
|Power Outages (hours)||||-0.007*|-0.007*|
|||||(0.004)|(0.004)|
|Finance Obstacle (0-4 scale)||||-0.353|-0.352|
|||||(0.238)|(0.238)|
|Overdraft Y:1 N:0||||3.300***|3.293***|
|||||(0.680)|(0.703)|
|Inflation (CPI, annual %)|||||0.129***<br>(0.039)|
|Primary Enrollment (Gross rate)|||||0.044*<br>(0.024)|
|Industry fixed effects|No|Yes|Yes|Yes|Yes|
|Year fixed effects|No|Yes|Yes|Yes|Yes|
|Constant|16.843***|21.028***|30.866***|32.888***|22.874***|
||(2.044)|(5.648)|(6.893)|(6.523)|(6.828)|
|Number of observations|<br>20,295|<br>20,295|<br>20,295|<br>20,295|<br>20,295|
|R-squared|0.042|0.057|0.085|0.096|0.097|
|Huber-White robust standard errors<br>*** (1%), ** (5%), * (10%).|and clustered o|n the country i|n brackets. Sig|nificance is d|enoted by|

46
# **<mark>Table A3 - Definition of variables</mark>**

|**Variables**|**Definition**|
|---|---|
|**_Children characteristics_**||
|Haz06|Height for Age Z score of under fiveyears old children computed usingthe 2006 WHO New standards|
|Stunting|Probabilityof HAZ less than -2 SD(Standard Deviation)|
|Birth Order|Birth order of the children amongsiblings|
|Male|1 if the under fiveyears old child is a male, 0 otherwise|
|Age|Child’s number of months|
|**_Parents characteristics_**||
|Secondaryeducation or more|1 if the mother/father completes the secondarylevel of education, 0 otherwise|
|Land Ownership|1 if the mother/father owns land, 0 otherwise|
|House Ownership|1 if the mother/father owns a house, 0 otherwise|
|Currentlyworking|1 if the mother/father is workingat the time of the survey, 0 otherwise|
|**_Refugees’ exposure variables_**||
|Resid.|1 if theparents move into theirplace of residence before 2001 and 0 otherwise|
|R|Refugee Influx Index(Weighted sum ofpopulation divided bythe distance to the 13 camps)|
|T<br>�<br>�|1=the Mother was born or exclusively spent her early childhood (0-5) during the refugee influx (1994-2001), 0=Otherwise|
|T<br>�<br>�|Total number of years a mother from the treatment cohort was exposed to refugees|
|T<br>�<br>�|Equal to 2 if the mother<br>jof the child<br>iwas exclusively born or solely spent her early childhood (0-5) during the first phase<br>of the refugees’ inflow (1993-1996), 1 if the mother<br>jof the child<br>iwas exclusively born or solely spent her early childhood<br>duringthe secondphase of refugee influx(1997-2001);and 0 otherwise|
|T<br>�<br>�|1=the Mother was born or exclusively spent her early childhood (0-5) during the refugee influx (1988-1993), 0=Otherwise<br>(Excluding those born between 1993 and 2001)|
|T<br>�<br>�|1=the Mother was born or exclusively spent her early childhood (0-5) during the refugee influx (1988-1996), 0=Otherwise<br>(Excluding those born after1996)|

41
water, low levels of system use were evident. Moreover, 15 percent of farmers reported nonavailability of water from the JCLIS during the Rabi season as well (Table A.5 in Appendix).

**Table 2. Median and average hours of operation of JICS in the crop year 2020-21**

|**Season**|**Median hours of operation**|**Average hours of operation**|
|---|---|---|
|Kharif 2020|0|16.7|
|Rabi 2020-21|60|84.4|
|Summer 2021|27.5|90.8|
|**Total in 2020-21**|**93.5**|**191.8**|

_Source: Created by authors using primary data collected from system operators_ The average irrigation fee for solar pumps is Rs. 34/hour. Operators of three solar-powered systems reported that the irrigation fee had yet to be decided. Nine other operators also said that while the irrigation fee has been decided, fee collection was not yet underway. Because solar pumps have no variable costs, water user group members do not feel the need to collect any fees even if money may be required in the future for repair and maintenance. Irrigation fees for diesel pumps vary from Rs. 120 to Rs. 140 per hour. Though the discharge from solar pumps varies during the day and across the seasons with the intensity of the sunlight, the irrigation fees remain the same across the year and have been set on an hourly basis for all systems. Among those who cultivated in the given season, 71 percent of farmers reported paying an irrigation fee in Rabi 2021, 30 percent in Kharif 2020, and 66 percent in summer 2020.

35 of the 50 systems experienced no breakdowns. Of the 15 systems that did, the most common problems were related to leakage in the distribution system and glitches in the control box, the control panel, or the wiring. In most of these cases the repair costs were negligible (5 cases) or small (less than Rs. 2,000). Managers of the two systems had to spend Rs. 27,000 and Rs. 35,000 on the repair. Water users mobilized the money for repair in both these cases. 45 of the 50 operators reported no significant disputes or discord in managing the irrigation systems. Where disputes did occur, they were triggered by disagreements over irrigation sequencing and water was scarce.

# _4.2 Farmer characteristics_

757 farmers were interviewed in the primary survey. Nearly 90 percent of the respondents were women. 39 percent of the respondents in the beneficiary group and 32 percent in the control group could not read or write. 65 percent of beneficiary households and 59 percent of households in the 11