rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4859

# **Abstract**

Lack of access to electricity is one of the major impediments to growth and development of the rural economies in developing countries. That is why access to modern energy, in particular to electricity, has been one of the priority themes of the World Bank and other development organizations. Using a cross-sectional survey conducted in 2005 of some 20,000 households in rural Bangladesh, this paper studies the welfare impacts of households’ grid connectivity. Based on rigorous econometric estimation techniques, this study finds that grid electrification has significant positive impacts on households’ income, expenditure, and educational outcomes. For example, the gain in total income due to electrification can be as much as 30 percent and as low as 9 percent. Benefits go up steadily as household exposure to grid electrification (measured by duration) increases and eventually reach a plateau. This paper also finds that rich households benefit more from electrification than poor households. Finally, estimates also show that income benefits of electrification on an average exceed cost by a wide margin.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to study the rural energy demand and the welfare impacts of rural electrification projects funded by the World Bank. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@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
Here, we provide more evidence on the influence of each group, reporting detailed data from the BIS and the **SEC.** These data sets help us isolate the behavior of investors and managers.

> The behavior of underlying investors is described in Figures **6** and **7.** Figure **6** shows the cash flows to Asian mutual funds over time, based in the **U.S.** and the **U.K.** This decision belongs to investors. The figure shows that inflows to Asia were high before the Asian crisis, particularly in **1995-96.** After the Thai devaluation of **1997,** large outflows took place in the second half of **1997.** The outflows continued in **1998,** being particularly large after the Russian crisis in the U.S.-based funds.

Figure **7** provides related information, plotting the evolution of aggregate net asset positions and the injections/redemptions into/from the **13** Latin American mutual funds.'<sup>9</sup> Again, the latter are part of the investors' decision set. Injections (redemptions) are measured **by** the percentage increase (decline) in the number of shares held **by** each mutual fund (to control for fund size changes due to capital gains and losses). The pattern of inflows and outflows corresponds to the recent crises. During the Mexican crisis, there were large redemptions from Latin American funds, accounting for **25** percent of the outstanding shares in the first quarter of **1995.** Injections resumed to Latin American funds

> **1s** Mutual funds here means open-end, non-index funds, which account for most of the funds that invest **in** emerging markets. For closed-end funds, investors do not control portfolio size. For index funds, managers have little control over portfolio holdings.

> **'9** The data come from Morningstar and the **SEC.** We work with holdings of the largest **13** Latin-America equity funds (open-end) from April **1993** to January **1999** (24 quarters). Those funds are Fidelity Latin America, Morgan Stanley Dean Witter Institutional Latin America, Van Kampen Latin America (formerly Morgan Stanley), BT Investment Latin America Equity, TCW Galileo Latin America Equity, TCW/Dean Witter Latin America Growth, Excelsior Latin America, Govett Latin America, Ivy South America, Scudder Latin America, T. Rowe Price Latin America, Merrill Lynch Latin America, and Templeton Latin America. Not all of these funds existed from the beginning of our sample; on average we have about **10** quarters of data (out of a possible 24) per fund.

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# Research Components of World BOm*<sup>Education Projects,</sup> since 1982.

|Area|Country and Loan/Credit<br>Iumber|<sup>Approved</sup><br>I|<br>Research Components<br> <br>I|Current Status of Studies,<sup>including</sup><br>Completed Documents/Data|
|---|---|---|---|---|
|IAC|Brazil LU 2366-BR|1984|Subsector Proiect for Technician<sup>Trainina.</sup><br>(i) Expnd iand strengthen graate<br>tracer<br>system (poras. 4.25-6 p.23);<br>(ii)<br>assess value of current evaluations<sup>of</sup><br>technician training system;<br>(iii)<br>assess pedagogicalandcost-benefit<br>aspects of "school-production"<sup>system</sup><br>(para. 4.18 p.18);<br>(iv) regional manpower studies<sup>(para. 4.28 p.24</sup>|(i) and (iv):<br>studies ongoing but not completed<br>(expected 06/90). Will mke interesting<sup>methodol-</sup><br>ogical contributions<br>to operational tracer studies<br>and labor mrket studies, but<sup>are not likely</sup><br>to<br>yield substance of general<sup>interest;</sup><br>(ii)<br>and(iii):<br>studies completed, but<sup>quality of</sup><br>reportsdoesnot merit wider distribution;<br>also:<br>linisterio<br>de Educecao/EDUTEC<br>(1987).<br>mAnalysisof Technician Training Project"<br>(doc.**9**G14997);|
|LAC|BrazilLN2489-BR|1985|Project for ScienceandTechnolo.<br>Noresearch<br>comfponent. Mid-term review due<sup>1986.</sup>|Confirmed.<br>|
|LAC|Brazil Ll 2810-BR|1987|Skills Formation. Studies and<sup>Evaluation</sup><br>division of theManpower<br>Secretariat to<br>undertake studies on:<br>Ci) the cost-effectiveness of training;<br>(ii)<br>systematic guidelines for evaluating<br>trainingmillbe introduced.<br>No ipecific research component<sup>(para4.28p.24)</sup>|Prolific<br>numberos studies - far more<sup>than indicated</sup><br>in the SAR**Allare**invarious<br>stages of completion.<br>Copies attached.<br>|
|LAC|Coltobia LN 2192-CO|1983|Subsector Project for Rural Basic<sup>Education.</sup><br>lmct<br>evaluation of progrm to<sup>comare</sup><br>efficiency**and**learningachievement<sup>between</sup><br>districts<br>incorporated in the progrm<br>ind<br>unincorporated districts<br>(pare. 4.36 p.29).|Original objectives chaiged.<sup>Project completion</sup><br>report due 01/90.|
|LAC|Coloabia LN 3010-CO|1989|SecondPrimryEducation Proiect.<sup>Introduction</sup><br>of systewatic testing to monitor<sup>the gains in</sup><br>primary<br>education. Analysis of this data<sup>to be</sup>|Project recently initiated.<br>Nostudies begun.|

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$400 million in 1996 to just over $800 million in 2001 (update based on latest UN Data).

These totals include a significant number of items for which there was no production as of the late 1970s. By the 1990s, locally assembled parts included diesel and petrol engines, cabins, chassis, various body parts, axles, seats and seat frames, clutch systems, transmissions, brake systems, fuel tanks, pistons, wire harnesses, and high-quality forged parts (ABH 2000: 232-233); and "Post crisis condition of automotive component industry," 2000). This expansion reflected increasing local content levels which, by the mid-1990s ranged from an average of 11% for sedans to 40% in light commercial vehicles, the latter includes basic utility vehicles, such as the Toyota Kijang, which provide an important market for auto body part suppliers. It merits note that although producers of auto body parts accounted for only 5 percent of the auto industry's gross output in 1995, they represented some 48% of the industry's firms and 30% of its employment.<sup>5</sup> Body parts, along with radiators and axels, were also principal contributors to the growth in parts exports. The export ratio of Indonesian auto parts rose from almost 0% in 1990 to 5.2% in 1995, and the value of component exports increased from $65 million in 1990 to almost $110 million in 1999.<sup>6</sup> Finally, measured by output per employee and total factor productivity, parts production exhibited some productivity growth between 1990 and 1995.<sup>7</sup> Yet this growth has been largely a function of macroeconomic factors and policy liberalization (discussed below) and has not derived from any significant improvements in Indonesian component production. Indonesian-owned parts producers are significantly weaker

> 5 Non-body part component producers accounted for 46% of total firms, 41% of gross output and 48% of employment. Okamoto and Shoholm 2000: 62.

> 6 Data on specific export items from "Post crisis condition of automotive component industry," 2002: 22-23. Ratios from Okamoto and Sjoholm 2000: 63. Values are from ABH (2000:230), who also calculate that the export share of both vehicles and components was around 4% in 1996 ( 228).

> 7 Yet productivity declined for body part suppliers. (Okamoto and Shojolm 2000: 67.)

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owned by foreigners grow faster than domestic firms. On the other hand, foreign owned firms innovate less than the domestic firms but the result is not significant.

**<u>Table 12 All Employment Growth</u>**

||||Em|ployment Gro|wth|||
|---|---|---|---|---|---|---|---|
|Export/Import|0.036|0.030|0.031|0.035|0.030|0.040|0.033|
||(0.004)***|(0.004)***|(0.004)***|(0.006)***|(0.005)***|(0.005)***|(0.006)***|
|Export Only|0.020|0.016|0.016|0.019|0.014|0.019|0.021|
||(0.005)***|(0.006)***|(0.006)***|(0.009)**|(0.006)**|(0.007)***|(0.009)**|
|Import Only|0.016|0.011|0.012|0.014|0.011|0.016|0.014|
||(0.004)***|(0.004)***|(0.004)***|(0.006)**|(0.004)***|(0.005)***|(0.006)**|
|Foreign (≥%10)||0.016|0.015|0.007|0.009|0.015|0.008|
|||(0.004)***|(0.004)***|(0.006)|(0.004)**|(0.005)***|(0.006)|
|R&D Ind|0.019|0.020|0.020|0.018|0.021|0.023|0.017|
||(0.003)***|(0.003)***|(0.003)***|(0.004)***|(0.003)***|(0.004)***|(0.004)***|
|Foreign (≥50%)|0.016<br>(0.004)***|||||||
|Foreign (<50%)|0.010<br>(0.006)|||||||
|Access to Finance||0.022|||||0.018|
|||(0.003)***|||||(0.004)***|
|% Financed|||0.0002<br>(0.000)***|||||
|Multi‐plant firm||||0.005|||0.005|
|||||(0.006)|||(0.006)|
|Total Hrs/Week|||||0.0003<br>(0.000)***||0.000<br>(0.000)***|
|log(Markup)||||||‐0.003<br>(0.003)||
|Observations|8390|7413|7413|3326|6196|4589|3284|
|R2/Pseudo R2|0.121|0.125|0.122|0.122|0.136|0.121|0.136|

Robust standard errors clustered by country, industry, and year are in parentheses. All regressions control for log(investment/worker), Non-production worker share, training, age, Log(Labor)t-3. They also control for 2-digit industry, survey year, and country fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

Access to finance is an important factor for firm evolution. Firms with easier access to external finance could find it easier to access to foreign markets. They can find it cheaper to compensate the sunk costs required to export or import. Using external financial sources for productive investment purposes, they are also likely to grow faster. Hence, when omitted, this variable can be a factor explaining the positive relation between foreign exposure and growth. To control for this, I include a dummy variable showing firms that use external sources to finance their investment. The 24
**Table 2: Median wage earnings in the Northern and Eastern provinces are slightly higher than in provinces other than Western**

|**Monthly**<br>**wages (Rs.)**|**Northern**<br>**Province**|**Eastern**<br>**Province**|**Western**<br>**Province**|**All other**<br>**Provinces**|
|---|---|---|---|---|
|10<sup>th </sup>percentile|4,929|6,074|9,355|4,830|
|25<sup>th </sup>percentile|9,858|10,124|15,572|9,585|
|Median|15,773|17,818|24,297|15,457|
|Mean|18,128|20,272|31,209|19,950|
|75<sup>th </sup>percentile|24,645|25,802|36,335|25,243|
|90<sup>th </sup>percentile|33,052|35,600|51,973|35,550|

Source: Labor Force Survey 2015 Note: Wages are spatially deflated The low employment rate in the North and East is largely explained by the low rates of female employment. Women in the Northern and Eastern provinces are just over half as likely to be employed (22% and 19%, respectively) as women in provinces other than Western (37%). If the employment rate of women in the Northern and Eastern provinces were the same as the national average, 132,000 more women would be employed in 2015, equal to about 2 percent of all workers. The employment rate of youth aged 15‐24 in the Northern and Eastern provinces is also slightly lower than the national average, although the disparity is not as stark as that for the employment rate of women. An examination of the hours worked by individuals in the previous week, using data from LFS 2015, does not show significant differences between the Northern and Eastern provinces and the rest of the country. The average hours worked by individuals in the Northern and Eastern provinces is 41.67 hours, slightly lower than 43.80 hours for the Western province and higher than 39.47 hours in other provinces.

**Figure 5: Employment rate in the Northern and Eastern provinces is low largely because of disparity in female employment**

<!-- Start of picture text --> Employment rate<br>74%<br>70% 69% 71%<br>37%<br>32%<br>22%<br>19%<br>Men Women<br>Northern Province Eastern Province Western Province Other Provinces<br>Source: Labor Force Survey 2015 (Sample: 15+ year olds)<br><!-- End of picture text --> 7
**Table 4. Average Cost of Treating a D&C or MVA Patient in Ecuador, by Hospital and Cost Category**

|Hosplta<br>**ad**Type<br>**of**<br>|Number<br>of<br>**cans**<br>|Medkal<br>**Instruments**|**Re-**<br>**sterllzadon**|**Itposable**<br>Supplesl|<br>**Drugs**|**staff**|SubTotal|lospltailzadc|Total|
|---|---|---|---|---|---|---|---|---|---|
|Proced|ure|__-_=|_<br>__|_._._._1_._._||||||
|ECUI<br>**(D&C)**|**13**|0.00<br>(NC)|0.00<br>(NC)|1.77<br>(58%)|0.36<br>(12%)|0.82<br>(27%)|2.95<br>(97%)|0.11<br>**(3%)**|3.06|
|ECU2<br>(MVA)|**11**|0.24<br>(6%)|0.62<br>(14%)|1.89<br>(43%)|0.41<br>(9%)|.68<br>(16%)|3.84<br>(88%)|**_*.51_**<br>(12%)|**4335**|
|ECUS|3|0.30|1.13|0.93|0.28|0.96|3.60|0.06|3.66|
|(MVA)||(8%)|(31%)|(25%)|(8%)|(26%)|(98%)|(2%)||

**Costs in** U.S. dollars; as **of** August 1991, **US$1** = **_SI._** 1105

* Indicates estimated cost

(NC) = not calculated (%) = Percentage of average total cost by hospital At Hospital ECU2 there were unusually long post-evacuation periods for MVA patients and a subsequently high hospitalization cost -- $0.51 compared to $0.11 at the D&C site (ECUI) but only $0.06 at the other MVA site (ECU5). Also, the cost of medical instnuments and sterilization (which were not accounted for at the D&C site) caused the cost of treatment to be higher at the MVA sites than at ECUI. Twenty percent of the total average patient cost at Hospital ECU2 was attnibuted to instruments and sterilization, and 39% of the average total patient cost at ECU5 was attributed to these components. Differences in cost and resource use may be explained partially by the fact that the hospitals selected for data collection in Ecuador were administered by different public sector authorities.

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# **3. Data and Methodology**

# _3.1. Data_

Our analysis relies on data from the Gallup World Poll for the period 2010-2020, unless stated otherwise. During this period, the Gallup World Poll surveyed annually between 1,000-3,000 randomly selected adult Brazilians (15 years and older). In total, our sample includes 13,911 observations. We have all the relevant information for 10,228 observations that constitute our common sample. Table 1 shows the socio-demographic composition of the sample, which is representative at the country level as the surveys are designed to cover entire countries except for unsafe or inaccessible regions. As shown in Appendix C, the Gallup World Poll in Brazil is reasonably spatially representative over the period 2010-2020, although some states (Espirito Santo, Rio de Janeiro) are slightly overrepresented, while other states (Minas Gerais, Pará) are slightly underrepresented. Sampling weights are used in all analysis to make the sample as representative as possible.

**Table 1. Descriptive statistics based on the Gallup World Poll data sample for Brazil, 2010-20**

|**Variable**|**N**|**Mean**|**SD**|**Min**|**Max**|
|---|---|---|---|---|---|
|SWB|10,228|6.65|2.35|0|10|
|Age|10,228|37.87|16.51|15|97|
|Female|10,228|0.51||0|1|
|Has a partner|10,228|0.53||0|1|
|Elementary education|10,228|0.39||0|1|
|Secondary education|10,228|0.54||0|1|
|Tertiary education|10,228|0.07||0|1|
|Per capita household income (international $)|10,228|4785|5119|0|31983|
|Full-time employed for employer|10,228|0.29||0|1|
|Self-employed|10,228|0.15||0|1|
|Part-time employed|10,228|0.16||0|1|
|Unemployed|10,228|0.08||0|1|
|Out of workforce|10,228|0.32||0|1|
|Lives in a rural area|10,228|0.49||0|1|

Note: Income is winsorized at the 99% level.

# _3.2. Variables_

Our dependent variable is SWB, which we also refer to as experienced welfare or life satisfaction. ‐ It is measured using the “Cantril ladder” or “Self Anchoring Striving Scale” (Cantril, 1965), mentioned earlier in the paper. Figure 3 displays the responses to the Cantril ladder question in Brazil, Latin America and the Caribbean, the Western countries (the Anglo-Saxon world and Europe), and the rest of the world. About one-third of Brazilian citizens (33%) score a 5 or lower on the Cantril ladder, but a considerable share of the population (21%) gives their life a score of 9 or 10. Many Brazilians with a below median income indicate that they are satisfied with their quality of life.<sup>16</sup> The relatively high SWB scores in many Latin American countries have been extensively discussed in the quality-of-life literature (Rojas, 2016). The high scores are largely explained by the quantity and quality of close social relationships and family life in this part of the world (Rojas, 2016) as well as the generally optimistic nature of Latin Americans (Yamamoto, 2016). Yet, cultural

> 16 In this regard, the poorest 40% of Brazilians score much higher on the Cantril ladder (6.4) than the poorest 40% of Russians (5.3), Indians (3.9), Indonesians (4.9), Chinese (4.7), and South Africans (4.4).

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Another feature of the _SP_ program also helps with the identification strategy; the program was launched in 2002, but was rolled out across municipalities over time. This variation in locationtime of _SP_ coverage provides a cleaner test than a simple before-after comparison.

Studies show that there has been significant uptake of the program, although the impact of _SP_ on health outcomes is mixed. King et al. (2007) assessed a sample representing 118,569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August, 2005, to September, 2005) and follow-up survey 10 months later (July, 2006, to August, 2006). The treatment consisted of encouragement to enroll in a health-insurance Program and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. They estimated intention to treat and complier average causal effects non-parametrically. They find that the Program resources reached the poor. However, the Program did not show some other expected health effects, possibly due to the short duration of treatment (10 months).<sup>4</sup> Scott (2006) also does an impact evaluation of _SP_ using data from 2004 and finds that there was significant uptake. However, he finds that the selection of beneficiaries does not target the poorest states and households as effectively as its stated objectives. This raises the possibility that the program could indeed be affecting the decision of whether to work in the formal or informal sector. If the program successfully targeted those in the lowest income deciles one might expect more limited impact on formality as these households are already significantly less likely to participate in the formal sector. However, this evidence that non-poor households are participating in _SP_ would suggest that it is affecting a portion of the population that might well be considering shifts between the formal and informal sectors.

# **3. Literature Review**

There have been several studies that examine the impact on informality of large social programs that may affect the incentive workers have to exit formal jobs. This section first explores the

> 4 Sosa-Rubi et al. (2007) analyzed the cross-sectional 2006 National Health and Nutrition Survey (ENSANUT), focusing on the responses of 3,890 women who delivered babies during 2001-2006. They find that the _SP_ program had a robust, significantly positive impact on access to obstetrical services.

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relative to the starting point of 2014, which is normalized to 100. As we can see, the poorest decile indeed experienced the sharpest drop in real cash income, which also came at the very beginning of the crisis. The poorest decile was also the first to recover from the crisis, and the recovery appears to be quite rapid. Deciles 2‐9 were also heavily affected by the crisis, and did not manage to recover fully by the end of 2017. The richest decile, by contrast, experienced the least income decline during the crisis, and still no recovery in the second part of 2017.

_Figure 7: Cash income by deciles of disposable income, in real terms, 2014 set to 100_ <!-- Start of picture text --> 100<br>98<br>96<br>94<br>92<br>90<br>88<br>86<br>84<br>82<br>80<br>2014 2015 2016 2017<br>Poorest decile Deciles 2-3 Deciles 4-7 Deciles 8-9 Richest decile<br><!-- End of picture text --> Note: Cash income measured in real 2014 roubles per capita, with the 2014 level set to 100. Source: Authors’ calculations based on quarterly HBS.

Consider Figure 8, where each circle represents a socio‐economic group as defined through residence, education, age, labor market status and household composition. As we can see, income changes differed significantly for different socio‐economic population groups. There is also a positive relationship between income growth rates and pre‐crisis income levels ‐‐ groups with the highest income level in 2014 experienced milder income declines during 2015‐2016. For example, the residents of Minsk, one of the highest‐income groups, only experienced 5.4 percent income decline. On the other hand, people residing in small cities and rural areas experienced 16.4 and 14.6 percent declines in incomes correspondingly. While people with higher education faced an income decline in the magnitude of 7.7 percent, for people with secondary specialized education the decline was 16.1 percent.

_Figure 8: Real income change during crisis (2014‐2016) for major socio‐economic groups positively depends on initial level of income_ <!-- Start of picture text --> 0.0%<br>150 200 250 300 350 400 450<br>-2.0%<br>Minsk<br>-4.0%<br>-6.0%<br>Retiree hh<br>-8.0%<br>Hh with 3 and more ch<br>-10.0%<br>Higher education<br>-12.0%<br>-14.0%<br>Rural<br>-16.0% Secondary spec edu<br>Small cities<br>-18.0%<br>Cash income in 2014<br>2014-2016<br>Real cash income change,<br><!-- End of picture text --> Note: Cash income and change in cash income are measured in real 2014 roubles per capita.

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