rafmacalaba/datause-encoder-data huggingface dataset:
_Note_ : Data correspond to state-owned installed capacity on commissioned generation plants, excluding regional projects. Dual fuel and cogeneration are recorded under natural gas. Other renewable energy includes geothermal, solar, wind, and biomass.

# **Table A4.2: State-owned transmission and distribution line data (km)  in the reference year or latest year with available data**

|**Country**|**HV (110 kV and**|**MV to HV (66**|**MV (1–66 kV)**|**Total above 1kV**|
|---|---|---|---|---|
||**above)**|**kV–109 kV)**|||
|Angola_2011|2,094|0|738|**2,832**|
|Benin_2013|136|0|3,408|**3,544**|
|Botswana_2013|3,023|0|—|**3,023**|
|Burkina Faso_2014|3,452|0|471|**3,923**|
|Burundi_2014|0|322|1,779|**2,101**|
|Cameroon_2014|0|2,232|16,785|**19,017**|
|Cape Verde_2012|0|0|859|**859**|
|Central African Republic_2014|0|0|225|**225**|
|Chad_2012|0|0|0|**0**|
|Comoros_2012|0|0|588|**588**|
|Congo,Dem. Rep._2013|30|0|4,483|**4,513**|
|Congo,Rep._2012|1,479|0|1,070|**2,549**|
|Côte d'Ivoire_2014|4,700|0|21,700|**26,400**|
|Equatorial Guinea_2011|0|0|0|**0**|
|Eritrea_2011|0|0|0|**0**|
|Ethiopia_2012|9,568|1,973|87,518|**99,059**|
|Gabon_2014|715|0|4,746|**5,461**|
|Gambia,The_2014|0|0|528|**528**|
|Ghana_2013|5,100|0|37,469|**42,569**|
|Guinea_2013|325|0|1,053|**1,378**|
|Guinea-Bissau_2014|0|0|458|**458**|
|Kenya_2015|4,054|1,212|54,193|**59,459**|
|Lesotho_2010|1,050|0|0|**1,050**|
|Liberia_2014|0|27|60|**87**|
|Madagascar_2014|0|0|3,217|**3,217**|
|Malawi_2014|1,274|1,121|12,250|**14,645**|
|Mali_2014|766|222|2,055|**3,043**|
|Mauritania_2013|0|0|0|**0**|
|Mauritius_2013|0|309|3,427|**3,736**|
|Mozambique_2014|4,781|579|15,269|**20,629**|
|Namibia_2013|7,513|3,605|21,877|**32,995**|
|Niger_2014|0|0|4,472|**4,472**|
|Nigeria_2014|12,325|0|125,000|**137,325**|
|Rwanda_2013|253|96|3,030|**3,379**|
|São Tomé and Príncipe_2014|0|0|149|**149**|
|Senegal_2013|501|0|7,822|**8,323**|
|Seychelles_2014|0|0|330|**330**|
|Sierra Leone_2012|205|0|0|**205**|
|Somalia_2011|0|0|0|**0**|
|South Africa_2014|31,107|0|48,704|**79,811**|
|South Sudan_2013|0|0|0|**0**|

89
5 personal constituencies, but weak incentives to screen candidates for their policy positions or competence. Such parties, again, do little to limit politician incentives to increase spending around elections.

Parties with limited incentives to screen for candidate competence are also likely to be shorterlived. Parties organized around charismatic individuals, for example, are not only less likely to screen for competence; they are also unlikely to survive the departure of their founding leaders. Parties organized around individual politicians with clientelist networks are likely to be ephemeral: it costs individual candidates little to leave one clientelist party and join or start another, since their individual characteristics constitute the primary basis of their appeal to voters. In contrast, the electoral appeal of parties that screen for competence and policy preferences depends less strongly on the particular candidates and party leaders in place. Their departure is consequently less likely to trigger party collapse. Younger parties, therefore, are less likely to mitigate the information asymmetries that can trigger political budget cycles. On the one hand, parties with less electoral experience are simply less likely to accurately identify pivotal voters. On the other hand, they are less likely to organize themselves to mitigate these asymmetries by screening candidates on the basis of competence and policy preference. The inability of politicians to make credible pre-electoral commitments to post-election public policies also creates incentives to trigger political budget cycles. Vote-buying is one important manifestation of this. In some countries, vote-buying is a large expenditure that occurs, by definition, in election years. According to the 2005-06 wave of Afrobarometer surveys, 19 percent of more than 20,000 respondents reported that they had been offered a gift in the last election. This type of spending is particularly relevant for political budget cycles since poor and younger democracies, which exhibit the greatest political budget cycles, also appear to exhibit greater vote-buying.

Hanusch and Keefer (2013) formally model the phenomenon of vote-buying and argue that it increases systematically as political credibility falls. Politicians who are unable to mobilize political
# **Introduction**

Wheat is the staple food in Afghanistan, contributing approximately 54 percent of average daily caloric intake. In 2008 due to a combination of international (increasing global food prices), regional (export bans in key trading partners like Pakistan), and domestic (drought) factors, domestic wheat grain and flour prices approximately doubled. These sharp price increases constituted a serious economic shock to Afghan households, who spend the majority of their budgets on food. D‘Souza and Jolliffe (Forthcoming) find that the price shock had a measurable impact on household food security across Afghanistan. Using ordinary least squares (OLS), they show that households reduced the value of food consumption in response to wheat flour price increases and that this reduction in the value of consumption is the result of reducing the quality (dietary diversity) and quantity (calories) of food consumed in approximately equal proportions. Such declines in consumption and nutrition indicators can have serious implications. Even short bouts of poor nutrition can have long-term repercussions, particularly for vulnerable populations such as children, lactating women and the elderly and those on the cusp of poverty and/or malnutrition (UNICEF, 2009). These bouts may exacerbate already high levels of malnutrition; Afghanistan has the highest prevalence of stunting in the world among children under five years old (UNICEF, 2009).

For the purposes of examining food security, or indeed any measure where the policy focus is on a particular portion of the distribution (such as the lower tail), or a particular threshold on the range of values (such as a poverty line or some fixed nutritional benchmark), the OLS estimator can be a potentially misleading tool. OLS constrains the behavioral response of households to be constant over the entire distribution. Specifically, OLS analysis provides an estimate of the (constant) partial derivative of the mean of the dependent variable with respect to changes in the independent variable. However, if we believe that households at different points of the distribution employ different coping strategies to deal with shocks, then it is essential to disaggregate the estimated behavioral responses in thinking about policy prescriptions.

The Quantile Regression (QR) estimator is frequently used to relax the assumption of a constant marginal response by allowing the responses to vary across the distribution of the dependent variable after conditioning on the observed covariates (for example, see Chamberlain (1994)). A disadvantage of this estimator is that it is based on the conditional 2
they experience, or misreport their regulatory burden (Pounov 2016). Fisman and Svensson (2007) exploit Enterprise Surveys data for Uganda and rely on cell-based instruments to estimate how bribery and taxation affect the growth rate of a firm’s sales. Their dependent variable is the annual growth rate of firm’s sales over the last three years. Their main explanatory variables are bribery and tax rates measured by the reported amounts of bribes and taxes paid by the firm, expressed as shares of firm’s annual sales. To deal with endogeneity concerns, the authors instrument for bribery and tax rates of each firm using averages of these variables over all other firms in each of the 42 industry-location cells. Other studies such as De Rosa et al. (2010) and Dollar et al. (2006) also use cell averages of other firms as instruments.

In constructing the cells, a choice must be made about location, industry and size. Location could either be the country or the city –sub-national sampling region in our case– where the firm is located. Similarly, industry selection could be made at the 2-digit level or at a more disaggregated level. There are both advantages and disadvantages of a narrow grouping. Consider, for example the choice of country vs. city for location. The advantage of using city-industry instead of country-industry cells is that firms in a narrower physical location (city) are more likely to experience similar levels of corruption than firms located further apart from each other. The disadvantage is that there are fewer firms in a city-industry cell than in a country-industry cell, making the average at the city-industry level less reliable than at the country-industry level.

Based on the discussion above, we define the level of informal competition faced by a formal firm as the proportion of all firms in the region-industry-size cell excluding the firm in question that report competing against informal firms. For firm-size, we use the ES classification of small (fewer than 20 employees), medium (20-99 employees) and large (100 or more employees). For region, we use the sub-national regions that match the ES strata. For industry, we 9
# **Baseline**

The first step is the process by which individual and household-level information is used to estimate a set of parameters and unobserved characteristics for various household income generation model equations. The model behind the micro-simulation is the household income generation model developed by Bourguignon and Ferreira (2005). This model allows accounting for multiple transmission channels for both family labor and non-labor income, as well as working at the individual/household level. The first component of the model is an identity that defines the per capita income in a household h as the ratio The total household income — the expression in brackets in equation (1) — results from adding two main sources of family income: _labor_ and _non-labor_ income. At the same time, the total family labor income is the aggregation of earnings in different employment sectors across members.<sup>7</sup> So, it is possible to see not only whether an individual does (or does not) participate in the sector, but also whether that individual receives (or does not receive) wages for that job.

The labor participation model relies on the utility maximization approach developed by McFadden.<sup>8</sup> Assume that the utility ( L ) for individual of household _h_ , associated with labor status _j=0,...,J_ , and level of education _L_ , can be expressed as a linear function of observed individual and household hii characteristics ( ) and unobserved utility determinants of the occupational status (UU ii L ). Furthermore, assume individual hiiLL _i_ chooses sector _j_ (the indicator function L = 1) if employment sector ii _j_ provides the ZZ vv highest level of utility:<sup>9</sup> hii II Ψ with and _L_ = education level (2) LLjj LL LLjj LLjj UUhii = ZZhii + vvii jj= 0, ... , JJ

> 7 Note that although it is possible to estimate specific models for salaried and non-salaried workers based on the microdata from the household survey, it was not possible in this case to use these models because this information is not generally available from the macro side. Macro-economic projections are calculated mainly for aggregate economic sectors, such as agriculture, industry and services, instead of wages or self-employed, formal and informal sectors.

> 8 McFadden (1974).

> 9 Bourguignon and Ferreira (2005) say that this interpretation is not fully justified because occupational choices may actually be constrained by the demand side of the market, as in the case of selective rationing, rather than individual preferences.

5
|Province|Laid-off workers<br>(10,000 persons)|People trained (10,000<br>persons)|Reemployment rate (%)|
|---|---|---|---|
|Heilongjiang|62|46|65%|
|Liaoning|35|19.7|40%|
|Chongqing|11|4|71%|
|Guangdong|45|24|70%|
|Hubei|32|11|58%|
|Hunan|44.8|8|72%|
|Fujian|3|1.3|46%|
|Henan|23.7|13.4|46%|
|Ningxia|3.5|2.6|30%|
|Xinjiang|4|1.5|27%|

Source: China Employment Training Center, 1998.

The results of these training programs were variant from province to province (table 15), for example, in Hunan, 72% of people were reemployed after training while in Xinjiang, only 27% were reemployed after training. An evaluation study by the research group at the World Bank on the retraining programs in Shenyang and Wuhan also showed mixed results.<sup>70</sup> In general, retraining programs had a positive impact on the reemployment prospects of trainees in Wuhan, while in Shenyang they seemed to be ineffective. In both cities, training didn’t put any impact on trainees’ earnings. Training results were also different for workers of different gender, age and education backgrounds. The study also showed that the design of training programs had an impact: the longer-duration programs usually yielded better results. Meanwhile, more positive results can be seen from those programs where trainees had to pay at least part of the training costs. In addition, a survey conducted among the unemployed in August 2001 by the Ministry of Labor and Social Security in 10 large cities showed that 70 percent of the people surveyed had participated in re-employment training, but only half thought that the training was useful in helping them find jobs.<sup>71</sup> In order to maximize the effects, retraining programs need to be tailored to the job opportunities and to the ages and backgrounds of the workers to be retrained. One good example can be drawn from Shanghai’s case. The economic restructuring and SOE reform led to the shedding of about 500,000 textile workers in Shanghai since the end of 1980s. Most of the workers made redundant were women in their 40s and 50s with little education and few marketable skills. The Textile Re-employment Service Center, sponsored by the municipal government, set up several dozen training bases focusing on computer techniques, accounting, cooking, sewing and other skills. After massive retraining, an estimated 70% of the redundant workers were reemployed, while the remaining 30% chose early retirement or self-employment. Only one year after the Textile Re-employment Center was opened, Shanghai’s textile sector had restructured 164 loss-making enterprises by either shutting them down, suspending production or shifting to manufacturing other goods. 72 This experience definitely has applications to the rest of China.

> 70 The World Bank: “Has Training Helped Employ the Xiagang in China? A Tale from Two Cities”, East Asia and Pacific Region, the World Bank, 2002.

> 71 “Urban Jobless Face Difficulty in Re-employment”, China Internet Information Center, China.org.cn, September, 2001.

> 72 Fruman, C., “Life-long Learning in China: A Case Study of Shanghai,” the World Bank, 2003.

30
Table 3: Effects on the use of childcare centers

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Household had any children<br>in a creche in<br>the past 24 hours|At least one child<br>attended a creche in<br>the past 12 months|At least one child<br>attended a public works creche<br>in the past 12 months|Maximum number of months<br>any child in the household<br>attended any creche|
|**Full sample**|||||
|ITT estimate|0.119***|0.241***|0.235***|0.720***|
||(0.026)|(0.026)|(0.027)|(0.145)|
|Observations|1961|1965|1965|1965|
|_R_<sup>2</sup>|0.051|0.127|0.181|0.081|
|Control mean|0.100|0.120|0.003|0.571|
|**Have child aged 0-2**|||||
|ITT estimate|0.112***|0.268***|0.253***|0.756***|
||(0.028)|(0.037)|(0.033)|(0.170)|
|Observations|1124|1126|1126|1126|
|_R_<sup>2</sup>|0.053|0.155|0.202|0.086|
|Control mean|0.094|0.091|0.003|0.472|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

35
Table 6: Flood fatalities and institutional quality

||6.1|6.2|6.3|6.4|6.5|6.6|6.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)|-0.438|-0.334|-0.454|-0.371|-0.262|-0.334|-0.417|
||(0.287)|(0.273)|(0.366)|(0.266)|(0.279)|(0.307)|(0.352)|
|Ln(_GDPj,t-1_)|-0.032|-0.004|-0.332|-0.144|-0.141|-0.131|-0.267|
|_CORR CONTjt_|(0.130)<br>-1.183***<br>(0.321)|(0.154)|(0.175)|(0.147)|(0.134)|(0.146)|(0.154)|
|_GOV EFFjt_||-1.014***<br>(0.295)||||||
|_POL STABjt_|||0.216<br>(0.235)|||||
|_REG QALjt_||||-0.946**<br>(0.445)||||
|_RULE LAWjt_|||||-0.770***<br>(0.153)|||
|_VOICE ACCjt_||||||-0.481*<br>(0.157)||
|_DEMOCRACYjt_|||||||-0.010<br>(0.024)|
|Ln(_POP jt)_|0.268|0.378**|0.151|0.341*|0.367*|0.218|0.193|
|<br>Ln_(POPDENSjt)_|(0.163)<br>0.306|(0.189)<br>0.201|(0.209)<br>0.112|(0.174)<br>0.251|(0.170)<br>0.077|(0.171)<br>0.078|(0.197)<br>0.086|
|_OPENjt_|(0.304)<br>-0.007|(0.261)<br>0.006|(0.299)<br>-0.014***|(0.290)<br>-0.006|(0.277)<br>-0.010**|(0.273)<br>-0.013***|(0.292)<br>-0.012***|
||(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|
|_MAGNITUDEijt_|0.090**|0.107**|0.120**|0.091*|0.119**|0.135**|0.125**|
||(0.043)|(0.049)|(0.054)|(0.046)|(0.051)|(0.056)|(0.056)|
|_NATHAZ – RISKj_|<br>0.077|<br>0.109|<br>0.169|<br>0.151|<br>0.192|<br>0.229|<br>0.184|
||(0.110)|(0.101)|(0.143)|(0.109)|(0.120)|(0.132)|(0.0141)|
|_LATITUDEj_|<br>-0.001|<br>-0.014|<br>-0.048***|<br>-0.033**|<br>-0.023|<br>-0.035**|<br>-0.043***|
|_ELEVATIONj_|(0.017)<br>0.001***|(0.015)<br>0.001***|(0.018)<br>0.001***|(0.013)<br>0.001***|(0.015)<br>0.001***|(0.015)<br>0.001***|(0.016)<br>0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Obs.|<br>1125|<br>1125|<br>1125|<br>1125|<br>1125|<br>1125|<br>1125|
|Log pseudolikelihood<br>Prob>χ<sup>2</sup>|-5093.929<br>0.000|-5103.844<br>0.000|-5123.730<br>0.000|-5101.884<br>0.000|-5108.906<br>0.000|-5119.118<br>0.000|-5124.959<br>0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

25
# **Tables**

**Table 1: Growth in household welfare in rural Bangladesh: 2000-2010**

|**Welfare indicators**|**2000**|**2005**|**2010**|
|---|---|---|---|
||**(N=5,030)**|**(N=6,031)**|**(N=7,840)**|
|Per capita income (Tk./month)|878.8|930.4|1,277.6|
|Share of nonfarm income (%)|36.3|38.1|38.3|
|Moderate poverty headcount (%)|56.2|40.8|33.3|
|Extreme poverty headcount (%)|41.8|26.5|19.6|

Note: Income figures are CPI-adjusted (2000=100). For nonfarm income, only income from activities is considered; that is, non-earned income (receipt from remittance, safety net programs, pension and so on). Income from interest, rental of assets, and property are not considered. Source: HIES 2000, 2005 and 2010

**Table 2: Income from nonfarm activities and its components: 2000-2010**

|**Nonfarm income**|**2000**<br>**(N=2,807)**|**2005**<br>**(N=3,325)**|**2010**<br>**(N=4,282)**|
|---|---|---|---|
|Per capita nonfarm income (Tk./month)|639.3|668.3|801.8|
|Share of enterprise income (%)|41.8|36.0|36.1|
|Share of nonfarm wage income (%)|31.3|35.0|35.1|
|Share of salaried income(%)|27.0|29.0|28.9|

Note: Income figures are CPI-adjusted (2000=100). The sample is restricted to households that are engaged in nonfarm activities. Source: HIES 2000, 2005 and 2010

**Table 3: Distribution of rural microenterprises by activity: 2000-2010**

|**Activities (%)**|**2000**|**2005**|**2010**|
|---|---|---|---|
||**(N=1,427)**|**(N=1,426)**|**(N=1,909)**|
|Manufacturing and processing|11.9|10.3|13.9|
|Transport|9.3|6.3|13.1|
|Trading|2.4|2.5|2.3|
|Service|65.3|75.8|61.5|
|Other miscellaneous activities|11.1|5.1|9.2|

Note: Manufacturing and processing activity includes manufacturing and processing in food and beverages, tobacco, textiles, wood and furniture, rubber/plastic, basic metal and nonmetal products. Transport activity includes operation and rental of various transport vehicles. Trading activity includes wholesale and retail trading of various farm and nonfarm products such as livestock, poultry, vegetables, fruits, rice, furniture, utensils, shoe, clothing, operating stores, shops and so on. Service activity includes skill-based or specialized activities such as that of carpenters, masons, 35
shifted, or whether wages were affected by _SP_ . Fifth, most papers have focused on the earliest rollout periods, when the program covered only a small fraction of the population and found little effect of the program. This paper looks at the longer period from 2000 to 2009.

In this paper, we have data on almost 10 million individuals in rotating panels from 2000-09. Individual panel data allows for improved control of individual characteristics and assessing whether _SP_ affects labor market dynamics. It also allows us to test whether different subgroups of households or different individuals within the household are more sensitive to _SP_ . We can also control for the potential endogeneity of the employment decision based on the decisions of others in the household.

We find that _SP_ does impact the decisions of workers, generating a moderate increase in the share of workers in the informal sector. Exploiting variation both over time and across municipalities, the effects of introducing _SP_ results in a decline in the share of households with SS of 0.4 to 0.7 percentage points, depending how time effects are controlled for. The results indicate that much of the adjustment occurs within a few years of the introduction of the program, with the effects becoming relatively smaller as the intensity of the program expands. The effect is associated with a 3.1 percentage point reduction (a 20 percent decline) in the inflow of workers into formality rather than an increase of outflows from formality. Thus, while the aggregate net change in the level of formality is not that large, the proportional change in flows into formality is. There is also evidence of important income effects, with the introduction of _SP_ associated with fewer transitions out of unemployment and lower rates of entry into the labor force. Our results also suggest higher sensitivity in larger households, as well as among household headed by persons with less than secondary education completed, particularly if the head is female. They also suggest a higher response among individuals who are covered by SS through someone else, or among individuals in a household that are not heads, spouses or dependents of heads. Finally, our results suggest that workers pay for part of the newly acquired _SP_ benefits with lower wages in the informal sector.

# **2. Background on** **_Seguro Popular_**

México‘s Social Security system (SS) only covers about 45 percent of the population, namely formally registered wage workers and their dependents. Self-employed workers are not obliged to 4