rafmacalaba/datause-encoder-data huggingface dataset:
Implementing this alternative strategy in developing countries is problematic as well. Measuring unemployment is difficult in a context where most people work for themselves. The sizeable gap between labor force participation rates depending on whether those registered with placement agencies or participating in the MNREGA program are counted in illustrates that difficulty. The different trends in unemployment rates depending on whether the Census or the NSS definitions are used show that even determining whether job opportunities are becoming scarcer or not can be challenging in practice.

This paper adopts a different strategy. The job opportunities faced by women are shaped by the situation of the local labor market. In the basic specification, tested for robustness further down, the job opportunities faced by the woman making the decision take the following form:

# Oij = Lj + Vj − Uj + φij

where:

- Lj is the local employment level, which can be disaggregated into farming, non-farm self-employment, non-farm regular wage employment and casual employment.

- V are local vacancies. j

- Uj is local unemployment.

- φij is the subjective assessment of local job opportunities by the woman making the decision to participate; this assessment is supposed to be independently distributed and to have zero mean.

Replacing in the individual decision to participate yields: In words, labor force participation depends on the individual characteristics of the woman making the decision ( Xij ), the characteristics of her household ( Hij ), the year ( t ), the rank in the rural-urban gradation of the location she lives in ( Rj ), the potential misclassification of that location as urban ( Gj ), and job opportunities at the local level. The latter are captured in two ways: local employment ( Lj ) and local excess demand for labor ( Vj −Uj ). The former can be measured using NSS data. The latter is unobservable but can be included in the estimation as a 18
13

# Filgure 1

<!-- Start of picture text -->
*<br>Private Investment and GDP Growth (1970-89)<br>80<br>70-  . .U<br>*  a<br>60  *  *  _<br>U<br>- 50 U*  *  *  .<br>40<br>°  30<br>20<br>10<br>-2  0  2  4  6  8  10<br>GDP Growth 21<br><!-- End of picture text -->

Note:

* For some countries the period covered is different due to data availabilty. 1 Private Investment as percentage of the total gross domestic investment.

2/ Average annual GDP growth over 1970-89.

Source: 'Trends in Private Investment in Developing Countries, 1990-91 Edition'. Guy P. Pfeffermann and Andrea Madarassy, IFC Discussion Paper, No.11, 1991. National Account, The World Bank.
easily.<sup>5</sup> Instead, we used a linear probability model (LPM) because the coefficients are easy to interpret and marginal effects are easy to compute. In fact, Angrist and Pischke (2010) documented that OLS estimates of LPM produce coefficients that are mostly statistically indistinguishable from the marginal effects of the probit model. Moreover, because our measure of the individuals’ WTP is non-negative and only three respondents stated a zero willingness to pay, we use the OLS method to estimate the effect of information provision on WTP that was gathered through the BDM bidding method.

# **4. Descriptive Statistics**

This section discusses the descriptive statistics of the outcome variables, treatment variables and key socio-economic characteristics of the households in our study. Table 1 presents descriptive statistics of key socio-economic characteristics of the households considered in our study for the overall sample as well as for treatment and control groups.

As shown in Table 1, on average about 61% and 58% of the information treatment and control households, respectively, used kerosene as the main source of light in their house. This means the majority of households in both the treatment and control group use a dirty fuel source for lighting in their homes. In terms of use of clean lighting energy sources, about 11% and 12% of the treatment and control households, respectively, used solar, and 2.7% and 1.5% of the treatment and control households are connected to grid electricity. This is consistent with previous national level data on rural households’ access to off-grid electricity. For example, in the World Bank’s MTF survey for Ethiopia, about 11% of households in Ethiopia get electricity access through off-grid electricity sources mainly through solar technologies (MoWIE, 2019).

On average, treatment household heads were about 47 years old, and had about 3 years of education; about 92% were married and 92% were male. Likewise, the control household heads were about 48 years old, and had about 2.6 years of education; about 92% were married and 94% were male. The treatment households had on average 5.8 household members and about 86,000 birr worth of assets; 98% of them owned houses with an average of 2.4 rooms and spent about 288 birr per month for lighting. Similarly, the control households had on average 5.8 household members and about 93,000 birr worth of assets; 98% of them owned houses with an average of 2.4 rooms and spent about 270 birr per month for lighting.

> 5 Although there is an “inteff” Stata command to estimate the “marginal effect” of interaction effects, this command does not produce the marginal effect of the main variables taking the interaction into account.

16
countries in 2007 and collected as part of Doing Business, a World Bank project that investigates the scope and manner of business regulations<sup>3</sup> .

The data provide detailed information on the different kinds of costs an exporter faces when moving his goods from the principal city to the port of exit. More precisely, the survey asks respondents the average and the maximum times in calendar days it takes for completing a series of export procedures. Each procedure can be classified into one of four main categories: documentation, inland transportation, customs, and ports.

The first category represents the time it takes for an exporter to complete all documentation activities such as securing a letter of credit, assembling and processing export and international shipping certificates and realizing all pre-shipment inspections and clearance.

Inland transportation includes the time it takes for the merchandise to be moved from the principal city to the port of exit, as well as the time spent arranging transport and waiting times for the merchandise's pick up and loading into a carriage. For landlocked countries, total transport times also include waiting times at the crossing border.

The customs category includes the time necessary to realize the technical controls of the merchandise. In addition, for landlocked countries this category comprises the total time it takes from the submission of request of clearance until the completion of the inspection and clearance procedure in the transit country. Finally, the ports category represents terminal handling times, including storage if a certain storage period is required, the waiting times for loading the containers into the vessel and customs inspection and clearance times.

An example illustrates the data. An exporter in Rwanda spends 43 days on average to complete all requirements for shipping its merchandise abroad: 17 days each on delays resulting from documentation and inland transit, while port and custom procedures take respectively 6 and 3 days on average (see Figure1).

Table 1 presents the summary statistics for each of the components representing the total time<sup>4</sup> necessary to fulfil all the requirements for exporting by region and regional arrangement. The data show that across regions, documentation procedures times are the longest. Furthermore, while getting a product from the factory to the ship is relatively quick in developed countries, this is not the case for Sub-Saharan Africa, where all time

> 3 For a detailed description of the data see Djankov, Freund and Pham  (2010).

5
- 46 -

charges coupled with scarce fiscal budgets eventually result<sup>in poor quality</sup> services and inequitable access to education. Moving toward lower subsidies, with governments exercising major efforts in the support<sup>of rural</sup> primary schools--or in the design of selective loan/scholarship programs-that largely serve the poor, is therefore becoming an increasingly popular view.

# B. THE PRACTICE

# B.1 Project Sample

4. Twenty projects (in 12 countries: Sudan, Jordan, Oman, PDRY, Bangladesh, Nepal, Papua New Guinea, Indonesia, China, Peru, Brazil and Haiti) were selected for this review. The sample accounted for 50 percent of the total number of education projects approved by the Bank during FY84 and FY85.

# B.2 Discussion of Cost Recovery **Issues**

5. Government recurrent expenditures on education ranged from US$28,000 for Djibouti in 1983/84 to $159 million for Oman in 1982, with<sup>a</sup> sample mean of a little over $41 million. Expressed as a proportion of total government recurrent expenditures, the education bill amounted from 9 to 23 percent in the African countries (or averaged about 17 percent); 7-19 percent (11 percent) in the other countries reviewed. Despite the notable differences among countries in the size of education expenditures, budgetary allocations generally declined with increases in education levels. Thus primary education usually received the largest portion of the budget, followed by secondary education, and then by universities and colleges as shown below:

|LEVEL OF EDUCATION|AVERAGE<br>RANGE<br>(8 of Education Expenditures)|COMMENTS|
|---|---|---|
|Primary|47.5<br>19-71|Less than 40%<br>in 5 cases|
|Secondary|25.9<br>7-44||
|Higher Education|17.2<br>0-36|0 in 2 cases|
|Teachers' Training & Other|<br>9.4<br>0-25|0 in 9 cases|

Nevertheless, annual expenditures per student rose sharply from primary to higher education in all the countries with relevant data, due mainly to the fact that higher education entailed much smaller student enrollments and more expensive type of services required. Thus per-student subsidy levels are consistently higher for higher levels of education.

6. Information on cost recovery through user charges was scanty, and was made available only in 10 out of the 20 SARs used for this review. Recovery of costs was nil in Djibouti and Sudan. Slightly over 3 percent<sup>of</sup> the recurrent education expenditures in Togo was recouped through parents' contributions for textbooks and participation by cousunities in the maintenance of school facilities; about 20 percent in Lesotho through student fees. Student fees levied at selected levels of education enabled a partial recovery of 13 percent of the expenditures for post-primary
As mentioned above, fertilizer use is risky and rural households in Nigeria are likely to be risk averse. Consequently we incorporate a risk premium of δ into our analysis (Sheahan et al., 2013; Anderson et al., 1977). As has been done in the literature, we use a risk premium of 1 to factor in risk and uncertainty and approximate for the rate at which nitrogen application is going to be profitable enough for rural farmers to be willing to use it (Xu et al., 2009; Sauer and Tchale, 2009; Bationo et al., 1992; Sheahan et al., 2013; Kelly, 2005).

In this framework, rather than AVCRnijt > 1 and MVCRnijt > 1 being used as the decision rule guiding farmers in their decision to use fertilizer, a higher MVCR of 2 is considered to be necessary for a risk averse farmer to find nitrogen application profitable. In line with Sheahan et al (2013) we consider MVCR and AVCR values greater than 2 to be an adequate indicator of profitability of nitrogen application for maize production.

Nigeria is a country with a significant fertilizer subsidy program. There are multiple layers of subsidy possible and when received, the subsidy rate could range between 25% and 100% (Liverpool-Tasie and Takeshima, 2014). Consequently it would be ideal to compare profitability of nitrogen application under subsidized and non-subsidized scenarios. We do not have information on the receipt of subsidized fertilizer in the LSMS-ISA data for our survey years. However, there is information on whether a farmer received some of their fertilizer for free to those who did not.<sup>14</sup> In both survey years, only about 2% of maize plots recorded that some of the fertilizer applied on the plot was received for free. Consequently, we restrict our consideration of the profitability differences across market purchased and subsidized fertilizer to simulations based on the expected price differences provided in recent studies on fertilizer subsidy programs in Nigeria.

# **4. Results**

Our descriptive statistics indicate that maize production is largely a smallholder activity in Nigeria. The average maize plot is between 1 and 1.5 hectares, managed by a middle aged male with limited use of irrigation and mechanization. While only about 20% of maize plots use purchased seed, almost 50% of farmers use some chemicals (herbicides and pesticides) in maize production and the average fertilizer use is between 36 and 38kg of applied Nitrogen This figure is

> 14 In our study sample from the LSMS-ISA data, only 1.2% of plots on which maize is grown did not pay in full for their fertilizer.

13
37 Atkinson, A.B. and F. Bourguignon (2000), **Handbook of Income Distribution** , Volume I, North Holland, Amsterdam Baker, J (2003), **Evaluating the Poverty Impact of Projects: A Handbook for Practitioners** , The Word Bank, for more details, see:

**<u>http://info.worldbank.org/etools/bSPAN/PresentationPrint.asp?PID=651&EID=327</u>**

Banerjee, A., R. Benabou and D. Mookherjee eds. (2004), **What have we learnt about poverty?** , Oxford University Press

**<u>http://www.bu.edu/econ/Working%20Papers/papers/Dilip%20Mookherjee/ddinf.pdf</u>**

Benabou, R. (2000), Unequal Societies: Income Distribution and the Social Contract”, _American Economic Review,_ 90(1), 96-129 Boland, J. and D. Whittington (2000), “The political economy of increasing block water tariff design in developing countries”, in Dinar (2002) Bourguignon, F. and L.A. Pereira da Silva (2003), **The Impact of Economic Policies on Poverty and Income Distribution—Evaluation Techniques and Tools,** Oxford University Press Brook, P. and T. Irwin (2003), **Infrastructure for poor people: public policy for private provision** , The World Bank, Washington, DC Chong, A. and F. Lopez de Silanes (2003). "The Truth about Privatization in Latin America," a preview is available as Yale School of Management Working Papers, number 436, Yale School of Management.

Clarke, G. and S. Wallsten (2002), “Universal(ly Bad) Service: Providing Infrastructure Services to Rural and Poor Urban Consumers”, World Bank, Policy Research Working Paper, 2868 Chisari, O., A. Estache and C. Romero (1999), “Winners and Losers from the Privatization and Regulation of Utilities: Lessons from a General Equilibrium Model of Argentina” (1999 _), The World Bank Economic Review,_ Vol. 13, No 2.), pp. 357-378 Chisari, O., A. Estache and C. Waddams-Price (2003), “Access by the poor in Latin America’s utility reform: subsidies and service obligations” in Ugaz, C. and C. Waddams Price, ed. (2003), **Utility Privatization and Regulation: A Fair Deal for Consumers?,** Edward Elgar, Northampton, MAS, USA (with O. Chisari and C. Waddams Price) Cremer, H., F. Gasmi, A. Grimaud and J.J. Laffont (2001), "Universal Service: an Economic Perspective Overview", _Annals of Public and Cooperative Economics_ , vol. 72, n. 1 Danziger, S. H. and R. H. Haverman (2001), eds. **Understanding Poverty** , Russell Sage Foundation, New York Deaton, A. (1996) **Microeconometric Analysis for Development Policy: Approach to Analyzing Household Surveys** , John Hopkins University Press, Baltimore Dinar, A. (2000), **The Political Economy of Water Pricing Reform** , Oxford University Press Duclos, J.Y. , P. Makdissi and Q.Wodon (2004), “Poverty-efficient Program Reforms: The Role of Targeting and Allocation Rules _," Journal of Development Economics_ , forthcoming Duflo, E (2003), Scaling Up and Evaluation, MIT, mimeo Duflo, E. and M. Kremer (2003), <u>Use of Randomization in the Evaluation of Development Effectiveness, , MIT, mimeo</u>
# **5.3 By types of diseases**

We find no significant relationship between job losses and deaths from aggregate diseases (see the first column). When we break down to cancer, <mark>cardiovascular disease (</mark> CVD) and “other diseases”, mortality caused by “other diseases” significantly grows due to unemployment (table 5.3a). This significant relationship is not driven by outliers (scatterplot not shown). The coefficient of 0.265 implies that a 1% increase in instrumented job losses causes a 0.265% increase in mortality in other diseases, which include diabetes, Alzheimer’s, chronic lower respiratory, liver and kidney.

Table 5.3a: Diseases and Unemployment (IV with controls)

|VARIABLES|Diseases|Cancer|CVD|Others|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Instr. Job Losses|0.0292|0.0196|-0.0784|0.265**|
||(0.0621)|(0.0773)|(0.0734)|(0.127)|
|Constant|0.0923**|0.0762|0.179***|-0.0791|
||(0.0441)|(0.0600)|(0.0461)|(0.0864)|
|Controls|Yes|Yes|Yes|Yes|
|Observations|2,212|2,213|2,213|2,212|
|R-squared|0.354|0.182|0.221|0.085|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Once we look at the different diseases under “Other Diseases”, we find only Alzheimer’s to be strongly counter-cyclical: unemployment causes higher deaths by Alzheimer’s. The relationship is not driven by outliers. Mortality for other types of diseases such as diabetes, liver and kidney diseases is not driven by employment.

21
As a first step, we cleaned both datasets, appending afterwards the information on returns to the holdings dataset. During the cleaning process we dropped duplicate entries, corrected the values of several variables, and generated an identifier variable for the securities in each dataset.<sup>31</sup> After cleaning and merging the datasets we obtain a panel of 7,501,210 observations, corresponding to 104,789 securities, which are grouped in 56 different instrument types. While 54 of these instrument types each account for a 0.37 percent of the observations (on average), there are two types of instrument that represent 80 percent of all observations: the former pension system bonds ( _bonos de reconocimiento_ ), which represent 43.71 percent of the data, and mortgage bonds ( _letras hipotecarias_ ), which represent 34.60 percent of observations.<sup>32</sup> We then group these instrument types into 12 general asset classes, considering both former pension system bonds and mortgage bonds as separate asset classes due to their importance.<sup>33</sup>

# **4. Pension Fund Holdings**

Pension fund administrators have become the largest institutional investors in Chile. This section briefly describes their relative importance in the Chilean capital markets, their broad patterns of asset allocation, and the concentration of their investments.

# **_4.1. Pension Fund Size and Relative Importance_**

During the period 1996 to 2005 covered by our data, the number of PFAs operating in Chile decreased by two-thirds while the number of pension funds doubled. The number of PFAs decreased from 15 to six due to a series of mergers and acquisitions

> 31 Since each dataset contained a different set of identifier variables we could not generate a unique identifier variable for both datasets. Therefore, we initially merged both datasets by the name of the instrument and the price to establish a correspondence between both identifier variables, later merging the datasets a second time in order to recover additional information on returns.

> 32 It is important to note that although former pension system bonds and mortgage bonds combined represent 80 percent of observations form the dataset, they only represent 16 percent of the total portfolio investments when considering the entire system for the 1996-2005 period, varying from a maximum portfolio share of 20 percent in 1996 to nine percent at the end of 2005.

> 33 The 12 asset classes are: domestic corporate bonds, instruments of domestic financial institutions, quotas of domestic investment and mutual funds, government paper, domestic others, domestic equity, foreign fixed-income, quotas of foreign investment and mutual funds, foreign others, foreign  equity, former pension system bonds, and mortgage bonds.

12
6 same framework, a wide range of simulations can be performed to investigate the effects of changes in specific parameters on any number of inequality or poverty measures (and then for any number of poverty lines or assumptions about equivalence scales). More fundamentally, it allows us to observe the effects of particular simulations on the entire distribution, as we do in Figures 6 - 15, rather than merely on a couple of scalar indices. Second, the evolving distribution which it decomposes is a distribution of household incomes per capita (with the recipient unit generally being the individual). Therefore, moving beyond pure labor market studies, the effect of household composition on living standards and participation decisions is explicitly taken into account. As it turns out, these are of great importance for a fuller understanding of the dynamics at hand.

The remainder of the paper is organized as follows. Section 2 briefly reviews the main findings of the literature on income distribution in Brazil over the period of study, and presents summary statistics and dominance comparisons for the four observed distributions we analyze: 1976, 1981, 1985 and 1996. Section 3 outlines the basic model and describes the empirical methodology. Section 4 presents the results of the estimation stage and discusses some of its implications. Section 5 presents the main results of the simulation stage, and decomposes the observed changes in poverty and inequality. Section 6 concludes and draws some policy implications.

# **2. Income Distribution in Brazil from 1976 to 1996: a brief review of the literature and of our data set.**

There is little disagreement in the existing literature about the broad trends in Brazilian inequality since reasonable data first became available in the 1960s. The Gini coefficient rose substantially during the 1960s, from around 0.500 in 1960 to 0.565 in 1970 (see Bonelli and Sedlacek, 1989). There was a debate over the causes of this increase, spearheaded by Albert Fishlow on the one hand, and Carlos Langoni on the other, but there was general agreement that the sixties had seen substantially increased dispersion in the Brazilian income distribution.
**Table A2.CU.C: Intergenerational Persistence in Schooling in Urban China - -** **<u>(18 35 Years Age Cohorts. First born Sample)</u>**

|||**LINEAR CEF**||**Q**|**UADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.28|0.36|0.083|0.11|0.34|0.24|
||(0.019)***|(0.018)***|(0.026)***|(0.053)**|(0.051)***|(0.074)***|
||[0.024]***|(0.021)***|[0.027]***|[0.067]|[0.066]***|[0.079]***|
|Quadratic Coefft.||||0.013|0.0015|-0.011|
|||||(0.0033)***|(0.0032)|(0.0046)**|
|||||[0.0037]***|[0.0040]|[0.0053]**|
|Intercept|9.26|8.47|-0.79|9.62|8.51|-1.11|
||(0.18)***|(0.17)***|(0.25)***|(0.22)***|(0.21)***|(0.30)***|
||[0.21]***|[0.30]***|[0.29]***|[0.28]***|[0.34]***|[0.33]***|
|No. Observations|2428|2617|5045|2428|2617|5045|
|||**Estima**<br>|**tes of Mobility**|**from Quadrati**|**c CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|9.62|8.47||**_IGME0 _**|0.12|0.36|
|**_ES6 _**|10.75|10.63||**_IGME6 _**|0.257|0.36|
|**_ES9_**|11.66|11.71||**_IGME9 _**|0.333|0.36|
|**_ES16_**|14.71|14.23||**_IGME16_**|0.51|0.36|

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.