rafmacalaba/datause-encoder-data huggingface dataset:
show clear differences between the mean (unweighted) step size for primary school (.089) and university (.167); a flattening of the earnings function at the primary and secondary levels when provincial dummies are included; and very large sheepskin effects associated with graduation from university.

Tables 4 and 5, finally, present the results which include parental education as controls (columns 1 and 2), and the within-sibling estimates (columns 3 and 4). Differences between these results and those in Tables 2 and 3 could be caused by inclusion of the additional controls, as well as by differences in the sample. In order to hold constant the effect of differences in the sample, I therefore present results for the smaller samples both with and without the additional controls. These results show, first, that the education of both parents has a significant, positive impact on log wages, with the effect of father's education being noticeably (and significantly) larger.'<sup>4</sup> Second, all of the coefficients on own schooling are smaller in these specifications which attempt to control for ability. The coefficient on primary education, for example, drops from .094 to .078 when parental education is included as a control, and from .103 to .086 in the within-sibling regression. Similarly, the coefficient on university education drops from .196 to .162 in the regression which includes parental education, and from .176 to .158 in the within-sibling regression. Third, the estimated sheepskin effects are quite robust to the inclusion of controls for parental education. However, there appears to be something peculiar in the sample of men with wage-earning siblings: The coefficient on the

> 13 Jaeger and Page (1996), for example, find much larger sheepskin effects than those reported by Hungerford and Solon (1987) when they use information on both years of schooling and actual degree awarded for a sample of workers in the United States.

> 14 Contrast this with the results in Heckman and Hotz (1986), who find that mother's schooling has a larger impact than father's schooling on wages for a sample of Panamanian men.

16
concentration is measured as: where _si_ is market share of firm _i_ given by <u>�</u> _<u>SALEj</u>_<sup>_SALE_and</sup><sup>_N_istotalnumberoffirmsinindustry</sup><sup>_j_</sup> in year _t_ . A higher HHI implies weaker competition.

While HHI measures industry concentration, it treats all firms in an industry identically. Thus, HHI ignores potential firm-specific indicators of market power such as firm size and market share. We use _Log(Assets)_ as a measure of firm size where assets are the Compustat item _AT_ . _Market Share_ is the ratio of firm _i_ ’s sales to total industry _j_ ’s sales in a particular year, to allow for the possibility that large market share firms in a concentrated industry realize different returns compared to low market share firms.

We also use firms’ markup of price over marginal cost, _Markups_ , as a firm-level measure of the firm’s pricing power. De Loecker and Eeckhout [2017] show that average markups have increased from 18% above marginal costs (where variable costs are measured by Cost of Goods Sold) in 1980 to 67% above marginal cost by 2014. Recent work by Traina [2018] however argues that _COGS_ grossly underestimate firms’ variable costs. Other expenses, such as _SGA_ are increasingly a lion’s share of variable costs for US firms. Traina shows that once we include _SGA_ in the calculation of marginal costs, there is no increase in public firm markups.

Consistent with Traina [2018], we base our measure of variable inputs on Operating expenses (Compustat item _OPEX_ ) rather than Cost of Goods Sold (Compustat _COGS_ ) as in De Loecker and Eeckhout [2017]. _OPEX_ includes SGA expenses whereas _COGS_ only includes costs of production such as material, labor, and overhead and does not include SGA expenses. COGS has been a declining share of variable costs for US firms as shown in Figure A6 of the Internet Appendix. We differ from Traina [2018] in our adjustment of operating expenses to include the correction for intangible capital. Specifically, following Peters and Taylor [2017], we treat research expenditures as an intangible investment and a portion of the SGA as an organizational investment. Thus, intangible investments such as R&D and a portion of SGA are subtracted from _OPEX_ in order to 14
Singh, I., Squire, L. and Strauss, J. 1986. Agricultural Household Models: Extensions and Applications. Baltimore: Johns Hopkins University Press.

Tyers, R. and Anderson, K. 1992. Disarray in World Food Markets: A Quantitative Assessment. Cambridge University Press, Cambridge.

Vietnam, General Statistics Office, 1998. Household Living Standards Survey Vietnam.

Hanoi: General Statistics Office.

Vietnam, General Statistics Office, 2004. Household Living Standards Survey Vietnam. Hanoi: General Statistics Office.

World Bank 2006. Making the New Indonesia Work for the Poor. World Bank, Washington DC.

World Bank 2008a. High food prices: a harsh new reality.

<u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/0,,contentMDK:2166588 3~pagePK:64165401~piPK:64165026~theSitePK:469372,00.html</u> World Bank 2008b. Rising food prices: policy options and World Bank Response. Paper prepared as background to the Meetings of the Development Committee, April, http://siteresources.worldbank.org/NEWS/Resources/risingfoodprices_backgroundnot e_apr08.pdf Zambia, Central Statistical Office, Republic of Zambia. 1998. Living Conditions Monitoring Survey. Lusaka: Central Statistical Office, Republic of Zambia.

25
year, while the largest project is expected to generate more than 10.4 million CERs per year. The median and mean of annual expected CERs from the projects are 49,000, and 130,220, respectively. Figure 1 shows the frequency distribution of the CDM projects within each capacity interval of 10,000 CERs per year. In terms of expected total CERs the size of individual projects ranges from 3,500 to 836.2 million, with a median at 0.4 million and mean at 1.1 million CERs.

Table 2 shows the mean and range of annual expected CERs and electricity generation by various types of CDM projects in the pipeline. In terms of annual expected CERs, H/PFCs & N2O reduction projects are the largest and demand-size energy efficiency projects are the smallest among the major categories. Fossil fuel switch is the second largest category while forest is the second smallest category. Renewable resource-based project category ranks sixth in terms of annual expected CERs.

Emissions reduction is the sole purpose of the H/PFCs & N2O reduction, forest, and transport projects. After excluding these categories, electricity generation is a joint purpose of 72 percent of the CDM projects in the remaining categories. While the average additional electricity generation capacity of these projects is about 206 thousand MWh per year, the capacity ranges from 10.0 to 29.8 million MWh (table 2). In terms of average annual additional electricity generation capacity, fossil fuel switching and supply-side energy efficiency are the largest and second largest categories, respectively, followed by the renewable resource-based category. More than 91 percent of the renewable resource-based and more than 80 percent of the supply-side energy efficiency CDM projects are capable of generating electricity. The largest electricity generating project falls into the category of supply-side energy efficiency projects. Geothermal and hydro-electricity projects are the largest and second largest among the renewable resource based electricity generation projects. In particular, electricity generation projects that have the capacity of generating more than one million CERs per year are in hydro, biogas, landfill gas, coal bed/mine methane capture, cement, fugitive, and energy efficiency supply-side and own generation sub-categories.

The UNEP Risoe Center reports initial capital investments in 4,418 of the projects in the pipeline. Annual operation and maintenance cost data for 122 projects are obtained from the PDDs with the help of Climate Solutions (2008). See Annex I for details on how operation and maintenance costs are 12
and cigarettes—; and iv) excise taxes on fuels, with a base determined by the amount of fuel expressed in cubic meters. Finally, social contributions from employees to health care, unemployment insurance, and contributory pensions are also included in the analysis. Contributions to health include FONASA, and the health systems of the armed forces (CAPREDENA) and police (DIPRECA).

**Table 2: Structure of Chile’s government revenues, 2013**

|**Government revenues, contributions to social security**<br>**andgrants**|**% of total**|**% of GDP**|**Included in**<br>**analysis**|
|---|---|---|---|
|**Total**|**100.0%**|**21.0%**||
|**Revenues**|**92.9%**|**19.5%**||
|Tax revenues|79.6%|16.7%||
|Direct taxes, of which:|31.5%|6.6%||
|Personal income tax|6.3%|1.3%|Yes|
|Corporate income tax|17.6%|3.7%|No|
|_Adicional_|5.4%|1.1%|No|
|Others|2.2%|0.5%|No|
|Indirect taxes, of which:|46.7%|9.8%||
|VAT|37.8%|7.9%|Yes|
|Sales tax (alcoholic/non-alcoholic beverages)|0.9%|0.2%|Yes|
|Sales tax (luxury goods, cars and others)|0.1%|0.0%|No|
|Excise taxes|6.9%|1.4%|Yes|
|Foreign trade taxes|1.1%|0.2%|No|
|Others|1.5%|0.3%|No|
|Non-tax accounts|-0.1%|0.0%|No|
|Non-tax revenues|13.3%|2.8%|No|
|**Contributions to social security**|**6.8%**|**1.4%**||
|From employees|6.6%|1.4%|Yes|
|From employers|0.2%|0.0%|No|
|**Grants**|**0.2%**|**0.1%**|No|

_Source_ : Author’s elaboration based on Direccion de Presupuestos (2014) and the 2009-2015 data on annual tax revenue published by Chile’s Internal Revenue Service (Servicio de Impuestos Internos, SII).

# **3. METHODOLOGY, DATA AND ASSUMPTIONS**

The analysis follows the so-called CEQ methodology (Lustig and Higgins, 2013; Lustig, 2017b) to assess the distributional impact of taxes, transfers and subsidies across income groups in Chile in 2013 based on household-level data and administrative records on taxes and social spending. Basically, the methodology consists of defining income concepts first, and then allocating taxes, social contributions, subsidies and public social spending to individuals included in the household survey in a consistent and methodologically sound way, so that it is possible to compare incomes and incomebased measures of wellbeing before and after taxes and public transfers.

The methodology comprises two standard scenarios depending on how contributory pensions are treated: as deferred income or as government transfers. In the analysis for Chile, each of both scenarios can be constructed by using two definitions of income that are employed in the estimation of official figures of income inequality and income-based poverty. The measurement of inequality in Chile uses 6
Environment and Enterprise Performance Survey (BEEPS) collected in 2009 from 28 countries in Central and Eastern Europe and the Commonwealth of Independent States to examine the impact of regulation and corruption on productivity. They find that an increase in the incidence of bribes from zero to one decreases productivity by 0.045 percent, while an increase in time spent on tax administration does not have a significant impact. They also show that corruption reduces productivity more when it is more prevalent and the legal framework is weaker.

# **3. Data and methodology**

In this section, we discuss the data and the estimation methodology used in the empirical analysis. The analysis is based on the surveys from 39,732 firms conducted by the World Bank’s Enterprise Surveys in 111 economies during 2009-2017. A common sampling methodology – stratified random sampling – was followed in all the surveys along with a common questionnaire.<sup>1</sup> The sample for each country was stratified by industry, firm-size, and location. Sampling weights, which take into account the industry-size-location strata, are provided in the survey and used in all the analyses, including regressions, descriptive statistics and graphs, to ensure that the sample is representative of the non-agricultural private sector of the economy.

We start our analysis with data from 40,613 firms in 111 economies that have observations on the main variables used in the analysis. After excluding the countries with three and fewer cells and treating the sample for outliers using a program developed for data with sampling weights, the sample size drops to 39,732 firms in 111 economies. The sample size reduces slightly to 39,650 with the inclusion of the variable proxying for the investment of firms, which is a dummy variable showing whether firms bought assets in the previous year.<sup>2</sup>

> 1 Details of the sampling methodology and surveys are available at www.enterprisesurveys.org.

> 2 ES data also have an indicator on the capital-labor ratio. However, since this indicator has too many missing observations, reducing the sample size from 39,000 to 12,000, we opted for the above-mentioned indicator.

8
<!-- Start of picture text --> Share of Male Rural Migrant Workers<br>w/Middle School Education<br>0.8<br>07<br>0.6<br>05<br>0.4<br>0.3<br>0.2<br>0<br>22 23 24 25<br>Age<br>mTM1990 TM2000 TM 2005<br><!-- End of picture text --> <!-- Start of picture text --> Share of Male Rural Migrant Workers<br>w/ High School Education<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>03<br>0 .21|ll | | | |<br>20 21 22 23 24 25<br>Age<br>m@1990 @2000 #2005<br><!-- End of picture text -->

<!-- Start of picture text --> Share of Male Rural Migrant Workers w/ Some<br>Post-Secondary Education<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.1<br>22 23 24 25<br>Age<br>m1990 m2000 2005<br><!-- End of picture text --> <!-- Start of picture text --> Share of Male Urban Workers<br>w/Middle School Education<br>0.8<br>07<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0<br>22 23 24 25<br>Age<br>—TM1990 #2000 2005<br><!-- End of picture text --> <!-- Start of picture text --> Share of Male Urban Workers<br>w/High School Education<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>03<br>0.2o4 | [| ge Bee Ben<br>22 23 24 25<br>Age<br>mTM1990 #2000 #2005<br><!-- End of picture text -->

<!-- Start of picture text --> Share of Male Urban Workers w/ Some<br>Post-Secondary Education<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.1<br>22 23 24 25<br>Age<br>1990 TM2000 TM2005<br><!-- End of picture text -->
# **Table A13: Second stage: Log Refugees at 50km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance) including Kampala district</u>**

|||||Lo|g of welfare|(consumption|aggregate per ad|ult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.02737|0.02721|0.07789*|0.07376*|||||||||
||(0.04452)|(0.04446)|(0.04452)|(0.04303)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.02927|0.02942|0.09207*|0.08941*|||||
||||||(0.04502)|(0.04500)|(0.04779)|(0.04608)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.03923|0.03882|0.07803|0.07521|
||||||||||(0.04945)|(0.04936)|(0.04803)|(0.04690)|
|HH head age||0.00210|0.01184**|-0.00013||0.00211|0.01182**|-0.00023||0.00212|0.01184**|-0.00014|
|||(0.00486)|(0.00558)|(0.01654)||(0.00486)|(0.00558)|(0.01657)||(0.00486)|(0.00558)|(0.01654)|
|Age squared||-0.00003|-0.00008|-0.00001||-0.00003|-0.00008|-0.00001||-0.00003|-0.00008|-0.00001|
|||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)|
|HH head sex (male)||0.00695|0.02475|-0.07368||0.00697|0.02490|-0.07279||0.00705|0.02475|-0.07360|
|||(0.03276)|(0.03745)|(0.06350)||(0.03276)|(0.03745)|(0.06351)||(0.03275)|(0.03744)|(0.06355)|
|Household size|||-0.02636***||||-0.02637***||||-0.02636***||
||||(0.00540)||||(0.00540)||||(0.00540)||
|Married monogamously|||-0.42106**||||-0.41955**||||-0.42105**||
||||(0.16637)||||(0.16682)||||(0.16628)||
|Married polygamously|||-0.36657**||||-0.36490**||||-0.36656**||
||||(0.17392)||||(0.17436)||||(0.17384)||
|Separated (divorced/widowed)|||-0.41133**||||-0.41025**||||-0.41132**||
||||(0.17406)||||(0.17451)||||(0.17400)||
|Completed primary sch|||0.22163***||||0.22170***||||0.22163***||
||||(0.03702)||||(0.03701)||||(0.03703)||
|Commercial farming|||0.10797||||0.10788||||0.10797||
||||(0.08938)||||(0.08940)||||(0.08937)||
|Wage employment|||0.00030||||0.00052||||0.00030||

17
periphery is a key link between the central direction of the project and any possible project effects.

# 5. Intermediate Oqtpluts such as Construction

Aside from disbursements the most consistent concern of project managers is with intermediate outputs relating to program objectives such as training, construction and the procurement of equipment and furniture.

The Bank, for various reasons discussed earlier, tends to provide more funding for construction than other donors. The construction component of projects is a matter of great concern to supervisory missions. The audit reports have also commented extensively concerning construction. Unfortunately, much of the commentary is negative. The Tunisia audit report, after indicating the parallels with problems in Jamaica details the delays and problems of poor design which plagued the project. In Bangladesh, according to the audit report, ... The construction component :.is farther delayed due to technical, organizational, procedural and contractual problems. Once completed, major difficulties were encountered in making facilities operational, largely due to design and quality control deficiencies. Nearly all the sites visited by the audit mission required major repairs and maintenance, while much of the equipment provided ounder the project was either not installed or not functional at the time.<sup>1 9</sup> The audit report for the first India project suggests that the design for sub-centers supported by the Bank was good and effective, but it is worth noting that during our visits to both Bangladesh and India, non-governmental observers made repeated comments concerning problems with the buildings constructed under Bank projects. In particular, the location of buildings with regard to the villages to be served and the degree of involvement of the community were seen as issues needing review. Delays in the construction component of projects seem to be a frequent source of delay in projects as a whol'-, and underuttilization of facilities constructed as a part of Bank projects is reported to be a problem in some countries.

The recruitment and training of different program staff is another major element in Bank projects. It receives !;omewhat less attention in the audit reports than does construction, but the poor performance of workers is often cited as a problem. For example in the Bangladesh PCR it is observed:

> 1 9 ProJect Performance Audit Report: Banaladesh, First Population Project, pages vi-vii.

46
The significant difference in returns to schooling for males and females observed in the Philippines (Figure 1.1) implies that male human capital and female human capital are not traded as perfect substitutes. Males’ and females’ skills are priced differently in the labor market. This can happen, for example, if males and females are specialized in occupations that require different types (and levels) of human capital. Because returns to schooling essentially determine the incentives to invest in schooling, the above observation implies that educational attainment would also differ between males and females.

The effects of improvements in school quality depend on the nature of dynamic human capital production functions. It is possible that males and females function differently, since, as indicated, male human capital and female human capital are not perfect substitutes and are possibly formed and valued in different ways.<sup>11</sup> This implies that although the improvement in school quality itself is gender-neutral, its effects can be gender-specific.

We use unique data from a student tracking survey conducted in the period from July 2010 to April 2011 in eight provinces and three major metropolitan areas. Four of the eight provinces had TEEP intervention, while the other four did not. The survey included 101 elementary schools, in each of which about 35 students were randomly sampled from the pre-intervention and intervention periods.<sup>12</sup> Students who had moved out of their school areas were tracked physically or by phone. The survey collected information on the schooling and work histories of 3,451 students. We use double differences estimation to identify the impact of the program by comparing grade 6 students enrolled in the 1999/2000 (pre-intervention), 2004/05 (intervention), and 2005/06 (intervention) school years in each school (school fixed effects), combined with school-level weights based on estimated propensity scores. Outcomes are measured based on repetitions in high school, college entry, years of schooling completed, migration, and earnings.

We have three major findings. First, the TEEP intervention widened the gender gap in schooling attainment. It increased females’ years of schooling completed (reducing repetitions in high school, increasing the likelihood of college entrance) relative to males. Second, the program increased migration rates and labor market earnings among females. Third, females seemed to be

> 11 For general discussions and literature reviews, see Altonji and Black (1999).

> 12In one northern Luzon province, the sampling design was modified, so the sample size differs from that of the other provinces.

4
||(70.14)|(70.15)|(76.40)|
|---|---|---|---|
|Household with two cows/buffaloes|218.6***|208.5***|126.4|
||(71.25)<br>|(71.43)<br>|(77.51)<br>|
|Household with three cows/buffaloes|270.8***<br>|257.6***<br>|166.4*<br>|
||(83.94)|(83.95)|(90.42)|
|Household with four or more cows/buffaloes|472.0***<br>(80.64)|457.8***<br>(80.54)|307.7***<br>(87.85)|
|Number of unemployed men (15‐60 yrs) in a||‐1.119|61.70|
|household||||
|||(54.58)|(118.0)|
|Number of unemployed women (15‐60 yrs) in<br>a household||‐134.4***<br>(36.07)|‐75.91<br>(101.8)|
|Minutes taken to collect a kg of firewood|||767.4***<br>(187.7)|
|Minutes to collect a kg of firewd squared|||‐531.3***<br>(130.0)|
|Constant|994.6***|997.0***|760.5***|
||(98.77)|(97.88)|(160.9)|
|Observations|4,750|4,750|3,329|
|R‐squared|0.115|0.117|0.112|
|Numberof wards|497|497|404|

Robust Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 2 shows the results of regressions of annual firewood collections in kilograms by households on the independent variables mentioned above. We find that households whose main cooking fuel is reported to be biogas on average collect about 1,060 to 1,160 fewer kilograms of firewood annually than households that use firewood as their main fuel, a reduction of somewhat less than 50%.<sup>7</sup> We emphasize, therefore, that biogas does not eliminate firewood collection, but it does reduce it substantially.<sup>8</sup> However, this reduction may be an over‐estimate of the impact of biogas adoption. Households who have a high opportunity cost of firewood collection (after

> 7 Mean firewood collection for households whose primary source of cooking fuel is firewood is about 2400 kg/year.

> 8 We performed a similar exercise using the second round of the NLSS from 2003‐04. At that time, about 1.7% of Nepali households used biogas as their main source of cooking fuel. Only about one‐ sixth of the households are common to the samples in the second and third rounds, so they are largely independent samples. The estimated reduction in firewood use associated with biogas is between 1240 and 1290 kg/year in 2003.

9