rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: Student performance (First follow-up in 2009)**

|||4th Grade<br>|<br>||6th Grad<br>|e<br>|
|---|---|---|---|---|---|---|
||WSD|Control|P-value|WSD|Control|P-value|
|_Reading test_|||||||
|Correct letters per minute|55|57|0.26|73|75|0.17|
||(1.23)|(1.23)||(1.15)|(1.1)||
|Correct words per minute|23|25|0.33|41|41|0.75|
||(1.18)|(1.15)||(1.08)|(1)||
|_Written test_|||||||
|Overall|47.2|48.22|0.5|60.59|61.79|0.4|
||(0.46)|(0.45)||(0.49)|(0.45)||
|Math|47.04|49.75|0.2|65.95|68.19|0.23|
||(0.65)|(0.66)||(0.67)|(0.62)||
|Literacy|45.82|45.94|0.93|57.19|57.76|0.67|
||(0.44)|(0.41)||(0.47)|(0.43)||
|Observations|411|403||431|460||

Notes: Standard deviations in parentheses. *** 1% Significance Level, **5% Significance Level,*10% Significance Level. Same students at the baseline. The score of the written test is the average score expressed in percentage.

**Table 9: Student performance (two years into intervention – 2010)**

||Test s<br>|core<br>a<br>|Percentage of st<br>rea<br>|udents who can<br>d<sup>b</sup><br>|
|---|---|---|---|---|
||3<sup>rd</sup>graders|5<sup>th</sup>graders|3<sup>rd</sup>graders|5<sup>th</sup>graders|
|WSD|-0.001|-0.08|0.01|-0.05|
||(0.08)|(0.09)|(0.03)|(0.04)|
|Grant|0.01|0.03|-0.01|-0.05|
||(0.08)|(0.09)|(0.02)|(0.04)|
|Observations|4537|4354|1241|1202|
|Mean of dependent<br>variable in comparison<br>group|35.32%<sup>a</sup>|52.06%<sup>a</sup>|11%<sup>b</sup>|38%<sup>b</sup>|

Notes: Standard deviations in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level.<sup>a</sup> Test score is normalized to 100 points. It is standardized only for the calculation of the treatment effect.<sup>b</sup> Percentage of students who can read 45 or more words per minute.

31
a considerable amount of time even if their income increased significantly. Restricting the sample to observations below this poverty line would thus induce a selection on the outcome variable, leading to biased results. Secondly, targeting is far from perfect as in the years under study only 58%-59% of households below their applicable poverty line receive the benefit, as do 38%41% of households in the second quintile, 19%-22% in the third, and even 6%-8% in the fourth quintile (Acosta, Avalos & Zapanta 2019). The average income of households with at least one child under the age of 18 is close to ~~P~~ 50,000, falling into the fourth quintile. On the other hand, not restricting the sample at all runs the risk of including too many high-income households for whom the program would be completely irrelevant. The analysis will start with a main sample of households earning less than ~~P~~ 50,000 and residing within 200km of the typhoon path (with a binary treatment group defined as living within 100km). Subsequently, results will be extended for different income thresholds and distances in both directions ( ~~P~~ 20,000 to ~~P~~ 80,000 and 20km to 400km).

Table 1 shows summary statistics for the main sample (up to 200km distance from the typhoon path and less than ~~P~~ 50,000 in household per-capita income). Six different outcomes are of interest. Unfortunately, FIES does not collect direct information on school attendance. To fill this gap, I created a a binary variable with value equal to one if a household reports any expenditure on education and zero otherwise. The analysis for this outcome 14
Policy Research Working Paper 10130

# **Abstract**

Using the Commitment to Equity methodology, this study investigates differences in the welfare impact of taxes and government spending on men and women in Ethiopia. It analyzes the incidence, progressivity, and pro-poorness of various taxes and transfers and their effects on income mobility, poverty, and inequality using individual-level data from the 2018/19 Ethiopia Socioeconomic Survey. The results show that the fiscal system as a whole is progressive, equalizing, and poverty-reducing. It moved about one in five individuals from one income group to another, and more women than men transitioned to a higher income group, making them relatively better off. However, some of its elements have differential effects on gender equality. Direct and indirect taxes have differential inequality-reducing and poverty-increasing effects for men and women. The inequality-reducing effects are stronger for men, whereas the poverty-increasing effects of some of them, including informal taxes and value-added taxes, are higher for women. On the transfer side, direct social protection transfers and indirect transfers, mainly spending on primary education and health services, promote gender equality better than other types of government spending.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aambel@worldbank.org, wtesfaye@worldbank.org and myonis@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
same increase in the quality of green management leads to a 36.2 percentage points increase in the likelihood of making both capital- and non-capital-intensive green investments in MENA, but a 41.6 percentage points increase in ECA – though the magnitude differences are not statistically different from each other at 5% level of significance. In ECA, a unit increase in the quality of green management is also associated with a 5.4 and 7.8 percentage points decrease in the likelihood of making capital-intensive and non-capital-intensive green investment, respectively. The estimates marginally confirm our hypothesis 1, but not hypothesis 3.

**_Table 5: Multinomial logistic IV regression for type of green investment (average marginal effects)_**

||No green<br>investment|Capital-intensive<br>green investment<br>only|Non-capital<br>intensive green<br>investment only|Both capital- and<br>non-capital-<br>intensive green<br>investment|
|---|---|---|---|---|
|||_Panel A: O_<br>|_verall sample_<br>||
|Credit constraints|0.268|-0.056|0.097|-0.309*|
||(0.224)|(0.073)|(0.190)|(0.134)|
|Green management|-0.351|-0.024|-0.050|0.426***|
||(0.260)|(0.079)|(0.169)|(0.057)|
|||_Panel B: Allow all coef_<br>|_ficients to vary by re_<br>|_gion_<br>|
|Credit constraints ECA|0.297*|-0.074|0.152*|-0.376**|
||(0.117)|(0.076)|(0.075)|(0.135)|
|Credit constraints MENA|0.229|0.306|-0.178|-0.357|
||(0.262)|(0.178)|(0.206)|(0.188)|
|Green management ECA|-0.284***|-0.054*|-0.078**|0.416***|
||(0.041)|(0.021)|(0.024)|(0.044)|
|Green management MENA|-0.460***|0.092|0.006|0.362***|
||(0.070)|(0.054)|(0.040)|(0.060)|
|Observations||17|,902||
|Clusters||3|,022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the type of green investment, based on multinomial IV logistic regression with the type of green investment as a dependent variable. Regression controls for locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); indicators for no firms in other sectors in a 15km radius with data on credit constraints and green management; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Table 4 provides the first stage of the IV regressions. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

To some extent, lack of more significant results could be because we do not know the actual capital intensity of each green investment the company reported. Investment in green energy generation on site, 26
- lIs -

Table 3: Effective Rate of Assistance to the<sup>Mexican Sugar Industry</sup>

||Unit|1989|1988|1987|1986|1985|
|---|---|---|---|---|---|---|
|lue of Output|PESOm|2,732,000|2,732,000|2,732,000|2,732,000|2,732,000|
|SS Total<br>Inputs<br> <br>|||||||
|o materials<br>In milling<br> <br>|PESOm<br>|171,500<br>|171,500<br>|171,500<br>210000|171,500<br>210000|171,500<br>210000|
|o depreciation<br>in milling<br>|PESOm|210,000|210,000|,|,|,|
|o materials<br>and deprecia-|||||||
|tion<br>in growing|PESOm<br>|343,000<br>|343,000<br>|343,000<br>|343,000<br>|343,000<br>|
|o materials<br>in marketing|PESOm|129,500|129,500|129,500|129,500|129,500|
|Valueadded|PESOm|2,075,764|2,075,764|2,075,764|2,075,764|2,075,764|
|PLUS Assistance to Value<br>|||||||
|Adding Factors<br>|||||1|100000|
|osugar sales subsidy|PESO.|100,000<br>|100,000<br>|100,000<br>|00,000<br>|,<br>|
|3mill<br>loss subsidy<br>|PESOm<br>|285,000<br>|285,000<br>|285,000<br>|285,000<br>88000|285,000<br>88000|
|oAZUCAR admin. cost sub|PESOm<br>|88,000<br>|88,000<br>|88,000<br>|,<br>|,<br>|
|3storage cost subsidy|PESOm|450,000|450,000|450,000|450,000|450,000|
|Assisted Value Added|PESOm|2,998,764|2,998,764|2,998,764|2,998,764|2,998,764|
|LESS Assistance to Outputs<br>|||||||
|odomestic pricing|PESOm|-740,245|31,365|656,640|802,975|1,172,890|
|LESS Assistance to Inputs|||||||
|ofertilizer<br>subsidy<br>|PESOm<br>|92,635<br>|92,635<br>|92,635<br>|92,635<br>2|92,635<br>200000|
|**o**fuel subsidy|PESOm|200,000|200,000|200,000|00,000|,|
|LESS Assistance to<br>||||||923000|
|Value Adding Factors|PESOm|923,000|923,000|923,000|923,000|,|
|,assisted<br>Value Added|PESOm|2,523,374|1,751,764|1,126,489|980,154|610,239|
|Net Subsidy Equivalent|PESO.|475,390|1,247,000|1,872,275|2,018,610|2,388,525|
|Effective<br>Rate Assistance|S|18.8394|71.1853|166.204|205.948|391.408|
|Nominal Rate Assistance|S|-21.318|1.16139|31.6398|41.6259|75.2281|

Source: AZUCAR

- Notes: (a) Data for 1989 have been used to calculate assisted value added in all years, as assisted value added information for 1988 and earlier are not available--this probably does not change<sup>much</sup> from year to year.

- (b) Assistance to output has been estimated for all years--this can change greatly from year<sup>to</sup> year as the wortd price changes.
# **Mexican Employment Dynamics: Evidence from Matched Firm-Worker Data**

# David S. Kaplan<sup>*</sup> World Bank and

Instituto Tecnológico Autónomo de México (ITAM) E-mail: dkaplan@worldbank.org Gabriel Martínez González General Secretariat Interamerican Social Security Conference E-mail: gabriel.martinez@ciss.org.mx Raymond Robertson Department of Economics Macalester College E-mail: robertson@macalester.edu

# **Acknowledgements**

This paper had been distributed under the title “Worker and Job Flows in Mexico. We gratefully acknowledge financial support from the Inter-American Development Bank for the project “Market Institutions, Labor Market Dynamics, Growth and Productivity: An Analysis of Latin America and the Caribbean.” We also acknowledge financial support from the Asociación Mexicana de Cultura.

> * Corresponding author.
<!-- Start of picture text --> Figure 7. Sources of Undue Private Influence on the State in Perua<br>1000-<br>70 -<br>M<br>co40<br>CL<br>-<br>10 ,<br>Drug Large Organized FDII ProfessionalLabor Unions<br>Conglomerates Economic Crime Transnationalkssociations<br>Conglomerates Corporations<br>Responses by: I U Firms Q Public officials<br><!-- End of picture text --> <!-- Start of picture text --> Source. World Bank Institute (2001).<br><!-- End of picture text --> 47
the budget constraint no longer constrains managerial behavior and becomes merely an accounting or book-keeping identity. Conversely, financial targets and binding profit constraints could increase managerial efforts to minimize costs under specific assumptions about managerial objectives.26/

# Empirical Evidence

15. Empirical studies have shown that costs tend to "drift" in firms which receive subsidies from government to cover deficits. Lumpsum subsidies in the regulated public buses in the U.S. were shown to result in cost increases.27/ A comparative study of publicly-owned bus companies in OECD countries showed that twenty-five to fifty percent of transfer payments were absorbed by increased unit costs.28/ The relevant question for welfare analysis, however, is a comparison of the gain in welfare from instituting optimal prices and the loss in welfare associated with cost drift. Heggie has demonstrated that a 202 increase in prices has the same absolute effect on welfare as a 2Z decrease in costs in a market with unitary elasticity of demand.29/ Reductions in costs have an even larger relative welfare gain when there are increasing returns to s:ale, as is the case in many transport enterprises. The implicit assumption of minimum costs in pricing literature, is therefore not a neutral assumption and may even contribute to the problem of public sector inefficiency. If pricing is to be the instrument of choice for cost recovery, care should be taken to insure that instituting a pricing scheme 26/ H. Gravelle and E. Katz, "Financial Targets and X-efficiency in Public Enterprise," Public Finance, Vol. 31, 1976, p. 218-234.

27/ Moshe Kim and Menahem Spiegel, "The Effects of Lump-Sum Subsidies on the Structure of Production and Productivity in Regulated Industry," Journal of Public Economics, Vol 34, 1987, p.105-119.

28/ P.H. Bly, F.V. Webster, and Susan Pounds, "Subsidization of Urban Public Transport," Supplementary Report No. 766, Transport and Road Research Laboratory, Crowthorne, U.K., 1983.

29/ Ian Heggie, Pricing, Cost Recovery, and Efficient Resource Use in Transport, The World Bank, INUTD Discussion Paper, December 1989, p. 17.

11
# Diagram 1

_Categories of the dependent variable in the multinomial regression model_ <!-- Start of picture text --> Did not<br>Economic Crime and<br>Family reasons Other reasons migrate<br>reasons violence<br>internally<br>Job search Study<br>Family Criminal<br>reunification insecurity People who<br>lived in<br>Mexico in<br>Natural 2015 and<br>Job change<br>disasters still resided<br>in the same<br>state in<br>Getting Violence 2020<br>married<br>Job offer Other<br><!-- End of picture text --> Source: Own elaboration.

**However, using the Census data for estimating the propensity to migrate has the limitation that individual characteristics are observed post-migration** . The Census has no information on the characteristics of individuals and their households before the migration decision. This is a challenge when modeling the migration choice since determinants of migration are likely to be related to individual and household socioeconomic conditions before choosing to migrate.

**Time-invariant characteristics and individual factors inferred at the time of migration are used as explanatory variables to overcome Census data limitations.** Time-invariant characteristics include ethnicity and race, and individual factors inferred at the time of the migration choice are age and if the person had children under the age of 14. Under the assumption that the highest level of educational attainment achieved does not change significantly after 25 years old for most individuals,<sup>13</sup> the sample is constrained to individuals 25 and over at the time of migration to allow for the inclusion of educational attainment as a regressor.

**Variables capturing socioeconomic conditions of the state of origin at the time of migration (2015) are also included as regressors.** Among these variables are labor market conditions (unemployment rate, employment rate, and informality rate), deprivations in non-monetary dimensions (deprivation index), the incidence of crimes, perceived levels of corruption, preschool education supply, and the percentage

> 13 According to the 2020 Census, only 1.1 percent of the population has a master's degree, and 0.2 percent have a Ph.D.

24
<!-- Start of picture text --> Policy Research<br>Debt and International Flnanoo<br>WPS 1044<br><!-- End of picture text --> This paper - a product of the Debt and Intemational Finance I ;vision, International Economics - Deparument is part of a larger effort in the department to study the determinants of<sup>secondary market</sup> prices of developing countries' debt. Copies of the paper are available free<sup>from the World Bank, 1818 H</sup> Street NW, Washington, DC 20433. Please contact Rose Vo, room<sup>S8-042, extension 33722 (November</sup> 1992, 37 pages).

The market prices of C ¶veloping countries' debts are imperfect indicators of the countries' payment capacity, for dthee reasoins: the concave shape of the debt's payoff structure, the presence of third-party guarantees, and the differences in the terms of various debt claims.

order of importance, the benefits of cleaning raw prices come first from correcting for the effects of different terms (such as fixed versus floating interest rates), followed by the value of thirdparty enhancements and then by the concavity of the payoff structure.

Claessens and Pennacchi derive an improved indicator of payment capacity by developing a - pricing model using option valuation techniques - that takes these three factors into account.

Applying the model to bonds issued recently by Mexico and Venezuela, they find that the estimated indicator of payment capacity often behaves differently from the raw bond prices themselves, confirming the importance of cleaning the raw prices for these three factors. In They find some evidence that the new indicator of repayment capacity conforms better than the raw prices themselves to generally held beliefs about which variables drive a country's repayment capacity. In particular, they find that variables that are often assumed to be related to - payment capacity such as oil prices and the countries' stock market prices - are more closely (and with the right sign) associated with the new estimteed measure of payment capacity than are the sc-ondary market prices of the bonds.

The Policy Research Working Paper Series disseminates the fmdings<sup>of work under way in the Bank. An objectiveof the series</sup> is to get these findings out quickly, even if presentations are less<sup>than fully polished. The fmdings, interpretations, and</sup> conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
roughly the same in 1960 as in 1980.

An interesting feature of average wages by sub-groups according to SII (Table 3.1) is, finally, the negative correlation between relative wage in 1960 and growth rate from 1960 to 1984, low-wage groups systematically growing faster than high-wage groups, to some extent even reversing the ranking from 1960. Mining & quarrying which was lowest in 1960 topped the list in 1984. Trade which was highest in 1960 was lowest in 1984. Minimum wage legislation could have this kind of effect although it should then also show up in the comparison between small and big establishments, creating faster increase in the former. Changes in coverage may be part of the explanation; trade is an example, possibly only covering high paid employees in 1960, slowly extending to lower paid employees, hence the combination of relatively high wages in 1960, a low growth rate, and relatively low wages in 1984.

Our descriptions and comparisons of SII and SMI (census) statistics have not disclosed any particular bias in either one that might help explaining the differences between these statistics for mining & quarrying and manufacturing, in particular not the much lower rate of increase of the SII statistics. It would appear that the. differences may be related to payrolls as reported by the individual establishments to the authorities.

Evasion cannot be excluded, of course. Both establishment

- 47 -
Improved biomass cookstoves ultimately are only useful for reducing indoor air pollution, mitigating climate change and improving livelihoods if people substitute them for more traditional cooking methods. Substituting, of course, at minimum implies that improved stoves are used. This paper focuses on the actual use of Mirt stoves over more than one year in 36 sites in rural Ethiopia. The study uses real-time stove use monitoring data derived from a 360-stove randomized trial to evaluate the degree to which – under six treatments –people use the Mirt stove. These results can, as discussed above, have important implications for climate change, human health and livelihoods.

There may also be carbon finance implications. The UN Collaborative Programme on Reducing Emissions from Deforestation and Degradation in Developing (REDD+) can credit reduced fuelwood consumption if it is documented that cooking technologies actually reduce deforestation and forest degradation. About a quarter of stove programs in 2010 indeed received or were planning to receive voluntary carbon market or CDM resources (Gifford, 2010).

Whether, how frequently and why low-income country residents actually use more efficient biomass cookstoves are very much open questions that go to the heart of whether stoves are truly “improved.” As discussed in the following section, in the past more efficient biomass cookstoves promoted in low-income countries have often not been accepted or used by cooks. Better understanding whether particular stoves really have the potential to address critical challenges – through use – is therefore of prime importance.

The following section discusses the literature on improved biomass cooking stoves, the potential linkages with climate change and forestry policies and the use of field experiments for evaluating the efficacy of such technologies. Section 3 presents the sampling and experimental designs used to collect the primary data. Part 4 of the paper reviews the variables and empirical methods used and section 5 presents the results. Section 6 concludes the paper and draws key policy implications.

# **2. Literature Review**

REDD+ is a still-evolving program to provide incentives for FCCC non-Annex 1 countries to reduce deforestation and forest degradation. Resources have been assembled to support forest management programs, including the World Bank Forest Carbon Partnership Facility and UN-

**4 |** P a g e