rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Average treatment effects of German acquisition on firm-level employment

||TWFE<br>(1)|Doubly robust<br>(2)|
|---|---|---|
|Average treatment efect (German acquisition)|0.075**<br>(0.035)|0.112**<br>(0.038)|
|Efect by treatment year|||
|2005|0.057**|0.059**|
||(0.026)|(0.028)|
|2006|0.043*|0.045|
||(0.026)|(0.030)|
|2007|0.040|0.068**|
||(0.033)|(0.029)|
|2008|0.087**|0.117**|
||(0.041)|(0.047)|
|2009|0.059|0.083*|
||(0.039)|(0.045)|
|2010|0.096*|0.140**|
||(0.052)|(0.056)|
|2011|0.117**|0.161***|
||(0.052)|(0.062)|
|2012|0.070|0.177**|
||(0.077)|(0.079)|

Notes: Authors’ calculations based on Orbis data. Results include only firms established before 2004. The doubly robust estimator of Sant’Anna and Zhao (2020) in column (2) has been implemented using pre-treatment levels of employment, age, region and sectoral dummies as covariates. *10% level, **5% level, and ***1% level.

Table 3: Effects of EU accession on firm performance across sectors

|Dep. variable:|Log TFPR<br>(1)|Log revenue<br>(2)|Log employment<br>(3)|Log exports<br>(4)|
|---|---|---|---|---|
|Post _×_ Germany’s export share (log)|0.048***|0.082**|0.029|0.162**|
|Firm FE|(0.017)<br>Yes|(0.032)<br>Yes|(0.023)<br>Yes|(0.064)<br>Yes|
|Year FE|Yes|Yes|Yes|Yes|
|N (obs.)|134174|134176|134178|63992|
|N (frms)|18355|18357|18359|13245|
|R-squared|0.105|0.110|0.073|0.110|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Germany’s export shares are measured in the period 19942004. TFPR is estimated using the method in Levinsohn and Petrin (2003). When using exports as dependent variable, the sample is restricted to the period 2002-2012 due to a change in the questionnaire, which does not allow for a consistent measure of exports throughout the entire period. The standard errors in parentheses are clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

22
For motor skills both program and interaction term coefficients are not statistically significant for any of the income subgroups. However, the trend is similar in the magnitude of the coefficients by income cohort: lowest quintiles of income show larger interaction effects compared to higher quintiles of income. This tendency reinforces the results for the communication skills trends. Although tenure of communications assets might be determined by income, these results show that even after contemplating income as an adjusting variable, households may be driven to purchase these types of assets because of the value added these may have in improving the cognitive development of children. These results may well also show that independently from parents’ education, there is a tendency of lower income households to have communications assets because in some way they value this asset as an important channel to reinforce educational messages across children at early ages. Indeed these results show some evidence that early life information that produce health outcomes, shape also childhood cognitive skills.

# _Matching results_

Table 12 summarizes the results of separate matching samples from DHS and IE surveys. All matched estimates used the same explanatory variables from the previously reported models<sup>13</sup> . For the PSM estimates the estimates between the longitudinal impact evaluation surveys and the imputed outcomes into DHS surveys is contrasting. The coefficients of the interaction effects (between the information campaigns and TV presence in the household), for the impact evaluation sample, are positive and significant for all socio-emotional scores (motor, communication and social). These interaction effects with the DHS matched samples are statistically insignificant yet with similar magnitudes in the coefficients. The heterogeneity of effects based on different samples indicates the presence of potential observed and unobserved biases. Nevertheless, the results from the imputed DHS samples are more precise since they entail matching of characteristics with larger samples. The value added of constructing counterfactuals based on statistical matching results in improving the balance of comparison groups,

> 13 Only estimators of the interaction effect between communication assets and socio‐emotional outcomes reported in the table.

27
Policy Research Working Paper 7658

# **Abstract**

This paper examines the effect of air pollution on labor supply in Lima, Peru. It focuses on fine particulate matter (PM2.5), an important pollutant for health according to the medical literature, and shows that moderate levels of pollution reduce hours worked for working adults. The research design takes advantage of rich household panel data in labor outcomes to address omitted variables and allows investigation of whether the response to air pollution is non-linear. The analysis finds that the effect of moderate pollution levels on hours worked is concentrated among households with susceptible dependents, that is small children and elderly adults, while the highest concentrations affect all households. This suggests that caregiving is likely a mechanism linking air pollution to labor supply at moderate levels. Further evidence of this mechanism is provided using DHS data on children morbidity for the same time period. Finally, no evidence is found of intra-household attenuation behavior. For instance, there is no re-allocation of labor across household members, and earnings decrease with air pollution.

This paper is a product of the Environment and Natural Resources Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at jjmiranda@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
relationship between property rights protection and a particular variable of interest is considered to be robust only if the coefficient remains statistically significant and of the theoretically predicted sign when the conditioning variable sets are changed in the regressions. So EBA would help us understand if the partial correlations established in the regression analysis are robust or fragile to small changes in the set of right hand side variables used in the regression. In this aspect it is similar to DAG since it uses the conditioning information set to determine the robustness of a particular variable. However, EBA looks at one coefficient at a time and the conditioning information set is restricted to triads of variables. DAG on the other hand allows us to examine the conditional independence relationships between all variables simultaneously and in addition shows us the direct and indirect effects of each of the variables<sup>13</sup> .

Specifically, suppose we are interested in knowing whether a variable Z is robust in predicting property rights protection, we estimate regressions of the form: Property Rights = a + bz Z + bxj Xj + e (1) where xj ∈ X is a vector of up to three variables taken from the pool X of N variables. The regression is estimated for all M possible combinations of xj ∈ X and for each model j, the estimate bzj and the corresponding standard deviation szj are identified. At the 5% level at which EBA is performed in the Levine and Renelt study, the lower extreme bound is the lowest value of bzj -2 szj and the upper extreme bound is the largest value of bzj + 2szj. According to EBA, variable z is robust only if both bounds are of the same sign. The extreme bounds consistent with a 1% significance level are bzj -3szj (lower extreme bound) and bzj + 3szj (upper extreme bound).

> 13 EBA may be considered to be a parameter estimation analysis where as DAG is a model specification analysis.

27
<!-- Start of picture text --> Policy Research<br><!-- End of picture text --> <!-- Start of picture text --> Financial Policy and Systems<br><!-- End of picture text -->

**WPS 1084**

This paper-a product of the Financial Policy and Systems Division, Country Economics Departmentis part of a larger effort in the department to promote the development of sound securities markets. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Priscilla Infante, room N9-005, extension 37665 (February 1993, 39 pages).

Programs to develop securities markets are now a common feature of World Bank financial sector loans. Stock market development in particular is receiving considerable attention, especially the legal and institutional underpinnings required for successfil stock market development.

The financial underpinnings needed have received less study.

He and Pardy contribute to such a study by exploring the relationship between the degrees of financial depth and stock market development in an economy.

Using a simple indicator of stock market development and several indicators of l;.-ancial depth, and using cross-sectional data from 32 developing countries ror 1984-90, they find a strong correlation between the two factors.

Time-series data from 19 of these countries (for 1978-90) show similar correlations for most Asian countries, but not for other countries in the sample. Also, the correlations of the Asian data are strongest after the mid-1980s.

He and Pardy test a "threshold hypothesis" that a certain level of financial depth may be necessary to allow stock market development to take off. They find that available data do not support the hypothesis.

The results suggest that financial depth is a significant factor in stock market development in most developing countries, but that countryspecific factors (such as industrial policy and structure, foreign investment controls, and stock market regulatory and operational infrastructure) have an equally strong influence on stock market growth.

Case studies of economies in which stock market development has been successful would help elucidate the interplay between these factors.

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

Produced by the Policy Research Dissemination Center
M. Regulatory substance- quality of supply index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchmark**|
|---|---|---|---|---|---|
|**Quality Regulation**|100%|100%|92%|71%|75%|
|**Quality of Service Standards**|100%|100%|100%|75%|82%|
|Requirement to meet quality of service standards|||||100%|
|Specific quality of service standards are formally<br>written and publicly available for- quality of the<br>product, quality of the service and customer<br>service|||||97%|
|Performance on quality of service standards is<br>public|||||71%|
|Fines for failing to meet quality of service<br>standards|||||59%|
|**Quality of Service Enforcement**|100%|100%|83%|67%|68%|
|Requirement to report technical data on a<br>periodic basis|||||100%|
|Regulator specifies how to collect technical<br>performance data|||||71%|
|Regulator reviews or validates technical<br>performance data|||||47%|
|Automated information management systems<br>are required to measure the quality or reliability<br>of the power supply|||||71%|
|Measurements of the quality or reliability of<br>power supply are made public|||||65%|
|Financial incentives to meet customer service<br>standards or increase customer satisfaction|||||53%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 68
Table 5. Variation in factor earnings and in aggregate welfare (% change from benchmark)

||Full Reform|All services<br>barriers|Only non-discrimina-<br>toryservices barriers<br>On|ly barriers against FDI in<br>services|Only uniform tariffs|CRTS|Steady State|
|---|---|---|---|---|---|---|---|
|**Variation in factor earnings**||||||||
|Subsistence Factor|2.52|2.11|0.49|1.50|0.37|0.73|5.75|
|Young workers|1.33|0.63|0.04|0.52|0.64|0.25|4.07|
|Female wages (Unskilled)|2.53|1.33|0.29|0.96|1.07|1.03|5.23|
|Female wages (Laborers)|1.79|1.03|0.16|0.78|0.69|0.49|4.57|
|Female wages (Technicians)|2.50|1.76|0.48|1.14|0.68|1.00|4.00|
|Female wages (Professionals)|7.96|7.23|2.10|4.59|0.68|3.14|11.25|
|Male wages (Unskilled)|2.15|1.35|0.28|0.98|0.71|0.82|4.89|
|Male wages (Laborers)|2.28|1.49|0.29|1.08|0.71|0.74|5.15|
|Male wages (Technicians)|3.90|3.21|0.92|2.05|0.63|1.56|6.00|
|Male wages (Professionals)|13.17|12.66|3.77|7.94|0.48|5.16|18.05|
|Return on subsistence Factor|2.52|2.11|0.49|1.50|0.37|0.73|5.75|
|Return on capital|2.63|2.07|0.50|1.40|0.50|1.04|-0.70|
|Return on land|0.52|0.04|-0.15|0.15|0.43|-0.27|3.13|
|Adult female wages|2.00|1.18|0.22|0.87|0.74|0.62|4.69|
|Adult male wages|2.78|2.01|0.47|1.39|0.69|0.97|5.61|
|Adult (female and male) wages|2.60|1.84|0.43|1.28|0.71|0.90|5.37|
|**Aggregate welfare**||||||||
|Welfare (EV as % of consumption)|2.23|1.95|0.46|1.32|0.26|0.67|4.52|
|Welfare (EV as % of GDP)|2.03|1.77|0.42|1.20|0.24|0.61|4.11|

Source: Author's estimates.

Table 6. Difference in wages by sex (change in percentage points of difference) and evolution of the wage gap (in %)

||Benchmark|Full Reform|All services barriers<br>Only non-discrimina-tory<br>services barriers<br>O|nly barriers against FDI in<br>services|Only uniform<br>tariffs|CRTS|Steady State|
|---|---|---|---|---|---|---|---|
|**Change in percentage points of diffe**|**rence betwee**|**n females wa**|**ges and males wages**|||||
|Female-male wages (Unskilled)|-|0.38|-0.02<br>0.01|-0.02|0.36|0.21|0.34|
|Female-male wages (Laborers)|-|-0.49|-0.46<br>-0.13|-0.30|-0.02|-0.25|-0.58|
|Female-male wages (Technicians)|-|-1.40|-1.45<br>-0.43|-0.91|0.05|-0.56|-2.00|
|Female-male wages (Professionals)|-|-5.21|-5.43<br>-1.67|-3.35|0.20|-2.01|-6.81|
|Female-male wages (All workers)|-|-0.78|-0.83<br>-0.25|-0.52|0.05|-0.35|-0.92|
|**Wage gap**||||||||
|Female/male wages (Unskilled)|67.66|67.91|67.65<br>67.66|67.65|67.90|67.80|67.88|
|Female/male wages (Laborers)|39.43|39.24|39.25<br>39.38|39.31|39.42|39.33|39.21|
|Female/male wages (Technicians)|52.22|51.52|51.49<br>52.00|51.75|52.25|51.94|51.24|
|Female/male wages (Professionals)|41.46|39.55|39.46<br>40.80|40.18|41.54|40.67|39.07|
|Female/male wages (All workers)|39.77|39.47|39.45<br>39.67|39.56|39.79|39.63|39.42|

# Source: Author's estimates.

Note: the wage gap is the ratio of female to male wages in percentage terms. Wages can be obtained by dividing total remuneration of each type of worker by the corresponding number of physical workers based on NBS (2002a) for the benchmark and in the simulation results for the remaining scenarios.

30
Credible and coherent strategic visions also existed in Korea, Singapore and other East Asian countries. These are described and documented in the East Asian Miracle study (World Bank 1993). Of course, the existence of grand visions does not necessarily imply that industrial and economic success should be fully attributed to them. In some cases, visions may have been too ambitious (e.g. Korea), in others they may have been too diffuse (e.g. Thailand or Indonesia). Moreover, visions and the industrial policies they have embraced may turn out to involve significant long-term costs. These are unlikely to offset the benefits of higher growth or to undermine the achievements of industrialization and development, but they are bound to lower the net benefits from this approach.

30
II.3 Summary **of** Industry **Sectors** Woven Texties. Korea's woven textile industry dates back to the establishment of a local silkworm experiment station in the 19th century. Later efforts to develop local cotton and wool textile production during the period of Japanese occupation (1909-1945) gave a significant boost to the industry. The industry is highly concentrated in Korea's Taegu region, approximately 200 miles south of Seoul. Large-scale modern spinning mills and synthetic chemical fiber plants were built in the region in the 1950s and 1960s. In 1965 the govenment made the Taegu region an industrial zone for woven textiles, and by 1989 the region housed 78.2 percent of Korea's woven textile firms.

By 1963, the woven textile sector had as many as 1,260 establishments (each with five or more workers), employing a total of 54,518 workers. This single industry accounted for 13.6 percent of manufacturing employment and 7.9 percent of manufacturing value-added. Despite rising labor productivity, relatively rapid growth in other industries, and virtually no increase in the total number of textile-weaving establishments, employment grew to almost 90,000 by 1973. Between 1973 and 1979, Korea began to increase its share of world textile markets at the expense of Japan. The number of textile weaving establishments rose by about 70 percent to 2,126 and employment rose to 170,871, thereby increasing the sector's share in manufacturing employment to slightly over 8 percent. Since 1979, while the nmnber of firms and the sector's real output have continued to grow rapidly, employment has fallen in both absolute and relative terms. This has been largely a response to the country's rapid increase in real wage rates."' As a result, in 1988 the woven textile sector accounted for only a little over 5 percent of manfacturing employment and 3.6 percent of manufactring value-added. Although labor productivity in this sector has increased steadily, it has generaly done so in small increments based on minor changes in technology, relying on gradual replacement of old equipment. Shuffle-loom technology remained virtually unchallenged unil the mid-1980s, when the switch to shmttleless looms begant1 10 Among all countries reporting data on real wage rates since 1969, Korea's increased the most rapidly between 1970 and 1989 [World Bank Development Report, 1992].

**11** Amsden 1989, chapter 11.

7
district average price index as a control variable. Because they use hard information on providers, the four indices described above are dubbed “objective” measures.

## 8.2.2. Subjective Quality Indices

To construct the subjective quality indices, we use the survey data on the patients’ perceptions of both used and bypassed facilities. The patients were asked to provide the reasons they chose the specific facility they visited through a battery of binary questions. Among the reasons listed one finds questions related to (1) availability of specific services, (2) waiting time, (3) proximity to markets, (4) quality of service, (5) drugs availability, and (6) consultation and drugs prices.

# 8.3. Results

Because some patients’ characteristics such as age, gender and education level may influence bypassing, they are included as controls in all regressions. Some additional controls considered are the type of ailment; the illness’ severity proxied by the number of days the patient has been sick, and whether the patient has multiple symptoms. Finally, we control for few characteristics of the health facility, such as its geographical location, whether it is a monopoly, the number of beds, whether it is for-profit, etc. Table A2 in the appendix presents summary statistics of variables.

Table 12 shows the results of the probit regressions of equation (3). The first four models (columns 1-4) show for the whole sample the determinants of bypassing specific type of providers, e.g. public health center. The last three models (columns 5- 7) focus on the modern sector, i.e. docteur choukous are not considered, and try also to identify differences across income groups by narrowing the sample to patients in the 1<sup>st</sup> and 5<sup>th</sup> income quintiles. Unlike Akin and Hutchinson (1999), we find that income is a key determinant of bypassing behavior. As a matter of fact, individuals in the richest quintile are significantly more likely to bypass a provider than anybody else. Moreover, the likelihood to bypass a provider increases with income (column 5). Patients in the poorest income quintile have a 10.8% lower probability of bypassing a modern health provider than patients in the richest quintile (column 5).

With respect to subjective quality variables, patients are more likely to have bypassed another facility when they perceive the facility they went to seek care as 23
# About AICD and its country reports

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a solid empirical foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.

The AICD is based on an unprecedented effort to collect detailed economic and technical data on African infrastructure. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. _Africa’s Infrastructure— A Time for Transformation_ , published by the World Bank and the Agence Française de Développement (AFD) in November 2009, synthesized the most significant findings of those reports.

The focus of the AICD country reports is on benchmarking sector performance and quantifying the main financing and efficiency gaps at the country level. These reports are particularly relevant to national policy makers and development partners working on specific countries.

The AICD was commissioned by the Infrastructure Consortium for Africa following the 2005 G8 (Group of Eight) summit at Gleneagles, Scotland, which flagged the importance of scaling up donor finance for infrastructure in support of Africa’s development.

The first phase of the AICD focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. The countries are: Benin, Burkina Faso, Cape Verde, Cameroon, Chad, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage was expanded to include as many of the remaining African countries as possible.

Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most severe infrastructure challenges. Some components of the study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated, therefore, the term _Africa_ is used throughout this report as a shorthand for _Sub-Saharan Africa._ The World Bank has implemented the AICD with the guidance of a steering committee that represents the African Union (AU), the New Partnership for Africa’s Development (NEPAD), Africa’s regional