rafmacalaba/data-use-mentions-extended huggingface dataset:
One could think of Jordan’s growth of local supply of public basic and secondary schools as a progressive public investment in human capital that increases the human capital production for children of _marginal_ parents in terms of income and educational attainment. Those are parents who would have chosen higher investment in the human capital of their offspring, but were constrained by the limited supply of schools in their subdistricts of residence and could not afford to send their children to more distant schools outside their jurisdiction or to provide them with homeschooling. However, the increase in public schools is expected to have less of an effect on richer or more educated parents, who are expected to provide education to their children regardless of the availability of schools in their subdistricts either by sending their children to distant schools or through homeschooling. On average, however, the increase in the local supply of public schools is expected to reduce the intergenerational correlation of educational attainment or enhance intergenerational educational mobility.

# III. DATA

Two new and unique data sources are employed in the empirical analysis. First, the Jordan Labor Market Panel Survey of 2010, carried out by the Economic Research Forum in cooperation with the Jordanian Department of Statistics, is a rich source of information on all aspects of the Jordanian labor market (JLMPS 2010). Most importantly for the purpose of the analysis is the fact that the survey provides individual-level data on own schooling and parents’ schooling for all adults in the sample, which is quite rare in household surveys from developing countries. Also, the survey provides the actual years of schooling completed and not only the highest educational degree attained, which allow observing the schooling variable with precision.

12
125)|(1.808)|(1.692)|
|Dummy_Mais Educação_* year 2009|–0.140|–5.194***|–2.140|
||(0.968)|(1.663)|(1.647)|
|_Mais Educação_* spending on|8.26e-05|–0.00331|–0.00633|
|education and culture|(0.00413)|(0.00877)|(0.00840)|
|_R_ <sup>2</sup>|0.736|0.755|0.741|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. Municipal spending on education and culture was deflated by IPCA, and is in 2007 prices. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is Portuguese grades in _Prova Brasil,_ in column (2) mathematics grades in _Prova Brasil,_ and in column (3) dropout rates. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade
16 to be high, current employer practices contain the costs for firms, especially for low-wage earners. Our recommendation is to find a workable compromise that not only balances employer concerns about high severance pay rates and employee concerns about income security, but also considers vulnerable workers who have been excluded from the debate.

It would be appropriate for Indonesia to establish an administrative entity to monitor compliance with the newly-negotiated severance pay regulations. Other countries with severance pay programs, such as Barbados and Slovenia, have established arrangements to oversee severance pay statutes. Indonesia may want to follow this lead.

Regardless of the policy pursued by the Indonesian government, further data collection and research are needed to assess the impact of severance pay or evaluate future proposed programs. Currently, the data available on severance pay in national surveys are limited. Both the IFLS and Sakernas have collected data for only one year (2007 and 2008, respectively). Continuing collection of severance pay is vital for examining the labor market outcomes associated with changes in severance pay regulations, especially the employment and wage effects for workers in the formal sector and those in the informal economy. Additional research is needed to understand the barriers to compliance: lack of knowledge, lack of enforcement mechanisms, costs of legal recourse, and how firms avoid compliance.
reductions in the national poverty rate of 1.9 percentage points resulted from the restoration of peace and stability to much of the country after Yoweri Museveni came in to power in 1986, the series of economic liberalization reforms that were implemented in the 1990s (such as coffee marketing reforms), and the investments by households and firms that these encouraged (see for example, Collier and Reinika 2003, Appleton 2004, Deininger and Okidi 2004). The narrative of Uganda’s continued, albeit slightly slower, progress in reducing poverty since 2006 is less familiar. This was a period in which growth started to slow as the gains from the reforms were fully realized, and weak infrastructure and corruption increasingly constrained private sector competitiveness (World Bank 2015).

In this paper we use a nationally representative panel data set of 2,356 Ugandan households visited four times between 2006 and 2012 to document the type of income growth they experienced. We take advantage of the panel nature of the data to control for unobserved, time-invariant, household characteristics and analyze the degree to which income growth was driven by exogenous changes in prices, weather and the cessation of conflict during the period considered. We utilize the methods outlined in Dell et al. (2014) in which the impact of weather shocks is identified by using panel data and including household fixed effects to disentangle the impact of the shock from the impact of omitted variables which could be causing bias. This framework has been used to analyze the relationship between weather and household welfare outcomes in Munshi (2003), Dercon (2004), Yang and Choi (2007), Thomas et al. (2012), Anttila-Hughes and Hsiang (2013) and Hill and Porter

> 2 Uganda reduced the extreme poverty rate by 2.9 percentage points a year, second only to Chad, which reduced the extreme poverty rate by 3.1 percentage points per year. This is using poverty numbers reported in Povcalnet as of January 2016, and using the surveys deemed comparable by World Bank 2016.
shape of the commune units in relation to district boundaries. Because communes are too small to sensibly plot them for the whole country, two map inlets display the heterogeneity of their boundaries. While in rural areas, boundaries can be large with diameters of more than ten kilometers, they can be very small in urban areas.

This study uses geocoded economic data for communes from the Enterprise Survey conducted annually by the Vietnam General Statistics Office.<sup>2</sup> This survey covers characteristics of firms such as employment, revenue, profits and industry classification. The firms are geographically identified at the commune level. This data is aggregated to the commune in order to work with the total count of enterprises and counts of employment at this small geographic scale. Economic activity of each commune is then approximated by the number of workers and the number of enterprises employing them. The data comes with several quality concerns which are discussed here. At first, it is limited by the fact that only formal employment is recorded. In a country with a potentially very high share of the informal economy (Cling et al., 2011), these numbers can severely underestimate the actual employment numbers and results should therefore be interpreted as measures of the formal economy. Second, it excludes self-operating farmers and only accounts for employment in agricultural enterprises. Further, the sampling scheme is designed to fully survey enterprises with more than 10 employees only, while a random sample is used for smaller firms, and is thus not fully representative. There are also inconsistencies over time as boundaries occasionally change.<sup>3</sup> Lastly, an issue with the geocoding arises due to the fact that the enterprise data is not based on plant location but rather on firm location. This potentially exaggerates measures of economic activity at the headquarter location and underestimates it at the branch location.

> 2The authors thank Klaus Deininger at the World Bank and Fang Xia at the University of International Business and Economics, Beijing for providing access and invaluable guidance relating to
- Piancastelli, M. (2001). Measuring the Tax Effort of Developed and Developing Countries: Cross-Country Panel Data Analysis, 1985/95. _IPEA Working Paper_ , No. 818. Rio de Janeiro: Institute for Applied Economic Research.

- Prichard, W., Goodall, A. and Cobham, A. (2014). The ICTD Government Revenue Dataset. _ICTD Working Paper_ 19, Brighton: International Centre for Tax and Development.

- Rashidi, K., and K.P. Cullinane (2019). Evaluating the Sustainability of National Logistics Performance Using Data Envelopment Analysis. _Transport Policy_ , 74, 35-46.

- Ray, S. C. (1991). Resource-Use Efficiency in Public Schools: A Study of Connecticut Data. _Management Science_ , 37(2), 1620-1628.

- Rodrik, D. (1998). Why Do More Open Economies Have Bigger Governments?. _Journal of Political Economy_ , 106(5), 997-1032.

- Stotsky, J. G. and Wolde-Mariam, A. (1997). Tax Effort in Sub Saharan Africa. _IMF Working Paper_ , No. 97/107. Washington, D.C.: International Monetary Fund.

- Tanzi, V. (1992). Structural Factors and Tax Revenue in Developing Countries: A Decade of Evidence. In: _Open Economies: Structural Adjustment and Agriculture_ , (Eds.) I. Goldin and L. A. Winters. Cambridge University Press.

Tukey, J. W. (1977). _Exploratory Data Analysis_ . Pearson.

- United Nations (2015a). _Sustainable Development Goals._ Available at (last accessed in February 2021): https://sustainabledevelopment.un.org/sdgs.

- United Nations (2015b). _Addis Ababa Action Agenda of the Third International Conference on Financing for Development_ . New York: United Nations.

- UNU-WIDER (2020). _Government Revenue Dataset, 2020_ . Available at (last accessed in February 2021): https://www.
Policy Research Working Paper 10372

# **Abstract**

Using multiple waves of two public opinion surveys and a two-way fixed effect model, this paper analyzes how people’s perceptions and attitudes towards public institutions shifted with the business cycle in El Salvador during 2004–2018. It finds that individuals’ levels of trust toward both the president and the municipal government are positively associated with higher levels of income at the municipality level. Income is also a strong predictor of trust in mass media, confidence in the judicial system and, to a lesser extent, trust in the national legislature but income does not affect trust in the Catholic Church. The relationship between income and trust toward the president and municipalities masks a relevant heterogeneity from a rural-urban divide as well as from differences in municipal state capacity. Further, views of income distribution fairness as well as preferences for democracy are positively shaped by municipality-specific business cycles. In contrast, neither generalized trust nor satisfaction with democracy is empirically associated with income at the municipality level.

This paper is a product of the Poverty and Equity Global Practice. 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 author may be contacted at mrobayo@worldbank.org and brude@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
Fourth, elementary strictly green jobs are home to many excluded workers that play a vital role in the green economy value chain but may be even more productive through organization. For example, waste pickers in South Africa are beginning to organize by registering with municipal authorities, organizing their work streams to increase the value added, and gaining some worker rights. As the green economy expands, such organized groups of workers may play a stronger role (Medina 2008).

Finally, as South Africa moves toward a green economy, it will be even more important to understand and track the impact on jobs. This paper used proxies to estimate the number and profile of green jobs in South Africa. But better measures will be needed. This highlights the need to develop detailed and regularly updated occupation-task databases, such as through web scrapes or other big data sources. It will also require academia-industry collaboration to more succinctly define the South African skill profile needed in emerging green jobs, as well as profiling those occupations that will disappear through the transition and related necessary reskilling.

26
vehicle taxes|218,114|||
|Social allowance onpersonal income tax|29,982|||

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data

**Table A-8. Programs simulated in RUSMOD, annual amounts (in million rubles), 2017**

||RUSMOD|RLMS-HSE|Ratio|External|Ratio|
|---|---|---|---|---|---|
|**Benefits**||||||
|**_Non means-tested_**||||||
|Birth grant|14,454|||30,705|0.47|
|Maternity leave allowance|47,528|||116,685|0.41|
|Lumpsum allowance for pregnant women|319|||552|0.58|
|Child allowance up to 1.5 years|117,890|95,108|1.24|210,972|0.56|
|Compensation of childcare costs|10,987|||20,253|0.54|
|Maternity capital|351,726|||328,651|1.07|
|**_Means-tested_**||||||
|Child allowance up to 16(18) years|47,866|113,748|0.42|46,447|1.03|
|Allowance for the third child up to 3 years|4,469|||55,026|0.08|
|State social assistance|18,064|61,932|0.29|18,211|0.99|
|Other social assistance|61,706|||||
|Housing subsidy|50,733|87,725|0.58|62,757|0.81|
|**Taxes and Social Insurance contributions**||||
we ended up with 321 strict green terms and 37 additional terms for the broad green list. Second, we use the South Africa-adjusted green dictionary and follow the method used by Vona (2019) to calculate the GTI scores for each occupation as per Equation 1. Third, to merge the GTI scores to PALMS, we used crosswalks to transform the ISCO-08 occupation codes to the ISCO-88 codes used in the PALMS.

Finally, we define an occupation – and the jobs within it – as green based on its calculated GTI. An occupation is defined as green if it has a GTI greater than zero. All the jobs corresponding to that occupation are assigned the same GTI and the same greenness status. We generate a list of strictly green occupations, which are those occupations with a GTI-strict value that is a positive number. The broadly green occupation list are those occupations with a GTI-broad value that is greater than zero.

We primarily use the strictly green occupations for our analysis, as the list of GTI-strict terms more concisely identifies jobs with tasks that are currently engaged in the green economy. But we present and discuss the results for both strictly and broadly green occupations, understanding that the results emerging from the analysis of strictly green jobs can be taken as the lower bound of green employment and broadly green jobs taken as the upper bound of green employment.

> 20 The OFO is DHET’s tool for identifying, reporting, and monitoring scarce and critical skills. This list cannot be easily mapped to household survey data because the structure of the occupation code differs from that of ISCO-88 (DHET 2013). For example, the OFO only has 8 one-digit ISCO occupations because major code 7 and 8 have been combined, making it hard to disentangle crafts and related trades and machine operators.

8
no registration status. According to the _Vietnam Migration Survey_ 2004 data, only 10.2 percent of the migrants who work for foreign firms possess permanent registration status (Fukase, 2013b).

> 27 Since there exist some errors in the panel identifiers in the original dataset, I used the revised identifier codes provided by McCaig (2009) to match the individuals between 2002 and 2004. Then, I eliminated some observations whose information on gender, age and education are inconsistent. In order to mitigate further the measurement error, I dropped one percent of individuals who experienced the largest wage changes. However, the results are not sensitive to these adjustments.

> 28 The data on panel individuals reveal that those who had a wage job in both years tend to be more educated (with 9.4 years of education on average) than those who moved from a non-wage job in 2002 to a wage job in 2004 (with 7.3 years of education) (VHLSS 2002, 2004). During the same period, the BTA might have accelerated the rate at which less skilled non-wage workers became wage workers. If that were the case, the wage workers who lived in a province more exposed to exports might have consisted disproportionately of “new” wage workers relative to other provinces.

16
direct impact on rice yield but also an indirect impact through the increase in fertilizer application.

On the other hand, by African standards, fertilizer use in the Sahelian schemes is remarkably high. In fact, in Niger, the average fertilizer application rates are well above the recommended rate, which is 400 kg/ha. This can be partly attributed to a relatively low fertilizer price and institutional support in these countries. According to national statistics, the ratio of urea price to paddy price is 2.5 in Burkina Faso, 1.3 in Mali, and 1.6 in Niger, which are close to the fertilizer price ratios in Table 3.<sup>8</sup> Note that the countries with strong institutional supports for fertilizer (see Mali, Niger, and Senegal in Table 2) show very low fertilizer price ratios. This ratio is not disadvantageous at all compared with the ratio of about 3.5 in Laguna in 1976-82 and 2 in major rice producers in Asia such as India and Pakistan in 2001 (Minten et al. 2006).

Related to this, we would like to stress that farming practices appear to be homogeneous in the Sahelian schemes. Regarding chemical fertilizer application, the standard deviation relative to the mean (i.e., the coefficient of variation) is much smaller than that in Doho and Chokwe. This may be one of the benefits of a well-managed irrigation scheme. This homogeneity implies that a serious constraint to rice production in the Sahel relative to Uganda and Mozambique might not exist, and thus many farmers use a large amount of chemical fertilizer to achieve yield comparable with yield in Asia.

Table 3 also shows labor use and the real daily wage in terms of kilograms of paddy.<sup>9</sup> A notable feature is found in Chokwe. The wage rate is higher and the labor

> 8 Chemical fertilizer reported in Table 3.3 consists of urea and other kinds of complete fertilizer packages.

> 9 Note that these are the
, given its economic structure. This puts the efforts of lowincome countries to increase revenue mobilization, i.e., their revenue-to-GDP ratio, in perspective: it may, indeed, be much harder for these countries to raise their revenue mobilization from the low absolute level unless and until their income level rises or economic structure changes substantially, thereby increasing their potential for additional revenue.

Figure 7 also shows that countries with low or high DEA scores are not concentrated in a particular income group or region. Rather, all income groups and regions cover wide ranges of scores, and median scores are relatively similar across income groups and regions (except for ECA and SSA). In particular, these results indicate that in case future efforts to support revenue mobilization were to assign strategic priorities to countries considering their efficiency in DRM as captured by the DEA scores, these priorities would not necessarily end up benefiting countries belonging to a particular income group or geographical region in a disproportional way.<sup>18</sup> Simply put, efficient government revenue mobilization appears to be a challenge across income groups and regions (including across countries with very different revenue-to-GDP ratios).

A series of robustness checks reported in Appendix A indicate that the main results of the DEA are robust to changes in the selection of indicators and the aggregation methodology underlying the construction of the variables used in the DEA. Due to imperfect data quality, however, we stress that the

> 17 An example is Uganda, a LIC, whose revenue-to-GDP ratio (0.12 in 2016-2019) is much smaller than that of, for example, Botswana (an UMIC with revenue-to-GDP ratio of 0.32 in the same period). Despite its lower revenue level, however, Uganda is achieving 81 percent of its potential revenue according to the estimated frontier, slightly more than Botswana (79 percent). The underlying reason is that Botswana features a much more favorable economic structure for generating revenue than Uganda, as captured by the large gap in the DEA input index across these
respondents in these communities may assume that women and young adults living in their households and engaged in the informal economy are not employed or working. The LOA module, on the other hand, may address the potential bias driven by the exposure to high informal employment by presenting to respondents a list of activities that are mostly from the informal sector. As a result, this seems to lead to no differences in the reporting of women’s and youths’ labor market indicators.

# **6.2 Social Norms around Gendered Work Activities**

Occupational and sectoral separation by sex is remarkably persistent over time (World Bank, 2011) and a major contributor to the gender wage gap. The prevalence of working women and men in particular occupations that classify jobs by type and skill and in different sectors of the economy varies greatly (Borrowman & Klasen, 2020). For example, women are more likely to be engaged in a wider range of activities compared to men. They combine unpaid domestic work or caregiving responsibilities, which traditional survey methods may not capture effectively, with income-generating activities. Thus, a hypothesis is that the unpaid domestic work women do (which is partly explained by social norms) is more likely to be underreported (Franck & Olsson, 2014), as well as women’s paid work may be prone to undercounting because it is conducted in shorter periods of time and they may consider that it is not worthy to be reported in household surveys.

Considering the types of activities in which women predominantly engage, especially in contexts where women devote a large portion of their time to domestic obligations, we test whether our survey methods measure women’s work accurately. To this end, we estimate a measure for the respondent’s exposure to social norms associated with domestic obligations. This measure consists of the average amount of time in hours that the women—excluding the respondent—in each EA devote to domestic duties, which we obtain from the main survey’s time use module. We then