rafmacalaba/data-use-mentions-extended huggingface dataset:
growth(based onpost-crisis GDP)||0.47|
|Average actual employmentgrowth|1.88|-0.08|
|Average overestimate of<br>employmentgrowth||0.55|
|Average magnitude of discrepancy||1.9|

_Forecasting employment changes on the basis of GDP alone typically produces inaccurate estimates for individual countries._ The extent of the discrepancy between actual and predicted employment ranged from -10.5 to 6.5 percentage points. On average across the countries, the prediction was off by a magnitude of 1.9 percentage points.<sup>24</sup>

# **From evidence to policy responses**

_Evidence on labor market adjustments is an important input in the design of policy responses._ While economic growth has bottomed out and appears to be recovering in the advanced economies, labor

> 23 These estimates come from regressions, based on quarterly data, of annual percent change in employment growth on annual percent change in employment, controlling for country fixed effects. Separate regressions were run for the 8 quarters before the crisis and for the four during the crisis. Results are shown in appendix table A4. The increase in the employment elasticity from 0.16 to 0.25 is not statistically significant, but leads to economically meaningful discrepancies between predicted and actual employment.

> 24 In other words, the average of the absolute value of the discrepancy across countries and quarters is 1.9

12
Devarajan (2013) and Jerven and Johnston (2015). Like them, we think that the quality of national accounts data is intimately linked to Africa’s growth and prosperity. Over the past decade or so, as growth in gross domestic product (GDP) has picked up in Africa, there has been a renewed focus on the quality of data—even leading to a rebasing of national accounts data for some countries. This is important, because of the rapid growth in small business activity that had previously been unrecorded. As a result, economies that did rebase saw significant gains in GDP per capita. In view of these issues, the authors of this book’s African chapters have tried to collect data from a wide range of sources and to account for inconsistencies. For example, in the case of Botswana, the authors consider two scenarios for structural change in recent years, depending on assumptions about the share of the labor force in agriculture.

**Failure to distinguish location.** Most agriculture takes place in rural areas, and most manufacturing and services take place in urban areas. Given the higher costs of living in urban areas (particularly high cash rents), urban wages must typically exceed rural wages simply because of higher living costs. Thus, comparing nominal urban service and industrial wages with nominal rural farm wage rates inevitably leads to higher urban prices and wages. A more appropriate and purely sectoral comparison would involve comparing farm with rural nonfarm earnings or urban agriculture with urban unskilled manufacturing and service sector wage rates. We would guess that rental costs alone would require a 20 percent higher wage in urban areas, simply to maintain a standard of living comparable with rural areas.

In summary, while all of the measurement issues discussed above are important, we think that there is adequate evidence to support the approach taken by the authors of the country studies in this book. Adjusting average productivities for measurement error may diminish the labor productivity gaps uncovered, but it is highly unlikely that it would overturn any
if, in other contexts, there is a shift in quality in the wake of a pay hike, it may take years to hire new teachers and improve the overall quality of the teaching force. Because of this, de Ree et al. (2018) make the argument that since the total stock of teachers turns over slowly, paying current teachers much more in the hopes of slowly achieving a more effective overall body of teachers may not be a cost-effective education investment.

The third explores cross-country differences in teacher salaries and their association with other outcomes. Finan, Olken and Pande (2017) document that low-income countries provide higher wage premiums to public sector employees. Mizala and Ñopo (2016) conduct an analysis of teacher relative pay using data from 13 Latin American countries and find that teachers earn less than “other professionals and technicians” with similar observable characteristics. Our analysis on low- and middle-income countries in Sub-Saharan Africa also finds that earnings premiums are higher in countries with lower incomes, but we also observe substantial variation in teacher earnings premiums given the income level.<sup>7</sup> The rest of this paper is structured as follows: Section 2 describes the data and our analytical and empirical strategy. Section 3 describes our results. Section 4 concludes.

# **2. Data, analytical strategy, and empirical strategy**

# **<u>2.1. Data</u>**

We draw on several sources of data. Our principal analysis of teachers and their earnings is based on household and labor force surveys conducted in 15 low- and middle-income countries in Sub-Saharan Africa (Table 1).<sup>8</sup> We chose these countries and surveys based on three criteria:

- (1) The surveys included detailed information on occupation, working hours, earnings, educational attainment and other basic demographic characteristics of surveyed household members or individuals.

> 7 One recent report examines teacher pay across 13 countries in Sub-Saharan Africa and finds that primary school
, such as survey-to-survey imputations. Instead, this study uses a sophisticated consumption-based modeling approach to estimate individual-level market income and simulate direct taxes and social contributions at the same time. This is the first such study to our knowledge and could be a useful example for low-income countries facing similar data challenges. The model described in this paper uses the latest household survey available at time of publication, but a future update of the model is expected to rely on updated data from the 2022 BLSS.

27
projection.<sup>13</sup> Moreover, the CDR model only partially reproduced the estimates of district-level average household wealth recorded by the household surveys. Although correlations of around 0.9 were reported at the district level—higher than in the Guatemala analysis—it appears that a few wealthy districts may have driven that result.

## **_Combining CDR Data with Public Observable Data_**

> 12 See Blumenstock et al. (2015). 13 See Beegle (2016).

**17**
example, a firm survey by Grupo de Servicios de Información (1999) indicates that for all firms the quality of skills ranks as the second most important constraint. For small firms, important for employment and income generation, the quality of skills is the main production constraint.

# **3. Measuring the Contribution of Education to Growth**

The accumulation of human capital through education has long been acknowledged to be an important factor in the development process of a nation. Education is thought to be beneficial because it decreases inequality, improves the quality of life, and in particular it is a factor in rising the income level and facilitating economic growth. This section will concentrate on the latter effect and summarize some evidence on the relationship between education and growth.

## **3.1 Augmented Solow Model and Endogenous Growth**

The existing literature contains a number of distinct conceptual rationales for the inclusion of human capital in models of economic growth. According to Sianesi and van Reenen (2003), the two main macro approaches are the augmented Solow model and the new growth theories.

## **_Augmented Solow Model_**

One way to estimate the impact of education on growth is to adapt the Solow (1956) model. The augmented version extends the basic framework to allow human capital as an extra input to enter the production function. In particular Mankiw et al. (1992) show that traditional growth theory can accommodate human capital and provide a reasonable approximation for empirical analysis. At the economy-wide level, it may also take into account human capital externalities. Still, one of the key insights is that the factor accumulation affects the _level_ of income, but per se is insufficient to achieve long-run _growth_ . Long-run growth depends rather on growth in technological progress. Human capital accumulation may therefore have only a short-term impact on the rate of growth.

However, rates of accumulation are expected to have explanatory power for growth rates during the transition to an eventual equilibrium growth path. In particular, considering the case of
the market income distribution and the consumable income distribution. Prefiscal inequality is plotted with dots and measured as the Gini index on the left-hand axis.

**Figure 11. Fiscal Policy’s Impact on the Poverty Headcount Ratio**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Market (incl. pensions) Net market Disposable Consumable<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and budget figures.

_Note:_ The poverty headcount ratios in this figure are based on poverty lines based on the methodology described in Lara Ibarra (2018). The average value is LE 5,748 per person per year, or US$3.98 per day in 2011 PPP.

45 When the government‐cost value of in‐kind services is added to Consumable Income, the poverty headcount rate drops to approximately 12 percent. However, a monetized value of an in‐kind service received does not _directly_ produce an increase in purchasing power (over all goods and services).

29
identify program effects. Moreover, some types of programs differ in impact with both exposure and intensity. King and Behrman (2009), for example, note that the impact of early childhood development (ECD) programs may be particularly sensitive to duration of program exposure.<sup>5</sup> For these reasons, this paper identifies impacts of the PMT program by exploiting differences in program exposure within communities while controlling for fixed community characteristics.

We find that the program had significant impact on children between 12 and 24 months of age at the time of the survey in 2000, but no effect on the nutritional status of children younger and older than this age group. Depending on the specification used, the program increased the standardized height-for-age of children 12 to 24 months of age by 0.48 to 0.55 standard deviations, and reduced the likelihood of stunting by 15 percent as of 2000.

The paper is organized as follows. Section 2 provides an analytical framework. Section 3 discusses the program and its implementation, and this is followed by an introduction to the data source, the IFLS, in section 4. The empirical strategy is discussed in section 5, results are discussed in section 6 and a final section concludes.

# **2. Analytical Framework**

The analytic approach is framed using a standard household model (e.g., Rosenzweig and Wolpin, 1986) to highlight the identification assumptions important when modeling the effect of a nutritional intervention on child health status. We assume that preferences of household � are inter-temporally separable and that the household

> 5Armecin et al (2006), Behrman, Cheng and Todd (2004) and Gertler (2004) examine ways in which impact of ECD programs vary with program exposure and also exploit duration of exposure in their identification strategies.

4
|**_global outputmt_**|**0.941*****|**0.755*****|(0.877)<br>**0.885*****|(0.886)<br>**0.662*****|
|---|---|---|---|---|
||(0.155)|(0.162)|(0.167)|(0.171)|
|**Constant**|**0.818*****|**0.672*****|**0.862*****|**1.032*****|
||(0.034)|(0.017)|(0.081)|(0.095)|
|Observations|450,629|450,629|450,629|450,629|
|R-squared|0.429|0.429|0.429|0.429|
|Exporter-Time FE|Yes|Yes|Yes|Yes|
|Importer-Time FE|Yes|Yes|Yes|Yes|
|Exporter-Partner-Sector|Yes|Yes|Yes|Yes|
|Sector-Time FE|Yes|Yes|Yes|Yes|
|Cluster|Exporter-Partner|Exporter-Partner|Exporter-Partner|Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

The availability of production data for years before 2020 also allows us to replicate the analysis for February-June 2019, to assess if our findings are driven by the Covid-19 pandemic. The results,
,917**|**461**|**12%**|**6,334,516**|**3,050,965**|
|SKS|India|961|166|16%|5,795,028|5,795,028|
|Grameen Bank|Bangladesh|817|127|23%|6,430,000|6,223,597|
|Spandana|India|787|215|21%|3,662,846|3,368,115|
|BRAC|Bangladesh|636|102|18%|6,241,328|5,921,753|
|ASA|Bangladesh|456|114|20%|4,000,401|3,535,145|
|**Summary statistics**|||||||
|**Total/Average for 2009**||**64,770**|**1,405**|**64%**|**82,501**|**63,767**|
|Number reporting||1,110|1,103|1,099|1,109|982|

_Source:_ Microfinance Information Exchange. http://www.mixmarket.org/mfi/indicators?page=1 [Accessed February 20, 2011.] For Thailand, 2004 data from Village Fund central office; 2009 data estimated based on Thailand Socio-Economic Survey. The comparator microfinance institutions shown here include the largest by gross lending, and by number of active borrowers, in each year, as well as a selection of other institutions of general interest.

3
# **I. Introduction**

There are three main reasons for informal employment to raise concerns. The first reason is related to the equity aspect, namely that informal workers’ well-being could be vulnerable if they are outside the formal social protection system and that poverty and inequality can increase if informal workers are systematically underpaid. Second, the informal sector operates largely outside the state’s regulation where tax evasion is the norm, both by firms and workers, which challenges the sustainability of the fiscal system. Third, informal employment could undermine productivity and economic growth if workers or firms choose to adopt small-size/low-productivity organizational and technology settings mostly to avoid tax payments and government regulations, or due to failures in other markets such as access to capital or entrepreneurial skills.

Rigid labor market institutions could lead to higher informality, but previous studies suggest that a combination of weak enforcement and universal access to basic pension and health care are also important drivers of informality in the Russian Federation. A large body of literature has shown that informal and formal labor markets are integrated rather than segmented, implying that workers tend to choose to be informally employed in Russia. In contrast to the large amount of research on the causes and drivers of informality, the welfare consequences of informality are much less researched and understood.

This paper has one main objective: to estimate the impact of informal employment on workers’ individual well-being. In order to do so, it also assesses the comparability of available sources of data on informal employment and updates the trends in informal employment through 2016. Several data sources are available for the purpose of studying the impact of informal employment on well-being in Russia. For the estimation of levels and trends of informal employment, we utilize four surveys to see how the trends compare across data sources and definitions. We then focus on one survey, the Russia Longitudinal Monitoring Survey (RLMS), which has rich and detailed information on employment, and complement the analysis with other surveys when appropriate. For all data sources, we maintain
), but it is critical to understand how well they measure air pollution and how useful they are for policy evaluation. Additionally, identifying how to combine different data sources into a cost-efficient air quality monitoring system could help low-income countries improve their air pollution measurement.

In this paper, we compare and evaluate three sources of air pollution data in a lower middle-income context. We focus on particulate matter with a diameter of 2.5 micrometers or less (PM2.5), which is a mixture of fine inhalable particles of various chemical compositions including dust, dirt, soot, and smoke. PM2.5 is a widely regulated air pollutant and has stronger health effects across a broader range of health outcomes than coarser particles (Bell _et al._ , 2004; Pope and Dockery, 2006). First, we compare the data sources in terms of accuracy of PM2.5 measurement across different spatial and temporal frequencies. Second, we use each of the data sources to evaluate a specific policy that reduced air pollution and compare the estimated treatment effects on PM2.5. Third, we consider policy makers’ constraints to air quality measurement in low-income settings to make policy recommendations about designing systems of air pollution monitoring.

We compare and evaluate three principal sources of air pollution data. First, the gold standard is regulatory-grade monitors that are widely used by governments in high-income countries to create a network of ground monitoring stations that capture detailed, precise and accurate measurements of air pollutants. However, the high set-up and maintenance costs of regulatory-grade monitors preclude lowand middle-income countries (LMICs) from developing dense networks of regulatory-grade monitors and from replacing them at recommended intervals. Second, satellite images can be used to approximate

> 1One exception is China, which is an upper-middle income country, and is the focus of a large number of papers in the literature (Chang _et al._ , 2019; Fu _et al._ , 2021; He _et al._ , 2019; Zivin _et al._ , 2020).

2
the value of one, if at least one new firm enters in sector _s_ , in district _d_ , and in year _t_ , and zero otherwise. β 1 and β 2 denote the immediate and lagged (by one year) effect of the number of new mining firms established in district _d_ on the probability of having a new firm in sector _s_ and district _d_ . To estimate equation 2, we use a district-year panel comprising 119 districts and 24 years (1990–2013) based on the location and the year of establishment of a firm, using 2014 IBES data. Since employment and other firm characteristics are available only for the census year, we use the number of firms to measure the outcome variable. For the same reason, the explanatory 14
2020). Better employment is not about formality or informality—but rather about the organization of work in a team where employees are paid for their work (rather than working out of familial obligation).<sup>17</sup> This allows a minimum of specialization and organization, which helps to boost productivity and for people to use their skills.

_Other employment_ is all types of employment other than better employment, and is calculated as the difference between total employment and better employment. The main categories usually in other employment are subsistence own-account/family agriculture, non-farm microenterprises, small-scale traders, and landless agricultural laborers, as these employment types are only common in low-income countries—suggesting they are more likely to have lower productivity.<sup>18</sup> The weights of better employment ( ) and other employment ( ) depend on the relative contributions of better employment and other employment in generating GDP (Equation 6). This depends on both ωω 1 −ωω the number of workers in better employment and other employment, but also on their relative productivity. I assume that those in “other employment” are only as productive as raw labor, which has normalized productivity of 1. The human capital of these workers does not affect their productivity: a doctor working as an agricultural laborer will be just as productive as someone with no education doing the same job. In contrast, workers with better employment are as productive as their human capital allows; they are h times as productive as raw labor (where h is human capital per worker).<sup>19</sup> I assume that in each case males and females are equally productive, and have the same human capital h = h = h (which is not a bad approximation based on the World Bank HCI, see Appendix Figure 3). FF MM (6) hLL<sup>B</sup> **3.2 Calculating the full GEGI in the data: Employment ratios and the HCI**
# **1. Introduction**

Municipal solid waste management (SWM) continues to be a major challenge for local governments in both urban and rural areas throughout the world. This challenge is particularly important for the developing world. The available statistics show that, although the municipal solid waste generation in the developing countries is still low per-capita level compared to that in the developed world, the developing countries account for a disproportionately high share of the world’s solid waste generation relative to their share of world income<sup>2</sup> . Moreover, from a dynamic point of view, the municipal solid waste management in developing countries faces even greater challenges in the future because of their rapid urbanization and economic growth. Empirical analyses using macroeconomic data<sup>3</sup> indicated that the per capita generation of solid waste was at least 0.3-0.4 kilograms per day even for the poorest people. In general, a 1 percent increase in population is associated with a 1.04 percent increase in solid waste generation, and a 1 percent increase in per capita income is associated with a 0.34 percent increase in total solid waste generation. Considering that most of the developing countries are still in the early stage of their urbanization and economic development process, people generally believe that a fast increase in solid waste generation should be unavoidable in the developing world.

The current practice of collecting, processing and disposing municipal solid wastes is also considered to be least efficient in the developing countries. The typical problems are ―low collection coverage and irregular collection services, crude open dumping and burning without air and water pollution control, the breading of flies and vermin, and the handling and control of informal waste picking or scavenging activities‖ (Bartone, 1995). Although some cities do spend significant portions of their municipal revenues on waste management (Coitreau, 1984, 1994; Thomas-Hope, 1998; Schübeler, 1996 and Bartone, 2000), they are often unable to keep pace with the scope
benefits, such as severance payments and unemployment insurance (UI). Severance payments are paid by the employer, who contributes every month 8% of workers’ earnings to a mandatory savings account (FGTS). Upon layoff, the worker can access the account’s balance, as well as an additional penalty for the firm equal to 40% of the balance. UI benefits amount to 1 to 1.76 minimum salaries and are paid for 3 to 5 months.<sup>6</sup> In contrast, if workers quit the job themselves, they are not entitled to any benefits. Quits are thus more likely to be driven by the workplace preferences of employees.

**Informality.** Brazil has a relatively large informal sector that accounts for roughly 45% of all workers in 2015 (Derenoncourt _et al._ , 2021). Our analysis will focus on separations from formal jobs and measure the racial composition among all formally employed coworkers which we can observe in administrative employment data. Survey evidence shows that formality rates are quite similar across racial groups (Gerard _et al._ , 2021), which suggests that conditioning on formality does not pose major limitations for understanding racial disparities in labor market

> 6Van Doornik _et al._ (2023) provide evidence that formal layoffs can be used to extract rents from the UI system by showing that firms are more likely to lay off workers when they become eligible for UI benefits, but continue to employ them informally while on benefits and recall them when the benefit period is over.

6