rafmacalaba/data-use-mentions-extended huggingface dataset:
Firm size and individual earnings are also correlated in AEs, which is consistent with models of firms that are heterogeneous in productivity and pay wages correlated with the marginal productivity of their workers, but the gradient is much weaker. The allocation of workers across size categories, rather than productivity (income) differences across them, explains 60% of the starker earnings gap of individuals in decile one versus the median in LATAM compared to the US.

These stylized facts have crucial implications. Our findings regarding individuals’ earnings imply that the bias in firm-level datasets against the self-employed without workers and microenterprises, tends to exclude low-income workers (including people in poverty). This omission implies a critical limitation for those interested in understanding inequality in LDCs and the causes of their income gap relative to advanced economies. Research not covering activity in the far left tail of the firm size distribution leaves out precisely the workers in the region for whom the income and productivity gaps relative to citizens of richer countries and the median worker in their own country are starkest. This calls for research integrating information on the universe of businesses in the economy, including self-employment initiatives without employees,

> 2 In fact, the ILO operational definition of the informal sector corresponds precisely to all productive units under the 10-employee threshold.

3
total, we tracked down 87 of the households. These households gave our enumerators the information that was needed to complete the employment sections of the survey form.

The second type of household is a household that at one time was living in the village (during our 2000 survey) and during 2008 was living out of the village in the city. In total, there were 89 such households (out of our entire sample of 1160) that fit into this category. In this case, there was no one in the village that knew on a day to day basis what the individuals in the household were doing employment-wise. In the case of these households, we tracked them to the city. In total, we tracked down 87 of the households. These households gave our enumerators the information that was needed to complete the employment sections of the survey form.

> 4 Enumerators attempted to ask the employment histories from each individual. If a household member or one of the children of the household head was not present, the respondent (which was almost always the household head or spouse of the household head) answered. Extensive pre-testing found that the data are fairly accurate. In addition, we conducted a practical test to see whether or not a respondent bias problem exists in the employment history part of our data. We replicated the analysis after excluding observations on individuals whom we did not interview directly and found that the results did not change.

In addition, we were worried about recall bias. Fortunately, we have data on the exact same households from an earlier wave of the survey in 2000. Because of this we are able to compare the household’s estimate of labor market participation in 2000 from the 2008 survey versus the information provided by the household from the 2000 survey). With this unique set of data, we are able to judge if there was a recall bias. As it turns out, there is almost none. Household participation in the off-farm labor market in 2000 was estimated to be 34.5
output per capita growth.

**Figure 17: Decomposition of growth in output per capita by sector (2015 RM), 1987-2018**

<!-- Start of picture text --> 14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>-2,000<br>-4,000<br>1987-1997 1997-2007 2007-2018<br>Within-sector changes Between-sector changes Changes in employment<br>Contribution to growth in output per capita (2015 RM) Agriculture Manufacturing Construction Trade Transport Business Other Total Agriculture Manufacturing Construction Trade Transport Business Other Total Agriculture Manufacturing Construction Trade Transport Business Other Total<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM 21
household welfare through capital gains due to changes in firm profits (Outreville 2015). The specialization of productive activities implies that, unlike the price channel that tends to affect all consumers, the other channels have localized impacts on a specific group of firms and workers belonging to the affected industry. Finally, competition reforms also might have dynamic effects by reallocating resources to more productive activities and higher growth industries (World Bank 2016). All these additional mechanisms have not yet been incorporated into the simulation approach.

The application of the tool to Mexican data is not aimed at any specific discussion of regulatory reform, nor does it imply that the firms in the markets discussed in this paper have or are abusing their market power (that is, that they are involved in anticompetitive practices). Instead, this paper focuses on the potential distributional impacts of abstract policy interventions or changes in market conditions affecting competition in specific markets, taking as a given the policies or behaviors under analysis. Similarly, this paper does not deal with the question of what type of behavior constitutes an abuse of market power or what the best policies are to remedy it (see Motta 2004; Buccirossi 2008 for discussions on these topics), nor with firms that may behave outside the conventional norm established in the microeconomic literature, such as public monopolies that provide subsidized services. Users of WELCOM should be aware of public sector participation in certain industries, such as energy, as this can have significant implications in measuring welfare effects. Users should therefore focus on products and services that are not provided by public entities, specifically because redistributive policies that are channeled through public entities are currently outside the scope of study of WELCOM.

# **6. Conclusion**

The labor share of income has declined consistently across the world over recent decades. In advanced economies, this share reached its lowest point between 2007 and 2008 (49.9 percent), while, among emerging markets, it reached only 34.7 percent in 2006 (IMF WEO 2017). Data
*</sup>|-0.106<sup>***</sup>|-0.106<sup>***</sup>|-0.117<sup>***</sup>|-0.117<sup>***</sup>|
||(0.007)|(0.007)|(0.018)|(0.018)|(0.006)|(0.006)|
|SOE dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Age dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Size dummies|No|No|Yes|Yes|Yes|Yes|
|Subsidies received|No|Yes|No|Yes|No|Yes|
|Industry effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<sup>**†**</sup>|2,253,381|2,253,381|2,165,653|2,165,653|2,249,388|2,249,388|
|Pseudo/Within R<sup>**†**</sup>|0.018|0.018|0.049|0.049|0.029|0.029|

Source: World Bank staff analysis using Romania MoF firm-level data from 2016-20. The sample includes all firms. Growth is calculated as the difference between the values in year t and t-1 divided by the average of the values in year t and t-1. Wage is labor cost divided by average number of employees. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-24.9%, 25-49.9%, or 50% or more
Establecimientos [DIEE]), a public database created by the Ecuadorian Statistics Institute (Instituto Nacional de Estadística y Censos [INEC]), which incorporates firm administrative data from the national revenue service (Servicio de Rentas Internas [SRI]) and firms’ employment data from the social security institution (Instituto Ecuatoriano de Seguridad Social [IESS]). The DIEE covers information for all formal firms registered with the SRI since 2012, totaling around 800,000 firms. However, it has limitations, providing only limited firm performance variables (sales and employment) and lacking data necessary for estimating value added and productivity, such as firms' costs.

The DIEE data is complemented with non-public firm administrative data obtained from the SRI, which encompasses the entire universe of formal firms in Ecuador. This second source includes key variables for estimating firm TFP, such as sales, gross production, costs, fixed assets, investment, employment, and materials at the firm level. The dataset also comprises relevant firm characteristic variables, including economic sector of activity, age, and geographical location, among others. The third source, the Labor and 5
(2) _Allocation, (Index, * Population,)_ The "FONCODES method" thus makes all districts in the country eligible for benefits, but weights the population of each one by its poverty index. For example, Coronel<sup>Castaffeda, the</sup> district with the highest value of the FONCODES index (36.38),<sup>and a population of 607</sup> inhabitants, would be allocated (36.38*607)/346,201,217 = .0064%<sup>of the total budget for that</sup> year. By contrast, Pacocha, the district with the lowest value<sup>of the FONCODES index (1.00),</sup> and a population of 6500, would be allocated (1*6,500)/346,201,217<sup>= .0019% of the total budget</sup> for that year. Per capita allocations to inhabitants of Coronel<sup>Castafieda Castaiieda would</sup> therefore be almost 37 times per capita allocations to inhabitants of Pacocha<sup>4Note that if the</sup> index is a poverty rate, allocations to district i simply correspond to the fraction<sup>of the poor who</sup> live in district i. I adapt the "FONCODES method" to other indicators by substituting<sup>the infant</sup> mortality rates and the imputed poverty measure for the FONCODES index<sup>in equation (2)</sup> above, and compute the corresponding district-level allocations.

The simulations in this paper make a number of assumptions. The most<sup>important</sup> assumption is that there is no targeting of program resources _within_<sup>a given district. This is</sup> clearly unrealistic: Paxson and Schady (1999) use non-parametric<sup>regressions to estimate the</sup> probability of benefiting from social
prices|702.5<br>(861)|614.2<br>(471)|-1.34|1007.8<br>(3573)|931.3<br>(1240)|-.032|
|Remittances as<br>percent of total<br>per capita<br>household<br>expenditure|NA|26.0<br>(42)|NA|NA|29.0<br>(63)|NA|
|N|5132|169||5122|179||

Notes: N=5301 households. Standard deviations are in parentheses. In 2000, 8422 Indonesian rupiah= US$1.00; in 2007, 9141 Indonesia rupiah=US$1.00. Source: Indonesia Family Life Survey (IFLS), 2000 and 2007. *Significant at the 0.10 level. **Significant at the 0.05 level. ***Significant at the 0.01 level.
– originally consisting of 434,253 individuals, which then decline to 308,625 valid observations.

Our selection rules include individuals aged 25-80 with a positive disposable income, harmonized according to the LIS procedure (variable DPI).<sup>7</sup> Incomes are converted to constant prices using the national consumer price index. Parental education is typically a categorical variable recording the highest educational attainment in the parental couple. In order to estimate a unique coefficient associated to the intergenerational transmission of education, we have converted them into years of education.<sup>8</sup> Descriptive statistics at survey/country disaggregation are reported in tables 1 to 5 in the Appendix.

Using these data, total inequality, absolute inequality of opportunity (namely inequality computed over incomes predicted according to circumstances) and relative inequality of opportunity (see equation (13)) have been estimated for each country and for each survey/year. These measures are reported in tables 6 to 10, including two indicators of inequality (standard deviation of logs and mean

> 7 To avoid negative values associated to logs, we have excluded all individuals with yearly incomes below 10. Data for the United Kingdom were rather volatile with respect to top incomes: in order to avoid confounding factors associated to differences in sampling procedures, we have trimmed them excluding incomes exceeding the 99.5 centile.

> 8 In the Italian file, recoding implies the following conversion: [1] illiterate=0 years; [2] primary=5 years; [3] lower secondary=8 years; [4] upper secondary=13; [5] tertiary=18. In the German file, recoding implies the following conversion: [1] school not attended =0 years; [2] no school degree =4; [3] Secondary General School ( _Hauptschule_ )=9 years; [4] Intermediate School ( _Realschule_ )=10 years; [5] Technical High School ( _Fachoberschule_ )=12 years; [6
accessibility and housing values in Mexico City.

The rest of this paper is structured as follows: Section 2 describes the context about job accessibility challenges in Nairobi. Section 3 discusses the methodology by explaining various data sets and analytical methods employed in this study. Section 4 presents the results on the job accessibility calculated for the city, the levels of job accessibility by different household groups, and hedonic regressions. Section 5 concludes with a brief summary of findings.

# **2. Context: Spatial challenges in Nairobi**

Recently collected surveys help to grasp spatial challenges in Nairobi. Most Nairobi residents are rent-paying tenants, and low-cost rental units are mainly located in informal residential areas. According to the Cities Baseline Survey (explained in Section 3), the distribution of housing rents in Nairobi is skewed with a small group of very high rents (Panels A and B in Figure 1). The average housing rent among the surveyed rental units is K Sh 5,315.<sup>3, 4</sup> A quarter of rental units have a rent of lower than K Sh 1,800; half the rental units have rents less than K Sh 2,500; and 75 percent of rental units have rents less than K Sh 4,400. About 10 percent of units have very high rents (more than K Sh 30,000). The mean rent value in informal settlements (K Sh 3,600) is less than half of that in formal residential areas (K Sh 9,000), and the majority of cheap rental units (less than K Sh 2,500) in the city are located in informal residential areas.

3 The housing rents used in this study include utilities. 4 K Sh 5,315 was roughly equivalent to US$61 as of 2013.

4
increase the average annual growth rate of physical capital per worker by 0.2 percentage point and enhance TFP growth by 0.6 percentage point per year.

# **4 Potential Growth Benefits of Greater International Trade Integration: A Comparative Statics Analysis**

This section uses the econometric analysis from section 3 to conduct a series of exercises through which we calculate the potential growth benefits of improving international trade integration in SubSaharan Africa. In this section, improving international trade integration involves illustrating the growth effects of: (a) expanding manufacturing trade volumes, (b) increasing export product diversification, and (c) increasing intra-regional trade. The (potential) improvement of the aforementioned dimensions of international trade integration implies comparing the trade performance of Sub-Saharan African countries (including country groups classified by their extent of natural resource abundance within the region) to a series of regional/international benchmarks.

Specifically, the comparative statics analysis conducted in this section consists of increasing the degree of manufacturing trade, reducing the Herfindahl index of export product concentration, and raising the extent of intra-regional trade to a determined benchmark. The comparative analysis is conducted across two different dimensions: first, we compute the growth benefits of narrowing the gap (using the latest time series observation for each country in the panel database) in manufacturing trade, export product diversification, and intra-regional trade for the whole region (and natural-resource-dependent country groups in Sub-Saharan Africa) vis-à-vis select regional benchmarks. Second, we compute the growth benefits from improvements in manufacturing trade, product diversification and intra-regional trade over time (say, over either a 10- or a 20-year horizon). Note that, in both cases, we compute the growth effects as well as the impact on physical capital accumulation, and TFP growth. Furthermore, the comparative statistical analysis is illustrative rather than conclusive because —among other simplifying assumptions— it is based on the implicit hypothesis that changes in trade do not lead to changes in any of the other growth determinants.

We will first look at the impact of narrowing the
4 on the importance of gaining popular support in conflict outcomes. According to practitioners, and scholars such as Kalyvas (2006) and Fearon (2008), the key strategic advantage from gaining the support of non-combatants is the provision of information about the armed forces of the opposition. Problems of within-group credible commitment (such as whether citizens can rely on leaders to pursue their interests in the event that they prevail in the conflict) are not the central focus of these analyses. Nagl (2002) places great weight on the importance for counterinsurgency strategy of gaining the trust of local actors. He focuses most on how trust matters and less on how it emerges. One way it can emerge, however, as is implicit in Kaplan‟s (2010) analysis, is if local actors are able to act collectively, allowing them to impose a larger sanction on leaders who renege on their commitments.

The literature portrays the battle for hearts and minds in two ways; collective action by citizens is relevant to both. One is the struggle by competing groups to make credible commitments to civilian populations to reward them for cooperation. Another is their struggle to deliver the benefits of good government to these populations, such as public security, social services, or predictable and non-predatory taxation. Collective action by civilian populations plays a central role in both cases: collectively organized civilians can more easily punish groups that renege on their commitments or who fail to provide the benefits of good government.

The most-studied institutional arrangement through which citizens can hold leaders accountable is voting. In fact, the absence of competitive elections is a notable feature of conflict countries. Systematic data supports this claim in the case of governments; casual observation offers little indication that insurgent-controlled territories are any different. The Sambanis (2004) conflict database records 71 conflicts from 1975 – 2000 for which data on competitive elections (from the Database of Political Institutions) are available. In only 15 of these cases were countries governed by competitively elected leaders in the year before the conflict started. In
<!-- Start of picture text --> in Table 4 are from OLS estimates of (1) including fixed effects, and from an<br>Arellano-Bond GMM estimator. We include all three tax rate variables in<br>our regressions: the personal income tax rate (PITR), the corporate income<br>tax rate (CITR) and the VAT rate (or sales tax rate for the US and Brazil).<br>The first two columns of Table 4 give estimates for the entire sample. We<br>are particularly interested in estimates for developing countries—as their in-<br>clusion is novel to this dataset; we therefore show estimates for the developing<br>countries within the sample in the third and fourth columns.<br>We find a negative relation between increases in the average marginal<br>personal income tax rate and GDP growth in the following period. For<br>the entire sample, a one-percentage-point cut in the average marginal tax<br>rate increases GDP growth in the following period by 0.16%. The effect is<br>larger for developing countries, where a tax cut of similar magnitude increases<br>output by close to 0.4%. This result is statistically significant at the 95%<br>confidence level for all specifications, and the 99% confidence level for almost<br>all specifications. It is possible that the tax codes in developing countries are<br>more distortionary than those in high-income countries. This is a possible<br>explanation for the higher effect of personal income tax changes in developing<br>countries.<br>Results for the corporate income tax rate and the VAT rate are less clear.<br>The effect of changes in the corporate income tax rate are never statistically<br>significant. The effect of VAT increases are statistically significant at the<br>95% confidence level only in the OLS regression for
When asked about targets, most boat owners report having a weekly target (rather than a daily target). While the nature of this weekly target is unclear, most boat owners report targets that are similar to their average weekly catches. This suggests that the weekly target may be a proxy of their income expectations, consistent with the work of K ̈oszegi and Rabin (2006), and not based on a minimum income needed for weekly expenses.

Previous studies that have analyzed whether hourly labor supply decisions are consistent with reference-dependent preferences have found mixed results. Camerer et al. (1997) was the first study to provide suggestive evidence in support of this hypothesis. They found that New York City taxi drivers work fewer hours on days with higher wages. Their econometric model and results were later disputed by Oettinger (1999) and Farber (2005).<sup>8</sup> In contrast, Farber (2005) found that the probability to stop working depends on cumulative hours worked on that day, but not on cumulative earnings, hence, at odds with the reference-dependent preferences assumption. However, Farber (2008) implemented a maximum likelihood estimation strategy allowing income targets to vary by taxi driver and shift. Farber estimated a substantial increase in the probability of stopping once the target was reached. More recently, Dupas et al. (2015) find that the probability that Kenyan bicycle taxi drivers stop working increases once they raise enough daily income to reach their self-reported cash needs for the day.<sup>9</sup> The paper closest to ours is Stafford (2015). She studies both the intensive and extensive margins of labor supply of fishermen in Florida. Her study shows that failing to control for self-selection in the participation margin, wage endogeneity and measurement error can lead to downward biased estimates. Our participation and earnings data come from administrative records and are thus less subject to measurement error. In addition, while our participation elasticities are somewhat lower than those in Stafford (2015),
## **2.4 Costs of CPS and other preschools**

Our cost estimates are based on the ingredient method<sup>11</sup> which aims to cost every resource required to make an intervention happen.<sup>12</sup> This includes construction costs as well as support costs, such as management, administrative and overhead costs. Total annual costs for running 500 CPS is estimated to be $3,136,743 for an average cost per school of $6,273 or $256 per child, which compares to roughly 22% of the GDP per capita ($1,160 in 2015). The estimated annual SPS costs are much higher at $12,602 per school and $426 per child.<sup>13</sup> Due to the lack of uniform quality standards no cost estimates for IPS were calculated. However, we anticipate the cost of IPS to be vastly lower. We obtain a back-of-the-envelop cost estimate of the IPS by using the CPS cost analysis from which we subtract all costs related to the construction (3%), the material (25%), the equipment (4%).<sup>14</sup> Based on this approach, the cost of IPS is estimated to be 68% of that for CPS i.e. $174.

# **3 Evaluation Design and Data**

## **3.1 Randomization and sampling**

This evaluation of the CPS program is based on a randomized controlled trial. All sample villages are situated in the south and northeast parts of Cambodia. Eligibility criteria for villages to participate in the study were: expressed demand for a CPS, a high poverty rate, and a large number of children aged zero to five years.

The study sample is composed of 305 villages. Before baseline, villages were randomly assigned to the control group or one of the three treatment groups: CPS (T1); CPS and D2D (T2