rafmacalaba/data-use-mentions-extended huggingface dataset:
8 secondary - informal - activities, a high proportion of the labor force has informal work activities as main occupation (see **Table 3** ). Another important form of possible informal activities can be self-employment. Although one could have expected to see self-employment develop rapidly during the transition, available data indicate that self-employment remains very limited in Ukraine. About _5_ percent of working men (and 3 percent for women) report self-employment as their main occupation and a very small proportion of the labor force combine self-employment with other types of occupation.

**ZablQ** 3

Labor **Force Participation Rates Employment Rates** in **Formal** e Informal **Sectors** - **1995 -**

||MEN|WOMEN|
|---|---|---|
|Labor force participation rate for population aged 15+|.79|.71|
|Total Employment rate (Population aged 15+)|.76|.70|
|Employment rate in the formal sector|.51|.37|
|% of those working in the formal sector:|||
|- also working on land plots|.66|.67|
|- also working as self- employed|.05|.04|
|Note:|||

1. In the calculation of employment rates, unemployed are excluded.

2. People working exclusively on land plots or as self-employed are assumed to belong to the informal sector.

Source: 1995 Household Survey The fact that a significant proportion of the labor force allocates time - sometimes exclusively - to occupations that can be qualified as informal and thus not subject to tax enforcement leads to a significant reduction in the number of contributors to the public social security system. **Chart** 2 shows total male and female activity rates as well as rates calculated on the basis of participation to formal employment5 . Employment rates in the formal economy are found to be substantially lower than total employment rates for
: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996. Per capita income imputes missing by the average per capita income of a community (instead of with zero). 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
.700|0.656|
|Latvia|0.784|0.627|||0.790|0.627|0.769|0.693|
|Lithuania|0.751|0.645|||0.606|0.564|0.870|0.801|
|Moldova|0.665|0.598|0.525|0.485|0.560|0.521|0.664|0.637|
|Poland|0.624|0.571|0.678|0.617|0.663|0.620|0.726|0.685|
|Romania|0.708|0.636|0.759|0.682|0.706|0.629|0.733|0.682|
|Slovak Republic|||0.647|0.608|0.552|0.519|0.768|0.726|
|Slovenia|0.702|0.616|0.673|0.621|0.927|0.781|0.912|0.814|
|Mean|0.693|0.605|0.673|0.611|0.682|0.605|0.762|0.708|

Source: DDP Quick Query database of WDI & GDF, World Bank.

# _5.2.1 Estimation of the propensity score_

The propensity score calculated to reflect the probability of a firm receiving overdraft facility, predicts that firms that are younger are more likely to receive benefits. The score, presented in Table 7, is predicted based upon a logit model controlling for factors such as age, age squared, and dummy variables (size firm, country income level, and domestic owners). Each additional year of existence (age) lowers the likelihood of receiving overdraft facilities. Country income level and
from this data set would not justify abandoning the idea of carrying out empirical work with only the formal variables in place. This is encouraging in itself and supportive of the work that we report in our companion paper, which uses the DPS data on regulatory governance – which does not have information on regulatory practice.

We have, however, noted that the Stern-Holder data set contains relatively little data on regulatory practice, even in its informal measures – much less than the recent Prayas Report on Indian State Electricity Boards. Hence, it remains a priority to collect more and better information on regulatory practice and its development _both_ in its own right _and_ to test whether the results from the simulation analysis based on the Stern-Holder data set generalise to up-to-date and better data sets.

32
In Panel D, we display the statistics of the firm-bank-month level data, summarizing the characteristics of bank loans given to private firms. In addition to the volume of all outstanding loans, we show the volume of loans that are destined to working capital and investment (all measured in logs). The average bank loan has an interest rate of about 14.4 percent, collateral covering 32 percent of its volume and maturity of around 18 months. Panel E summarizes the yearly real economic outcomes of firms. Namely, we display the volume of firm liabilities, total assets, fixed assets, and sales, all measured in logs of thousands of dollars.

In Panel F, we display the summary statistics for the monthly bank-balance-sheet outcomes. The first variable corresponds to the overall volume lent to the government (including subnational governments) and private-sector firms. The next two outcomes disaggregate this measure into the lending volumes channeled by banks to the government and private-sector firms. All lending volumes are measured in logs of millions of pesos. The next two outcomes correspond to the interest rates charged on loans to the government and private sector firms, measured in percent. The next outcome, _BankExposureGovb,Mar16,_ measures the share of lending that bank _b_ channeled to the government in March 2016. We use this variable to proxy for the ex-ante exposure that banks had to public debt. The last variable, _BankExposureGovs,b,Mar16_ , reports the share of lending of bank _b_ to state government _s_ in March 2016. This measure proxies for the ex-ante exposure of a given bank to a given state government prior to the Law.

Finally, Panel G displays the summary statistics of two firm-level time-invariant variables. The first one reports the share of revenue obtained from selling inputs to government entities and is measured at the 4-digit economic NAICS sector using U.S. Input-Output tables. Higher values indicate firms operating in sectors with greater dependence to government spending. The second one is an indicator that equals one for firms headquartered in states of the North of
|
|Classified road density|km/1000 km<sup>2</sup>|88.2|**80**|278.4|
|GIS Rural accessibility|% of rural population within 2 km from all-<br>season road|23.1|**32.2**|31.5|
|Paved road traffic|average annual daily traffic|1,287.7|**843**|2,558.3|
|Unpaved road traffic|average annual daily traffic|38.5|**47**|74.7|
|Paved network condition|% in good or fair condition|86.2|**79.9**|82.0|
|Unpaved classified network<br>condition|% in good or fair condition|55.8|**73.0**|57.6|
|Perceived transport quality|% of firms identifying transport quality as<br>major business constraint|27.6|**38.2**|18.2|
|Overengineered|% of main road network over-engineered|29.6|**24.0**|18.4|
|Underengineered|% of main road network under-engineered|13.5|**2.6**|20.0|

Source: Gwillliam and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data.

## **Challenges**

<mark>The Road Fund is supported by a fuel levy, which as of 2006 was set at $0.05 per liter. This is far below the estimated $0.15 per liter needed to fully maintain and rehabilitate the country’s classified road network.As a result, as in other neighboring West African states, road sector spending in Côte d’Ivoire falls far short of what
The remaining projections are determined by assuming that long-term trends in Peru remain constant and by performing steady state calculations. The labor share in the non-resource sector N = 0.49 is calibrated by adjusting the aggregate labor share proportionally in relation to non-resource GDP (see Loayza et al. 2022).<sup>7</sup> Using the calibration of Loayza ββ et al. (2022), the capital share in the mining sector ( )=0.84 is calculated with the natural resource rents shares ( ) from the Global Trade Analysis Project (GTAP), and averaged over 2004, 2007, 2011, and ii 2014. The tax rate in the resource sector, = 0.621 −γγ , is calibrated using data on government natural ii γγ resource revenues and resource GDP from the IMF’s World Commodity Exporters Dataset (IMF-WCE). For Peru, the = 0.62 is set to match the historical average in the last 20 years of resource revenues as a ττ<sup>NN</sup> share of resource GDP. Following the standard model of the LTGM-NR, we calibrate the fiscal rule like a Balanced Budget-Hartwick Rule, which implies a marginal propensity to invest of ττ<sup>NN</sup> . GDP for 2021 is taken from World Bank’s World Development Indicators (WB-WDI), in constant 2015 U.S. θθ= 1 GDP for 2021 is taken from World Bank’s World Development Indicators (WB-WDI), in constant 2015 U.S. dollars. In the absence of comprehensive information on GDP at the copper sector level, we proxy GDP in the copper sector by the exports of copper. For Peru, GDP in the copper industry is set to match the value of exports as a share of GDP in 2021. The export data is taken from the UN-Comtrade Database (UN-CT), which provides information on export value for all 11 commodities and all 56 countries, with a time series that usually starts in 2002.

As
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey graduates in Brazil, the observed decline in experience premium was 26.6 log points, whereas the predicted drop based only on changes in supply results in -6.9 log points. These figures are similar for Argentina and Chile. These results point out to a prominent role of changes in relative demand in explaining changes in the return to experience.

A complementary explanation to the observed reduction in the premium to skills in Latin America is that the increase in the supply of workers with higher education was followed by a reduction in the quality of education received by the new generations. Based on this “degraded tertiary hypothesis”, the lower quality of schooling is the reason behind skilled workers’ lower relative remuneration, since they are less productive than their previous counterpart (Messina and Silva, 2017). If such hypothesis is correct, we should expect the skill premium for younger generations to be smaller than for older generations, holding constant potential experience.

The evidence on this hypothesis is mixed for Latin America. Rodr ́ıguez et al. (2016) find evidence of heterogeneous returns for post-secondary education in Chile, with some individuals facing negative returns to their first-best choice compared to their second-best choice, depending of their unobserved ability. Camacho et al. (2017) use administrative data from Colombia and find that once selection based on ability and socio-demographic characteristics by the end of high school is accounted for, the penalty for attending a recently created post-secondary program instead of an existing one disappears. Bassi et al. (2012) use data from Brazil, Argentina, and Chile to compare what are the skills demanded by firms in these countries with what schools teach students. They find that firms not only demand academic skills, but also behavioral and socioemotional skills, with the great majority of firms reporting that these skills are in short supply. Wang (2015) tries to explicitly test for the hypothesis of degraded tertiary using Brazilian data, by comparing the
significantly lower access to basic amenities. This pattern holds across different types of amenities and is robust across delineation methods and the inclusion of various controls. Furthermore, when controlling for income (columns 2, 5, 8), the pattern remains consistent, indicating that that lower access to water, sanitation and electricity in less densely populated areas cannot be fully explained by higher incomes in more densely populated areas. Additionally, the results do not significantly change when controlling for demographic characteristics, education, and employment. In summary, our findings provide evidence that residents of less densely populated areas not only experience higher rates of monetary poverty but are also more susceptible to non-monetary forms of deprivation.

# _Employment across locations_

We also examine the employment patterns across different locations, considering the potential connection between spatial transformation (from rural to urban) and structural transformation (from agricultural to non-agricultural jobs). To investigate this, we estimated household-level regression models, using the employment shares in the agriculture and service sectors as the dependent variables. The results, which are presented in Table A6 in Appendix A, indicate that areas with lower population density tend to have a higher proportion of agricultural workers, while more densely populated areas tend to offer more employment opportunities in the service sector. This result remains consistent even after controlling for a set of observed household characteristics. Although this analysis is exploratory in nature, it suggests the significance of population density in the generation of productive jobs.

# _Robustness checks_

We replicate the analyses presented above based on WorldPop for 16 SSA countries by changing the gridded population layers to GHSPOP and by adding 4 non-SSA countries (Bangladesh, Colombia, Egypt, and Vietnam). The results in Appendix B and Appendix C, respectively, show that key messages from the main analysis hold.

23
4 the past, and not unlike what has often been observed with alternative household energy technologies such as improved stoves. But, this time it may not be because of defunct technologies or support systems, but rather because of rising rural wages and urban migration (Zhang, Yang, and Wang, 2012). Better empirically grounded insights regarding the benefits from biogas use as well as the factors affecting the adoption _and_ suspension of biogas utilization are needed. This will help gauge the potential of smallholder biogas for improving household welfare and health and greening growth, both in China and beyond.

To shed preliminary light on these issues, this paper analyzes a comprehensive survey of 2,700 households from 225 villages collected under a World Bank co-funded _Eco-farming project_ aimed at providing household biogas systems to 400,000-500,000 rural smallholder households in 5 provinces of rural China. In its analytical approach, the study uses “thick” descriptive analysis based on multivariate analysis of cross-sectional data, consistent with most of the renewable energy literature so far. However, contrary to most of this literature, the sample used here is much larger and documents the experience from a large nationally implemented program, as opposed to a small NGO-run program. Also, using a purposively-designed sample and questionnaire, the paper analyzes the use of biogas in conjunction with the broader rural household energy mix, thereby enabling a more comprehensive and comparative analysis of rural household energy use, including the identification of fuel switching patterns and its correlates, as well as the identification of entry points for better targeting of biogas programs to increase adoption and reduce suspension.

In particular, the following issues are addressed:

- What are the current fuel use patterns in this part of rural China, and what is the role of biogas in promoting transition to clean fuels?

- Does the preliminary evidence support the purported benefits of biogas?
-9-

3.07 The consequence of this lack of a well-defined sample frame is that it is not possible to be as confident in the extrapolation of the findings from the sample to the general population of Onitsha. This increased level of uncertainty must be explicitly addressed by managers and decisionmakers working in the water sector. It is not a limitation of this study per se in the sense that there is no reasonable alternative to the sampling approaches used given the time and budgetary constraints. The real question for policy makers and managers working in the water sector is thus not whether they would prefer to have more reliable information obtained from studies carried out in accordance with rigorous social science research protocols versus the kind of information provided by the type of rapid reconnaissance surveys described in this paper. Rather the choice is between information which can be obtained from the kind of surveys carried out in this study (or information which can be obteined from other kinds of fast, relatively inexpensive studies).

3.08 Because of the uncertainty introduced by the lack of a welldefined sample frame, the surveys were designed to include as many cross-checks on the data obtained from the various interviews as possible. For example, the technique of having enumerators ride on the tanker trucks made it possible to verify much of the information obtained from the interviews conducted with different actors in the water vending system and with households. Since the results could be cross-checked with information from more than one source, it is possible to be confident about the accuracy of the general picture of water vending in Onitsha which is presented in this paper.
sector to match the 2018/19 NLSS to real GDP growth rates in each sector. Those household heads whose sector could not be distinguished because the household contained multiple enterprises or those who were not working at all were assigned a weighted average of the per capita real GDP growth from agriculture, industry, and services.

The backcasted series starts by assuming that the 2018/19 NLSS effectively corresponds to 2019, for the purposes of mapping it to the macroeconomic data. This seems like a reasonable assumption since the data were collected between September 2018 and October 2019, covering two-thirds of 2019. We apply the growth rate between 2018 and 2019 to the survey estimate to backcast an estimate for 2018, then for 2017, and so on for all the other years until 2009.

The main formula for the backcasts can be written: where gt−1cons,s = pass× gt−1s −pt−1 C<sup>s</sup> is household consumption for households whose head is employed in sector s . pass is the assumed pass-through rate value between growth in national accounts and in household consumption. g<sup>s</sup> is real sectoral GDP growth in sector s . p is population growth.

The pass-through rate is initially assumed to be the same across sectors and across richer and poorer Nigerians.<sup>13</sup> The main results assume a pass-through rate of one, that is, they assume that growth in national accounts (in real, per capita terms) is fully passed onto household consumption. Nevertheless, sensitivity analysis, which applies different pass-through rates to check the robustness of the results, is presented in Section 6. The backcasted consumption vector is converted to US$ 2011 PPP terms to estimate poverty rates at the international poverty line.<sup>14</sup>

# _<u>4.2. Survey-to-survey imputation</u>_

Survey-to-survey imputations use non-monetary indicators and household consumption data from a “baseline” or “training”
Table E11: Share of Significant Temperature Coefficients in Tanzania

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.50|0.42|0.33|
|Median Daily Temperature|0.33|0.58|0.50|
|Variance of Daily Temperature|0.67|0.67|0.58|
|Skew of Daily Temperature|0.42|0.33|0.33|
|Growing Degree Days (GDD)|0.00|0.00|0.67|
|Deviation in GDD|0.25|0.00|0.50|
|Z-Score of GDD|0.42|0.00|0.50|
|Maximum Daily Temperature|0.33|0.50|0.42|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Tanzania.

Table E12: Share of Significant Temperature Coefficients in Uganda

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.58|0.58|0.42|
|Median Daily Temperature|0.58|0.58|0.58|
|Variance of Daily Temperature|0.42|0.50|0.17|
|Skew of Daily Temperature|0.50|0.50|0.33|
|Growing Degree Days (GDD)|0.25|0.50|0.50|
|Deviation in GDD|0.33|0.67|0.17|
|Z-Score of GDD|0.33|0.33|0.42|
|Maximum Daily Temperature|0.50|0.25|0.67|
|Observations|96|96|
provisions. With the exception of services and investment, coverage of more than 75 percent of essential provisions is rare and happens in less than 15 percent of the mapped agreements.

One important caveat regarding this data set is that it does not cover all of the trade agreements that have been in force during the period under study. Specifically, our information on provisions is limited to agreements that are in effect in present day, i.e., excluding any agreements that are no longer in effect. For this reason, we drop observations associated with agreements no longer in effect. This means that the effects of newer agreements are identified by changes in trade relative to when that pair did not have any agreement rather than relative to pre-existing agreements. The majority of the observations that are dropped are due to pre-accession agreements that new European Union (EU) members sign before joining the EU. Thus, to use one of these cases as an example, Italy-Croatia is included in our data for years 1992-2000 (after Croatian independence and before the initial EU-Croatia PTA in 2001) and for year 2016 (after Croatia joins the EU in 2013). The EU is treated differently in our analysis for this reason, as we discuss further in Section 4. To identify agreements no longer in effect, we consult the NSF-Kellogg database created by Jeff Bergstrand and Scott Baier crosschecked with data from the WTO. The EU and the earlier European Community are treated as the same agreement for these purposes, though it is allowed to evolve as new provisions are added.

Table 2: Coverage of essential <u>provisions</u> by <u>policy</u> area

||Share of|agreements c|overing:|
|---|---|---|---|
|Policy Area|0 to 25%|25% to 75%|over 75%|
|Anti-dumping and Countervailing Duties|99%|1%|0%|
|Competition Policy|48%
- Schweinhart, L. J. (2005). _Lifetime effects: the High/Scope Perry Preschool study through age 40_ . Number 14. High/Scope Foundation.

- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. _Journal of the Royal Statistical Society: Series B (Methodological)_ , 58(1):267–288.

- Valk, J.-H., Rashid, A. T., and Elder, L. (2010). Using mobile phones to improve educational outcomes: An analysis of evidence from Asia. _The International Review of Research in Open and Distributed Learning_ , 11(1):117–140.

- Walker, S. P., Chang, S. M., Powell, C. A., Simonoff, E., and Grantham-McGregor, S. M. (2007a). Early childhood stunting is associated with poor psychological functioning in late adolescence and effects are reduced by psychosocial stimulation. _The Journal of Nutrition_ , 137(11):2464–2469.

- Walker, S. P., Grantham-McGregor, S. M., Powell, C. A., and Chang, S. M. (2000). Effects of growth restriction in early childhood on growth, IQ, and cognition at age 11 to 12 years and the benefits of nutritional supplementation and psychosocial stimulation. _The Journal of Pediatrics_ , 137(1):36–41.

- Walker, S. P., Wachs, T. D., Gardner, J. M., Lozoff, B., Wasserman, G. A., Pollitt, E., Carter, J. A., Group, I. C. D. S., et al. (2007b). Child development: Risk factors for adverse outcomes in developing countries. _The Lancet_ , 369(9556):145–157.

- Walsh, C. S., Power, T., Khatoon