rafmacalaba/data-use-mentions-extended huggingface dataset:
implies a long-term welfare loss from even transient unemployment at given income. It also suggests that high unemployment benefits do not attract people out of work, but they may well discourage a return to work. Also, the unemployment effect was not found to be robust to restricting the sample to those for whom incomes were almost certainly better measured. This suggests that there could well be a bias in past estimates of the (income-compensated) welfare effect of unemployment, arising from the structure of income measurement errors.

These panel data models impose considerable structure on the way in which latent heterogeneity is deemed to matter to subjective welfare, namely that it takes the form of a individual specific time-invariant and additive component of the error term. I turn next to another source of data that offers a more flexible and robust way of addressing concerns about latent heterogeneity in scales.

# **_Using vignettes to address heterogeneity in scales_**

The essential idea of vignettes in this context is to include in the survey (or a sub-sample) reasonably detailed descriptions of the lives of certain stylized individuals and ask the respondent the same subjective question about those vignettes that was posed about the respondent herself.

Vignettes to have been used in a number of recent studies of subjective data on ( _inter alia_ ) health status, political efficacy and job satisfaction. King et al. (2004) and King and Wand (2007) designed vignettes to establish common points on the heterogeneous reference scales regarding political efficacy in China and Mexico. Kristensen and Johansson (2008) used vignettes in anchoring subjective scales for job satisfaction. Bago d’Uva et al. (2008) used them for correcting self-assessed health data for reporting bias. Vignettes have also been used in testing the competence of doctors (Das et al., 2008). Kapteyn et al. (2008) used vignettes to compare life satisfaction between respondents in the U.S. and the Netherlands.

Beegle, Himelein and Ravallion (BHR) (2012) used vignettes to provide various tests for bias due
## **3.1.3 Participant Selection**

Toward the end of 2017, in collaboration with the Project Implementation Unit, we conducted a census of 45,883 households across the 280 localities to identify eligible girls. Girls were considered eligible for community safe spaces if they were 8–24 years old and had never been to school or did not go to school during the academic year 2017/2018. Priority was given to orphans, single mothers, pregnant girls and girls considered most vulnerable to early marriage and early pregnancies based on a household vulnerability index.<sup>7</sup> From the eligible population, we sampled a fourth of the total eligible girls who were aged 12–24 to be part of the impact evaluation sample.

# **3.2 Data**

We use two rounds of data collection: first, a baseline survey conducted before the implementation of the interventions between December 2017 and January 2018; and second, a follow-up survey conducted approximately one year after the end of the interventions, between January and March 2021. The follow-up survey was done in-person despite the disruption caused by the COVID-19 pandemic, and followed national health guidelines. Two main questionnaires were used: the household questionnaire and the adolescent girl questionnaire. The household questionnaire, completed by the household’s head or their representative, collected data on the household’s living conditions (e.g., nutrition, assets, and shocks) as well as demographics from every household member (e.g., education, marriage, and pregnancy).

The adolescent girl questionnaire collected information on various domains, including marriage and fertility behaviors, aspirations, socio-emotional skills (collected only at followup), mental health (collected only at follow-up), gender attitudes, gender-based violence (collected only at follow-up), decision-making in the household, income-generating activities, and access to finance. Gender-based violence (experience of emotional, physical, and sexual violence) is measured using questions adapted from the DHS survey
# <u>Institutions</u>

We use two variables obtained from Freedom House to measure the influence of institutions on agricultural productivity. The measure of political rights reflects the electoral process, political pluralism and participation, and functioning of the government. The civil liberties measure includes aspects of freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights. The hypothesis is that the physical, legal, and regulatory infrastructure and institutions support overall, including agricultural, development; however, it is possible that these effects may already be captured by the development indicator variable.

# <u>Incentives</u>

Three measures of incentives are included in the estimation. The effects of these variables would be over and above their indirect effect through resource allocation. The mechanism for the direct effect is through the choice of technique. Relative prices reflect the terms of trade between the agricultural sector and the overall economy. Price variability is a measure of the market risk faced by agricultural producers. In addition to the sector-specific risk, there is an economy-wide market risk, that of price volatility for the economy as a whole, measured by the rate of inflation. Inflation may also serve as an indicator of overall macroeconomic instability, which is thought to inhibit growth.

# <u>Physical environment</u>

Two country-specific variables are included in the analysis to represent the physical environment: potential dry matter and factor of water availability. These are expected to have a positive impact on agricultural productivity.<sup>29</sup>

# **Sample description**

The sample was determined by data availability. It consists of annual data from 30 countries for a 29-year period (1972-2000). The information conveyed by the sample is summarized in Table 4. In the first column, we report the average annual growth rates of the variables for the whole sample period. Agricultural output grew at a rate of 5.4%. All three components of agricultural capital grew faster
1|0.03|6.2|0.10|
|No Risk Data|1,411|0.6|16|18.2|0.10|17.1|0.10|
|Total: Formal Firms|8,941|3.6|100|7.4|0.27|10.4|0.38|
|_Panel B: Formal Firms + Natural Persons_||||||||
|Formal Firms|8,941|3.6|78|7.4|0.27|10.4|0.38|
|Natural Persons|2,563|1.0|22|18.2|0.19|17.1|0.18|
|Total: Formal Firms + Natural Persons|11,504|4.6|100|9.8|0.45|11.9|0.55|

This table shows the distribution of the aggregate of the credit allocation, for natural persons and the formal firms sample. Panel A reports statistics across the firms’ risk distribution. Panel B reports statistics separately for formal firms and natural persons. Column 1 shows the total amount of guaranteed credit in dollar terms, and Column 2 normalizes Column 1 by GDP. Column 3 shows the share of guaranteed loans for each category. Column 4 shows the default probability of each category, using the model in Table 1, Columns 4 and 8. Column 5 shows the total expected loss as a share of GDP (Column 2 times Column 4). Column 6 shows the _ex post_ default rate of each category, using a dummy equal to one if the firm defaults on a loan after May 2020 (has payment past due over 90 days) and zero otherwise. Column 7 shows the total _ex post_ expected loss as a share of GDP. Values in Columns 4 and 6 are weighted by the total amount of guaranteed credit granted to each
</sup> The data record the jurisdiction implementing the policy change, the direction of the measure (i.e. trade liberalizing or restrictive), the type of measure (e.g. export ban, tariff reduction), the timeline of the measure and the sectors covered by the policy.

The data reveal several stylized facts on trade policy changes over the first nine months of the COVID-19 pandemic:

- There was a big jump in trade policy activism starting in February 2020. This accelerated in March, with the initial increase occurring in tandem with the rise in the number of COVID-19 cases. At the global level, there is an approximate balance between temporary and open-ended measures, but there is significant heterogeneity at the regional/country level.

- Measures targeting medical products and PPE dominate, accounting for two-thirds of all trade measures taken. Food is less in focus. In terms of the value of 2019 trade covered, trade policy changes in medical goods outweigh those in food products by a factor of 3 or more.

- Export curbs in medical goods covered international trade worth $135 billion (of 2019 trade). Whereas import reforms in the same sector covered $165 billion. In the case of food and agri-food products, the comparable totals are $39 billion and $42 billion, respectively. Because these estimates are based on 2019 (i.e. pre-pandemic) trade data, they may understate the magnitudes of implicated trade, especially for medical products.

- Countries responded to the COVID-19 pandemic with different combinations of export controls and import liberalization measures. Some countries acted both on the restrictive and liberalizing side creating long term changes in their pre-COVID trade policy structures for the food and

> 16  Weekly data are available through October 15, 2020. Looking forward, the data will continue to be compiled monthly.

17
17 on implementing adjustments to the basic average years of schooling to account for the above-mentioned deficiencies to obtain a better proxy for the human capital stock.

# **Accounting for Rates of Return to Education**

To transform a measure of the average years of schooling into a human capital stock variable, we follow the specification used by Woessmann (2000): where

- _h_ is the average per worker human capital stock

- _r_ is the Mincerian rate of return to an additional year of schooling

- _s_ is the number of years of schooling.

Mincer (1974) proposed a method to estimate the market rates of return to education and this method is currently known as the “basic” earnings function method. This method involves fitting a semi-log ordinary least squares regression with the natural logarithm of earnings as the dependent variable, and the years of schooling and years of labor market experience and the square of years of labor market experience as independent variables. In this semi-log earnings function specification the coefficient on years of schooling can be interpreted as the average private rate of return to one additional year of education<sup>26</sup> .

Country specific data on Mincerian rates of return to schooling were obtained from Psacharopoulos (1994) and Bils and Klenow (2000). The former provides estimates on the returns to schooling for 62 countries for different years, while the latter has estimates for 52 countries for different years. In order to have the widest global coverage, we incorporate both data sets, which results in estimates for 64 countries for different years<sup>27</sup> .

Given that there are more than 200 countries in the world, having Mincerian rates of return for just 64 countries would drastically reduce the potential number of

> 26 Note that the Mincerian rate of return does not take into account the educational level at which this additional year of schooling take place.  Due to current data constraints, we were not able to incorporate educational rates of return that varied by educational level into our human capital stock
more violence, as women had to step in as household heads and in local political roles. Another study finds reduced prevalence of risky sexual transactions among female sex workers as a result of violence, presumably in response to increased insecurity (Manian, 2021).

Regarding the indirect effects of conflict, the literature to date has focused on the impacts across generations (e.g., Phadera, 2021). Much less is known about the impacts on distant areas. A few cross-country panel studies show that conflict can have spillover effects on economic growth in neighboring countries (Murdoch and Todd, 2002, 2004).<sup>4</sup> We add to the literature by focusing on spatial spillover effects of conflict on both individual economic and non-economic outcomes within the same country. Also, to the best of our knowledge, we are the first to examine the indirect effects of conflict on women’s empowerment.

This study also relates to previous evidence on the impact of the arrival of IDPs or refugees on host communities. Most of the literature focuses on how the arrival of refugees affects labor market outcomes of the local population (see, e.g., Ruiz and Vargas-Silva, 2013; Ruiz, Isabel and Vargas-Silva, Carlos, 2015; Verme and Schuettler, 2021). In general, studies find small impacts on employment and wages and some studies find short-term adverse effects on young women working in the informal sector.

More recent studies analyze the impact of the displaced population on other outcomes beyond labor market outcomes. These studies find that the arrival of displaced people has adverse effects on health outcomes (Soazic Wang Sonne and Verme, 2019) and consumption by host-community members (Ayenew, 2021). Other studies find no impact on overall poverty indicators for the host population (Azevedo _et al._ , 2016) but an improvement in access to services and infrastructure due to investments being directed to refugee-hosting areas (Zhou _et al._ , 2023). Still other studies find that hosting refugees can
MONGOLIAS INFORMAL SECTOR Size and Trends 13 30. A second approach to estimating the size of employment in Ulaanbaatar's informal sector suggests that the above estimates are reasonable. An analysis of the data from the SSO's household survey reveals that 47-51% of households have some informal income. Since a household would have informal income if even one household member had informal income, the approximate percentage of people with informal income can only be inferred. A simple adjustment suggests that roughly 30-33% of the working age population of Ulaanbaatar earns some informal income, an estimate that is consistent with the more elaborate estimation derived above.<sup>32</sup>

# **Aggregate Household Income in the Informal Sector**

<!-- Start of picture text --> 31. This section olf the report reviews the available evidence on incomes in the informal sector? 3 An<br>analysis of data from the SSO Monthly<br>Survey of Household ][ncome and Percentage of Household Income by Type<br>Expenditure reveals a wealth of 1/93 through 12/96<br>information about both the trends and 7o/<br>the current size of the informal sector, 60% A<br>as measured by the contribution to<br>household income. First, it will be 501/ X<br>shown that the growth of informal<br>activity during the transition is 30<br>confirmed by the household survey > 30%<br>data. Second, an analysis of a single 20%./<br>month's data will be used to estimate<br>that, in the aggregate, 32% of 10%/i<br>household income in Ulaanbaatar comes from informal sources. 93.01 ____ 94.01 95.01 96.01 97.01<br>Trends in Informal Income _ Income from Individual Activities Wages and Salaries<br><!-- End of picture text
indicator variable that equals 1 if district _d_ was part of phases I or II of the MNREGS roll-out and zero otherwise; _Postt_ is a time dummy; **_X_** is a vector of time varying controls at individual (sex, age, age square, education, education square, marital status), household (land ownership, marital status size, female shares in different age categories, head’s gender and age) and village (population, share of SCs and STs, share of agricultural households, availability of paved road, distance to next town) level; _μi_ a household fixed effect; and _εidt_ an error term.<sup>14</sup> Our main interest is in _β2_ , the estimated impact of MNREGS exposure and we also introduce an additional interaction with

> 12 The sample includes all of a household’s descendants who still reside in the village in case of a split.

> 13 As discussed above, for Andhra Pradesh, Bihar, Chattisgarh, Haryana, Madhya Pradesh, Maharashtra, Rajasthan, and Tamil Nadu, we were also able to obtain administrative data on employment generated by MNREGS overall and for irrigation-related activities, mainly cleaning and rehabilitating canals. If implemented in an effective manner, the latter would quickly improve agricultural productivity even for non-participants while program-induced wage payments would affect risk coping mainly by direct beneficiaries. Due to delays with implementing the survey in Kerala, all of the state’s districts had been covered by MNREGS at the time the survey was administered, forcing us to drop observations from this state. Our final analysis sample thus includes 16 of the original states.

> 14 For households who split between our two periods, successor households resident in the same village are included with the same id in the second period. In practice, dropping them does not affect the substantive results.

8
Scales.**For calculating the value of the consumption basket for families other than the reference<br>one, higher equivalence scales than those used by the OECD and the European Commission were used, leading<br>to a potential overestimation of the consumption basket.<br>**Significant discrepancies exist between this estimate and the estimate presented in this paper.**The reason<br>for the discrepancy is probably the fact that the “decent” wage is more than the “living” wage. The basket estimated<br>by FES includes, for example, savings close to 10% of total income, vacation, and relatively high spending on<br>clothing, education, and culture. While these estimates may measure a “decent life,” this basket offers a significantly<br>higher standard of living than the usual living wage definition. In our case, the estimate of the corresponding<br>minimum wage is also inflated by the fact that only one of two adults works full-time.|

**Our main finding, indicating a positive gap between the net statutory minimum wages and the living wage estimate, aligns with earlier assessments.** Fabo and Belli (2017) show that the minimum wage adequately encompasses the living wage in the core EU countries when using data from the Wage Indicator Foundation (WI). However, this does not apply to Eastern European countries (Visegrad countries, Bulgaria, and Romania), where the minimum wage falls 10%–60% short of covering even the lower living wage threshold. Further assessments are needed to understand the drivers of this gap.

**Finally, the consumption basket can also serve as a baseline to evaluate the adequacy of other social benefits and pensions in addition to the minimum wage.** Beyond its primary role in determining the living costs to produce a benchmark for minimum wage settings, the consumption basket can also be a reference point for assessing the adequacy of various social benefits and pensions. By
0%<br>Q2 Q2 -1.2%<br>-16.0%<br>-11.6%<br>Q3 Q3 -7.8%<br>-15.0%<br>-10.0%<br>Q4 Q4 -12.6%<br>-17.4%<br>-8.8%<br>Q5 Q5 -17.8%<br>-19.6%<br>Projected Observed - Without AE Observed - With AE<br><!-- End of picture text -->

Notes: Estimations obtained using the PNAD-Continua 2019 and 2020.

To understand the sources of the difference between the observed change in household per capita income and our projections, we calculate the changes in employment and the changes in labor earnings between 2019 and 2020 using the actual 2019 and 2020 household surveys. Figure 11 shows that quintile 1 suffered the largest decline in employment and quintile 5 faced the largest 24
Swedish carbon tax as a facilitating factor for the expansion of bioenergy in the town of Enköping, but such a case study does not

> 3 Authors‘ estimation for year 2008 based on data for global gasoline and diesel consumption in the transport sector from the International Energy Agency and ethanol and biodiesel production data from Timilsina and Shrestha (2010).

> 4 Biofuel penetration in this study is defined as the ratio of biofuels (ethanol and biodiesel) to the total liquid fuel consumption in the road transportation (i.e., biofuels, gasoline and diesel).

3
rates of access to upper secondary education in China. Yunnan Province has a relatively low upper secondary (gross) transition rate at 82 percent in 2015 in comparison with other provinces in China.

Figure 4.2 Composition of population by education level, Yunnan versus national average (2015) <!-- Start of picture text --> 45.00%<br>38.70% 38.80%<br>40.00%<br>32.90%<br>35.00%<br>30.00%<br>25.70%<br>25.00%<br>20.00% 16.90%<br>12.90%<br>15.00% 11%<br>8.70% 8.70%<br>10.00%<br>5.70%<br>5.00%<br>0.00%<br>Illiterate Primary Junior Seconior Tertiary<br>Education Secondary Secondary Education<br>Education Education<br>National 2016 Yunnan 2016<br><!-- End of picture text --> _Source_ . National Bureau of Statistics of China 2016b. http://www.stats.gov.cn.

Yunnan has gradually narrowed the education gap, except at post compulsory level. In recent years, Yunnan has been bridging its education gap with the rest of China. Compared with the results of NBS’ household survey between 2007 and 2016, the share of Yunnan’s population attaining at least junior secondary education has largely increased—from 38 percent to 53 percent. Nonetheless, compared with the rest of China, Yunnan is still lagging in its share of population reaching upper secondary education level and above at 20 percent compared with 30 percent nationwide. In light of this gap, the provincial FY13 development plan outlined specific targets— by 2020, the Yunnan aims to achieve 90 percent GER at upper secondary education
data have been taken from Chao rather than using the data published in the Fixed Asset Yearbook. For the period 1966 to 1974, no annual data are available for capital construction in the government or infrastructure sector. The available data for plan periods have been divided equally across the years concerned.

The data for the business sector have been estimated as the difference between the non-residential FAI and the government and infrastructure FAI.

# **_Estimates of capital formation in the residential sector_**

A particular effort is needed to estimate capital formation in the housing sector due to the importance of land purchases in the cost of housing construction. The purchase of existing assets should be excluded from estimates of gross fixed capital formation, but the Chinese practice is to include land purchases in fixed asset investment. The scale of the bias introduced by these differences between FAI data and the SNA basis of estimates of gross fixed capital formation has been growing over time. In the 1980s and 1990s, there was little bias, as there was no market in real estate. The bias became more important during the decade starting in 2000 as market-based transactions dominated the housing market.

The NBS has published its methodology for overcoming this bias. The steps it takes are set out below: Gross fixed capital formation = Residential fixed asset investment _Less_ land purchases (which includes the costs of land improvement) _Plus_ difference between sales prices and construction cost multiplied by sales quantity _Plus_ investment in land improvement.

Effectively, this means that GFCF is being measured as property sales less the cost of unimproved land purchased for residential development. This definition is not quite compliant with the SNA, as the deduction for land purchases is made when the developer buys the land rather than when the land is sold to the final purchaser. Thus, if the land has increased in value between the date at which the developer made the purchase and when it is sold to a final purchaser, this increase in value will be included in GFCF. Equally, the quantity of land purchased refers to land bought