rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2. Data and Methodology**

# **a. Census and Survey Data**

The primary source of data on labor market outcomes is the 2020 Census of Population and Housing, carried out in March 2020 by the Mexican National Institute of Statistics, Geography, and Informatics (INEGI).<sup>3</sup> The census was collected the month before the COVID-19 pandemic led to widespread shutdowns. INEGI publishes census statistics publicly at different geographic levels, with the lowest level being the Área Geoestadistica Básica (AGEB). INEGI publishes aggregate statistics only for urban AGEBs. We use these to generate municipality statistics that are weighted by the population of urban AGEBs. Because only urban AGEBs are included in the analysis, the results in this paper pertain to urban state and municipality-level rates. Urban AGEBs total to around 96.4 million people, or more than 75% of Mexico’s estimated 127.6 million people.

We are primarily interested in examining the feasibility and effectiveness of combining geospatial data with survey data to improve municipal estimates of labor force participation rates and unemployment rates, separately for women and men. We also examine the ability of the procedure to improve state-level estimates of these four labor market outcomes. We restrict the analysis to urban AGEBs in order to utilize the census data as a credible benchmark against which to assess the performance of the small area estimates.

From the available urban census data, we construct a data set with six different variables: the total number of women in the labor force, the total number of women employed, the total number of women 12 years or older, plus the same three variables for men.<sup>4</sup> Since this is census data, we take these values to be the true values for each AGEB. We then randomly sample AGEBs from the full set of urban AGEBs to form a pseudo survey. We select AGEBs with probability proportional to size within states, which serve as the strata. We then choose
3).<sup>27</sup> First, while a higher percentage of occupations that can be performed remotely in a sector still mitigates the negative trade effect of a production shock in exporting countries, the coefficient of 0.77 in column (4) is 0.35 percentage point smaller compared to the original country sample (Table 3) and also shows a lower statistical significance. Second, third-country effects in column (3) have the correct sign and are statistically significant, suggesting that China’s exports grew in destination markets as other exporters were affected by Covid-19. This coefficient remains with the right sign but loses significance in column (4). Finally, regarding the GVC channel, the positive role of an exporting country’s share of imported inputs in mitigating shocks is 0.2 percentage point larger in the extended country sample including China, whereas the negative role of GVC participation in a partner country’s sector is 0.15 percentage point smaller. The upstream shock, however, matters more strongly for bilateral trade growth, consistently with the centrality of China in modern global value chains.

# **_Including developing countries_**

In a final extension we use mirror trade data, i.e. import data to our 28 countries, to obtain a larger sample of exporting countries. Since the weights used for the computation of the upstream shock variable are based on 64 countries in the TiVA data set, we need to limit the number of exporters to the TiVA sample too. This country sample includes several upper- and lower-middle-income countries and thus allows us to examine whether the previous findings also hold when more middle-income countries are included. The caveat is that the trade data only consider bilateral exports to 26 EU member states, Japan and the United States.

The results reported in Table 7 show that the general findings still hold in the larger country sample. The only exception is the role of remote work in a country and sector which is no longer significant and shows a much
_Social Capital, Household Welfare and Poverty in Indonesia_ 39

**6. The Effects of Social Capital: Asset Accumulation, Access to**

# **Credit, Collective Action**

Why are households interested in acquiring social capital by investing time and money in local associations? In Indonesia, the partial answer is that the government created many nationwide associations with mandatory membership. However, Indonesia also has a strong local tradition of mutual help and associational life to support it. The survey questionnaires of the LLI study provide insights into why households join local associations. Only 17% of households cite mandatory memberships as the prime reason. The other responses are about equally divided between the direct impact on the household's livelihood, the impact on the community, and safeguards in case of future emergency or need (Grootaert, 1999).

In this section we investigate some of these processes directly. In a relatively poor rural setting, a prime consideration for households is to build up coping strategies to deal with the risk of income fluctuations. This involves accumulating assets (which can be sold or borrowed against in time of need) or arranging access to credit. In the rural areas that are included in this study, asset accumulation is still at a low level. Out of a list of 15 household durable goods, the average household owned only 2.2 items. Most frequently owned were a radio, a pressure lamp and a bicycle. Improving access to credit and savings is a major reason why Indonesian households join local associations. Onefifth of all memberships are primarily for Ihis purpose, with a stronger concentration in Jawa Tengah which has a tradition of rotaLting credit and saving associations (Werner, 1998). Many other groups have the provision of credit as a secondary objective.
certain industries.

It is critical to understand that the consequences of product market concentration are not always confined to the product market and may impact other, related markets. Ultimately, an analysis of the ways reforms to promote competition affect household welfare would need to consider the direct effects through price and other channels, such as wages, employment, and profit shares, to determine the distributional effects of competition through its impact on three overlapping groups of economic agents, that is, consumers, workers, and capital owners. The competition conditions among firms affect the welfare of households in their role as consumers through changes in the prices, quality, and diversity of alternatives available for purchase (Atkin et al. 2016; Begazo and Nyman 2016). Although desirable, changes in the quality and diversity of products, as well as the effects of new consumers that were previously priced out, are not within the scope of analysis of WELCOM. The first is due to the use of household surveys, which do not account for the quality of the products consumed. An appropriate method is currently in development in an updated version of the tool and will be incorporating potential welfare gains in the extensive margin of the products of interest.

It is also important to highlight other channels, such as wages, employment, profit, and capital income. For instance, competition can affect household welfare through labor market adjustments induced by competition and the resulting effect on nominal wages, the number and composition of firms demanding labor (due to the potential entry or exit of firms), and labor’s bargaining power or the presence of oligopsonies in factor markets such as labor (CEA 2016b). Moreover, competition affects 22
|**Kuwait**|2009|, , <br>Reform enabled restructuring of companies facing<br>financial difficulty or insolvency. The assets of any<br>local companies declared bankrupt will be valued<br>based on current market values, with priority on any<br>profits designated to repaying government loans. Any<br>assets left after repayment of the loans should go to<br>company shareholders, followed by companies and<br>individual creditors who have an equal claim. (World<br>Bank, 2010)|The Hawkamah-World Bank-OECD survey,<br>based on the Principles and Guidelines for<br>Effective Insolvency and Creditor Rights<br>Systems developed by the World Bank, scored<br>nations according to a 155 point system, with<br>higher scores denoting stronger insolvency<br>regimes. Kuwait achieved a score of 104 out<br>of 155, which is below the OECD average of<br>124, but above the MENA average of 88.<br>(SidiN2009)|
|**Lithuania**|2008|The reform shortens time limits on bankruptcy<br>procedures by eliminating the minimal term of 3<br>months, which must be passed in order for the<br>creditors to apply to a court. The reform also aims to<br>secure the interests of the creditors, i.e. to transfer<br>control of the company to the administrator in order to<br>prevent directors or owners from unfairly selling or<br>hiding the assets of the bankrupted company. This<br>transfer<br>also<br>establishes<br>higher<br>standards<br>of<br>responsibility<br>for<br>the<br>persons<br>executing<br>the<br>proceduresofthebankruptcies.(WorldBank2009)|a, ., <br>Currently in Lithuania,
We will then compare the bootstrap estimates of the drought parameter across the candidate models. The model with the least bootstrap variance would be selected. Thus, bootstrapping can be considered as a test of internal validity. The relevant internal validity question can be phrased as: given a model that has been estimated using a nationally representative sample, if we resampled many times, would we estimate a stable relationship?

Note here that either method (bootstrapping or k‐fold) can be used to validate the parameters of an estimated model.

# **<u>Outputs from the analysis</u>**

The shortlisted functions of D(.) in the conceptual section are candidate models 1‐4 outlined above. We proposed that the control variables should be selected on the basis of economic theory/empirical findings/common sense. The interactions should also be selected on that basis, however the number of interactions, and the functional form is to be decided using the statistical analysis. The next step in the exercise is to test the performance of the candidate models in predicting consumption in different contexts (or out of sample predictions) using different values of X, than those based on which the parameters for the D(.) functions were estimated (i.e. minimizing or reducing “test” MSE).

# **How to select candidate functions of D(.)?**

Recall that D(.) comprises the hazard **_I_** and household characteristics **_X_** . There are two issues: a) Specifying the functional form for D( **_h_** ) whilst holding **_X_** constant and b) Which characteristics enter the vector **_X_** ?

For a) we investigate vulnerability functions specific to household “types” as noted in the description of the regression model above. We also investigate ii) whether the vulnerability function for all types should be non‐linear by including squared and cubed terms in the model.
focus groups gave replies to qualitative questions that are almost wholly consistent with these quantitative estimates of improved attendance and savings of time. In one matter, however, they tell another story: the household data reveal no improvement in completing grades, but the respondents in most villages were adamant that their new road had made dropping out of secondary school a rarity, even among girls.

Turning to the effects of all-weather roads on health, the quantitative household data reveal none where both morbidity and mortality are concerned, though the time elapsed since the provision of the PMGSY roads was far too short for long-term effects to make themselves felt. The provision of such roads did, however, affect the choice of facility for treatment: the ensuing switch from PHCs to hospitals is both substantial and involves longer travel times, both induced by higher average speeds. Such an effect on behavior is a clear indication that there was an improvement in welfare in this domain. The survey respondents’ replies to the qualitative questions and the participants’ in the focus group interviews leave no doubt on this matter. In particular, the latter were of the firm opinion that their roads had brought about a definite fall in mortality: the average of the estimate was 2.5 deaths a year in the nine villages.

We close with two remarks. First, whereas the traditional method of estimating the benefits arising in the ‘commercial’ domain is well-established and relatively easy to implement in practice, the task of estimating those arising in the domains of education and health is altogether more difficult. Since the respondents ranked the combined benefits in the domains of education and health at least as highly as those in the commercial one, simply doubling the latter yields a conservative estimate of the total (money-metric) benefits, provided this restricted view of consumer sovereignty be accepted. Second, as expected, the before and after comparisons yield sharper results than their cross-section counterparts. Obtaining more precise estimates of the effects of providing villages with all-weather roads therefore demands a more systematic and larger panel survey
9.3.1<br>More persistent confounding shocks<br>. . . . . . . . . . . . . .<br>44|
|||9.3.2<br>Changing the target horizon for Max-Share and NAMS<br>. . .<br>45|
|||9.3.3<br>Where do long-run restrictions work well? . . . . . . . . . . .<br>46|
||9.4|CKM (2008) simulations . . . . . . . . . . . . . . . . . . . . . . . . .<br>47|
||9.5|US Data Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>50|

2
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# **OECD**

# **Australia**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Central Government, Non central, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to December 1991. In our data, federal employees correspond to central govemment, whereas non-central government employees include employees of both the state government and of municipalities. Although education and health care in Australia are states' responsibilities, the federal government contributes heavily in providing funding. Accordingly, we have subtracted education and health employees from non-central government employment.

Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1994.

# **Austria**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993.

Data on Central Government, Non Central Government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1990. In our data, federal employees correspond to central government, whereas non-central government employees include employees of both the state government and of municipalities. Responsibility for education is shared between the Lander and the federal government. Teachers (for primary and secondary education) are Lander employees but are paid by the federal government. Teachers of general and secondary education are the responsibility of the federal government. According to our definition, therefore, they are considered central government.

Consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.

# **Belgium**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1992. Data on Central Government, Non-central Government,
7. [Community question] Why is this road impassable during these months?

- A. Bad weather, such as seasonal flooding or snow

- B. Poor maintenance, such as poor drainage or bushy roads

- C. Insecurity

- D. Other (specify)

## • **Access to Urban Centers**

1. [Community question] Is your community in a major urban center?

- A. Yes (End this section)

- B. No (>> Question 2)

2. [Community question] How far is it from your community to the nearest urban center?

3. [Community question] How much does it typically cost to go from your community to the nearest urban center by public transport?

4. [Community question] How frequently can you expect a bus or minibus to stop in this community or at the nearest bus station? (Already integrated in some countries)

# **6. Conclusion**

This paper reviews household-level information on transport using multi-topic Household Surveys for many countries and provides a standard set of guidelines to design transport-related questions in future household surveys. More than 30 LSMS surveys conducted after 2010 as well as other World Bank harmonized household survey databases (SSAPOV, Global Consumption Database, etc.) are reviewed to uncover transport-related questions. Household survey data can be used to better understand transport behaviors and frictions for households in subsequent work. They can also be used to quantify the impact of road crash fatalities and injuries to monitor SDG 3.6 on reducing traffic deaths and serious injuries, which is substantially underreported in official statistics of most LMICs, and also for identifying vehicle stock and transportation related costs for different activities. A more careful and harmonized design of the transport module in future household surveys, as suggested in the paper, would allow for more research questions to be covered in the developing country context where transport affordability and accessibility are of prime importance.

27
Mozambique and $504 in Ghana (see Table 1). The Future of Business Survey is the only study in the data set with no data on earnings.

Apart from the STEP surveys, samples used for this analysis are typically not representative of the general population but come from selected subpopulations. This implies that results are not always externally valid, but representative surveys very rarely contain detailed measures of socio-emotional skills.

Table 2 shows summary statistics on the 13 socio-emotional skills measures for the pooled sample (Panel A) and at the study-level (Panel B). Overall, 323 items were used to construct the socio-emotional skills measures. Importantly, more than 70% of socio-emotional skills items refer to intrapersonal skills, which reflects the larger effort made to collect data on intrapersonal skills (mostly positive self-concept, personal initiative and PSDM). In comparison, interpersonal skills, and especially empathy and teamwork, suffer from a thinner information base. This translates into a lower number of observations being used for interpersonal skills and a lower reliability in these indices as indicated by systematically lower Cronbach’s alpha across studies compared to intrapersonal skills. Panel B indicates for each study which socioemotional skills measures are available and the number of items they are based upon with summary statistics when available. Note that each study measures between 2 and 9 specific socio-emotional skills (excluding aggregate measures), the average study measuring 5.7 specific socio-emotional skills: 3.8 intrapersonal skills (out of 6) and 1.8 interpersonal skills (out of 4).

# **3.3 Estimating gender differences in socio-emotional skill levels**

To measure gender differences in socio-emotional skill levels, we regress each of the 13 measures of socioemotional skills on the gender of the respondent controlling for a set of project dummies, using the pooled sample described above.

# Where:

- Skillis is the socio-emotional skill measure for individual _i_ , either skill-specific or aggregated.

- Womanis is a dummy equal to one if the individual _i_ in study _s_ is a woman.

-
statistically significant. Hence, accounting for second-order effects leads to results that suggest that human capital accumulation is a significant determinant of economic growth. Note that the educational quality variable still positive but not statistically significant. However, this could be due to the data not being able to accurately measure quality differences, rather than educational quality not being important in explaining differences in long-term economic growth across countries<sup>51</sup> . As such, we continue to include the educational quality variable as an educational pillar regressor, even though it is not statistically significant.

Next, we seek to improve our estimates by accounting for country differences in Mincerian rates of return to education. As such, we employ the human capital stock measure that again uses exponential compounding of the average years of schooling, but multiplied by country specific rates of return (See Equation 16).

51 Recall that more than 50 percent of the data from Hanushek and Kimko (2000) on educational quality were in fact predicted values from a regression with various schooling inputs as the independent variables.
Regardless of the underlying reason for _why_ youths heavily discount future payouts, the implication is that jobs with lower starting wages but high future wage growth are relatively less attractive.<sup>30</sup> Table 7 tests how discount rates are correlated with participants’ reservation wages. For the non-incentivized measure, we indeed find a strong negative relationship. Participants who discount the future by 10 percentage points more (i.e. their discount factor is lower) have reservation wages that are about 4.2 percent higher (Column 2). However, this correlation is small and not significant in the smaller sample for which we elicited incentivized discount factors (Column 3).

# **5.4 Beliefs about wage growth**

The job search framework posits that beliefs about wage growth affect job seekers’ reservation wages and search effort. We elicit these beliefs in an incentive compatible way in the endline survey. Specifically, we present participants with a scenario of a person starting a formal job that pays 5,500 pesos per month. We ask them to estimate how much that person is earning in six months if she is still employed separately for jobs in the formal and informal sector. For each guess that is within 100 pesos of the correct answer, they receive a bonus of 30 pesos.

Figure 8a shows the cumulative distribution of wage growth beliefs for _formal_ employment. The dotted line marks the actual wage growth of about 25% based on social security data. We find that the majority of participants substantially underestimate formal sector wage growth: 20% believe that there is no wage growth and the median belief is less than 10%. Only about one third either has correct beliefs or overestimate wage growth. Beliefs about _informal_ sector wage growth appear to be more accurate. Figure 8b shows that more than 40% believe there is no wage growth and two thirds of youths believe that informal wages grow by less than 10%. While we do not have administrative data for informal wages, ENOE survey data suggests