rafmacalaba/data-use-mentions-extended huggingface dataset:
main caregivers, before reporting results on health center utilization. While children’s illnesses could be interpreted as a final outcome, we report it along with other intermediary outcomes in the health domain because health center utilization is measured conditionally on reported illnesses.

S _timulation practices (Intermediary outcome domain #3)_ . The BCC component encouraged psycho-social stimulation through language and play. We build two indices based on items capturing the family care environment in MICS surveys. The first index captures the frequency at which children have interactions with adults through activities such as reading, storytelling, going out together, playing, naming, counting or drawing. The second index captures the number of types of toys children play with, including homemade toys, manufactured toys, household objects, animals, toys that can be pulled, and balls.

_Child protection practices (Intermediary outcome domain #4)_ . The BCC component promoted positive disciplining to manage conflict and avoid harsh punishments. It also encouraged birth registration. We first build an index aggregating positive disciplining behavior (explain or redirect) minus negative disciplining behavior (forbid, shake, yell, spank, spank with an object, berate, slap, hit on the hands, and hit with an object). Second, we measure whether children have a birth certificate.

Ultimately, we are interested in testing whether the promotion of behavioral changes in parenting practices contribute to improve human capital outcomes among young children. We consider final (or downstream) outcomes in three domains: anthropometrics, cognitive development and socio-emotional development. We again measure indices for each set of outcomes.<sup>24</sup> _Anthropometrics (Final outcome domain #1)_ . We measure height-for-age (used to identify stunting), weight-for-height (used to identify wasting) and weight-for-age (used to identify underweight) z scores for all children aged 6-59 at the time of the follow-up survey.<sup>25</sup> _Cognitive development (Final outcome domain #2)_ . We implement a cognitive test adapted
higher in the India data compared to that in the Bangladesh data, thus the resulting truncation bias is likely to be relatively lower in India. For example, in the father-son sample the coresidency rate is 79 percent in India, while the corresponding rate is only 52 percent in Bangladesh. In the mother-daughter samples, the coresidency rates are lower: 39 percent in India and 26 percent in Bangladesh, reflecting the fact that women leave the natal family following marriage in both countries.

For each country we present three graphs: (i) son-father, (ii) daughter-mother, and (iii) all children-father. The figures show that the slope of the fitted line is _smaller_ in the coresident sample which is consistent with Hausman and Wise (1977). The widely held belief that the coresidency bias in the estimates of IGRC is substantial thus appears clearly visible in the graphs.

In the graphs for the “all children” sample (both sons and daughters), the coresident line intersects the full sample line from above (see Figures 2.A for Bangladesh and 3.A for India). This implies that the surveys miss less educated children from households with low parental education, but miss better educated children from households with high parental education. We thus have both truncation from above and from below.

A closer look at the other graphs reveals some interesting differences across gender and countries. In Bangladesh, the fitted lines in father-son sample (see figure 2.B) intersect each other at a very low level of father’s education, implying that most of the coresident line lies below the full sample line. This implies that, for most of the distribution, the better educated sons leave the parental household. For Mother-daughter sample in Bangladesh (figure 2.C) the pattern of sample selection is different; the line for the coresident sample intersects the full sample line from above at about 5 years of mother’s schooling which is very high given that the average education for mothers is only 1.47 years
separates the importer types between general and producer.<sup>9</sup> The data covers the universe of all Indonesian exports in the period, except for those from export processing zones. Thus our data accounts for more than 90% of overall exports. The data contains a total of 69,383 firms, of which 37,260 are importers and 44,363 are exporters. One of the appealing features of the data is the connection between importing and exporting activities at a highly disaggregated level (10-digit HS code). The data contains the monthly dollar value and quantities of imports and exports at the firm-month-HS-10 product level along with information on the country of origin for imports and the destination for exports, whether the importer is a producer or a pure trader, and the customs’ treatment (e.g. privilege customs status). Since we are interested in how NTMs applied on imports affect exports when there is a demand shock, we only use data on the population of Indonesian exporters which also import directly.

A total of 12,735 firms, representing about 18% of all exporters, both import and export directly during 2014-2018. Despite of being fewer than a fifth of exporters, these firms account for over two-thirds of Indonesian export value (figure 2). The other 37% of exports came from firms that do not import directly but may purchase imported inputs through third parties. This is consistent with stylized facts globally where importing exporters tend to dominate trade (World Bank, 2020). The share of importer exporters, though dominant in the export market, has been declining over the recent years, as evident in figure 2 b. During the same time, the share of exporters who face at least one NTM has been increasing, as discussed in section 3.2 below.

# **3.2 Indonesian NTM and data**

NTM-related regulations have been spread out among a total of 13 government institutions (ministries, agencies and some are issued as a presidential decree)
representative of the consumption pattern of the poor. This view aligns with Recommendation #9 of the Monitoring Global Poverty report, which specifies that countries should consider using a price index for the poor. It is also consistent with the belief that price changes implied by the Bangladesh survey data are closer to the changes of the poverty line than the CPI.

While the CPI shows how the expenditure to purchase a specific bundle of goods consumed by the average consumer changes nationally, there are a few problems with using the CPI as a deflator for poverty measurement: (i) unit values changes of some consumption items can vary differently from the CPI for this item, (ii) the CPI measures how price changes nationally, and not by region, so it does not reflect price changes of specific regions of the country; or more importantly, it may not accurately identify the poor in regions with high levels of deprivation, and (iii) the goods in the poor’s basket may not align well with the CPI basket, as the international extreme poor may consume items that are different from what a typical consumer consumes in a country. This last point is validated by Dupriez (2007) and Deaton and Dupriez (2009 and 2011) that document that the expenditure patterns of poor households are different from the pattern observed in the System of National Accounts.

<mark>Overall, this section shows that there are many sources that can substantially contribute to the total error of poverty estimates and influence international poverty comparisons. The comparison of survey design and sampling methodologies shows that differences in data collection methods across countries are relatively minor. The main source of total error involves differences in how consumption is collected and aggregated at the household level, particularly related to the way in which countries treat actual and imputed rent. In addition, the use of poverty lines to deflate intertemporal prices instead of the CPI can also contribute to total error in the international poverty rate estimates in the case of Bangladesh.</mark> 13
productivity and consumption result in a higher tax burden for female-only and female-headed households. (2) Our data shows that 65 percent of female-only- and 58 percent of female-headed households are smallholders with less than 0.5 hectare of land, compared to about 40 percent of their male counterparts. Smallholders face the largest per-hectare tax rates. Our results suggest that area-based land taxation is likely to reinforce gender inequities in agriculture and constitutes an implicit bias arising from gender norms in agriculture, household structures, and the gender gap in agricultural productivity.

Because of the regressivity of current tax schedules, we conduct a tax incidence analysis of a hypothetical tax schedule with progressive per-hectare rates and exemptions for smallholders from paying agricultural income tax. We find that this is likely to decrease the tax burden of female-headed and female-only households, but the tax incidence continues to be regressive because land rights are prevalent among poor rural households. This illustrates how difficult it is for area-based land taxes to be vertically equitable.

Tax schedules vary by region. This analysis finds a substantial difference between self-reported tax payments and imputed tax liabilities using land area and the tax schedules from the regions of Amhara, Oromia, and Southern Nations, Nationalities and People (SNNP). We discuss possible sources of tax discrepancies, which include (1) the Revenue Bureau not having the most current titleholders and precise land area data; (2) different methods used to measure land area; and (3) possible errors by tax collectors assessing taxes or by household survey respondents reporting tax payments. These discrepancies highlight the importance of administrative tax data and land registries to complement survey data to examine whether the most current title holder and area information are used for estimating tax liabilities.

This paper contributes to the growing literature on the gender dimensions of taxation in low- and middleincome countries. Much of the literature discusses the gender implications of personal income tax and payroll taxes (see Elson 2006; Grown and Valodia 2010; Joshi et al. 2020; Lahey 2018; Stotsky 1997). Grown and
geographic structure of the sample design we can estimate mean consumption of ‘neighbors’ using the survey data for the primary sampling unit containing each sampled household. The mean consumption of neighbors is estimated by the leave-out mean for the EA for each sampled household, i.e., the mean consumption of all sampled households in the respondent’s EA, excluding the respondent.

As noted above, it is not clear that this provides a good measure of the economic welfare of neighbors. However, there is an additional source of information for studying social effects (that appears to have been largely ignored in the literature), namely to ask survey respondents about their comparison groups. We also use questions asking people about how they perceive the economic welfare of the two most obvious comparison groups: friends and neighbors. (Workplace comparisons are another possibility, but these are of limited relevance in a poor, largely rural, country such as Malawi.) It is left to the respondent to judge who are their friends and neighbors, and by what standard their economic status is to be judged. With regard to the respondent’s friends we use the friends’ economic welfare ( _FEW_ ) question: “Imagine six steps, where on the bottom, the first step, stand the poorest people, and on the highest step, the sixth, stand the rich: (show a picture of the steps). On which step are most of your friends today?” The same question is asked about neighbors’ economic welfare ( _NEW_ ).

Notice that consumption is measured post transfers and that it is reasonable to presume that _OEW_ also reflects post-transfer economic welfare. Thus, a strict interpretation of our model in section 2 would suggest that any effects on _SWL_ of either mean consumption in the locality or of _FEW_ or _NEW_ , controlling for our measures of own-economic welfare, could only reflect RD (since the transfer effect operates through own-economic welfare). That provides a seemingly 22
tend to agree with this statement. A higher value of this variable is interpreted as stronger demand for government redistribution. Information on demand for redistribution is available for more countries and years than equality perceptions. However, we only use those country-years for which equality perceptions is also available.

In order to have some quantitative measure of ideology, we choose two different variables. The first of them, available only for a subset of respondents, corresponds to the political placement in a leftright axis. This variable is obtained from a direct question to interviewees on their position in that axis or inferred from their affiliation or sympathy to a political party. Out of our sample of 46,894 individuals, 30% have missing information on political ideology and 13% express no ideology; thus, we have valid information of the political ideology for 57% of our sample – around 26,800 individuals. To overcome this sample limitation, we also look at an additional variable that we take, as robustness check, to be a proxy for political ideology. This question derives from Question 12 of the Social Inequality module of ISSP which is available for almost all the sample. Question 12 asks respondents to give their opinion on the importance of several factors in determining how much people ought to earn for a job. We focus on one factor: what individuals need to support their family. We argue that this variable correlates with ideology: rewarding individuals taking into account their needs seems related to social justice.

In terms of the economic context, the paper uses data from different sources: (i) Gini indices on per capita household income mainly drawn from the Luxembourg Income Study Database (LIS) and, when not available, from “All the Ginis” data set of Milanovic (2018) (ii) data on unemployment rate and government expenditures are taken either from Eurostat (1999 and 2009), from the Milanovic's Household Expenditure and Income Dataset for Transition Economies (HEIDE) data (1992), or from the World Development Indicators
From here on, the term 'green' is used to indicate the proposed definition of jobs, and more broadly, to refer to output, technologies, tasks, or skills that contribute to reducing environmental impact. In the subsections that describe a specific measurement approach, 'green' will refer to the specific definition of that subsection.

Accurately identifying green jobs is challenging since a green job involves two different dimensions: the firm and the worker. The firm and worker dimensions complement each other, and both contribute to making a job green. These dimensions sometimes, but not always, overlap.

The _firm-level dimension_ relates to the **output** (goods or services) that the job contributes to producing, and the **technologies** applied to do so. The firm’s environmental footprint naturally relates to the outputs produced. At one end, firms with a negative environmental footprint produce highly polluting or high carbon-intensive outputs (e.g., coal). At the other end, firms with a positive environmental footprint produce environmentally friendly outputs (here on green outputs or environmental goods and services, EGS) (e.g., solar panels). Green outputs include those that may have carbon-intensive production processes but still contribute to the greening of the economy (e.g., a firm manufacturing electric cars). In the middle, there might be firms that have a neutral environmental footprint, with zero or low carbon-intensive outputs (e.g., a leasing firm). In addition to the firm’s output, the job may be performed at a firm that intentionally utilizes technologies<sup>7</sup> to reduce its own environmental footprint. These are usually referred to as green or environmental technologies. The adoption of green technologies could be triggered by different objectives: increasing profits, compliance with regulations, social responsibility, accessing new market opportunities, or raising consumer awareness on climate change issues (e.g., a paperless office or a firm reducing
There have been a number of studies trying to isolate which country specific factors are important in explaining capital structure choices. Booth, Aivazian, Demirguc-Kunt, and Maksimovic (2001) find that while similar factors such as profitability and asset tangibility affect debt ratios in both developed and developing countries, there are also systematic differences in the way these ratios are affected by institutional factors such as capital market development, GDP growth, and inflation.<sup>23</sup> Giannetti (2003) uses data on unlisted companies from 26 European countries and finds that good creditor rights protection is important in obtaining financing for firms investing in intangible assets that cannot be provided as collateral (e.g. R&D) and also those in sectors with high volatile returns. Fan, Titman, and Twite (2010) find that a country‟s taxation and inflation policies as well as its legal and financial institutions have an important effect on capital structure and debt maturity choices. Firms use more debt when dividends are more highly taxed, use less debt and of shorter maturity when inflation is lower, and use more short-term debt in countries that are viewed as more corrupt. Controlling for corruption, the legal tradition, i.e. common law versus civil law only influences the debt maturity decision and not the leverage ratio.<sup>24</sup> While the above cross-country studies of capital structure are focused on domestic firms in different countries, Desai, Foley, and Hines (2005) analyze the capital structures of foreign affiliates of U.S. corporations. They find that multinational affiliates use less external debt in countries with underdeveloped capital markets and weak creditor rights and make greater use of internal capital markets (borrowings from parent companies) to overcome capital market imperfections. They also find the overall level and composition of debt to be very sensitive to tax incentives.

Overall the body of evidence suggests that institutional factors such as legal institutions, the level of banking and stock market development are important determinants of firms‟ leverage
Results for most other variables in equation 1.2 are very similar to those in equation 1.1. The most notable difference is that the coefficient for non-DAC bilaterals increases in magnitude from -15 to -23. Controlling for their very high levels of support for aid, Korea and Turkey stand out even more strongly as negative outliers on use of country systems. It is not clear how to interpret the public opinion results for these two countries, however. Korea and Turkey were historically aid recipients, and their aid outflows are still small. The public opinion index for them is measured using only the GI 2002 survey, in which 87% of respondents in each country thought that “the wealthier nations should give more financial help to the poorer nations.” Many respondents in both countries might not have considered their own country to be among the “wealthier nations,” and some (particularly in Turkey) may even have considered their own country to be among the “poorer nations” on the receiving end. In equation 1.3 we therefore drop the 52 observations in which Korea or Turkey are the donors. Results for the public opinion variable (and for the other regressors) are unaffected by this change in sample, however.

The remainder of this section addresses three potential problems with the basic results reported in equations 1.1-1.3. First, those estimates are potentially affected by sample selection bias. Second, there may be omitted variables correlated with use of country systems. Third, coefficients for some key regressors may incorporate endogeneity bias.

There are two sources of potential selection bias in the data. First, aid-recipient governments self-select into (or out of) the PDMS. Of the 151 eligible aid recipients on the official DAC list, only 80 participated in one or more rounds of the survey. Second, not all donors choose to provide aid to all recipients in all years (or, in some cases, recipients may choose not to accept aid). In fact, for the vast majority
.970***|-2.636***|-1.965***|-1.433***|-3.242***|-3.222***|
||(0.092)|(0.237)|(0.487)|(0.435)|(0.363)|(0.369)|(0.483)|(0.535)|
|Number of observations|619|469|630|473|679|522|554|427|
|r2_w|0.224|0.342|||||||
|r2_b|0.091|0.069|||||||
|r2_o|0.079|0.065|||||||
|rho|0.629|0.679|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

51
country. A score of 0 indicates no restrictions while 100 signals severe implementation of lockdown policies in a country (Hale et al., 2021).

The first lockdown measure in Indonesia was on January 18, 2020, when COVID-19 screening of international travelers was introduced. This was followed by the ban on arrivals from some regions on February 5, 2020. Closure of workplaces, total border closure, closure of public transport, restrictions on internal movement and stay-at-home requirements followed in that order from March 15 and they were all operational by April 1. This is depicted by the consistent rise in the stringency index which remained above 70 until May 20, 2020, when the closure of public transport was temporarily lifted. The index has since fluctuated depending on the severity of the COVID-19 wave. Indonesia had the second highest index among the countries in Figure 1, after Australia, by the end of 2020. All the lockdown measures were in operation in 2021 and the index was relatively stable at around 72. Indonesia had the third highest index, after Vietnam and the Philippines, at the end of the sample period on August 31, 2021.

Among the neighboring countries, Singapore was the first to impose lockdown measures (January 2), followed by Vietnam (January 25), India (January 26), Papua New Guinea (January 27), Malaysia (January 30), the Philippines (January 31), Australia (February 1) and Thailand (March 6). All the countries started with international travel controls and progressively introduced domestic measures. Singapore’s stringency index was the lowest of all Indonesia’s neighbors at the end of August 2021 because of the relaxation of the conditions on internal movements and public transport. The Philippines, Vietnam and Australia had the highest stringency indices by the end of August 2021, respectively.

# **2.2 Trade data**

Our export and import data is a monthly product-country data series obtained from the Indonesia Statistics Authority (BPS _Badan Pusat Statistik_ ). The