rafmacalaba/data-use-mentions-extended huggingface dataset:
6 general, and has this tendency lessened since many countries began reducing their trade barriers in the 1980s? Are trade restrictions noticeably different in periods of international price spikes than in non-spike periods? Do various governments respond differently in periods of upward versus downward spikes in international food prices? In particular, do developing countries alter their trade interventions more than high-income countries, and are food-deficit countries more inclined to vary their trade interventions than food-surplus countries? The next section describes a new database capable of addressing these questions, and the following section provides a summary of empirical evidence provided by that database.

# **Database on government distortions to domestic prices of farm products**

A new World Bank database provides, in a single source, a set of indicators of the extent to which export restrictions and other price-distorting trade and domestic policies have altered annual average domestic producer and consumer prices of farm products away from their international price levels over the past half century (Anderson and Valenzuela 2008, with summary estimates in Anderson 2009 and Ch. 2 of Anderson 2010). The sample includes 75 countries that together account for all but one-tenth of global agriculture, and the 75 most important products so as to cover around 70 percent of the gross value of agricultural output in each focus country.

While those estimates only go up to 2007 (and only to 2004 for most developing countries), and so do not include the most-recent price-spike period, price data are now available for sufficient countries to enable at least a preliminary update of the estimates in Anderson and Valenzuela (2008) for rice and wheat. These new estimates are based, for highincome countries (including those that recently acceded to form the EU-27), on producer
RIGA/RuLIS database closely follow the definition given by the International Labour Organization (ILO 2003), which considers as income receipts those that (1) recur regularly, (2) contribute to current economic well-being, and (3) do not arise from a reduction in net worth (Carletto et al. 2007). These three criteria are embodied in each of the components of income; irregular payments such as lottery earnings or inheritances, investments, savings, and the value of durables are not included in the RIGA/RuLIS definition and measure of income. Furthermore, costs are also considered to ensure that

> 5 See FAO, “RuLIS—Rural Livelihoods Information System,” database. <u>http://www.fao.org/in-action/rural-livelihoodsdataset-rulis/en/.</u>

> 6 Burkina Faso, Enquête Multisectorille Continue (2014/15); Ethiopia, Ethiopia Socioeconomic Survey (2013/14, 2015/16); Malawi, Integrated Household Survey (2013, 2017); Niger, National Survey on Household Living Conditions and Agriculture (2011, 2014); Rwanda, Integrated Household Living Conditions Survey (2013/14); and United Republic of Tanzania, National Panel Survey (2009, 2012/13).

> 7 Notice that both the RIGA and the RuLIS projects cover more SSA countries and provide datasets that are sometimes more recent than those used in the present analysis. However, limited coverage of the population and issues of accuracy caused us to focus only on the countries (and years) mentioned in the text.

6
standard errors because individuals’ financial behaviors are likely to depend on unobserved countrycharacteristics.

We use data from the World Development Indicators, the Global Findex database and the EBA database (table 3).

**Table 3.** Data description

|**Variable**|**Definition, year**|**Source**|
|---|---|---|
|FINC|Respondent has an account at a financial institution, 2014|Demirguc-Kunt et al. (2015)|
|REGULATION|Regulation index obtained from EBA Finance data, 2017|World Bank (2017)|
|_Individual control_|_s_||
|FEMALE|Respondent is female, 2014||
|AGE|Respondent age, 2014||
|AGESQUARE|Squared value of AGE, 2014|Demirguc-Kunt et al. (2015)|
|EDUCATION|Respondent completed secondary school or higher, 2014||
|INCOME|Within-economy household income quintile, 2014||
|_Country controls_|||
|LOGGDPCAP|Log value of GDP per capita in current US$, 2014|World Develoment Indicators (2015)|
|LOGPOP|Log value of total population, 2014|p|

Column (1) in table 4 presents the estimations on account penetration using the continuous score of the regulatory index. The results indicate that the regulatory index score (0-100) for a country is positively associated with an individual within this country having an account at a financial institution. However, the association is not significant. We then try to divide the sample into four quartiles based on their scores of the regulation index. Column (2) in table 4 displays the results with the further segmentation. The estimations for regulatory quality have higher significance level, as shown in column (2), implying that radical rather than incremental changes in the regulatory framework reflected as significant change in the score of the regulation index are necessary to improve the financial inclusion scenario. For instance, adopting a dedicated legislation to regulate
is computed by using a weighted average; with weights being proportional to the GDP at constant prices, 2010 USD of each country in the union. “Adjusted” means that the inflation rate is equal to 3 percent for countries with a p-value of the Wald test (of significance of the threshold variable) above 10 percent. “Unadjusted” average refers to the weighted average without considering the significance of the threshold variable. Models 4, 5 and 6 are ECM models; and Models 1-3 are ARDL models.

Overall, these results show that from the current inflation thresholds there are opportunities to increase the current thresholds that are used by regional central bank in each currency union. Results from the ARDL models and ECMs consistently confirm these findings in both currency unions. In CEMAC countries, the level is broadly consistent with the one found with panel data analyses (Augmented Model 1 - Table 3). The adjusted average suggest that the regional inflation threshold is around 5.6 percent (Model 5). However, the regional inflation rate of CEMAC countries could have been higher if one was considering the best-fitted model with significant threshold coefficients. In WAEMU countries, the regional averages, which is based on country thresholds, are higher than the one estimated with panel analyses. This result reinforces the existence of structural differences that expose countries to asymmetric shocks. Robustness analyses provide similar results by considering inflation rate as an endogenous variable (Appendix 6 and Appendix 7). Based on the above analyses and robustness analyses, it could be concluded that it is possible to increase the threshold levels used by both regional central banks to 5.4-5.6 Page | 21
|114|303|73|201|296|
||shareintotal 'buyer-driven'|94.2|88.7|89.3|2.2|4.1|5.4|3.6|7.2|5.3|
|Imports of parts (|US$ mill.)|1,833|2,150|4,226|12|12|16|27|59|154|
||share in total 'buyer-driven'|97.9|96.8|96.1|0.6|0.5|0.4|1.4|2.7|3.5|

Source: Computations based on UN COMTRADE Statistics.

Export growth rates of each network exceeded the growth of the EU’s external import demand for these products. As a result, producers in ‘buyer-driven’ networks have succeeded in increasing their share in EU external imports through the combination of increases of exports of both parts and final products. Suppliers of footwear had the largest increase but from a very small base. Turkish T&C chain accounting now for around 15 percent of EU imports of textiles and clothing has also seen a substantial, 25 percent increase in its share in 2000-03. The presence of furniture network in EU markets is also visible, as suppliers of furniture parts account for more than 5 percent of these EU external imports (Table 18).

It appears that Turkish firms operating in ‘buyer-driven’ chains are not merely assembly shops for EU multinationals taking advantage of cheap labor. While without the survey of firms in sectors or access to input-output tables it would be impossible to provide an unequivocal proof of this assertion, an examination of trade data tabulated in Table 18 suggests a two-way involvement going beyond simple outward processing.

In fact, trade flows, encompassing two-way movements of both parts and final products within each chain point to the development of backward linkages. Import intensities in each chain, i.e
whose index fell below a percentile threshold roughly equal to the prevailing national poverty rate as non-monetarily poor. The analysis combines survey-based estimates with publicly available geospatial indicators using an Empirical Best Predictor model following Molina and Rao (2010). Relative to direct survey estimates, the correlation with the census rose from 0.72 to 0.88 in Sri Lanka and from 0.77 to 0.88 in Tanzania. Unlike previous papers integrating survey and geospatial data, this one estimates the efficiency gain as well as the gain in accuracy due to incorporating geospatial data and finds that it is roughly equivalent to expanding the size of the sample by a factor between three and five.

Van der Weide et al. (2022) generate small area estimates of monetary poverty in Malawi for Traditional Authorities by combining survey data with publicly available geospatial features. Like Steele et al. (2017), their study uses a Bayesian structural equation model that accounts for spatial correlation across areas and validates the prediction against census-based estimates. It finds a correlation between the geospatial features and census estimates above 0.9, although some individual target areas show substantial discrepancies between the census and geospatial-based estimates due to differences in the data used for prediction.

Krennmair and Schmid (2022) propose a mixed effect random forest model, which is tested in a designbased simulation using household-level covariates in Mexico. The results demonstrate the benefits of applying machine learning methods over traditional linear models. Design-based simulations from the Mexican state of Nuevo León indicate that this approach reduces median relative bias by about 20 percent relative to the more typical approach of applying an empirical best predictor (EBP) model with a transformation. The paper also evaluates a random effect block residual bootstrap approach to estimating uncertainty and finds that it performs well.

Merfeld and Newhouse (2023) evaluate small area estimates of an asset index for four countries: Madagascar, Malawi, Mozambique, and Sri Lanka. In addition, the paper evaluates small area estimates of poverty for Malawi obtained using publicly available geospatial auxiliary data. This
for labor in the economy (Walras’ law). People who are legally eligible to work are assumed to spend their time either working or consuming leisure. The model permits capital mobility across the production sectors. However, the total capital stock in the economy is assumed to remain unchanged with a policy change. The difference between total value outflow (e.g., imports of goods and services) from the country and the total value inflow (e.g., exports and transfers from the rest of the world) to the country is defined as the current account balance, while total investment is the sum of total savings comprising of household saving, government saving and the current balance.

# **3.2 Incorporating energy subsidy removal into the CGE model**

In order to incorporate the direct government subsidy to the electricity sector, we compared the size of the subsidy to the total value of electricity sold. In 2012, the base year of our model, direct electricity subsidy was equivalent to 33% of the value of electricity sales reported in the social accounting matrix (SAM), the main database required by a CGE model. This means that the non-subsidized average price of electricity for consumers would have been 33% higher than the reported value. The elimination of this subsidy would mean increasing the electricity price to 1.33 times its starting (subsidized) value. This is implemented in the model as a 33% tax added onto the subsidized price of electricity. It is assumed that the government saves the amount which has been transferred to the electricity sector as a subsidy. This saving could be used in the economy for other purposes. Alternative options to utilize the saved subsidies will be discussed later.

In the case of natural gas, the situation is different. The gas sector does not receive a direct subsidy from the government; instead gas is sold at a price lower than its opportunity cost. The opportunity cost is the cif price of imported LNG. Under the baseline (business as
**|16.9***|22.7**|33.3**|
|Leisure|106.8***|133.5***|86.5***|106.3***|127.4***|160.5***|
|Unpaid work|31.9***|49.6***|56.0***|82.6***|7.5***|17.0***|
|Transport|13.7***|34.5***|7.0***|20.5***|20.6***|48.4***|
|Eating/drinking|21.7|21.1|20.7|21.4|22.7*|20.8*|

Notes: (1) T-tests of equality of means conducted across treatment groups; ***p<0.01, **p<0.05, *p<0.10.

(2) Based on a pooled individual-level sample for days 5, 8 and 10 (each individual appeared three times in the sample).

(3) Grey shading = recall arm yields significantly lower estimates.

One possibility for these differences is respondent fatigue in the recall arm, given the structure and relatively long duration of the interview. Respondents might be more actively reporting time in different productive activities at the start of the interview, and by the end of the narrative (i.e., for hours later in the day) they lose interest or track of their time. Figure 6 gives some indication that this may be the case, revealing a bimodal sleep distribution for recall respondents that is particularly pronounced for women. A significantly greater share of recall respondents, particularly women, report earlier sleep times that would conclude
that gender norms are more progressive in urban areas in South Asia (Asher et al., n.d.), we find no significant difference between urban and rural marriage penalties. This may in part reflect the nature of urban labor markets, where employment opportunities are concentrated _outside_ the home, adding additional constraints to women’s participation in the context of conservative gender attitudes (Jalota and Ho, 2024).

We then turn to testing the impact of education and social norms on marriage penalties in South Asia by interacting our post-marriage indicator with these characteristics. We find that educated women have much smaller marriage penalties, with post-secondary 4
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# I. JORDAN’S GROWTH OF EDUCATIONAL ATTAINMENT AND SCHOOL SUPPLY

Educational attainment in Jordan witnessed dramatic growth in the second half of the twentieth century. The evolution of the average years of schooling of males and females by year of birth is depicted based on data from JLMPS 2010 (figure 1).<sup>3</sup> As shown, there was a rapid increase in educational attainment across cohorts for both males and females. For males, the increase occurred earlier, with their mean years of schooling rising from under six years for the 1940 birth cohort to ten years for the 1955 cohort. It then stagnated at about 11 years for cohorts born between 1960 and 1975, only to start rising again for younger cohorts. The increase in female mean years of schooling occurred later and was more sustained. The mean years of schooling starts as low as fewer than two years for the 1940 cohort and rises rapidly to about ten years for the 1965 cohort. Although the rate of increase of female schooling slows after that, the mean years of schooling for women exceeds that of their male counterparts for the youngest cohort born between 1975 and 1985.

To investigate whether this dramatic increase in educational attainment corresponds to the expansion of public schools in the country, the average number of basic and secondary public schools per 1,000 individuals in subdistrict of birth for the JLMPS 2010 sample is depicted (figure 2). The data on the number of public schools is obtained from the school census carried out in 2010 by the Jordanian Ministry of Education.<sup>4</sup> The focus on public schools is because they

> 3. Mean years of schooling for older cohorts may be biased upward due to selectivity resulting from the likely higher mortality of less educated individuals.

> 4. Since these figures are based exclusively on surviving schools in 2010, they ignore school closures and school conversions from one type of school to another. Basic schools currently go from first to tenth grade. Before 1994, they were
compared our sample to the 2008 Nigeria DHS, restricted to the South-Western region, and found that the two were quite similar in terms of religion (a third Muslim and two thirds Christian), years of education (around 11 years) and television-ownership rates (around 90 percent).

At the end of the survey, the main study participants received invitations to attend Screenings 1 and 2, organized in the two weekends following the interview. Those in the ‘Friends treatment’ also received invitations to bring up to two friends of their choice. Note that invitees were not told what they would watch, neither before Screening 0 nor before Screening 1. It is only when attending Screening 1 that they learnt they were watching Shuga or Gidi Up.

# **Network members**

We complemented our data collection on the main study participants with a sample of con-

> 1The normalized difference is a scale-free measure of the difference in distributions, recommended by Imbens and Wooldridge (2009):

where _X A_ and _X B_ are the means of covariate _X_ in groups A and B, respectively, and _SA_<sup>2and</sup><sup>_S_</sup> _B_<sup>2arethe</sup> corresponding sample variances of _X._

> 2In some locations we have 65 respondents instead of 64, because our teams consisted of eight enumerators working in parallel. Enumerators were given a target number of respondents to interview per day and they did not know how many their colleagues had interviewed until the end of the day.

ii
column shows the percentage of enterprises that have attempted to secure government contracts through bribes. The last column of Table 1 shows Türkiye’s overall bribery rate in 2013. This rate shows the percentage of enterprises that encounter bribe requests during any of the six public transactions included in the bribery incidence index, plus the enterprises that used bribes to secure a government contract and the enterprises that reported positive average bribery spending.

Next, I present the amount of bribery spending in order to ‘get things done’. Table 2 summarizes bribery payments along both the intensive and extensive margins. Average bribery payments are reported as the percentages of value added (VA) to be consistent with the model I will present in the next section. The gross output to value added ratio in Türkiye is 2.1 according to input-output tables. Therefore, the bribery to value added ratio can be obtained by multiplying the bribery to sales ratio by 2.1. By doing so, I assume that total sales can be interpreted as gross output.

The second column of Table 2 reports the average percentage of VA spent on bribery in the extensive margin. This measure includes the enterprises that state that no such payments or favors are needed ‘to get things done’. The last column of Table 2 shows similar statistics in the intensive margin. In other words, the bribery amount to VA ratio in the intensive margin indicates the average bribe to VA ratio of the enterprises that admitted that they paid bribes.

The bribery data reveals that big enterprises pay larger bribes than small enterprises. However, bigger enterprises’ bribery payments, relative to their annual sales are smaller than the small enterprises’ bribery payments proportional to their annual sales.<sup>5</sup> On average, 1.4% percent of the annual VA of the enterprises was spent on bribery. However, if only the enterprises that were asked to pay bribes are considered, they spent 18.1% of their VA on bribery. The difference between small and big
2014a); World Bank staff calculations based on 2013 labor force survey.**

**Notes: Full time workers; data are for 2013 (2008 for Laos; 2009 for Cambodia, Mongolia and Thailand; 2010 for China, Indonesia and Philippines); average minimum wages for China and Vietnam. Non-government sector includes state-owned enterprises, domestic private firms and foreign-owned private firms.**

Figure 3 presents the growth of Vietnam’s inflation-adjusted, non-government, domestic sector minimum wages. It includes a projected path of minimum wages from 2016 to 2018 based on the

> 2 Cf. Appendix A for more detailed empirical evidence on the relationship between Vietnam’s statutory minimum wage and the country’s entire wage distribution.

- 8 -
green tasks used therein.<sup>17</sup> This data set, while quite rich, faces three primary shortcomings in our context. First, it may not be applicable to countries outside the US, especially middleincome and developing nations since production methods and technology may differ across economies or the required task profiles to realize those occupations may differ. Second, the GEP data were collected before 2009 while green technologies have evolved rapidly in the past 14 years. Additionally, it only considers jobs in 12 industries. Third, researchers report significant measurement error (Vona et al. 2019; Granata and Posadas 2024) when transforming 8-digit O*NET SOC codes to 4-digit ISCO codes.

Other studies have developed a methodology using text analysis of occupational tasks data to identify green jobs. Janser (2018) applied text analysis to a German occupational tasks database (BERUFENET) to identify green jobs in Germany. Granata and Posadas (2024) applied this method to ISCO occupational descriptions to profile green jobs in Indonesia.

Given that currently there is no occupational tasks database for South Africa, and the limitations of the O*NET GEP, we follow Granata and Posadas (2024) who apply a text analysis methodology to the ISCO-08 (developed in 2008) to identify occupations that perform green tasks. Granata and Posadas (2024) create a dictionary of green terms that are used to build a green occupation database. The dictionary of green terms is basically a collection of terms (words, roots, or expressions) that are commonly found in environmental economics literature. To make their list as comprehensive as possible, they draw green terms from various databases.<sup>18</sup> They reviewed more than 70 references to fine-tune the list. The final green terms dictionary includes 347 green terms, of which 308 are considered strict green because they are directly linked to the green economy. Another 39 terms are added to generate the broad green list of terms. These terms are not explicitly linked to environmental sustainability and climate change