rafmacalaba/data-use-mentions-extended huggingface dataset:
and another noting that now “I try to talk [with] customers in a nice and professional way. I try to make delicious meals and make my place of work look attractive to them...I make sure [my cooking] it's up to the customers’ standard, not watery food” (ICRW, 2015). In qualitative interviews with those who had gone through mentoring, one participant said “I used to not care much about my business, I could open late, and sometimes the shop would remain closed if I don’t feel like opening. Now I know that it is important to put effort into my business.” We incorporated this potential channel into the three-year follow-up survey, asking whether the business always opened at a set time each day. Column 5 shows only 34 percent of the control group do this, and training increases this by a significant 6 percentage points. Mentoring appears to have a greater impact than training alone on having regular opening hours. Appendix 11 shows that the route to more customers does not appear to be through price competition, with no significant impact of treatment on the prices charged (or input costs of) the most commonly sold goods.

A second reason firms were able to attract more customers was by diversifying the range of products they sold. Banerjee and Duflo (2008) note the preponderance of small businesses in developing countries all selling similar things to one another, with no reason for customers to seek them out in terms of either product line or shopping environment. Column 6 shows that firms assigned to training were 9 to 11 percentage points more likely to have introduced a new product to their business, significant at the 1 percent level. There was tremendous variety in the new products introduced, with almost every business that said it had introduced a new product giving a different answer for what product it had introduced. Examples included avocados, oranges, carrots, garlic, fertilizer, body oil, cowpeas, belts, hair oil, tea,
at pre-recession (2006) levels, and (2) the way that these fixed characteristics interact with changes in formulaic parameters determined by the state and federal governments.<sup>16</sup> Instrumenting for the state-level benefits per population, the IV _Z_<sup> ̃</sup> _st_ is constructed by aggregating the simulated benefits per recipient, _B_<sup> ̃</sup> _it_ ( _Xi_ 0; _pst_ ( _Xi_ 0)), up to the state-level using sampling weights in the QC data and then dividing by _ns,_ 0, the population of state _s_

> 15The government uses other observed characteristics as well, but in this subsection, we do not focus on these characteristics, which include legal child-support expenses, dependent-care expenses, and medical expenses for households with elderly or disabled members; in 2008, deductions based on these expenses comprised only 2.2% of total expense deductions granted by the program.

> 16As mentioned in Ganong and Liebman (2018), the impact of state-level reforms such as electronic applications on SNAP enrollment mostly occurred before the recession in the early 2000s. During the recession, there was indeed increased state-level adoption of Broad-Based Categorical Eligibility (BBCE), which they calculate to only account for about 8% (1.57/19.1 in Table 2) of SNAP enrollment increases. They discuss that the benefit calculation rule sharply limited the scope of this eligibility expansion. Given these facts and our sample period, we focus on intensive margin changes in this paper.

9
facilities in Kenya reported that the average time between ordering new supplies and receiving them is more than two weeks. During emergencies, a more efficient supply chain may be required.

As mentioned previously, large facility surveys like the SDI do not collect information on available stocks of medicines and treatments (a facility will be recorded as having the medicine even if only one tablet is available). It is therefore not possible to say whether, for example, facilities identified as having a supply of azithromycin would have adequate stock to meet rising demand.

19
survey (2012-13) and the experience of adverse events after lagged consumption was observed (between the second and third waves of the survey) on the current level of depression. As GHS-Panel is a panel survey, consumption expenditure information is available from three waves of the survey (2011-12, 2012-13, 2015-16) for the same set of interviewed households.

The results of our analysis suggest that experiencing conflict or idiosyncratic shocks has a significant impact on self-reported level of depression (higher CES-D scores), as well as an increased probability of having depressive symptoms. These results remain consistent when controlling for covariates such as past consumption expenditures. Experience of conflicts in the past two years has the largest and strongest impact on the respondent’s depressive symptomology, as it is associated with a 15.3 percentage point increase in the probability of reporting depression.

After establishing the potential causes of depression, we further examine how depression affects household decision making and well-being. Our results indiciate that individual depression is associated with lower labor force participation and child educational investment. Persons with depressive symptoms have a lower likelihood of engaging in any work, particularly in agricultural work (for men) and non-farm work (for women). Finally, parental depression is correlated with a lower likelihood that the family spends any money on education for their children.

We also go a step further to examine measurement and reporting of depression in household surveys. We conducted a survey experiment testing priming effects on depression reporting by randomizing the order in which respondents were asked about negative events and depressive 3
21 approximately equidistant to (equally remote from) radio towers and roads inside the commune.<sup>9</sup> Complementing the fragmentation of the Benin radio market, such a sampling frame yields variation in radio access driven by station location and transmission conditions that are exogenous to the sampled villages.

Most of the variation in access to radio signals is explained by differences in access to outof-commune radio stations. The average household has access to .63 private within-commune community radio stations, with a standard deviation of .51. It has access to 1.78 out-of-commune stations, with a standard deviation of 1.45. Exposure to signals from neighboring communes is particularly likely to be exogenous, since the location decisions of out-of-commune community broadcasters are unrelated to the characteristics of villages in neighboring communes.

Examination of observable village characteristics reinforces the conclusion that withincommune variation in radio access is exogenous to village characteristics. If observable characteristics do not differ systematically across villages with more and less access to community radio, it is unlikely that radio access would be associated with unobservable characteristics that have independent effects on education. The village survey includes data on village distance from urban centers and distance to a train or bus stop; smaller and more remote villages could both be likely to have fewer accessible radio signals and lower investments in education. Larger villages might also 9 Unfortunately, no data was available on the precise GPS locations of stations and villages to directly control for this in our analysis. The best we could do was rely on pictorial maps provided by our local consultants to ―eyeball‖ neighboring villages that were located equally distant from radio towers. However, as we discuss below, we are able to verify that our sampled villages exhibit no correlation between radio access and observable characteristics of location that could independently impact education outcomes.
absence of other work attempting such classification, we summarize their results as giving a rough outline of the relative importance of the different types of income earners in the economy.

The authors analyzed the data available from the Social Dimensions of Adjustment Survey for 1988. They listed six socio-economic groups to which the households were assigned as follows;

- i) Households producing the exportable crop, cocoa;

- ii) Households producing other agricultural goods-basically food;

- iii) Households with one or members who are retail or wholesale traders;

- iv) Households with one or more members in wage employment producing non-traded goods and services ( excluding traders and government and services);

- v) Households with one or more members in wage employment producing manufactured goods;

- vi) Households with one or more members employed in providing government services.

The extent of multiple occupations can be seen from the following Table 1.1 of incomes derived by the households in the sample from different sources which Appleton and Collier have calculated.

**Table 1.1: Percentage in per Capita Income Deciles**

||l|2|3|4|_5_|6|7|8|**9**|10|
|---|---|---|---|---|---|---|---|---|---|---|
|Govt.|3.1|6.7|8.5|10.4|8.6|10.0|10.9|13.7|15.6|12.6|
|NTG|8.4|8.5|9.7|11.8|11.7|8.8|11.1|8.9|10.9|10.2|
|Manu.|5.9|9.2|10.1|12.5|8.
|Agriculture|31.5|0.635|0.62|24.6|
|Livestock|16.0|0.729|0.45|10.3|
|Forestry and fishing|11.1|0.986|0.81|17.4|
|Non-farm income|31.9|0.828|0.79|40.8|
|Self-employment|27.1|0.927|0.82|40.7|
|Wage-paying|4.9|0.767|0.03|0.2|
|Other income|9.5|0.781|0.47|6.9|
|Private transfer|3.1|0.895|0.16|0.9|
|Others|6.4|0.849|0.56|6.0|

The LSMS data also show that the distribution of farm income is more equal than that of non-farm income. 94.3% of the households participate in farm activity, while only 68.2% participate in non-farm activity. 49% of the households have income from wage-paying activity, and 39% of households have income from self-employment activity. The distribution of the self13
# 4.2.1 Cleaning Rules: Standard cleaning and quality control

The ownership and financial modules in ORBIS went through various cleaning steps before we were able to identify SOEs and extract their financial and economic data. Building on the lessons and literature using ORBIS information (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych, & Yesiltas, 2015), the following cleaning steps were applied:

- _Remove the duplicates from the ownership Links data._ Using the subsidiary id, shareholder id, and type of relation to identify each unique observation, we retain the most recent ownership information if duplicates exist.

- _Reconstruct the Year variable._ The variable Year takes the value of year for the closing date if the closing date falls on or after June 1<sup>st</sup> . Otherwise, we assign the previous year to that variable. The Year variable indicates the fiscal year for which the financial data were reported.

- _Remove inactive links._ For each year, we remove all obsolete links that were no longer valid by using ORBIS’s variable “Active archived” that indicates whether a link is active or obsolete. This step is essential to ensure that the information on ownership is correctly specified every year.

- _Convert local nominal currency._ The balance sheets and income statement data are stored in local and nominal currency in the ORBIS files. For our purposes, we converted all nominal values to real US Dollars with 2005 as the base year.

The cleaned version of the Ownership module is the starting point for the creation of the global BOS database. After the ownership module is cleaned, the following cleaning rules were further applied:

- _Drop entities that are no longer active in the markets_ : Using the “Active” status variable in the ORBIS interface, we delete all firms flagged as inactive because they correspond to firms that were dissolved, liquidated or under bankruptcy.

- _Complement data gaps when information as of 2019 was not available:_ When there was no information available in terms of sector of
Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott 2013); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018); US (Higgins et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to equality is the difference between the Gini coefficient without the particular fiscal intervention and the Gini coefficient of all income components together. There is no marginal contribution calculated for Argentina and Brazil. USA results are preliminary.

With regards to transfers, all exhibit a progressive pattern; Kakwani coefficients are all positive (Figure 9). These transfers are also inequality-reducing as most of the marginal contributions are also positive, but there is marked heterogeneity across them. Social assistance transfers are strongly progressive, given their poverty-targeted design, though taken individually their marginal impacts look relatively small, given their limited benefit levels. Despite having low progressivity, the AGI program has the largest distributional impact among all programs, since it channels a relatively large budget.

From a policy perspective, the AGI offers an interesting entry point to consider. It has a budget that is almost two-thirds of the budget of all social assistance transfers, and has a design with good space for improvement in terms progressivity. Reforming AGI towards a design more targeted to low-income households could be a policy option to explore to improve the equalizing impact of the fiscal system in Turkey. For example, an alternative design could consider introducing a ceiling around the minimum wage for eligibility to receive AGI. This would generate savings from the current AGI budget, that could then be allocated to: i) increase the amount that 27
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The shares are assumed to remain constant over the three years of the model.

We have also estimated shares of capital and labor in government production, using the wage bill as the share of labor. These shares are taken to have their actual values for each of the years 1983-85. They are: Table 5.2. Factor Shares in Government Production A/

||Share of Capital<br>Share|of Labor|
|---|---|---|
|1983|0.463|0.537|
|1984<br>1985|0.461<br>0.447|0.539<br>0.553|

A/ See Informe Annual. 1985 (1986), p. 163.

Similarly, factor shares in investment were estimated as shares in the construction industry with the share of capital being 0.291 and the share of labor being 0.709.15 We constructed initial allocations of factors and financial assets in the following way. We took the total returns to capital and labor in 15/ See Sistema de Cuentas Nacionales de Mexico (1981), Volume I, Table 16, p. 94.
omitted, again suggesting that the result is not merely reflecting India.

# **2.2.2 A statistical coincidence?: Omitted and placebo variables**

Across rich and poor places, good conditions are often found together, and problems are often found in places with other problems. Would _any_ measure of infrastructure, governance, or welfare be as correlated with height as is sanitation?

Column 6 of table 1 adds time-varying controls. These include female literacy, which is an important predictor of child welfare, and accessibility of water supply, all also from the DHS.<sup>6</sup> Development outcomes are often attributed to good “institutions;” the set of controls includes the polity and autocracy scores from the Polity IV database. With these controls added, the association between height and sanitation is essentially unchanged.<sup>7</sup> Table 3 isolates each of these alternative independent variables in turn. None of these “placebo” predictors matter for child height, conditional on sanitation and and GDP.<sup>8</sup> In particular, conditional on sanitation and GDP, child height is not associated with other types of infrastructure (electrification, water), governance (a democratic polity, autocracy), female literacy, or nutritional measures such as food availability, the breastfeeding rate,<sup>9</sup> or the fraction of infants who are fed “other liquids” beyond breastmilk in the last 24 hours.

> 6Despite the frequency of undifferentiated references to “water and sanitation,” improving water supply and reducing open defecation have very different effects on child health and other outcomes and should not be conflated (Black and Fawcett, 2008).

> 7If controls for the fraction of infants ever breastfed, the fraction of infants breastfed within the first day, and the fraction of infants fed “other liquids” in the past 24 hours are further added as measures of the quality of infant nutrition, the coefficient on open defecation in panel A
# **4. Data and Empirical Approach**

# _Data_

We use multiple sources of data to assess the distributional impact of fiscal policy in Turkey. The main source of information is the 2016 Household Budget Survey (HBS) collected by the Turkish Statistical Institute (TUIK). The HBS is a nationally representative household survey that collects detailed information on consumption (302 expenditure items in total), labor income, social assistance, pensions, remittances, financial income, assets, housing characteristics, accessibility to labor and financial market, health services, education opportunities, and individual characteristics such as education, health, and labor market status and experience. The survey has been collected on an annual basis since 2002. We use the 2016 round, which was the latest available HBS round at the start of the study. That year, the survey interviewed 12,092 households, encompassing 42,605 individuals.<sup>9</sup> In addition, we use the Survey of Income and Living Conditions (SILC), collected by TUIK to monitor living standards following a methodology consistent with the European Union (EU) SILC initiative. The survey aims to provide comparable data on income distribution, living conditions, access to services, material deprivations, and relative poverty. Although it does not collect data on consumption, one of the main advantages of SILC is that its sampling design allows to conduct analysis representative at subnational NUTS2 level (26 regions). We use the 2016 round, which includes 22,441 households, to allocate in-kind benefits in education across households while taking into account regional differences in spending and personnel. Annex A includes a detailed explanation of the approach.

Third, we use IPSOS Consumption Expenditure Panel, a nationally representative survey that collects detailed information on household purchases (food and beverages, cleaning products, personal care, and other products), their channel of purchase, and socio-economic status of the household. We use this data set to account for informality in consumption, by identifying the share of
# **_8.1 What do the firm intensive and extensive margins of export growth reveal?_**

Customs transaction-level data for Ethiopia, Kenya, and Madagascar are available only for periods after the major US trade policy changes. Nevertheless, the decomposition of apparel export growth to the US into intensive (within-firm for incumbent firms) and extensive (new firms and exiting firms) margins yields some insights. If firms which benefitted from privileged access to foreign markets learned by doing, reduced costs through scale economies, or innovated and became internationally competitive, then we would expect to observe steady export growth along the intensive margin even after trade preferences were reduced (by the MFA end). Export growth along the extensive margin would be consistent with the presence of external economies, e.g., demonstration effects by incumbent exporters on new exporters, country reputation in new foreign markets, or movements of workers across exporting firms with embedded tacit knowledge. The relative importance of internal versus external economies may also reflect differences in types of domestic policies complementing the trade preferences - policies relieving constraints internal to firms versus policies addressing the broader economic environment in which firms operate, respectively.

We consider apparel export growth decompositions in the long-run (2010-2016) shown in Figure 4 and in the short-run (year-to-year) shown in Online Appendix D. First, in Ethiopia and Kenya, much of the strong apparel export growth to the US between 2010 and 2016 came from new exporters rather than from the expansion of exports by incumbent exporters. In Ethiopia, firms did not take any significant advantage of AGOA in the early years and the dominant role played by new exporters in later years is not surprising. Kenyan apparel exports did grow during the early AGOA period but even if firms operating during this period enhanced their competitiveness, the MFA end and the Great Recession seem to have wiped the slate clean by inducing significant 29
economic growth via the savings rate and overall labor productivity. We have adopted this approach for a first-stage regression that relates national income growth since 2010 to the age-dependency ratio. We substitute the regression prediction for income per capita in our EKC model that excludes all other righthand variables except the time trend and seasonal variations by hemisphere. The resulting EKC estimate is included in Figure 8 (labeled “IV”). Its peak occurs at a lower CO2 anomaly than its OLS counterpart, and at an income that is substantially higher. We should emphasize that this IV result is far from the last word, and future research should use satellitemeasured CO2 data to address the simultaneity issue in more depth.

We should also note that the income-related results in this study are only intended to provide a benchmark for judging urban performance in reducing CO2 emissions. The same would be true if our results had rejected the EKC (β12 > 0, β13 = 0), yielding a relationship in which CO2 emissions increase continuously with income per capita. To avoid any misunderstanding, we should emphasize that _our EKC results have no normative or policy implications in themselves_ . They do not imply that additional public resources are not needed for reducing CO2 emissions because “the problem will take care of itself” with continued economic growth. In fact, the opposite is true. The most recent IPCC report (IPCC (2021), Figure SPM.10) affirms a near-linear relationship between cumulative CO2 emissions and the increase in global surface temperature. Our EKC results (Figure 8) show that even high-income countries have not approached zero CO2 emissions, and growing industrial giants like India and China are still on the rising portion of the curve. The clear implication is that waiting for the EKC to reduce emissions from all countries would produce an enormous increase in cumulative CO2 and a potentially-catastrophic global temperature increase. This conclusion will simply be compounded if future research finds insignificance for the EKC regression parameter (β13 = 0).

Finally, we should note that our EKC results only provide