rafmacalaba/data-use-mentions-extended huggingface dataset:
(Fryer, 2013; Fryer Jr, 2011). Given these mixed results, it is clear that context and the details of design matter greatly for determining the impacts of performance-based rewards (Bruns et al., 2011). In some contexts teachers may lack the skills and knowledge to respond effectively to incentives, even if they wished to.

Our contribution is both to the design and to the discussion of context specificity of these program. First, we document the contrast in the impacts between in-kind and recognition rewards, and explore heterogeneity in how these effects are mediated by teacher and student gender. To the best of our knowledge, this is the first paper with a clear identification strategy to look at the contrasts between financial and recognition rewards for government civil servants. A similar study experimentally compared financial incentives with providing recognition for pro-social behavior, although the agents were private-sector hairdressers (Ashraf et al., 2014). Second, we document impacts on learning outcomes even in an environment where teacher skills are relatively low.

# **2 Intervention, Sample, and Evaluation Design**

Guinea, located on the west coast of Africa, is one of the poorest countries in the world. It is ranked 174/189 on the Human Development Index and, as of 2012, 36 percent of the population lived in extreme poverty.<sup>1,2</sup> School enrollment at all education levels have been increasing in recent years, but serious challenges remain both in access and quality. For example, an assessment administered in 2017 revealed that only 25 percent of grade two and three children could read a simple text,<sup>3</sup> and according to the Human Capital Index a child born today can expect to complete 7 years of schooling, but adjusting for quality suggests that amounts to only 4.5 years of high-quality schooling (World Bank, 2018).

These apparent problems induced a newly elected government in 2011 to seek innovative approaches in personnel and human resource management reforms
13 The Value of Children Survey did not provide analysis separately for old age support but combined it with data on financial assistance from children. The study found significant negative correlations between economic motives for child bearing and parental education and income in all six of the countries in their sample at that time. (Arnold et al, 1975, p.109). A recent study of husbands and wives in Pakistan showed educational difi3rentials in the amount of support expected from children. (Sotoudeh-Zand, 1987). A study of Egyptian husbands and wives showed strong negative relationships between extent of old age support expected from children and parental education in both urban and rural areas. Differences across income and expenditures groups were also sharp, but not always monotonic. (Hallouda et al, 1983). The effect of reliance on old age support on desired family size is not well established. The Value of Children Study found that among individuals in Korea, Taiwan, Hawaii and the Philippines these was a significant positive association between the economic motivation for childbearing (including old age support) and ideal family size. No significant relationship was found in Japan or Thailand. A study using the Egyptian data mentioned above found a significant effect of expectation to live with children in old age and desired family size among women in urban and rural areas. There was no significant effect for men. Nor was the extent of financial support expected from children significant for men or women. (Cochrane, Khan and Osheba, 1988).

There is little data on whether the old age motive for childbearing causes a couple to use contraception earlier or more frequently as would be expected if it had important effects on desire family size. Bulatao in analyzing the Value of Children data compared the attitudes of those who were high parity
the area median income, they found substantial shifts away from affordability in neighborhoods with poor safety, job accessibility, and schools. According to this index, housing considered unaffordable may be considered affordable after improvements to job accessibility, public transportation, and other amenities are made.

**Housing plus Transportation (H+T) Affordability Index.** The H+T Index, developed by the Center for Neighborhood Technology, incorporates transportation costs into measures of affordability and maps these relationships across metropolitan areas in the United States. It posits that “the relationship between housing cost and transportation is essential to the development patterns of urban form, suburbanization, and housing markets. Households must make trade-offs on how much they spend on housing and how much they spend on transportation.” Moreover, the rationale for this type of index is that “the failure of conventional measures to incorporate transportation costs into neighborhood affordability has led to the exacerbation of sprawl and the location of households in areas far from civic, social, and economic opportunity. The H+T Index provides an estimate of the typical cost of housing and transportation in different neighborhoods and compares this estimate to a typical household’s income.” A neighborhood is considered affordable if a given household would spend less than 45 percent of its income on housing _plus_ transportation costs (Homelessness Policy Research Institute 2018).

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of 66%. This amounts to over 120,000 tons annually in Shanghai alone. The imposition of a 10% annual increase in the levy leads to a fall in emissions from 8.3 million tons to 1.1 million tons nationally and 88% reductions for the five cities: 160,000 tons annually in Shanghai, 145,000 in Beijing; 60,000 in Chongqing, 55,000 in Guangzhou and 21,000 in Shenyang.

All three scenarios lead us to an optimistic view of the potential for solving China's industrial water pollution problem, at least in the case of COD. The best available evidence suggests that total COD loads will increase only modestly as economic reform proceeds, even if regulations are not tightened. Moreover, the projected results of higher pollution levies are quite striking. We project that a 10% annual increase in the levy through 2020 would eliminate most of the COD emissions from regulated factories in China.

<!-- Start of picture text --> Oao /lfloLebn *<br>; . _<br>SU ...<br>man A. awva<br>X G*h.<br>Chm-g*0<br>0o<br><!-- End of picture text -->

**Figure 24: COD Loads in Three Scenarios**

# **Air Pollution**

For TSP and S02, we project national and city emissions using the same approach:

- We use projected pollution levies and shares of large plants, SOE's, 'dirty' sectors and 'clean' sectors to project national and provincial pollution intensities. We assume that

26
data history which can then be leveraged for better access to credit. Access to credit often depends on lenders being able to assess the credit risk of potential borrowers based on their credit history. However, many low-income adults lack a documented credit history, which might reduce their ability to secure a loan. Including payment data on regular bill payments such as utility or telephone payments can help adults build credit history and qualify for better loan terms. In the United States, for example, the inclusion of utility and telecom payment data into credit files reduced the share of adults for whom no credit score could be calculated from 12 percent to 2 percent. The greatest benefits accrued to lower-income adults, members of minority communities, young adults, and the elderly (Turner et al., 2012; Turner and 9
Our study quantitatively compared caregiver perceptions of child intelligence with an assessment of child development in a low-income country using a perceived ranking scale and examined correlates of discordance. Previous quantitative studies have used the Neonatal Perception Inventory to measure maternal perceptions of early child development (Hernández‐ Martínez, et al., 2011; Scher & Tirosh, 1997). This measure assesses parents’ perceptions of the newborn relative to their concept of an average newborn in relation to crying, vomiting, sleeping, feeding, and predictability (Broussard, 1979; Broussard & Hartner, 1970). Others have used the Knowledge of Infant Development Inventory, which examines parents’ knowledge of childrearing practices, developmental processes, developmental milestones, and health and safety concerns (Bornstein, et al., 2010; Hess, et al., 2004). However, these measures primarily focus on early infancy, but early child development extends well beyond this stage. While previous studies have identified that parental perceptions of child health diverge from objective measures, we examined a wide range of child, caregiver, and household factors on caregiver estimation of child developmental abilities. Additionally, our results are robust to using interviewer-observed items of the ASQ-I in a sensitivity analysis, suggesting that reporting bias was not a major issue in our analysis.

# 6. CONCLUSION

Our findings demonstrate a discordance between caregiver perceptions of child intelligence and an early child development measure in a low-income, rural Malagasy population. With the increasing number of parenting interventions and programs in low-income contexts, it is important to consider caregiver perceptions of children’s developmental abilities early in life and how these perceptions affect educational investments. Future work should examine whether providing specific feedback on child developmental milestones to parents would alter caregiving or 23
BHSCZECZEMLTMLTOMNOMN VGBESP ESPITAITANZLNZLISRISRKORKORFRAFRAFINFIN GBR GBRBELBEL CAN CANISLISLDNKDNKDEUDEUBHRBHRAUSAUSAUTAUT SWE HKGHKGNLDNLDSAUKWTKWTUSAUSABRNBRN NOR NORCHECHEAREIRLIRL Primary SecondaryLUXLUX MACMACQAT<br>7 8 9 10 11 12 7 8 9 10 11 12<br>Log GDP per worker in PPP (2015) Log GDP per worker in PPP (2015)<br>Net Enrollment Rate (%, 2015)<br>Average Years of Schooling (2015)<br><!-- End of picture text --> _Notes_ : Panel (a) plots average years of schooling in the population above 25 against log GDP per worker (in PPP terms) in 2015. Panel (b) plots the net enrollment rate in primary and secondary school against log GDP per worker (in PPP terms) in 2015. Average years of schooling is taken from Barro and Lee (2013), the enrollment rates are from the World Development Indicators, while GDP per worker is from version 9.0 of the Penn World Tables (Feenstra et al., 2015).

**More recent work, however, points to large gaps in learning achievements and educational quality across countries, which compound the effect of gaps in schooling quantity** . Figure III displays the average learning scores of primary and secondary school students from the Harmonized Learning Outcomes Database (Angrist et al., 2021a), a collection of average scores in standardized tests administered across 164 countries, against log GDP per worker. Students in rich countries vastly outperform their peers in poor countries, conditional on a given level of educational attainment; as a result, the vast majority of the young generations in the developing world do not achieve the skills needed to participate effectively in modern economies (Gust et al., 2024). In addition, Singh (2019) exploits panel data to show that an

6
<!-- Start of picture text --> Regression with the<br><!-- End of picture text --> Figure 7: Intercept Coefficients and their Significance in the Fixed-effect Regression with the semi-parametric Method and Updated Data <!-- Start of picture text --> Intercept Estimates For Gini - UNIDO 2-digit Employment Data<br>°|<br>-<br>©<br>=, ‘<br>x ,ih<br>; i<br>es<br>Income midpoint<br><!-- End of picture text -->

Source: Authors’ calculations Similarly, Figure 7 plots the intercept of the semi-parametric fixed-effects regression of each subsample against its midpoint GDP per capita. Consistent with our earlier evidence that between-effects account for much the U-shaped relationship between diversification and GDP per capita, the estimated intercept curve is U-shaped and the intercept is estimated with consistent precision.

<!-- Start of picture text --> and the intercept is estimated<br><!-- End of picture text --> <!-- Start of picture text --> with consistent precision.<br><!-- End of picture text --> <!-- Start of picture text --> Diversification for<br><!-- End of picture text --> <!-- Start of picture text --> and Highly<br><!-- End of picture text --> 4.5 Patterns of Diversification for Resource-Poor, Resource-Rich, and Highly Resource-Rich Countries.

<!-- Start of picture text --> Countries.<br><!-- End of picture text --> <!-- Start of picture text --> and<br><!-- End of picture text --> Figure 8 displays the lowess curve for the Gini index of employment, value-added and exports for resource-rich and resource-poor
5 ani estimate of both the direct and simultaneous effects of mortality and fertility determinants in Indonesia and giving an example of policy applications.

3. Some of the major policy questions examined are, (a) Do family planning programs have a substantial effect on infant mortality?

(b) Do programs to reduce infant mortality have a substantial effect on fertility?

(c) What is the relative effectiveness of hospital expenditures and non hospital expenditures on infant mortality? How does the cost effectiveness of expenditures on both of these programs compare with the effectiveness of family planning programs?

(d) Does interaction significantly affect estimates of the cost effectiveness of family planning and health programs?

4. To answer these questions the paper examines aggregate data for Indonesia. The paper is organized as follows. Section II describes the Indonesian setting. Section III sets out the analytical framework and describes the data. Section IV presents the empirical results, and Section V gives estimates of the cost effectiveness of family planning and health
negative impact on men’s expenditures are robust to different OLS specifications, a larger set of potential control variables for the PDS Lasso algorithm and a inverse hyperbolic sine transformation. The negative effect on men’s expenditures is also found when using the husbands’ data (though the coefficient is smaller and insignificant at -0.69 percentage points), and the husbands report a very small increase in women’s involvement in decision-making index of 0.02 SD.<sup>81</sup>

# **5.3 Mechanisms**

The evidence shown in the last subsection makes it unlikely that the increase in exposed women’s decision-making power and the negative effect on men’s expenditures is driven by an income effect.

> 77I follow the strategy described in Section 4.4, and match on all variables for which I test balance in Table 2, in addition to factory, enumerator and month dummy variables.

> 78Table C.33 in the Online Appendix shows the matching results for all main outcomes.

> 79Table C.34 in the Online Appendix presents the results for this subsample for all main outcomes. Note that due to data limitations I am unable to impose the condition of having been on the line before trial start on the comparison group and that the subsample of the exposed group is therefore likely more experienced, which may explain the larger impact on income shown in the table.

> 80See Tables C.35 and C.36.

> 81I do not find a positive impact on women’s decision-making when I use data from the same sample of exposed and non-exposed operators at factory follow-up (about three months after exposure started), when women’s involvement in four different spheres of decision-making was enumerated. The coefficient is negative at -0.12 SD and insignificant.

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Figure B17: Theil indexes at the national and across urban versus rural areas, sorted by GDP per capita <!-- Start of picture text --> (A) DOU (B) DB<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>Urban Rural National<br>NERMWITCDETHGNBBFAUGAGINTZALSOSENGHA CIVMRTAGOGAB NERMWITCDETHGNBBFAUGAGINTZALSOSENGHA CIVMRTAGOGAB<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Countries are sorted in ascending order of log of GDP per capita, measured in PPP (constant 2017 international $). GHSPOP 1km is used for the DOU and DB methods. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. See Figure B18 for the same chart with countries reordered by the highest to the lowest Theil indexes.

Figure B18: Theil indexes at the national and across urban versus rural areas, sorted by Theil index <!-- Start of picture text --> (A) DOU (B) DB<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>National Urban Rural<br>MWIAGOLSOUGA BFAGHA TZAGABNERETHSEN CIVTCDGNBMRT GIN MWIAGOLSOUGA BFAGHA TZAGABNERETHSEN CIVTCDGNBMRT GIN<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, GHSPOP 1km is used. For the DOU and DB methods, GHSPOP 1km is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method.

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Trade and FDI affect the incentives to migrate in multiple ways through increased job creation.<sup>16</sup> First, as discussed, better jobs increase incomes for many, which makes migration less appealing. This flow of capital is widely considered to be a _solution_ to the problem that workers cannot (or do not) move quickly and flexibly enough. FDI can, therefore, reduce migration in the short term (Kugler and Rapoport 2007), particularly among the people for whom FDI creates jobs. In the long term and/or among other groups of workers, FDI and migration may complement each other.

**Figure 4 Economic integration through trade increases with development**

_Sources_ : Original calculations based on trade data from World Bank 2022 and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figures show the relationship between trade (as a share of GDP) with GDP per capita. Each line on the background traces the evolution of trade and GDP for each country between 1960 and 2020 (with trade shares capped at 300 percent of GDP). The solid lines in the foreground plot the smoothened locally linear estimate pooling all countries and years between 1960 and 2020. CI = confidence interval.

But trade and FDI also reduce other barriers to migration, which can encourage migration. Trade and FDI result in a strong network of firms across countries that facilitate better information flows. Through these linkages, firms get better information about the ability of specific workers or groups of workers and have a credible way to recruit them. More generally, the advances in and adoption of communication technologies due to globalization has lowered search costs for employers and workers. The reduced information friction in the labor market can facilitate migration. More broadly, globalization has facilitated increased information flow about life abroad—including lifestyles, cultures, norms, and specific amenities and services—that could help lower

> 16 Trade creates more jobs due to expansion of market
to credit, rather than subjective measures of financing obstacles, because the former is more comparable across countries and it does not depend on cultural biases that might influence individuals’ perceptions; plus it is more reliable and easier to interpret. Appendix A1 gives a detailed description of the process used to construct our measure of access to finance.

We also include several firm-level variables that may influence the extent of firms’ access to finance, such as firm size, measured as log of the number of employees, a dummy for manufacturing industry (the omitted category is service and other industries), a dummy for exporting firms, a dummy for foreign-owned firms, a dummy for government-owned firms, and the log of firm age in years.

The bank-level data come from Bankscope, a commercial database by Bureau Van Dijk including annual balance sheet and income statement information for banks across the world. Only banks classified as commercial, cooperative, Islamic, savings, and bank holding companies are considered in the analysis. We leave out central banks and investment banks, because they are not directly involved in providing loans to firms.

We use bank-level data to construct the _Lerner index_ , a direct measure of pricing behavior by banks, which captures the markup in prices – i.e., the difference between prices and marginal costs, measured as a ratio of prices. Higher values of the index indicate higher markups or lower levels of competition.<sup>11</sup> We start with annual bank-level data and estimate a translog cost function using all available data for each country. We then calculate the marginal cost equation (by taking the derivative of the translog cost equation) and finally the Lerner index for each bank, which we then average for each country and year. Appendix A2 describes in detail the process we use to calculate the Lerner index.

We also use bank-level data to construct two commonly used measures of concentration: _Concentration 3_ is the share of banking system assets held by the three largest banks and

> 11 There is an
rather than relying solely on PMM or its variants to compensate for any model specification shortcomings.

Although the methodological approach described in this section applies to both the 2010 and 2016 surveys, for the sake of simplicity, this section will focus on the 2022 to 2016 imputation. However, it is essential to note that the same principles and procedures apply to the 2022 to 2010 imputations.

Data availability consists of at least two cross-sectional consumption surveys, the donor (i.e., HIES 2022) and the recipient (i.e., HIES 2016 or 2010). The official consumption aggregates are different from the two surveys. As mentioned, in 2022, many changes were introduced, like additional items, improvements in fieldwork and data collection, etc. First, to reconstruct trends in distributional indicators, a consumption aggregate is estimated for 2022 based on the commonly collected items in the two surveys. In both surveys, i is the comparable per capita consumption for household , (i.e., ), in survey ( ) with sample size , and i is a vector of characteristics observed yy ii ii= for household in survey . These household characteristics, observed in both survey years, include ii ii 1, ... , NN jj jj= 2016, 2022 NN XX household head characteristics (gender, ethnicity, civil status, education, age, etc.) as well as dwelling ii jj features (access to water, electricity, sanitary facilities, etc.) and other household variables (size, labor conditions, composition, etc.). Second, the linear model where the -th household's comparable log per capita consumption<sup>7</sup> i is explained by household characteristics i for each survey is given by: ii yy i i XX i (7) TT where i is an error term and yy = ααii + ββi is a vector of ii XX + εε _K_ regressors, and jj = 2016, 2022 is the intercept. In this
VHLSS methodology and asked for an aggregate of all FAFH for only three categories: (1) total FAFH of present household members, (2) total FAFH of temporarily absent household members, and (3) other FAFH. We expect that these household-level aggregated categories will lead to mismeasurement of FAFH and this arm to perform least well compared to the benchmark. Although FAFH was technically collected for three different categories or lines, we shall still refer to T1 as the “one-line” treatment arm since the majority of FAFH consumption is reported on the first line (FAFH of present members).

The next treatment arm, T2, collected FAFH using a daily individual-level diary, and is the benchmark against which all other arms will be compared. Finally, the next two arms, T3 and T4, tested possible alternatives which are less intensive than a diary (T2), but more intensive than collecting from three highly aggregated categories (T1). T3 was collected at the individual-level directly from all adult members of the household. By contrast, T4 (like T1) was collected from a single individual informant for the entire 7