rafmacalaba/data-use-mentions-extended huggingface dataset:
>Original Scores Test Scores<br><!-- End of picture text -->

# **4.2 Interpretation**

Regardless of the optimization object chosen, the resulting data set has a similar structure. Each row contains the results for one simulation. The `obj` variable contains the optimization object, while the rest of the variables contain the parameters of the 6th-degree polynomial transformation that achieves that object and the initial random starting values for these parameters. This format allows the user to not only have the full distribution of results but also access the necessary parameters to replicate the desired scale transformation if needed.

For the Gap Growth maximizing and minimizing options, one should be aware that the program finds the most extreme transformations, some of which might be extremely unlikely (although theoretically plausible). Thus, for instances where the bounds are too wide, it might be useful to benchmark against likely transformations (such as those found by the `scale` ~~`t`~~ `ransformation` program that maximize correlation or R-squared) and explore other properties of test scores that could help us discard some of the most extreme and very unlikely transformations. For instance, Ho and Yu (2015) present values for skewness and kurtosis from 330 United States state-level examinations. Based on this, one could look at the moments of the resulting distributions and discard those
Child Development Workers educate and talk to parents or children, 57 percent conduct infant feedings, and nearly 80 percent engage in home visits and growth monitoring. These results show an intensification of the provision of a range of crucial health and ECDrelated services for both young children and parents in program areas.

**Table 1. Functions of Service Providers by Type of Provider and ECD Program Area,** **<u>Philippines ECD Study (Round 3)</u>**

|Provider Type:|Rural<br>Mid|Health<br>wife|Bar<br>Health|angay<br>Worker|Day<br>Wo|Care<br>rker|Child<br>Development<br>Worker<sup>a</sup>|
|---|---|---|---|---|---|---|---|
|Program (P) or non-<br>program (NP) area:<br>_Functions:_|P|NP|P|NP|P|NP|P|
|Examine patients|95.5|81.7*|3.1|6.1|n.a.|n.a.|n.a.|
|Provide diagnosis|30.5|12.0*|0.24|0|n.a.|n.a.|n.a.|
|Dispense medicines|88.7|91.7|15.4|16.5|0|0|2.8|
|Make referrals|57.1|36.7*|14.2|10.8|7.7|0*|11.1|
|Educate or talk to<br>clients|67.2|36.7*|19.5|15.4|54.1
# **1 Introduction**

Air pollution has causal impacts on mortality, physical and mental health, labor supply and productivity, migration, cognitive performance, human capital accumulation, and decision-making (Hoffmann and Rud, 2024; Aguilar-Gomez _et al._ , 2022; Chen _et al._ , 2022; Guidetti _et al._ , 2021; Anderson, 2020; Deryugina _et al._ , 2019; Zhang _et al._ , 2017; Arceo _et al._ , 2016; Graff Zivin and Neidell, 2013). The majority of the evidence on the impacts of air pollution comes from high-income settings where there is high-quality data available to study pollution and its impacts.<sup>1</sup> However, individuals in low- and middle-income countries face greater exposure and negative impacts of air pollution than those in high-income countries. Approximately 80% of people exposed to unsafe levels of air pollution live in low- and middle-income countries (Rentschler and Leonova, 2022) and 89% of the mortality and morbidity attributed to air pollution globally occurs in developing countries (WHO, 2024).

As there are important differences in the effects of air pollution across higher- and lower-income settings (Arceo _et al._ , 2016), more research is needed in low-income countries to evaluate both the extent of the problem and the effectiveness of policies in tackling it. A necessary first step is to develop approaches that can accurately measure air pollution, taking into account the particular constraints faced by governments in low-income countries. Recent studies have expressed enthusiasm about new frontiers in monitoring air quality, such as the advent of low-cost monitoring devices and the availability of satellite data (Snyder _et al._ , 2013; Di _et al._ , 2016), but it is critical to understand how well they measure air pollution and how useful they are for policy evaluation. Additionally, identifying how to combine different data sources into a cost-efficient air quality monitoring system could help low-income countries improve their air pollution measurement.

In
*Total**|18.54|24.14|37.12|20.20|100.00|

_Source:_ Author’s computations based on Economic Census data.

# **Misallocation is High and Persistent in Mexico**

Misallocation in Mexico is significant, as evidenced by the fact that the marginal revenue product of capital and the labor of firms that produce similar goods are different. (See appendix S1 for definitions and for descriptions of the data.) In fact, misallocation is higher in Mexico than in the United States or in other Latin American countries with comparable data (Busso, Madrigal, and Pagés 2013). While there is debate over the exact nature of the distortions that induce this phenomenon, three results are robust: distortions result in large productivity losses; they operate in the direction of allocating too much capital and labor to low-productivity firms that are less intensive in more educated workers; and they are persistent through time.

Following Hsieh and Klenow (2009), total revenue productivity, TFPRis, is defined as the value produced by firm _i_ in sector _s_ with one peso of capital and labor (a weighted average of the marginal revenue products of the labor and capital used by the firm). In the absence of any 9
World Bank) are the appropriate weighting scheme. In contrast, for measuring living standards and aggregating welfare, PPP weights would be appropriate since they capture the consumption affordable to households for comparable consumption baskets.

While the theoretical purposes of the weighting schemes are clearly distinct, they also have different features in practice. First, PPP exchange rates are subject to greater measurement challenges. Since they are constructed from prices of the same baskets of goods and services, they rely on price surveys by the World Bank’s International Comparison Program (ICP; Callen (2007)). These are conducted infrequently and not available for all countries, hence subject to considerable measurement error and extrapolation to countries and years with missing data. PPP exchange rates tend to be more stable than market exchange rates (Schnatz, 2007). Hence, for weighting purposes, market exchange rates are typically fixed at the value of a specific year or period by the World Bank, and can therefore be influenced by short-term fluctuations that occurred in the chosen period.

The large majority of convergence studies have used versions of the Penn World Table (PWT) as a data source of PPP-adjusted income per capita due to its large coverage both the time and country coverage dimensions (Feenstra et al., 2015; Johnson et al., 2013). In recent iterations, the PPPadjustment is estimated on a time-varying basis, rather than simply using survey-based evidence from a particular year. The PWT version 9.1 is used in this box as a source of PPP-adjusted labor productivity levels and growth rates.

The productivity gap between advanced economies and EMDEs is substantially smaller once productivity is measured at PPP-adjusted exchange rates ( **Figure 12** ). The average EMDE worker produces 34 percent (16 percent at market exchange rates) of the output of the average advanced economy worker, while LICs produce 4 percent (2 percent when measured at market exchange rates). While the scale of productivity differentials relative to advanced economies is smaller under PPPmeasurement, the relative ordering of productivity
, 2016). A contiguous cluster of grid cells that falls above the applied threshold is then classified as constituting a “metro” area.

More recently, however, DMSP-OLS NTL data have been superseded by NTL data collected from a new satellite sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor, launched in 2011. This sensor collects NTL data at a far higher resolution than the old DMSP-OLS sensors and the derived data products are also not subject to the overglow problem. We use the new VIIRS satellite data to delineate metro areas. Specifically, we use the 2015 annual composite product which has recently been released by NOAA.<sup>11</sup> This product reports luminosity values, calculated as an annual average over all cloud-free nights in 2015, at a resolution of 15 arc seconds, which is equal to 460 m<sup>2</sup> at the equator. Prior to averaging, NOAA applies filtering techniques to remove data that are affected by stray light, lightning, and lunar illumination. NOAA likewise filters out lights from aurora, fires, boats and other temporary lights. Although the VIIRS NTL data do not suffer from the same overglow problem as the DMSP-OLS NTL data, they, nevertheless, record light emitted to the nighttime sky by all human activities, including light at very low levels outside of what may be considered metro or even urban areas. For this reason, the use of a threshold may still be required to properly delineate metro from non-metro areas. As with papers that have used the DMSP-OLS data for the same purpose, we consider a contiguous cluster of grid cells that falls above any imposed threshold as representing a “metro” area.

# **3. Application to Indonesia**

# _3.1. Data sources_

The data that we use to apply the four approaches to delineating metro areas to Indonesia come from a variety of sources. For Duranton’s algorithm, we use data on O-D commuting flows between Indonesian districts that we derive from the
World Bank document that lists the reasons for delays once World Bank projects are completed. For each of the listed projects, delays were mostly due to the slow implementation of initial administrative tasks connected to weak institutional capacity, the slow resettlement process, the slow adoption of laws to facilitate the project, and the limited capacity of the responsible investment party. A main road rehabilitation project was delayed between 2017 and 2018 due to repeated cases of genderbased violence, ranging from sexual harassment to sexual exploitation and abuse, and rape linked to the implementation of the project and the influx of workers on the project site.<sup>4</sup> We check whether an increase in gender-based violence, classified as violence against civilians in ACLED data, during the project implementation only could drive our results by

> 4 `https://www.inspectionpanel.org/panel-cases/high-priority-roads-reopening-and-maint enance-2nd-additional-financing-p153836`

12
_Source: Reform Committee data Note: Figure based on 107 economies with a functioning business reform committee._ In Azerbaijan, the Commission on Business Environment and International Rankings relies on different strategies to monitor progress, including reform strategy action plans, progress reports, closed meetings with supervisors, firm surveys, user feedback, and progression towards international benchmarks. For example, progress reports to monitor progress are provided by working groups to the Commission every three months and the final evaluation report is prepared by the Commission every 9-12 months. Similarly, in Peru, the website of the National Council of Competitiveness and Formalization (CNCF)<sup>5</sup> provides information about the fulfilment rate of the policy measures of the National Plan of Competitiveness and Productivity.

# **4.3. Stakeholder Engagement and Communication**

International organizations such as the World Bank and other development banks provide relevant technical and advisory support to clients regarding their business regulatory environment, including on setting up adequate institutional structures to implement reform agendas. These organizations can provide funding, technical advice, or training to the reform committee.

According to the data on reform committees, as expected, these international organizations have supported the creation of these reform committees mostly in low and lower middle-income setups. None of the reform committees in high-income economies have engaged international organizations in their development. The data seems to suggest that economies seem to rely on international development organizations in instances where they lack the requisite technical skills and financial capacity domestically. An exception to this is the case of Uzbekistan, a lower-middle income economy, where no international organizations were involved in the creation of the Republican Council for working with International Ratings and Indices.

Notably, more than a third of all reform committees have engaged the World Bank Group to support their creation (Figure 10). For example, the International Finance Corporation extended technical assistance towards the design and operationalization of the Ethiopia National Doing Business Reform Steering Committee. In other cases, the interventions from international organizations have been limited to providing technical
# **4. Construction of the Main Variables**

## **_4.1 Short-term Performance_**

To examine the hypothesis by Shin and Stulz (1996) that investment policy of segments in diversified firms differs from that of similar specialized firms, we employ the "chop-shop" procedure of Lang and Stulz (1996) to construct our short-term and long-term performance measures. In addition to adjust for sectoral differences in performance, the measures can be interpreted as the performance of a multi-segment firm relative to singlesegment firms in its industries. These measures allow us to compare the performnance differences between multi-segment (diversified) and single-segment (focused) firms and associate the perfornance differences with their different investment strategies.

We measure a firm's short-term performance by its profit margin, calculated as one minus the costs of goods sold over sales. We first use the sub-sample of single-segment firns in each country to compute the median profit margin for each two-digit SIC code industry. We then multiply the sales share in each segment of a firm by the corresponding industry median profit margin. We sum the sales-weighted profit margin across segments to obtain the imputed profit margin of the firm. Lastly, we subtract the imputed profit margin from the actual profit margin to obtain the industry-adjusted excess profit margin (EPM).<sup>8</sup> In the computation of industry median profit margin, we restrict the number of single-segment firms to be at least three. In some cases, we do not have sufficient number of firms to compute the median profit margin. In these cases, we use the median profit margin of broader industry groups as defined by Campbell (1996). This procedure avoids the loss of observations. Table 2 compares the differences in the excess profit margin measures between single- and multi-segment firms. Overall, multi-segment firms are less profitable than single-segment firms. The difference in mean and median EPM between the multi- and single-segment firms is negative and statistically significant at the 1-percent level.

## [Table 2 here]
**Table 2:** Consumption Aggregates and Poverty Rate

||(1)<br>Mean|(2)<br>sd)|
|---|---|---|
|Per capita monthly consumption expenditure (in Afghani)|2614.22|1728.48|
|Components of per capita expenditures (in Afghani)|||
|Food|1473.40|982.47|
|Durables|111.81|196.34|
|Rent|315.11|438.00|
|Non-food|713.90|691.18|
|National poverty line (in Afghani)|2239.62|530.72|
|Urban food poverty rate|.48|.50|
|Rural food poverty rate|.52|.50|
|Food poverty rate|.52|.50|
|Urban poverty rate|.45|.50|
|Rural poverty rate|.48|.50|
|Poverty rate|.47|.50|

_Note_ : This table reports summary statistics for consumption aggregates and poverty rates from IE-LFS 2019-20.

26
><!-- End of picture text --> Anumal Data DEVOFF Devaluation Rate in of ficdel £xchaoge Itatc REX : Real Exchange Race tndex CAB : O.crent AccOunlc Balances i,liow _UJS$_ WRA Real Wage Index (SowuCC: fundaCioti 141diterrafteg) YGI : AnNmacl Ggwch Rate of Real GDO P2) Other variable definitions, ibide, Figure 3. Sources World Bank Date cc _oJ_
730-70.

- [45] gollier.c and j.w.pratt (1996) “Risk Vulnerability and the Tempering Effect of Background Risk,” _Econometrica_ 64: 1109-23.

- [46] guarnieri.e and h.rainer (2018) Female Empowerment and Male Backlash, CESifo WP7009.

- [47] haushofer.j, j.shapiro, c.ringdal, x.y.wang (2018) Income Changes and Intimate Partner Violence: Evidence from Unconditional Cash Transfers in Kenya, mimeo Princeton.

- [48] hes.s, d.jaimovich and m.scheundeln (2018) Development Projects and Economic Networks: Lessons From Rural Gambia, mimeo Goethe University.

- [49] himelein.k, m.testaverde, a.turay and s.turay (2015) The Socio-economic Impacts of Ebola in Sierra Leone: Results from a High Frequency Cell Phone Survey (Round Three), Washington DC: The World Bank.

43
|India||||
|1990–1999|2.9|1.7|1.3|
|2000–2004|6.5|6.2|0.3|
|Vietnam||||
|1990–2008|5.1|1.9|3.1|
|1990–2000|5.2|1.0|4.2|
|2000–2008|4.9|2.7|2.2|
|Brazil||||
|1995–2005|0.8|0.6|0.2|
|1990–2005|0.8|0.8|–0.0|
|1993/1995–2007/2008|0.5|0.3|0.2|

Source: _Botswana_ —Value-added and employment data are from the Groningen Growth and Development Centre Africa Sector Base; _Ghana_ —Economic Survey of Ghana 1961–1982; population and housing censuses 1960, 1970, 1984, 2000, and 2010; Ghana Living Standard Survey 1991–1992 and 2005–2006; Singal and Nartey (1971); Androe (1981); Ewusi (1986); GSS (2010); and World Bank (2010); _Nigeria_ —Output data are from the Nigerian Bureau of Statistics. Employment data are from the Nigeria General Household Survey (GHS) [1996–2011]; Zambia—Data are from the Central Statistics Office [1993, 2004, 2011, and 2012.]; _India_ —Value-added and employment data are from the Groningen Growth and Development Centre; _Vietnam_ —Employment, gross domestic product (in constant 1994 prices), and labor productivity (also in constant 1994 prices) data are from the General Statistics Office of Vietnam; _Brazil_ —For the period 1950–2005, value-added and employment data are from the Groningen Growth and Development Centre. For the period 1993/1995–2007/2008, data are from Pesquisa Nacional por
whose relationships with crop yields we are trying to assess.

# **2.3 Descriptive statistics**

Table 1 presents summary statistics of the main plot and household level variables used in the analysis. The first part of the table displays plot characteristics. The variables extracted from the GPS coordinates show that most of the plots are located on similarly flat terrain within a 5 km radius of the household’s dwelling. In terms of land tenure, 95% of the plots are reportedly owned by the households cultivating them. The plot managers of these plots are mostly middle-aged men with low literacy levels. The average size of the associated households was 19,<sup>8</sup> and households were headed overwhelmingly by middle-aged men most of whom were illiterate. The average number of cultivated plots is further indication that agriculture is the main activity for the households. Furthermore, the average GPS-based plot area is considerably larger than the available estimates observed in smallholder farming systems elsewhere in Africa. For example, Desiere and Jolliffe (2018), Gourlay et al. (2019), Abay et al. (2019) report average plot sizes ranging from 0.12 to 0.37 hectares in their sample. The incidence of agricultural input use was, on average, 33% for hired labor and 24% for fertilizer.

Table 2 reports estimates for plot-level for sorghum production (kilograms) and yields (kilograms per hectare).<sup>9</sup> The competing figures are based on plot-level self-reported sorghum production versus estimated sorghum production that is tied to the crop harvest for the 8m x 8m crop cut sub-plot and separately, a randomly chosen 4m x 4m quadrant within the sub-plot.<sup>10</sup> All yield measures use the GPS-based plot areas. As we do not find statistically significant differences between crop cut yields as a function of the size of the crop cut, in the rest of the analysis, CC yields are tied to the 8m x 8m sub-plot.

We
Figure 1: Timeline of Study <!-- Start of picture text --> Baseline/Census<br>1027/1104 HFs (93% response rate)<br>Jan-Dec 2015<br>Partial census update [1]<br>1258 HFs<br>Oct-Nov 2016<br>Randomization listing<br>273 Markets, 1258 HFs<br>Nov 2016<br>(+90 HFs found later = 1348 HFs<br>operational at randomization) [2]<br>Treatment sample Control sample<br>186 Markets, 856 HFs (913 HFs 87 Markets, 402 HFs (435 HFs<br>operational at randomization) operational at randomization)<br>Treatment rollout [3]<br>Nov 2016 – Dec 2017<br>Endline/Census<br>Markets: 268 [4]<br>HFs: 1285/1319 (97% response rate)<br>Mar-Aug 2018<br>Treatment sample Control sample<br>Markets: 182, HFs: 883 Markets: 86, HFs: 436<br>T1<br>Markets: 89, HFs: 393<br>T2<br>Markets: 93, HFs: 490<br><!-- End of picture text --> _Notes._ [1] Due to the high turnover of facilities and delay in the implementation, we conducted a partial update of the census in markets of size 1, 2, and 3 between October and November 2016. We used this partial update of the census of 1,258 facilities located with available GPS coordinates for the randomization. [2] 90 facilities were missed or listed as temporarily or permanently closed during the randomization census. These facilities were added using a nearest-neighbor algorithm to the nearest market by endline. [3] Another partial update to the census was conducted at the end of July 2017 when the first round of inspections was completed in all counties. At this stage, only the
# 5.3. Heterogeneous treatment effects

Analysis of two crucial heterogeneous treatment effects makes a valuable contribution for policy decisions. The first is the impact of moving out of camps on female-headed households. The estimate shows that female-headed households are in general more vulnerable than male-headed households, whether they live in or out of camps. Table 3 shows that, on average, female-headed households earn less, are more likely to be at risk of deprivation in basic needs, and own fewer household assets. They are also less likely to report increased life satisfaction if they move out of camps. The gendered disaggregated DiD analysis, based on common-support regions, estimates that moving out of camps increases the steps in the Cantril ladder by 0.76 point for male-headed households, but only 0.67 point for femaleheaded households. In general, after controlling for pre-crisis differences, female-headed households living in camps earn JD4.34 less than male-headed counterparts. One positive result is that women headed households are less likely to live in overcrowded houses both in and out of camps. This result may be because of absence of the male head. About 78 percent of female-headed households in camps are at risk of abject poverty compared to 61 percent of male-headed households. Poverty nevertheless reduces for both groups when they move out of camps. Moving out-of-camp also tends to decrease female-headed household poverty more than for male-headed households: the share of households below the abject poverty level reduced by 47 percent for female-headed households who moved out of camps, but the reduction is only 31 percent for relocating male-headed households.

_Table 3: Heterogeneous treatment effect on gender of the household head_

||Female|-headed ho|useholds|Male-h|eaded house|holds|
|---|---|---|---|---|---|---|
||Camp|Out-of-<br>camp|ATT|Camp|Out-of-<br>camp|ATT|
|Life satisfaction|||-