rafmacalaba/data-use-mentions-extended huggingface dataset:
we use average effective tax rates as marginal effective tax rates. The main advantage of following this method is that all exemptions and deductions of the tax code are embedded in our measure. Hence, we do not have to model the intricate details of the tax code. The main drawback of our approach is that the observed tax rate is potentially endogenous to certain firms’ characteristics and past behavior. We conduct several robustness checks to verify that our results are not driven by other specific characteristics and behavior of the firm.

We set the rental rate of capital to _r_ = 0 _._ 05 and the depreciation rate to _δ_ = 0 _._ 05, to make our results comparable with Hsieh and Klenow (2009). The elasticity of substitution between varieties is fixed at _σ_ = 3, so that firms’ price is 50 percent higher than their marginal cost. In Section 5.1, we evaluate the sensitivity of our results with respect to these assumptions. The capital share _αs_ in industry _s_ is equal to 1 minus the labor share in that corresponding industry for the United States.<sup>10</sup> These shares are obtained from the NBER Productivity Database.<sup>11</sup> Using the data and parameter values described above, we back out the capital and output wedges in the following manner. For firms with positive accounting profits, we use equations (5) and (6) to obtain the firm-specific wedges. Since _μ_<sup>_t_</sup> _si_<sup>= 0,theoutputandcapitalwedgesare</sup> On the other hand, for firms with negative accounting profits the capital and output wedges are obtained from equations (7) and (8). In this case, _μ_<sup>0</sup> _si_<sup>=0</sup> 10. Following Hsieh and Klenow (2009), we set the capital shares for each industry equal to those of the United States as we suppose that the US
determinant index att−5.<br>(2011 = 1)|
|---|---|
|**Table B2-a. TFP de**<br>|**terminants and Related Indicators**|
|Innovation|R&D expenditure, public and private (percent of GDP)<br>Number of patents, residents, and nonresidents (per 100 people)<br>Number of scientificjournal articles(per 100people)|
|Education|Government expenditure on education (percent of GDP)<br>Proxy for primary education: secondary enrollment rate (gross percentage)<br>Proxy for secondary education: PISA score<br>Proxyfor tertiaryeducation: completed tertiaryeducation(percent ofpopulation 25+)|
|Market Efficiency|Goods market: Doing Business index.<br>Financial market: IMF Financial development index<br>Labor market: Minimum wage (percent of VA per worker), severance pay for redundancy dismissal<br>(weeks of salary),and share of women in wage employment in non-agricultural sectors|
|Infrastructure|Fixed telephone and mobile subscription (per capita)<br>Length of paved roads (kilometer per capita)<br>Electricity production (kilowatt per capita)<br>Access to improved water source (percent of population)<br>Access to improved sanitation facilities(percent ofpopulation)|
|Governance<br>Source: Kim and Loayza|Voice and accountability, corruption control, government effectiveness, political stability, regulatory<br>quality,and rule of law<br> (2019).|

# **4.1. Reforms to boost TFP growth in Peru**

Identifying ways to boost TFP growth is often challenging because TFP growth is a residual. While reforms to schooling and the investment climate (such as access to credit) are better understood, TFP is generally regarded as a residual component of growth representing economic efficiency and technological improvement. However, this loose and broad definition impedes a
<!-- Start of picture text --> Producer Price Index by Industry: Deep Sea Freight Transportation: Deep Sea Freight Transportation<br>Services<br>380<br>360<br>340<br>320<br>300<br>280<br>260<br>240<br>Jan 2015 Jan 2016 Jan 2017 Jan 2018 Jan 2019 Jan 2020 Jan 2021 Jan 2022<br>Shaded areas indicate U.S. recessions. Source: U.S. Bureau of Labor Statistics fred.stlouisfed.org<br>Index Jun 1988=100<br><!-- End of picture text -->

**Figure 1:** PPI for deep sea freight transportation services between January 2015 and January 2022, taken directly from FRED.

cation is motivated by the behavior of the producer price index for deep sea freight trans1. portation services, depicted in figure This index increased by approximately 12% from its pre-pandemic level of 330 to around 370 in December 2021.

It is worth noting that the aforementioned producer price index may not capture the full increase in the cost of cross-border trade, as other trade cost components could have also risen. Nevertheless, iceberg trade costs in trade models also include non-physical factors such as marketing or research costs for entry into a foreign country, which may not have risen as much as physical costs. Overall, we view the 12% increase in the iceberg trade cost as a suggestive benchmark and assess the sensitivity of our results to different values of this parameter.

The choice of a 3-year duration for the shock is based on current evidence suggesting that global supply chain disruptions have largely subsided by early 2023. Thus, we assume that high trade costs are in effect for 2020, 2021, and 2022, but then dissipate by 2023.

12
—||99|
|Distribution losses|% production|40||37||29|
|Cost recovery|% total costs|67||56||86|
|Operating cost recovery|% operating costs|94||79||121|
|Labor productivity|connections per employee|96||—||203|
|||**Cameroon**<br>(2009)|Co|untries with non-<br>scarce water<br>resources|Other<br>re|developing<br>gions|
|Average effective tariff|US$ per m3||0.8|0.6||0.03–0.6|

_Source_ : AICD water supply and sanitation database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note_ : Access figures from Demographic and Health Surveys (1991 and 1998) and Multiple Indicators Cluster Survey (2006) as reported by WHO 2010a and 2010b. A country is considered to have non-scarce water resources if renewable internal freshwater resources per capita are greater than 3,000 mm.

— = data not available.

Open defecation has also declined, if slowly, with the expansion of traditional latrines (figure 15b). The practice declined from 9 percent to 7 percent between 1998 and 2006, both figures being only about a quarter of the typical level of open defecation in resource-rich countries (table 9). Over this period, use of traditional latrines increased from 33 percent of the population in 1998 to 35 percent in 2006. But because the overall level of access to improved sanitation in 2006 remained just 47 percent, the country was far behind the goal of achieving the Millennium Development Goal of 74 percent of population with access to improved sanitation (AMCOW 2010b).

26
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The SDI surveys collect a broad set of school, teacher, and student specific information, with an approach that relies as much as possible on direct observation rather than on respondent reports. Data are collected through visual inspections of fourth-grade classrooms and the school premises, direct physical verification of teacher presence by unannounced visits, and teacher and student tests. Bold et al. (2017) document how African teachers perform along three dimensions: Time spent teaching, teachers’ knowledge of the subject they are teaching, and teachers’ pedagogical skills. Teachers, on average, are absent from class 44 percent of the time and about half of that classroom absence is due to teachers not being at the school during regular teaching hours (Table 1). As a result, while the scheduled teaching time for fourth graders is relatively long—5 hours and 25 minutes—the actual time students are taught is about half that time—2 hours and 46 minutes. Pedagogical knowledge is low, with only one in ten teachers deemed to have minimum pedagogical knowledge, and even fewer teachers are judged to be able to properly assess students’ learning shortcomings and progression.

In each school, 10 students are sampled from a randomly selected grade 4 classroom. The choice to test students who had completed the third grade was made with the following objectives in mind: on the one hand a desire to assess cognitive skills at young ages when these are most malleable; and on the other hand a desire to assess the learning outcomes of students who have completed at least some years of schooling and to assess language learning at a time when all children would have had lessons in the official language of their country (English in Nigeria and Uganda, English and Swahili in Kenya and Tanzania, French in Senegal and Togo, and Portuguese in Mozambique). In each school, the students’ current, and to the extent possible, previous language and mathematics teacher are selected for testing.<sup>8</sup> In more than 80 percent of the
0.006|0.398***|0.884|
||(0.887)|(0.198)|(0.033)|(0.020)|(0.079)|(1.081)|
|Observations|1206|1206|1206|1206|1206|1206|
|_R_<sup>2</sup>|0.101|0.232|0.488|0.483|0.317|0.184|
|Control mean|17.032|1.602|0.256|0.148|0.184|1.254|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

41
032|17.7|
||(0.063)||(0.11)||(0.091)||(0.11)||
|Perc. Change Income|0.053***<br>(0.0021)|1.0|-0.0069**<br>(0.0035)|1.0|0.051***<br>(0.0029)|1.0|-0.00030<br>(0.0037)|1.0|
|Observations|23,568||||23,496||||

_Source_ : Authors’ analysis based on survey data.

_Note_ : WTP= willingness to pay. Columns (1) and (5) show the baseline coefficients for men and for workers with primary or lower education, while Columns (3) and (7) show interactions for women and for workers with a secondary or higher education, respectively. The WTP for each attribute for women is calculated by summing the coefficients on that attribute in Columns (1) and (3), respectively, and dividing by the sum of the coefficients on income in Columns (1) and (3). A 46
# **6 Heterogeneous Treatment Effects**

It is not clear whether digital market access can substitute for in-person exposure among low-mobility women. Although these might be more market-constrained, they might also have limited capital, time, skills, or aspirations. If that’s the case, there might not be a significant share of productive but market-constrained women for the intervention to impact.

To shed light on this tension, I first present heterogeneity in treatment effects by women’s mobility levels. Second, I present consistent heterogeneous treatment effects as predicted using machine learning algorithms, following Chernozhukov et al. 2018. Finally, I present results by whether a business is home-based, which is a pre-specified measure of business owners’ mobility and market exposure.

## **6.1 Heterogeneity by Baseline Mobility**

I create an index for respondents’ physical mobility using mobility variables collected from the baseline survey. Following Kling et al. 2007, the index averages the standardized values of baseline mobility variables. These variables are summarized in table 1 and consist of: (1) Ability to take weekly trips alone for business-related reasons; (2) Willingness to operate the business outside the home if rent is covered; and (3) The number of times the respondent went out alone last month.

### **6.1.1 Differences across high vs. low-mobility women:**

In table 5, I document how high vs. low mobility women differ in demographics and business outcomes, defining high and low mobility as above vs. below median mobility index. As one might expect, high and low mobility women have significant differences in their demographics, values, digital practices, and business characteristics. These differences are not necessarily confounders for mobility, but underlying reasons for it.

For example, high mobility women are significantly less likely than low mobility women to live in rural areas, in clan communities, to be married, to have children, and to have a working husband. They are also more
Our effect is roughly the same size as that found in Dechezleprˆetre et al. (2019). This is somewhat surprising given the dramatically higher levels of ambient pollution in India when compared to Europe. On average, in their sample, the population faces annual average exposure of 10 _μg/m_<sup>3</sup> compared to average exposures of 50 _μg/m_<sup>3</sup> in our sample. But our effects are roughly double those found in Avila Uribe (2023), more consistent with the hypothesis that higher levels of exposure may lead to more severe impacts.

The effects of these differences in ambient concentrations are likely exacerbated by differences in the sources of economic activity between India and Europe and the United States, and the implied differences in vulnerability to air pollution. Data from the Reserve Bank of India suggests that as much of 50 percent of India’s GDP comes from sectors that are exposed to heat - a rough approximation for the share of GDP generated by outdoor work. This is compared to less than 25 percent of European GDP generated by the same sectors based on European Central Bank figures.

Indian workers work in an environment with substantially higher levels of ambient pollution than European workers and a substantially larger share of Indian GDP is generated by – – workers who are likely to be frequently working in conditions outside that expose them to ambient pollution. Both of these facts suggest that the impact of increases in pollution on GDP should be larger in India than Europe.

On the other hand, recent work examining the mortality impacts of very high levels of air pollution using exposure to wildfire smoke in the United States suggests a strongly concave dose-response function for air pollution exposure (Miller et al., 2017). While small changes in pollution have large mortality effects at low levels of pollution, similarly small changes at higher levels of pollution have much smaller effects. This is supported by work examining the consequences of exposure to pollution on outdoor workers around the world that finds a similarly
historical events commonly rely on population as a proxy for prosperity.<sup>29</sup> Third, as argued by Bairoch (1988), population density is closely related to urbanization, and it has been used as a proxy of urbanization in many studies of history (e.g., Acemoglu et al. 2002; Chen and Kung 2016; Jia 2014).<sup>30</sup> Finally, by Malthus’s argument, only areas with high productivity can afford a high population density (Malthus 1798). We recognize that after the Malthusian regime (Galor and Weil 2000), a better measure of development is income per capita, and that slower population growth, in fact, could be a key sign of high-quality development.

We use two data sets for population. One is from the commonly used Cao (2001), covering 266 of all 317 prefectures in China (Chen and Kung 2016; Jia 2014). It records population once about every three decades between 1820 and 1953. The other source is the national censuses for 1953, 1982, and 2000. Using the historical GIS maps of China, we merge the contemporary census data set with the historical population data sets, after considering different administrative boundary changes.<sup>31</sup> In the end, we rely on seven snapshots across two centuries (i.e., years 1820, 1851, 1880, 1910, 1953, 1982, and 2000) for population evolution.

To see the long-term development impact, we also consider modern outcomes. Income is captured by GDP per capita in 2010.<sup>32</sup> Fiscal capacity is measured by fiscal revenue per capita in 2010,<sup>33</sup> industrialization by the shares of manufacturing and of non-agricultural employment in 2000, and human capital by the average years of schooling and the mortality rate.

_The Taiping Rebellion_ **.** Based on Guo (1989) and Hua (1991
This study examines the potential moderating role of child gender in the association between ECD outcomes and selected risk and protective factors. The factors studied here are those for which there is prior evidence of direct effects on ECD outcomes in Pakistan (Hentschel et al., 2023). Specifically, this study focuses on children from two age cohorts: ages 0-35 months and 36-72 months. It examines the extent to which child gender moderates the associations of parental distress (risk factor), psychosocial stimulation (protective factor) and enrollment in ECE (protective factor) with ECD outcomes. This study seeks to address the following research questions:

1. How does the distribution of key risk and protective factors and relevant demographics vary by child gender for children aged 0-35 months and 36-72 months?

2. Are the relationships between risk and protective factors and early child development outcomes moderated by child gender for children aged 0-35 months and 36-72 months?

Given the dearth of evidence surrounding this topic in the Pakistani context, this study is exploratory in nature and aims to inform future investigations of early influences of child gender on ECD in Pakistan.

# **Methods**

## **Procedure**

This paper uses data from a nationally representative phone survey in Pakistan conducted by Gallup Pakistan from December 2021 to February 2022.<sup>1</sup> Data were collected by phone due to health protocol restrictions in place during the COVID-19 pandemic. Participants included caregivers with at least one child aged 72 months or younger. For each participant, one focal child was selected. Focal child selection was stratified according to child gender (ensuring a 1:1 ratio of boys to girls) and age (ensuring a 1:1 ratio of 0-35-month-olds to 36-72-month-olds). Participants were identified using random digit dialing (RDD) of mobile phones, drawing on Pakistan’s four telecom providers with active numbers. Interviewers made up to three calls to reach individuals identified through RDD. Upon accepting the invitation to participate, individuals were administered a screening questionnaire,
0.0%|2.8%|
|Sri Lanka|Non-urban|12|17|28%|0.0%|0.2%|
||Urban|80|22|48%|0.0%|7.1%|
|SAR|Non-urban|15|11|41%|0.0%|9.1%|
||Urban|23|14|45%|1.4%|10.5%|

_Source: Interim Enterprise Survey, 2009/10; Own calculations_

> 10 The sample for Pakistan only consists of firms located in urban areas.

15
’ effort, and (iii) providers’ inputs to work. SDI surveys allow for assessing how these elements come together to produce quality health services in the same facility. Data collection is undertaken by enumerators visiting selected medical facilities in announced and unannounced visits and observing providers’ performance, the availability of inputs, and the infrastructure in place.

For this analysis, we rely on the SDI health survey implemented in Kenya in 2018, the largest to date, for which 3,094 facilities, of which 1,718 public and 1,313 private facilities, were visited between March and July 2018. The survey is representative of each of the 47 counties, the location of facilities (i.e., urban or rural), facility ownership (i.e., public or private), and facility level (i.e., hospital, health center, or dispensary and clinic).

Data on service delivery performance, including the availability of inputs and medical providers’ knowledge, are aggregated at the facility level. We focus on questions related to the availability of resources, such as medicines and vaccines, and providers’ knowledge, focusing on early childhood and maternal health. For the former, enumerators verify firsthand the availability of non-expired medicines and vaccines from a list of essential pharmaceuticals and maternal and reproductive products. For the latter, Patient Case Simulations (PSC, also called vignettes) are performed to assess the providers’ diagnostic and treatment accuracy for specific illnesses of wide prevalence in the country. A random sample of staff is selected in each facility<sup>5</sup> with one of the enumerators presenting different cases of a sick patient to each health provider while the other acts as an observer. Section 3.2 describes the variables built based on SDI questions.

> 5 Up to ten medical personnel from those who regularly treated patients.

6
regional trade for Central America and quantify the relationship between trade intensity, trade structure and business cycle synchronization and discuss how trade integration within CAFTA is likely to shape future business cycle patterns in the region. Third, we provide some policy advice on the appropriateness of macro coordination for Central America conditional on its trade structure. As El Salvador unilaterally dollarized in 2000, it seems highly relevant to inform the debate on this front.

Data availability for Central America seriously limits the scope for any econometrical analysis. To provide some inference about the level of business cycle synchronization and the link between trade structure and business cycle synchronization in Central America we make use of annual data on GDP from 1965 to 2002 and monthly data on economic activity from 1995 to 2003.

This paper is organized as follows. Section 2 provides measures of business cycle synchronization for Central America based on different econometrical filters and based on annual and monthly data. Section 3 analyzes the link between Central America’s trade structure and business cycle synchronization with the United States. Section 4 concludes.

# **2. The degree of Business Cycle Synchronization in Central America**

# **2.1. Data and methodology**

The degree of business cycle synchronization is important as it provides information on the necessity of independent fiscal and monetary policy. If the business cycles are similar and shocks are common, then a coordination of macro policies can become desirable, with a common currency as the ultimate form of policy coordination. On the other hand, if shocks are predominately country-specific, then the ability to conduct independent monetary and fiscal policy is usually seen as important in helping an economy adjust to a new equilibrium.

As shocks are not observed directly, empirical studies rely on econometric methods for their identification. Helg et al. (1995) and Bayoumi and Eichengreen (1993) adopt a structural VAR approach, whereas Artis and Zhang (1995) develop an identification scheme based on cyclical 3