rafmacalaba/data-use-mentions-extended huggingface dataset:
micro, meso, or macro level, which each measure and define worker productivity in their own way. Micro studies focus on observed individual worker performance, output or wages as the left-hand side variable.<sup>xxxii</sup> Studies at the sector or economy wide level typically consider output per worker, using an aggregate economic indicator such as output, value-added, or GDP, divided by reported number of workers employed.xxxiii Increased temperature generally has an adverse effect on labor productivity, and this seem to hold across settings, specifications, levels of observation, and types of output. This is confirmed by a recent review of a subset of studies (Lai et al., 2023). Micro-level analysis in developing countries finds a negative relationship between productivity and temperature. In India’s manufacturing sector, a 1<sup>_◦_</sup> C increase on a hot day reduces labor productivity by 2–4% (Somanathan et al., 2021). For garment factories in India, productivity effects of relatively hot days are in the magnitude of -2 pp/+1<sup>_◦_</sup> C for temperatures above 27<sup>_◦_</sup> C-28<sup>_◦_</sup> C (Adhvaryu et al., 2020). Workers’ data from non-climate-controlled manufacturing firms in China suggest a U-shaped relationship: temperature is positively correlated with productivity up to 78<sup>_◦_</sup> F (~25<sup>_◦_</sup> C), after which productivity declines with further warming (Cai et al., 2018). Call center workers in India see a productivity decline of 1.8% for a 1<sup>_◦_</sup> C rise over a temperature range from 21 _._ 9<sup>_◦_</sup> C to 28 _._ 5<sup>
each country study, a major concern would be to collect information that will be of most use while avoiding overloading the questionnaire or the capacity of the organisation responsible for implementation of the survey.

Another consideration that needs to he taken into account in the design of a WHS is the benefits that could be obtained. from repeating country health studies after an interval of a few years. An 'ad hoc' survey may do little to improve the planning of health interventions and its findings soon become out of date. It might be better to concentrate resources on developing the capacity of national institutions to conduct a continuing programme of field research in those countries in which the findings will be fully exploited. Repeated surveys could yield a much clearer view of the dynamics of the health situation and the improvements in health care resulting from various initiatives. It should be noted, however, that even in developed countries considerable difficulties have been encountered in interpreting trends in self-reported measures in ill-health (Wilson 1984).

# 2.5 Investigations of the determinants of health

The study of the determinants of health is beset with methodological problems. Research into this subject needs to elucidate the complex processes whereby environmental, behavioural and economic factors interact to cause disease and death. It is also necessary to distinguish the effects of multiple contexts on health. For example, the health of a child depends on its own constitution, on childrearing practices adopted by its mother and on the characteristics of the household and the community in which it grows up. Other problems apply to research into factors affecting the use of services. Complex interactions can be expected beteween service provision and individual and household characteristics in determining the use of services. Moreover specific problems arise from reciprocal relationships whereby differences in individual fraility influence the use of services as 13
Expenditures (PCE) Chain-type Price Index, as there are no ocial statelevel price data.<sup>14</sup> As state-level growth can be extremely volatile, especially for small states, I drop outliers from all specications where the annualized growth rate of per capita labor income growth or GDP is more than 20% in absolute value. Standard errors are cluster-robust (clustered at the state level), which allows for heteroskedasticity and arbitrary serial correlation. See Appendix 1 for descriptive statistics and additional information on data sources and construction.

2.3.1. Cumulative Multipliers. As an extension in Section 3.6, I estimate dynamic cumulative multipliers several quarters after the transfer is paid. The cumulative multipliers _Ch_ are dened as the cumulative change in an income variable relative to the cumulative spending on the transfers over a horizon of h quarters beyond the impact quarter ( _h_ + 1 quarters in total). I use two specications, both of which collapse to Equation 2 for _h_ = 0 .

For permanent transfers, I use a direct projections specication (Jordà 2005), which has been used by Miyamoto, Nguyen, and Sergeyev (2018) and others to estimate cumulative multipliers.<sup>15</sup> The estimated equation is: where<sup>�</sup><sup>_h_</sup> _j_ =0<sup>(</sup><sup>_Yi,t_+</sup><sup>_j −Yi,t−_1)</sup><sup>_/Yi,t−_1is the sum of</sup><sup>_h_+1growth rates over horizons</sup> up to _h_ periods (left side) and<sup>�</sup><sup>_h_</sup> _j_ =0<sup>(</sup><sup>_tri,t_+</sup><sup>_j−tri,t
earnings for workers who were previously employed in informal sectors. The estimated coefficients range from (-0.054) to (-0.077). The coefficients are slightly higher for agriculture compared to manufacturing and services in the specifications with control variables, reflecting large inefficiencies in the sector. Finally, these coefficients are lower than those reported by Maloney (2001) and Loayza and Rigolini (2011) for Latin American countries.

Table 2. Regressions results of informality and labor productivity.

||(1)<br>Agriculture|(2)<br>Manufacturing|(3)<br>Services|(4)<br>Agriculture|(5)<br>Manufacturing|(6)<br>Services|
|---|---|---|---|---|---|---|
|Labor|-0.07***|-0.077***|-0.055***|-0.064***|-0.054**|-|
|Productivity|(0.017)|(0.022)|(0.019)|(0.018)|(0.029)|0.056***<br>(0.021)|
|Controls|No|No|No|Yes|Yes|Yes|
|Country fixed|||||||
|effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|545|543|544|532|530|531|
|R-squared|0.37|0.34|0.55|0.04|0.104|0.25|
|# Countries|87|87|87|79|79|79|

Note: The first three columns show panel data regression results for each sector. The last three columns include control variables
location and corporate structure. Unsurprisingly, most of the observations (more than 90 percent) are recorded in the three most populous regions in Portugal (Norte, Centro and Lisboa), and the overall degree of domestic firm ownership is relatively high (almost 90 percent). Our data refer exclusively to economic activity that takes place in Portugal, which constitutes all economic activity for the vast majority of firms. There are some firms in our data that also have plants located abroad, but these are very few.<sup>12</sup>

## [ Table 1 here ]

Table A3 in the Appendix reports key moments on the distribution of export destinations of Portuguese manufacturing firms in 2006, both in the full customs data and in our estimation sample. The main export destinations are other EU member states that are part of the eurozone (Spain, Germany, France), but also include countries outside the eurozone and/or the EU, notably the United Kingdom, United States, Angola and Singapore. For all destinations, export shares in the estimation sample are remarkably similar to those in the full customs data.

Figure A2 in the Appendix shows the variation of forecasted and actual GDP growth in each of the top 18 destinations for Portuguese exports. We observe significant variation across destinations with regard to both these variables. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets.

Figure 1 shows that there exists significant variation across firms in the estimation sample with regard to weighted actual and forecast growth. The range of forecast and actual growth is often greater than 10 percentage points, which is considerably higher than the averages for both these variables. The range of forecast growth is especially wide (and considerably larger than that of actual growth) in the initial years of the sample period, but remains sizeable over the whole period.

## [ Figure 1 here ]

# **5.2 Average effects on firm performance and worker compensation**

We
**Table E.1:** Average Reported Cases in Sections with Specialized Ebola Facilities

|Facility Type|ETU|EHC|CCC|Any|
|---|---|---|---|---|
|Total Cases|||||
|No Facility|25_._2|26_._6|27_._9|22_._6|
|Facility Present|484_._0|126_._5|99_._6|131_._3|
|Confrmed Cases|||||
|No Facility|3_._2|3_._3|3_._6|2_._8|
|Facility Present|62_._6|18_._5|11_._3|17_._8|
|Negative Cases|||||
|No Facility|18_._1|19_._1|20_._0|16_._2|
|Facility Present|340_._0|92_._4|74_._0|94_._9|

_Notes_ : Data on facility locations taken from UNMEER.

**Table E.2:** Health Care Worker (HCW) Training Schedule

|Week Ending|HCWs Trained|% Total HCWs Trained (4,264)|
|---|---|---|
|11/28/2014|2_,_440|57%|
|12/05/2014|3_,_450|81%|
|12/12/2014|3_,_980|93%|
|12/19/2014|4_,_200|98%|
|12/26/2014|4_,_200|98%|

_Notes_ : Approximate counts extracted from report, “Infection Prevention and Control (IPC) and Screening of Suspected Ebola Cases,” p. 4.

A32
financial viability of KPLC and the sector as a whole. The methodology used to determine the end-user tariffs is based on KPLC’s revenue requirement, that is, that the company’s revenue must be sufficient to provide a return and cover taxes on the regulated asset base, the cost of purchases of capacity and energy, and the company’s operating and maintenance costs. ERC is also responsible for approving PPAs between KPLC and various power providers—among them KenGen, IPPs, TANESCO, the Uganda Electricity Transmission Company, and the Ethiopian Electric Power Corporation. Regulatory objectives (i.e., allocative efficiency, productive efficiency, equity, and financial sustainability) are acknowledged and clearly specified in the sectoral legal framework, specifically in ERC ́s 2005 Retail Electricity Tariffs Review Policy. The relevant policies not only address substantive issues, but also the following formal objectives: simplicity, transparency, stability, and administrative feasibility. The ERC, with World Bank support, developed the sixth Cost of Service Study, which will provide the baseline for the next tariff cycle, expected sometime in 2019.

Kenya’s comprehensive regulatory framework for tariffs includes an automatic tariff adjustment mechanism (introduced in 1997) that allows exogenous costs related to inflation, exchange rate, and oil price to pass through to the user. Application of the mechanism has fortified the sector against external shocks by reducing the discretion involved in making tariff adjustments. However, the requirement for tariff reviews—which are to be carried out every three years according to the 2005 policy—has been inconsistently applied and delayed in some cases, jeopardizing the financial sustainability of the system. It is widely believed that the delays have occurred for political reasons, such as to forestall a drop in public opinion ahead of or immediately after national elections. As a result of these parallel institutional features—robust technical capacity and comprehensive 37
Table B.8: **Effects of Inpres on Coresidency Rates**

||**Sons**|**Daughters**|
|---|---|---|
||(1)|(2)|
|Born 1968-72_×_Inpres|-0.036|-0.036|
||(0.022)|(0.023)|
|Constant|0.520***|0.517***|
||(0.005)|(0.005)|
|R2|0.042|0.058|
|Observations|1057100|720747|

Notes: Robust standard errors in parentheses clustered at the district of birth. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1) Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The census 2000 collected data on number of children born alive to an adult woman. The coresidency rate is the ratio of number of children in the household divided by number of children born alive. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

71
0006)|(0.0005)|(0.000656)|
|Temporary|0.018<br>(0.012)|0.282***<br>(0.024)|0.164***<br>(0.018)|0.050***<br>(0.008)|0.0509***<br>(0.00785)|
|Firm size|0.043***|0.043***|0.045***|0.024***|0.0190***|
||(0.002)|(0.001)|(0.001)|(0.0006)|(0.000741)|
|Year||Yes|Yes|Yes|Yes|
|Sector||Yes|Yes|Yes|Yes|
|Occupation|||Yes|||
|Region|||Yes|||
|Worker||||Yes|Yes|
|Observations|147943807|147943807|147943807|137063393|109720650|
|R-squared|0.413|0.490|0.563|0.910|0.916|

_Note:_ This table reports worker-level regressions where the dependent variable is the logarithm of the hourly wage. Firm size is measured as employment. The inclusion of fixed effects for year, sector (4-digit CNAE code), occupation (2-digit CBO code), region, and worker is indicated at the bottom of the Table. Data from RAIS, 2016-2020. Robust standard errors clustered at the firm level in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

26
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a trade agreement comes into effect, for the 227 trade agreements that were signed between 1958 and 2015. Importantly, our specification controls for country-pair and country-time fixed effects.<sup>6</sup> The country pair fixed effects not only accounts for standard time invariant covariates of gravity models such as geography, institutions, and culture, but also for unobserved time invariant heterogeneity. Country-time

> 5In order to avoid outliers, we keep 92.5% of the observations.

> 6The directional trade data we use is rich enough that we could expand on the country-time fixed effects to control for country-time by exporter and importer status. Country-time fixed effects by exporter and importer status would however absorb all the variations that we use to separately identify the effects of trade agreements on inflows and outflows.

8
The first three columns of Table 5 present the intent-to-treat estimates of the impact of the program on total school enrollment, as well as enrollment by grade and by gender. We transformed enrollment data using the formula Loge(Enrrolment+1). We present the regression with a full set of covariates, which include schools characteristics at baseline as well as regional fixed effects. The results are stable to the inclusion of these baseline controls. Each column reports the results corresponding to information collected at Checks 1, 2 and 3.

We observe large and significant enrollment effects for lower grades and small and always insignificant impacts for higher grades that were not part of the program. At all Checks, we find an impact between 0.285 and 0.32 for grade 1, which translates to increments of between 33% and 38% in enrollment. At Check 3, in the second year of implementation, we observe effects in enrollment at both grades 1 and 2, of the order of 34% and 58% respectively. In the bottom panel of Table 5 we show that the increase in enrollment is roughly proportionate across genders. If anything, female beneficiaries account for slightly more than half of the students at checks 1 and 2. The last three columns of Table 5 show the IV estimates, which roughly scale up by a factor of 3 the ITT estimates. The same pattern as the ITT estimation is present in the IV model. The most conservative IV estimate (Check 3, point estimate 0.737) suggests an increment that doubles the size of the schools induced to participate in the program by the lottery.

Table 6 presents the impact of the program on test scores from NAPE in 2011 and 2012. We include both years of the test given that tested cohorts are variously exposed to the PPP program, with the 2011 cohort potentially exposed for 6 months while the 2012 cohort is exposed for 18 months. We report both on the overall test scores but also on the composition of students, a potential mechanism underlying