rafmacalaba/data-use-mentions-extended huggingface dataset:
In 1993-1995, the annual growth rate reached 21.8% as a result of additional rebates in import tariffs, exchange rate appreciation and GDP expansion, these last two events having occurred as a result of the implementation of the Real Plan.

During the 1996-1999 sub-period, the control on imports together with the decline in economic activity reduced the annual variation rate of foreign purchases to 8.5%.

Finally, after changes were introduced in the exchange rate regime, imports showed a downward trend with a negative annual variation of 3.5%.

Figure 1 Indexes of Import Volumes, Real Exchange Rate, Real GDP, and Nominal Tariff for the 1987-2003 Period (base year: 1987 = 100) <!-- Start of picture text --> Import quantum Real exchange rate GDP Tariff<br>500<br>Import quantum<br>450<br>400<br>350<br>Flexible exchange rate<br>300<br>Real Plan<br>250<br>200<br>Trade liberalization<br>150<br>GDP<br>100<br>Real exchange rate<br>50 Tariff<br>0<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Import volume from FUNCEX; real exchange rate and GDP, from IPEA-DATA, and nominal tariff, from Kume, Piani and Souza (2003). Author's calculations.

9
 territory (map 1). These selected units cover five of the six regions of the country northern, central,  eastern, western, and southern relatively well. Only for the northeastern region is the coverage of the sample poor. This is due to the shortcomings of village boundaries in the states of Arunachal Pradesh and Meghalaya and the low quality of the household surveys in the states of Manipur, Mizoram, Tripura, Meghalaya, and Assam.

**Map 1. The spatial distribution of sample units**

# **3. The urban status of units in the sample**

Cities have diverse characteristics both across and within countries. India has a continuum of urban and rural spaces, ranging from a glamorous metropolis such as Mumbai, capital of Maharashtra, to a tiny shabby town such as Bagula in West Bengal, to any among hundreds of thousands of villages. An objective method based on one or two metrics with fixed thresholds for distinguishing between urban and rural areas may not adequately capture this diversity. A richer combination of criteria would help describe the multifaceted nature of a city’s environment, but the joint interpretation of these criteria would invariably involve an element of judgment. Building on this intuition, we rely on subjective assessments to determine whether each administrative unit in the sample is urban or rural in practice.

8
* p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. Equation (3) additionally controls for the unemployment rate at destination at the time of return migration (for return migrants) and at the time of the survey in 2012 (for current migrants). For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

59
related to finding indicators that demonstrate both cross-country comparability and relevance.

A final strategy is to develop a database of within-country studies of governance determinants and classify them according to common avenues of inquiry. These can then be subjected to procedures applied by synthetic reviews – clearly identifying the question about governance that is being asked, establishing criteria for the quality of the underlying evidence and assessing findings accordingly.

Ekman (2004) demonstrates how such synthetic reviews can be conducted on a governance determinant. In this case, the focus of the study was the impact of community health insurance schemes on utilization of health care services, out-of-pocket expenditures and health status. By clearly identifying the hypotheses under consideration, pre-establishing criteria for assessing the quality of evidence, and conducting a systematic search for studies, Ekman was able to take studies conducted in specific contexts and determine what evidence was available for generalizable lessons.

It is this latter strategy that is most likely to generate the kind of evidence that policymakers are seeking when they ask how to improve health service provision by improving health sector governance.

# **6. Conclusion**

After reviewing the many ways governance has been used and studied, this paper argues for defining governance as the combination of political, social, economic and institutional factors that affect the behavior of organizations and individuals and influence their performance. Applying this concept to applied research in the health sector requires clarifying which organizations are the subject of analysis (e.g. health insurers, public health services, regulatory agencies) and how their performance is measured (e.g. what are their missions in terms of service provision or health outcomes). It also requires distinguishing governance determinants, such as ownership, decentralization, formal procedures and stakeholder participation, from governance performance (e.g. whether formal procedures are implemented, workers fulfill their responsibilities, or stakeholders have substantive input into decision processes).

Measures of governance performance in public health care provision can be gathered through facility and population surveys. Facility surveys can directly measure certain aspects of governance
Heterogeneous impacts on enrollment by parental education are evaluated across two groups: those children whose parents have a completed secondary education degree or less and those whose parents have more than a secondary education degree.<sup>5</sup> In the two groups of children the decrease in enrollment (both gross and net) is similar, substantial, and statistically significant. Enrollment in private education is hereby an important exception: for this outcome, the relative decrease is stronger among the children of loweducated parents.

# **4. Estimates of education losses**

In this section we follow the framework of Neidhöfer, Lustig and Tommasi (2021) (NLT thereafter) to approximate the educational loss associated with school closures. NLT nowcast the instructional losses associated to the pandemic using country level data on days of school closure, educational mitigation strategies, number of COVID-19 cases and deaths, and survey data on individual characteristics. The basic assumption is that the school closures in reaction to the pandemic affected the human capital accumulation through lost days of school. This loss could be compensated partly by parent’s investments and actions, especially in more educated households, and partly by educational mitigation strategies enacted by countries.

In this section we extend the calculations of NLT based on 2020 by adding information from administrative data for 2021. More important, whereas the estimates in NLT are based on Latinobarometro, our calculations are based on microdata from national household surveys, which allows us (i) to have large samples, (ii) to include data at the household level on some key factors for the educational impact of COVID-19, such as parental education and access to

> 5 In principle, the socioeconomic status of the family could also be approximated by household income. However, since this outcome is directly affected by the pandemic, the position of the household in the income distribution might not be a correct way of comparison over time. Hence, in this part of the analysis, we only evaluate differences by parental educational background, assuming that this
# **9. References**

Acharya, A., Blackwell, M., Sen, M., 2016. Explaining causal findings without bias: detecting and assessing direct effects. Am. Polit. Sci. Rev. 110 (3), 512–529.

Agnolucci, P., Fischer, C., Heine, D., Oca Leon, M. Pryor, J. Patroni, K. Hallegatte, S. (2023) “Measuring Total Carbon Pricing” World Bank Policy Research paper 10486.

Black, S., Liu, A. Parry, I and Vernon, N,. (2023). “IMF Fossil Fuel Subsidies Data: 2023 Update.” IMF Working paper WP/23/169.

<mark>Favennec, JP. (2022). “Economics of Oil Refining”. In: Hafner, M., Luciani, G. (eds) The Palgrave Handbook of International Energy Economics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-86884-0_3</mark> Frankel, J. A. (2010). The natural resource curse: a survey (No. w15836). National Bureau of Economic Research.

Fuchs, Alan. (2023). Poverty and Distributional Consequences of Air Pollution in Tbilisi. World Bank. Washington D.C.

Heine, Dirk and Simon Black _Benefits beyond Climate: Environmental Tax Reform._ Fiscal Policies for Development and Climate Action. December 2018, 1-6. https://doi.org/10.1596/978-1-4648-1358-0_ch1 International Energy Agency 2023. World Energy Outlook.

International Monetary Fund (2019) Policy Papers “Fiscal Policies for Paris Climate Strategies—from Principle to Practice (May 1).

International Monetary Fund (2019) Fiscal Monitor “Fiscal Monitor: How to Mitigate Climate Change” (October).

Kojuma, M. and Koplow, D. (2017) “Fossil Fuel Subsidies: Approaches and Valuation” World Bank Policy Research paper
We assess the model’s in-sample performance using five-fold cross-validation to determine the subset of variables that best perform across folds.<sup>9</sup> In-sample performance tests show that both urban and rural models display a high R-square, low Mean Square Error (MSE), and can predict welfare ranking well (Spearman rank correlation). Overall, in-sample tests show very small differences between observed and predicted poverty levels of between 1.6 and 2 percent in urban and rural models, respectively. The higher predictive capacity in the urban samples is likely the result of the lower variability in income levels and a limited number of variables to fully model rural households' income-generating processes, particularly assets used in agricultural production (Table 4).

**Table 4: SWIFT-Plus model validation tests**

|||**R2**|**Mean**<br>**MSE**|**Spearman**<br>**correlation**|**In-s**<br>**I**<br>**Observed**<br>**poverty**|**ample predic**<br>**E-LFS 2019/2**<br>**Predicted**<br>**Poverty**|**tion**<br>**0**<br>**Difference**<br>**(%)**|**Out-o**<br>**Observed**<br>**poverty**|**f-sample pre**<br>**IE-LFS 2021**<br>**Predicted**<br>**Poverty**|**diction**<br> <br>**Difference**<br>**(%)**|
|---|---|---|---|-
million, about 0.5 percent of total imports from China in 2008). Among the top 15 trade partners whose exports to Belarus increase the most because of the CET, only Uzbekistan and Moldova are members of the CIS; six are EU countries (Table 7). At the same time, the 15 countries—7 of them EU members—that see the largest drop in exports to Belarus together account for 45.4 percent of the total decrease in Belarus import value (Table 8). Georgia is likely to see the largest drop: its exports to Belarus will go down by $2.5 million (22 percent of its exports to Belarus in 2008).

Apart from the specific effects on Russian exports to the rest of the ECU to provide a context, it is worth looking at some general results for imports after Belarus and Kazakhstan adopted the CET.

For Kazakhstan, using tariff data for 2008 and 2012, the change in the total import value is measured against a 2008 baseline of $35.4 billion, from which imports from all partners are simulated to have declined to $34.6 billion (2.3 percent) as a result of adopting the CET (combined with Kazakhstan’s negotiated exceptions to it). Welfare losses arose from a $71.4 million decline in consumer surplus. As adoption of the CET replaced the MFN tariffs Kazakhstan had applied in 2008, it appears that there was a decrease in imports for 65.9 percent of Kazakhstan’s product lines and an increase for 5.5 percent, with trade unchanged for 28.6 percent. For products affected by a tariff increase between 2008 and 2012, the trade-weighted average tariff increased by 6.0 percentage points from 3.8 to 9.7 percent. Ten of the top 20 products whose import values dropped are meats; 5 are vehicles for the transport of people; and 2 relate to pneumatics. Meanwhile, for products whose tariffs were reduced, trade-weighted average tariffs went down
Source: World Bank staff calculation with World Input Output Database data.

# **2. Literature and Contribution**

Over the past decade, a significant body of literature has emerged to examine the effects of trade integration on labor markets. Notably, Autor, Dorn, and Hanson (2013) made a seminal contribution by employing a Bartik (1991) approach to estimate the causal impact of the "China Shock" on employment and wages at the community zone level in the United States. Their findings reveal that employment was the primary margin of adjustment to the China Shock, accounting for approximately 25 percent of the decline in US employment. These results prompted further exploration into the effects of trade shocks on labor markets, leading to subsequent studies by Pierre and Schott (2016), Acemoglu et al. (2016), Autor et al. (2014), Dix-Carneiro and Kovak (2017), Kovak (2013), Topalova (2010), and others. This literature has expanded to assess the impacts of various trade shocks on local labor market outcomes in both developed and developing economies. The key finding of this literature is that trade shocks within a country can have substantial and persistent impacts on local labor markets, giving rise to distributional concerns (Engel et al., 2020).

It is crucial to note that trade affects not only tradable sectors, but also non-tradable sectors within the same local labor markets. While increased import competition or market access directly impacts specific tradable sectors, there are indirect effects on non-tradable sectors such as retail, health care, or hospitality in the same region. For instance, Autor et al. (2013) find that wages in non-tradable sectors in areas most exposed to Chinese imports decreased, while preliminary evidence by Wang et al. (2018) suggests that these sectors experienced employment expansion. Dauth et al. (2012) also report wage gains in the service sectors of export-oriented regions in Germany, and Menezes-Filho and Muendler (2011) show that the service sector and the
and just requires adjusting expression (3) so that 1 0 are replaced by their time-demeaned analogues 1 0. XX , XX To better understand the uncertainty in our estimates, in addition to the permutation test proposed XX ̈, XX by Abadie et al. (2010) we also consider the prediction interval procedure proposed in Cattaneo, Feng, and Titiunik (2021).

# **3.3 Estimation Details**

The primary difficulty in applying synthetic control in our context is that Australia is an outlier in terms of emissions per capita. Australia’s electricity generation is mainly coal-based, and it is also a major exporter of coal and natural gas. Coal-based electricity, coal mining and natural gas production are energy-intensive industries. In addition, many natural comparison economies that might help form a plausible counterfactual for Australia’s emissions must be omitted from the sample because they implemented policies that would significantly affect emissions during the sample period. For instance, the implementation of the European Union’s Emissions Trading System (EU ETS) in 2005 disqualifies all EU economies.

We form the donor pool for Australia in three main steps. First, we construct a global panel dataset of emissions, GDP per capita, and energy consumption using the data sources previously described. We only consider economies with no missing values in any year for any of the relevant indicators. Second, we use data provided by the World Bank (primarily from the Carbon Pricing Dashboard)<sup>9</sup> to identify every economy that implemented a national-level carbon price or ETS from 1980-2017 and disqualify them from the donor pool. Third, to address Australia’s position as a large economy with relatively high emissions per capita, we limit our estimation sample to the top 35 largest emitters per capita with a population of at least 1 million (both measured on average from 1980-2000). The resulting donor pool is listed in Appendix B.<sup>10</sup> This third restriction speeds computation and reduces concerns of interpolation bias
_Figure 10. Current Industry for Employed, Working-Age Syrian Refugees (20-60 years_ _<u>old)</u>_ <!-- Start of picture text --> 0.66<br>33.12<br>2.59<br>4.43<br>4.76<br>10.92<br>24<br>1.55<br>9.47<br>0.17<br>8.33<br><!-- End of picture text --> _Notes_ : Authors’ own tabulation using data from SRHCS.

16
7 _κ_ Equation ( ) shows that it is possible to estimate , based on firm-level exports and imports data. In particular, if _DV Ait_ can be controlled nonparametrically by _Dit_ ( _·_ ), then (7) can be reformulated into a regression with a classical measurement error term, _εit_ : where the coefficient on imports, _κ_ , has a structural interpretation as the share of imported inputs used specifically for the export production.

With an unbiased estimate of _κ_ , we can adjust firm-level imports to determine _DV ARit_ for the ordinary exporters:

# **3.2 Econometric Specification**

The import coefficient has a structural interpretation as the share of imports used for exports, when regress firm’s exports on imports according to (8). This is analogous to the production function estimation, where regressing firm’s output on inputs, resulting in the structural interpretations of the input coefficients as input shares or elasticities. Similar to the production function estimation which is subjected to simultaneity bias, estimation of _κ_ according to (8) could be inconsistent due to factors that affect both exports and imports of firms. One such factors is firm’s productivity: productivity increases could simultaneously raise both imports and exports, leading to an upward bias in the least squares estimate. In contrast, another factor could be currency depreciation, which might reduce imports while boosting exports, causing a downward bias. The net effect of these opposing forces is empirical and cannot be determined without further investigation.

To control for the potential bias in the regression of (8), firm-level exchange rate data can be constructed according to Kee and Tang (2016) and introduced directly in the regression. However, is unobserved and econometric to address. One firm productivity requires specific techniques way is to use an instrumental variable (IV) correlated with imports but unrelated to exports. Given that firm-level IVs are often difficult to identify, here we adopt the control function approach from Olley and Pakes (1996) (OP) to control for the unobserved
**Table 1 Data sources**

|Indonesia|The sample includes information from the Manufacturing Survey of Large<br>and Medium-Sized Firms (Statistik Industri). It provides plant-level data<br>from 2000 to 2015.<br>The sample focuses on a subset of Indonesian enterprises. The 2016<br>economic census indicates that 98.3% of enterprises are micro and small<br>business with less than 20 employees. However, the remaining 1.7% of<br>firms, the ones interviewed for the Statistik Industri, employ a non-negligible<br>amount (23.7%) of workers.|
|---|---|
|Malaysia|2000, 2005 and 2010 census of manufacturing firms with at least 10 workers.<br>There are 37,211 firms across the three waves of census.<br>In the 2010 manufacturing census, 36.7% of firms have at least 10 workers.<br>They account for 66.4% of employment in the manufacturing census.|
|Philippines|The sample includes data from 2001 to 2014 surveys on manufacturing firms<br>with at least 10 workers.<br>The economic census data is not disaggregated by firm size, so we refer to<br>the latest Enterprise Survey which indicates that firms with 5-19 workers<br>account for 52% of the sample. Firms with less than 20 workers employ<br>almost 12% of the labor force.|
|Vietnam|The sample includes data from 2009 to 2014 on manufacturing firms with at<br>least 1 worker. There are 259,721 manufacturing firms between 2009 and<br>2014.|

The potential gains from reallocation are large, even after accounting for measurement error (Table 2). We present three different measures of the potential productivity gains that can be achieved from reallocation. First, consistently with Hsieh and Klenow (2009), Panel A provides the estimates
>16</sup> The results, shown in Appendix A,

> 15 Figure 2 shows estimates that are obtained using the normal linear regression models. The estimates obtained using the empirical distribution of the error terms are similar (Appendix A, Figure A.1).

> 16 The poverty lines for 2020/21 and 2019/20 are based on data from the NPS and may vary due to changes in the household consumption questionnaire. While the food components of both years' poverty lines remain consistent, the

22
|
|log GDP per capita, PPP USD<br>l ld 1|6.643***|3.333**|7.751***|4.040***|-0.69|-0.653|
|(annua average, agge year)|(1.636)|(1.538)|(3.005)|(1.366)|(1.282)|(1.630)|
|Real GDP Growth, y-o-y,|0.034|0.129*|0.049|0.149***|-0.124|-0.107|
|quarterly data lagged 1 quarter|(0.063)|(0.069)|(0.074)|(0.057)|(0.113)|(0.112)|
|Union Density,|-0.194***|-0.252***|-0.252**|-0.411***|-0.073|-0.077|
|% of employees|(0.052)|(0.072)|(0.114)|(0.081)|(0.050)|(0.059)|
|Population with immigrant|0.178***|0.205***|-0.090|0.017|-0.100*|-0.122*|
|background, %|(0.050)|(0.066)|(0.107)|(0.090)|(0.058)|(0.065)|
|Root MSE|1.8761|2.0460|3.8393|2.9874|3.6423|3.6560|
|R-squared|0.8135|0.
0.0090|−0.0043|−0.0070|−0.015|
||(0.0095)|(0.0099)|(0.010)|(0.010)|
|Observations|862|862|862|830|
|_R_<sup>2</sup>|0.90|0.90|0.90|0.90|
|Household level controls|Yes|Yes|Yes|Yes|
|Agricultural controls|Yes|Yes|Yes|Yes|
|Group controls|No|Yes|Yes|Yes|
|Control for other group|No|No|Yes|Yes|
|Additional group controls|No|No|No|Yes|

_Source_ : Author’s analysis based on the 1982 and 2006 rounds of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ :<sup>*</sup> _p <_ 0.1,<sup>**</sup> _p <_ 0.05,<sup>***</sup> _p <_ 0.01. Robust standard errors clustered at the village level are reported in parentheses. The coefficients on the other controls are reported in table S1.1 in the supplementary appendix S1. The reference group is defined as individuals from the same caste and the same occupation. The other group refers to individuals from the same caste that do not have agriculture as their main occupation. Group controls and controls for other groups include the number of individuals in each group (in log) and the mean value for the demographic characteristics in each group. Additional group controls include the average amount of land in each group and the amount of transfers within groups.