rafmacalaba/data-use-mentions-extended huggingface dataset:
survey format where the scope (in terms of question complexity) and length (in terms of interview time) are much reduced compared to a typical F2F surveys. For example, it is not feasible to collect all information necessary to construct an aggregate measure of household consumption expenditures, the prevailing welfare measure in many developing countries. Phone surveys utilizing existing representative surveys as a frame have much more scope for effective bias adjustments through reweighting as a result of the rich set of information available for the representative sample that serves as a frame. There are a variety of approaches to counteract the biases present in phone surveys utilizing the detailed information from the representative F2F survey, though three prominent methods are explored here.

The first is through **weighting class adjustments,** whereby the phone survey sample is divided into cells that represent different “classes” of respondents (Little, 1986). These cells are typically constructed by crossing several characteristics that may be correlated with the likelihood of nonresponse (e.g. gender, age, education, location, etc.). The phone survey response rate is then calculated for each of these cells and the weights for interviewed households in the cell increased by the inverse of the response rate.

> 5 See Himelein et al (2020) for a more complete comparison of the advantages and disadvantages of these three sampling approaches.

6 One disadvantage of using a representative F2F survey as a frame (compared to RDD and list-based frames) is that F2F surveys are often a two-stage clustered design with census enumeration areas typically serving as the clusters from which households/respondents are selected. This clustered design introduces design effects that reduces the effective sample size and increases the size of the overall sample required to achieve the desired level of precision (relative to a simple random sample). These design effects are at least partially retained in a phone sample drawn from the clustered F2F survey sample. These imported design effects imply that a larger sample is required to achieve the same level of precision for a phone
share of unskilled labor intensive products rapidly expanded to levels well above the earlier peak in 1993 and their expansion extended over a much longer period of time. The share of both skilled labor and capital intensive products in Bulgaria’s EU exports after increases in 1994-95 has subsequently, whereas unskilled labor intensive drove Bulgaria’s exports until 2001, with their share rising to 40 percent and stabilizing at this level.

Bulgaria’s idiosyncratic factor intensities extended to total exports still in 1996 (first year for which reliable foreign trade data are available). Given the relative endowments in capital across regional peers, one would not expect such a significant presence of capital intensive products in Bulgaria’s total exports. Yet, the share of capital intensive products was higher in Bulgaria’s exports than in total exports of all other comparators in 1996 (Table 21). This may come as no surprise vis-à-vis SEE-4, but it is rather startling when cast against much more developed CEEC-8 economies taken as a bloc. The share in CEEC-8 exports was four percentage points lower than in Bulgaria’s, while the share of unskilled labor products four percentage points larger. In a similar vein, unskilled labor intensive products had much larger presence in both Croatian and Romanian exports than in Bulgaria’s.

15 See the calculations reported in World Bank 1999.

16 The value of total EU-oriented exports increased 85 percent in 1993-96. The value of skilled labor intensive products quadrupled and that of capital intensive exports. Simultaneously, the share of unskilled labor intensive declined
<!-- Start of picture text --> 2<br>2<br>o2<br>a<br>2<br>(5) Cc6<br>«<br>©<br>2<br>2x<br>®<br>(a)<br>N<br>fo)<br>o * *" 5 10<br>landholdings (ha)<br>Source: Baseline villager survey. Only regions of Arusha, Kilimanjaro and Morogoro<br><!-- End of picture text --> <!-- Start of picture text --> fo)<br>N<br>LO<br>-<br>c<br>Oo<br>2<br>oO<br>ial e<br>Lo<br>co)<br>1 2 3 4 5<br>Quintile of landholdings<br>IY) 2008 beneficiaries [ENN 2010 new beneficiaries<br><!-- End of picture text --> Source: Baseline villager survey. Only regions of Arusha, Kilimanjaro and Morogoro
above the interpolated target, and an increase represents an increase in substance consumption. If the value is negative, the country is “in compliance.”<sup>10</sup> We merge these country-year emissions data to our data on RTAs and RTA content and construct a “stacked” country-level panel with a _±_ 3 year event window around signature of any RTAs. We consider every RTA that a country signs and include three years before and three years after. If a country signs multiple RTAs in adjacent years, each of _±_ 3 year event windows enter into the analysis data set separately and are stacked or pooled. The resulting panel consists of 265 RTAs and 874 individual seven-year country sequences. We only consider RTAs signed after the Montreal Protocol was signed in 1987, which also corresponds with the start of ODS consumption reporting in most countries.

# **3 Empirical Strategy**

We consider two types of linkages between RTAs and the Montreal Protocol. First, we investigate “participation linkages,” referring to the potential for trade agreement content to induce accession to the Montreal Protocol or its Amendments. Because the Montreal Protocol bans trade in ODSrelated products with countries not party to the Protocol, countries may include provisions in trade agreements to ensure their trading partner’s status with the Protocol to maximize gains from trade liberalization.

The Montreal Protocol is made up of the original 1987 treaty and five amendments introducing new substances (1990, 1992, 1997, 1999, and 2016), each of which needs to be ratified by a country’s legislature. Figures A.16 – A.18 in the Appendix display histograms of the years in which the original treaty and its amendments were ratified. Besides the most recent amendment in 2016, all five amendments and the original treaty exhibit near-universal ratification. If a country has not ratified an amendment, it is considered a non-party for the substances included in that amendment (Brack, 2017). For example, TCA was introduced in the London Amendment
Secondly, by using high resolution population layers to identify areas with economic activity some noise from adjacent non‐economic areas is removed. Hopefully providing a better data set to pinpoint where a change in light value would be due to a change in economic output and not due to stray light or other light sources such as forest fires. One potential downside of this is that some agricultural land will not be included in the analysis. Finally, instead of aggregating up to a provincial or municipality level, the data are kept at a cell level.

More specifically with regards to the methodology, one started with localized damage data modeled from actual intensity measures. These values were matched with any intersecting VIIRS cell and assigned to the corresponding month. For light cells that intersected several damage cells an average value was used. As earthquake damage estimates were modeled based on centroids, only the centroid intersection was used.

Once the nightlight and damage data had been merged, population data was included. The population data sets were aggregated up to the same cell size as the VIIRS data and then matched to find the total population in each nightlight cell. Due to slight differences between the HRSL and WorldPop data sets, two different cutoffs for populated cells were utilized. As HRSL specifically provide settled areas and set other areas to having 0 population, any VIIRS cell with no settled areas in it, would be excluded from the final data set. As for the WorldPop data, any VIIRS cell with less than 5 people in it was cut.

Finally, for potential future analysis, the VIIRS cells were assigned to administrative subdivisions such as provinces and municipalities. In cases where a cell crossed province borders, the province that contained the centroid was used.

# _B. Case Study Methodology_

Once the data sets were constructed, one case study for each natural hazard type was chosen to graph the effect the hazard would have on the local nightlight values. The choice of hazards was based on reported damage and “fame”. In the case of floods,
every month—implies that our main estimator in this specification, when positive, does not represent an _increase in crimes_ but rather an increase in crime reports by Venezuelans. Naturally, the estimators for _CrimeReports_<sup>_TOT_</sup> _ct_ are all positive and statistically significant, since this variable explains a huge deal of variation of the dependent variable: the more crimes there were, the more crime reports Venezuelans made. Reasuringly, the results in Panel D are similar to the ones presented in Panels B. Yet, in those estimates we not only document increments on migrants reports of domestic violence and sex crimes but also homicides. Since the effects in homicide rates are not consistently observed in the 2SLS squares estimates we only see them as suggestive evidence.

As an additional robustness test we also estimate the effects of the program by aggregating the national data at the department-month level. We initially decided to only account for cities as rural 22
# **7. Conclusion**

We have illustrated how growth regressions can be a useful tool for country-specific policy analysis of growth patterns. Economists performing such analysis will find the data and models presented in our paper a useful reference and can further tailor those to their specific needs (e.g., by exploring further heterogeneities, applying different estimators or model averaging, adding or updating variables). Our reference data can additionally be used by researchers and analysts to explore certain growth-relevant aspects in more detail (e.g., looking at threshold relations or other non-linearities for variables like inflation).

Future research could further improve policy modeling with growth regressions. One apparent angle is to reconsider the dynamic implications of the econometric neoclassical growth model, which assumes that persistence is uniform across countries and variables. Country heterogeneities could easily be addressed with recent advances in the grouped heterogeneity panel econometrics literature (e.g., Bester and Hansen, 2016; Muris and Wacker, 2022). But it may also be necessary to consider alternative dynamic modeling strategies that account for the plausibility that improvements in infrastructure, for example, have more persistent correlations with growth than certain macroeconomic stabilization policies like taming inflation. Another angle worth exploring is to develop metrics that can plausibly compare the relative importance of different policy packages. For example, economists are often curious about the extent to which domestic policies or a favorable external environment can explain a country’s growth episode. Explicit modeling of such relative importance appears futile.<sup>28</sup> Researchers may rather consider using clustering techniques to separate common time-varying shocks in groups of reasonably similar countries (which can then be interpreted as external factors) from variation in growth that is explained by domestic policy variables.

Since our analysis is mostly descriptive and aims to facilitate country-specific growth analysis, future work could also further the identification of peer and reference groups in growth patterns. Moller and Wacker (2017) provide an example by using a cluster analysis to reveal the uniqueness of Ethiopia’
# **Table 7 (continued)**

|Minera|9/2/91|Court action.|
|---|---|---|
|Vapores|6/6/92|Company is fined by government.|
|Emos|10/17/93|Accident: drinking water contamination.|
|Puerto|7/23/92*|Government complains about health hazard in the vicinity<br>of the company.|
|Victoria|12/2/93|Government black list of air polluter.|
|lansa|5/29/93|One of the plants ordered to shutdown.|
|Coloso|4/1/92<br>12/2/93<br>2/5/94<br>3/11/94<br>3/18/94|Government action: fine.<br>Government action: company shutdown for few hours.<br>Court action:fine.<br>Government action: company shutdown.<br>Citizens complaint: accident.|
|Iquique|4/1/92 *<br>12/21/93<br>2/5/94<br>3/10/94<br>3/11/94|Government action: fine.<br>Government action: fine.<br>Court action: fine.<br>Government action (Company closed for 72 hours).<br>Court action for bad smell problem.|
|Lirquien|7/15/92|Government black list of air polluter.|
|Chilectra|7/11/92|Citizens complain against company expansion.|
|Molymet|1/19/92|1Governnent complaint: company major air polluter.|
|||<br>**PHILIPPINES**|
|Apex|4/24/91 *|Government action.|
|Ayala|12/8/
clientalism) have been the subject of a large body of research (e.g. Chattopadhyay and Duflo 2004; Besley et al. 2004, 2005; Bardhan and Mookherjee 2006; Beaman et al. 2008; Ban and Rao 2008).

A small and growing body of scholarship has examined the _sabha_ itself, and whether it serves as a mere “talking shop,” or constitutes a true deliberative forum in which citizens are able to raise and resolve issues of public relevance. Besley et al. (2005) examine survey data from 5,180 randomly chosen households drawn from 537 villages in the four South Indian states of Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu. They find, like Crook and Manor (1998), that with the exception of Kerala, _gram sabhas_ are often not held as scheduled; however, when they are held, governance sharply improves. Focusing on a specific policy administered at the local level – access to a Below Poverty Line (BPL) card, which provides an array of public benefits – they find that policies were more effectively targeted to landless and illiterate individuals when a _gram sabha_ was held. And these effects were large, raising the probability of receiving a BPL card by 25 percent.

The reason why _gram sabhas_ result in better poverty targeting is related to one of their primary roles in village government. BPL lists are first determined on the basis of a census conducted by the government which identifies poor households with a given set of criteria. However, in many states these lists have to be ratified by the _gram sabha_ , which allows the quantitative definitions of poverty to be deliberatively interrogated by the community. Rao and Sanyal (2009) examine transcripts of deliberation from 290 _gram sabhas_ from a subset of villages studied by Besley et al. (2005) and find that this process acts as a vehicle for questioning governmental definitions of poverty and creating a shared, intersubjective<sup>3</sup> understanding of what it means to be poor. They argue
# _6.1. Village-level Poverty Targeting Using Detailed Household Surveys_

We begin the analysis of the household-level data by using the subset of our observations among which detailed household surveys were collected. These data allow us to calculate household-level consumption aggregates, and therefore to conduct an analysis of poverty at the local level. We use sampling weights to make this sample representative of the population in the 100 study villages.

Figure 2 shows the CDF of the consumption aggregate by stratum. We can see that the village elite have the lowest likelihood of being poor followed by non-vulnerable individuals and TASAF II VG group leaders. Vulnerable households, exogenously defined using the criteria devised by TASAF II, are poorer than non-vulnerable households and those who ended up in TASAF II VG groups as „rank and file‟ members (i.e. **not** group leaders) form the poorest stratum. Using a lower bound poverty line (of 18,000 Tanzanian Shillings per capita per month), fewer than 20% of village elites are defined as being in poverty, whereas almost 60% of TASAF VG group „rank and file‟ members are poor.

The vulnerability criterion is, in and of itself, a decent targeting criterion: the poverty rate among vulnerable households is 10 percentage points higher than non-vulnerable households (51% vs. 41%). However, inclusion and exclusion errors are many: there are many poor households that are not defined as vulnerable and many vulnerable households who are not poor. The within-village targeting, conditional on the vulnerability criterion, is very different for group leaders (who are substantially richer than the average eligible beneficiary) and group rank and file (who are substantially poorer). Hence, while the villages do a good job of forming TASAF II VG groups that are poorer than the eligible population in general, group leaders (who comprise 20-30% of the membership in these groups) are significantly richer than the average eligible household. Again, the combination of the targeting criteria from the center (vulnerability)
that climate regulations could have on selected advanced economies and globally. For instance, Kleimann et al. (2023), Attinassi et al. (2023), and Bernoth (2023) analyze the impacts of the US IRA on Europe, while Gründler (2023) discusses global effects. However, the potential effects of these policies on developing countries have been largely overlooked.

**This paper examines key mitigation policies implemented by the EU, US, and China, seeking to address following questions:**

- What are the differences in the policy approaches of the EU, US, and China?

- What are the economic channels through which the selected EU, US, and China policies impact developing countries?<sup>4</sup>

- Which sectors and countries are most exposed to these impacts?

- How should developing countries respond?

- What measures can advanced economies take to reduce negative spillovers for developing countries?

**The paper focuses on three main channels through which these climate policies can affect developing countries:** shifts in supply and demand in trade sectors, price changes, and access to technology. Using descriptive statistics and existing research, the paper identifies the sectors and countries most exposed to the policies. The paper does not cover adaptation policies or the impact of developing countries' own policies to meet their Nationally Determined Contributions (NDCs), which are discussed in Brenton et al. (forthcoming).

> 2 World Bank’s World Development Indicators.

> 3 Under the broad ‘subsidy’ category, we include tax credits for companies and consumers, grants to support programs, and the provision of technical assistance.

> 4 In this paper, the term "developing countries" will specifically refer to non-China developing nations.

2
**Figure 17. Latin America: Export Shares by Broad Type of Product**

<!-- Start of picture text --> (a) Commodities (b) Manufactures (c) Services<br>80 80 40<br>70 70 35<br>60 60 30<br>50 50 25<br>40 40 20<br>30 30 15<br>20 20 10<br>10 10 5<br>0 0 0<br>México América Central América del Sur México América Central América del Sur México América Central América del Sur<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> _Note_ : The manufacture and services series are drawn from the WDI database. The commodities series is obtained as a residual from total exports of goods and services. Central America includes Costa Rica, Dominican Republic, El Salvador, Guatemala, Honduras, and Panama. South America includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, and Uruguay. _Source_ : WDI, World Bank.

# **VI.   Growth in the 21**<sup>**st**</sup> **century**

We now explore the impact of macro and trade shocks and the role of China on the growth of LA countries during the Commodities Cycle (2003-2020). To help tighten the links between trade, macro, and growth, we look at Mexico and break down the South American and Central American countries into two sub-groups, depending on features of their trade composition. A simple regression model is used to find the systematic connections between the growth
Policy Research Working Paper 7591

# **Abstract**

Using firm-level data for Jordan, the paper estimates the extent to which growth spillovers from foreign direct investment (FDI) to local firms stem from persistent learning externalities (i.e., they endure even after foreign investment leaves as knowledge has been transferred to local firms) or from transitory effects (e.g., demand increases that evaporate following disinvestment). The paper find that spillovers have a significant transitory nature, with employment and capital growth declining when FDI falls, particularly in downstream industries supplied by locals. This suggests that if FDI-attracting policies are intended to promote sustainable growth, it may be more effective to attract and retain FDI via long-term structural policies, for instance, through low corporate tax rates rather than temporary tax holidays or through policies that strengthen the domestic absorptive capacity and linkages between foreign and local firms.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mschiffbauer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team