rafmacalaba/data-use-mentions-extended huggingface dataset:
tools for economic analysis exist that can inform such plans, ranging from computable general equilibrium models to microsimulations tools, all of which have strengths and weaknesses.<sup>16</sup> Monitoring performance against these benchmarks poses a number of serious data challenges. There has been huge progress in collecting primary household survey data. When the World Bank’s current global poverty-monitoring effort began in 1990, the estimates used 22 surveys for 22 countries (Ravallion et al. 1991). Today approximately 900 surveys are used by the Bank’s researchers, spanning 125 countries, with more than six per country. The latest estimates use a “global” sample of 2.1 million households. However, many problems remain. There are persistent lags and uneven coverage. (The surveys used here cover 90 percent of the population of the developing world as a whole in 2008, but this varies from 94 percent in East Asia to only 50 percent in the Middle East and North Africa.) Understandably, there are continuing concerns about the comparability of the surveys over time and across countries. Furthermore, there are concerns about underreporting and selective compliance in household surveys; the rich are difficult to interview, and this task is not becoming any easier. Moreover, the weak integration of macro and micro data is a long-standing concern that warrants more attention than it has received. Better data will help to determine how close—or far—we are from reaching our development goals, including poverty reduction, and in assessing the efficacy of alternative policies for attaining those goals.

> 16 A useful compendium of the tools available can be found in Bourguignon et al. (2008). On microeconomic simulation methods, see Ferreira and Leite (2003).

18
for the year 1989 – 2008<sup>12</sup> . After cleaning the data, the final dataset contains 6,296 firms. Since there is limited data for other district-level variables, the analysis is restricted to fewer years (1999-2004). The analysis is limited to the manufacturing sector (i.e. National Industrial Classification 2-digit unit 14 to 36). I also exclude firms for which data on sales, gross assets and wages is missing, since these are crucial to the computation of firm-level productivity. Of the firms in the final dataset, 3,638 firms enter the export market at least once over the period of study. There is also a large degree of firm heterogeneity in terms of size and age.

However, some caveats should be mentioned here. It is not mandatory for firms to supply data to the CMIE, and one cannot tell exactly how representative of the industry is the membership of the firms in the organization. Prowess covers 60-70 percent of the organized sector in India, 75 percent of corporate taxes and 95 percent of excise duties collected by the Government of India (Goldberg et al 2010<sup>13</sup> ). Large firms, which account for a large percentage of industrial production and foreign trade, are usually members of the CMIE and are more likely to be included in the database. And so, the analysis is based on a sample of firms that is, in all probability, taken disproportionately from the higher end of the size distribution. As Tybout and Westbrook (1994) point out, a lot of productivity growth comes from larger plants, which are also more likely to be exporters, providing confidence in the comprehensive scope of the study.

Measures of agglomeration are constructed using unit-level data for the years 19992004 from the Annual Survey of Industries (ASI), conducted by the Central Statistical Office of the Government of India. The ASI covers all factories registered under the Factories Act of 1948 that employ 20 or more workers, or that
rates in the country. By contrast, the country’s Southern provinces and urban areas registered very high poverty rates despite their traditionally superior nonmonetary poverty indicators (e.g. child malnutrition) and higher regional productivity.

Three types of error may have contributed to producing these counterintuitive results. First, measurement errors in expenditure data could be caused by consumption underreporting (especially consumption of household produce, which is common in rural areas) or by flaws in the survey questionnaires. Second, the methodology used to compute poverty lines<sup>5</sup> (PLs) differs from the standard used in many other developing countries, and methodological weaknesses might result in unexpected provincial patterns. Third, the interaction between consumption underreporting and the particular methodology used to compute the PLs could magnify inaccuracies in the data.

> 2 _Inquérito sobre Orçamento Familiar._

> 3 _Ministério de Planificação e Desenvolvimento – Direcção Nacional de Estudos e Análise de Políticas_ : Ministry of Planning and Development – National Directorate of Policy Studies and Analysis.

> 4 The Northern, Central and Southern regions are grouped according to the official country definition. _Northern_ includes the provinces of Niassa, Cabo Delgado and Nampula; _Central_ includes Tete, Zambezia, Manica and Sofala; and _Southern_ includes Gaza, Inhambane, Maputo Province, and Maputo City.  For an overview of Mozambican provinces, see Map 1 below.

> 5 All the three NPAs report a different poverty line, based on local prices and consumption patterns observed in the crosssectional rounds of, respectively, the IAF ( _Inquérito aos Agregados Familiares sobre Orçamento Familiar_ ) 1996/97, the IAF 2002/03 and the IOF 2008/09 survey, by grouping provinces and areas according to 13 locations: Niassa and Cabo Delgado, rural and urban; Nampula, rural and urban; Sofala and Zambezia, rural and urban; Manica and Tete, rural and urban; Gaza and Inhambane, rural and urban, Maputo Province, rural and urban, and Maputo City.

2
**Table 1: Characteristics of EMS by service providers, %**

||Federal|State|Autonomous|Private|
|---|---|---|---|---|
|Share of total enrollment|21.8|47.4|12.3|18.6|
|Students with insufficient math achievement level|62.6|73|52|61|
|Students that have a job|35.1|37.8|37.5|33.3|
|Students with scholarship|46.4|47.3|36.7|20.9|
|Mother with higher education|7.3|4.2|11.7|22.6|
|Father with higher education|9.2|5.2|14.4|24.2|
|More than 25 books at home|66.5|60.3|69.9|77.9|
|PC at home|70.2|56.5|73.2|83.4|

_Sources: Context survey PLANEA 2016. The sample includes students who answered the 12_<sup>_th_</sup> _grade context survey; INEE, Panorama Educativo 2016 and National results of PLANEA 2016-2017._ EMS schools run by the federal government tend to be large with an average of 1,159 students usually split in morning and evening shifts. Federal EMS schools located in the metropolitan area of Mexico City (CDMX from now onwards), are particularly large with an average of 2,200 students with most of them having morning and afternoon shifts. The EMS system is characterized by some strict promotion criteria. Students must pass five out of eight disciplinary subject areas and practical modules per semester. Otherwise they must repeat the semester. Students who fail three or fewer subject areas can enroll in the next semester, but they must attend and pass leveling courses during a
**Table 1: Evolution in Inequality – Key Indicators, Age 25 years or more**

|||**Ci**|**rca 20**|**01**|||**C**|**irca 2**|**009**||
|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Median**|**SD**|**Dif 75 25**|**Gini**|**Mean**|**Median**|**SD**|**Dif 75 25**|**Gini**|
|**Costa Rica**|7.3|6|6|4.3|0.32|7.9|6|5|4.3|0.30|
|**Panama**|9.1|9|6|5.0|0.31|9.4|9|6|5.0|0.30|
|**Guatemala**|3.7|2|6|4.6|0.63|4.4|3|6|4.9|0.58|
|**El Salvador**|5.8|5|8|5.2|0.50|6.4|6|9|5.3|0.47|
|**Honduras**|5.1|4|5|4.6|0.49|5.4|6|5|4.4|0.46|
|**Nicaragua**|5.0|4|
# **Vc. Exchange rate and price competition**

The increasing spending per tourist in STP reflects the REER appreciation and can also be linked to high airfares. In order to evaluate this dynamic, consider the evolution of the real exchange rates; in particular, evaluate the relative trends in prices and exchange rates with respect to the euro-euro zone (RER_EURO), the US dollar-US (RER_US), and the British pound-UK (RER_POUND). This analysis is done for the 1999-2016 period for STP (Figure 18), for the five countries of comparison (Figure 19 to Figure 21), and the 2016 values for a larger comparison group (Figure 22 to Figure 24). All the figures indicate that STP’s currency has appreciated the most in the relevant comparison group. Note, however, that the average spending per tourist includes the air ticket; therefore, a high expenditure in STP could be a reflection of comparably more expensive tickets to STP due to low airlift competition rather than actual high-spending tourists. As discussed above, a more detailed data set on how tourists spend their money would be needed to separate expenditures by categories.

# **Vd. Digital presence and Instagram posts**

ICT and digital marketing allow countries to reach potential tourists in a more direct way and can be a boost for niche destinations. Many tourism destinations have taken to the social media and social networks to market their products, reaching markets previously inaccessible to them.<sup>11</sup> This can be particularly more intense in the type of tourism that STP wants to promote, “discovery” tourism, targeting travelers who are looking for lesser known destinations that offer attractions like nature, culture, etc. with strong emphasis on environmental protection and positive social impact.

The collected digital presence data proved to be very volatile, and only Belize and the Maldives showed some persistent improvement in this metric. Following the relatively new literature about the digital presence effects on tourism and other industries, consider now the Instagram posts
rounds of surveys covered the same enumeration areas and interviewed the same respondent or household, if possible.<sup>3</sup> Community-level questionnaires were also conducted in 2007 and 2008 NPS.<sup>4</sup> In our analysis, we aggregate the community-level data to the enumeration area, which we then merged with the NPS household-level data at the enumeration area level.

Unfortunately, the questions on the 2007 and 2008 surveys only include a limited number of questions that were included in the 2005 surveys. As a result, the emphasis of this paper is on the analysis of the 2007 and 2008 rounds of survey data, which includes a richer set of measures of service delivery, administrative competence and honesty.<sup>5</sup>

# **4.2 Measures**

## **4.2.1 Dependent Variable**

There is a large literature that models generalized trust, or how much citizens trust the government. Survey questions that probe respondents about their trust in government are problematic for several reasons. First, there is an issue of the extent to which the same questions capture comparable attitudes in different segments of the population or across time. Second, trust is usually quite domain-specific. As Cook, Hardin and Levi (2005, p. 164) argue, trust varies with the task, context and people involved. Consequently, we do not know the domain which respondents have in mind when answering questions about trust. If a respondent answers that they trust a president, we do not know with what tasks the respondent trusts the president. It is highly unlikely that an individual would trust a president to handle all tasks in every possible context.

To deal with this problem, we model beliefs about the trustworthiness of local government officials rather than a vague notion of trust in government as an institution. Specifically, respondents were asked: “In your opinion, do you believe local councillors or do you have to be careful dealing with them?” By
Policy Research Working Paper 10291

# **Abstract**

How can regularization programs improve forced migrants’ resilience to shocks? This paper leverages panel data collected during the COVID-19 pandemic to assess whether Venezuelan forced migrants who were eligible for a regularization program in Colombia were more resilient and less affected by the pandemic than similar but non-eligible forced migrants. The results indicate that access to the program promoted better health access for eligible migrants, facilitating adherence to prevention guidelines and increasing detection rates. Additionally, eligible migrants had better housing and labor conditions, relative to non-eligible migrants.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at sandrarozo@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
# sectors.

In Section 4, I briefly review the empirical literature to the extent that it relates to this paper and then, in Section 5 I make an empirical analysis based on the predictions from my theory using Colombian data for the 1983-1998 period. I analyze the effect of productivity on trade protection by following my theory closely. I confirm that the (4-digit ISIC<sup>4</sup> level) sectoral tariffs are inversely related to the import penetration ratio and import demand elasticity, whereas they are positively related to total factor productivity. The results also indicate that the sectors with more productivity gain (or less productivity decline), as compared to the other sectors, are liberalized less.

In the estimations, I account for the extra channels which I mention above and, more specifically, I account for their elimination which leads to a big decline in tariffs. For this purpose, I allow for a shift in the common terms across sectors over time. I tackle the potential endogeneity of the right-hand-side variables, namely the inverse import penetration to import demand elasticity ratio and productivity, by using instrumental variables. The instruments are the capital to output ratio, materials prices, a measure of scale economies (value added/number of firms), and the TFP of the upstream industries which I confirm to be valid based on a test of overidentifying restrictions and also explain intuitively in Section 5.

My productivity estimates come from Eslava, Haltiwanger, Kugler and Kugler (2004) and they are originally calculated at the firm-level which are then aggregated to the 4-digit ISIC level using production shares of each firm. The dataset has the great advantage of using plant level input and output prices. This enables obtaining good estimates with smaller bias as compared to the majority of the studies which need to employ non-parametric estimations and sector level price due to lack of data.

By showing how trade policy depends on productivity both theoretically and empirically, I provide solid evidence for the endogeneity of trade policy with respect to productivity. Finally, in
as in the GTAP Data Base, (ii) import-augmenting technical change, _amsk_ **_k,r,s_** , that lowers the effective price of a good in the destination market, and (iii) the import penetration as captured by the change in composite imports of subsector commodity _k_ , _qimk_ **_k,s_** . Note that the substitution effect for a particular flow _(k,r,s)_ increases in divergence of import tariff for good _k_ from regions _r_ to _s_ , from the weighted-average tariff of _s_ . Since a higher weight means lower divergence, this effect decreases in import-shares of region _r_ in the total imports by region _s_ of the good _k_ .

## **For all sectors k in SECT, regions r and s in REG:**

**_qxskk,r,s = -amskk,r,s + qimkk,s -σM,k  * [pmskk,r,s - amskk,r,s - pimkk,s]    ............... (1)_**

Global transport margins are treated in the same manner as in the standard GTAP model, with the quantity of international trade, transport and insurance services required being a fixed proportion of the volume of goods shipped. Technical change in this sector is represented with the variable atmfsdkk **_,r,s_** and is obtained by adding up the changes at different levels, which are directly translated from the aggregate changes in the corresponding variables. Trade and transport services are provided at a common price, _pt_ , which represents a Cobb-Douglas aggregation of trade and transport services exports from all regions in the model. Deducting the rate of technical progress from this price change gives the percentage change in the commodity and route-specific transport margin, _ptransk_ **_k,r,s_** . The price linkages in the model also include export taxes, _txsk_ **_k,r,s_*
financing information. The PPI also provides the current status of the project (active, concluded, distressed, or canceled).

Many projects in the PPI database lack data for all characteristics. Missing data on the variables essential for the analysis—the contract period and the level of government that granted the contract—were added for all projects using the individual project descriptions provided in the database if available.

The Polity IV data were used to identify the institutional characteristics of a country. We used yearly executive recruitment, constraints on the executive, and political competition concept variables. We linearly interpolated the values for the interruption, interregnum, and transition periods, during which the variables are coded out of their respective ranges in Polity IV.

From the WDI, we took the annual series on the per capita growth rate and nominal exchange rates, which we used to create series of detrended and demeaned series of per capita growth rate and exchange rate shocks, using the filter suggested by Hamilton (2018). The data on financial crises come from the Systemic Banking Crises dataset of Laeven and Valencia (2020).

The PPI database records 8,420 projects in the developing world, encompassing 130 countries, with financial closure dates from 1990 to 2020. The sample includes projects from five sectors: ICT, energy, transport, water and sewerage, and municipal solid waste. As the municipal solid waste data are a recent addition to the database and cover only currently active projects with financial closures starting in 2009, they were dropped from the sample to make all projects comparable. ICT projects, merchant and rental greenfield projects, management and lease projects, and divestitures were also excluded from the sample, because they are not PPPs as defined in this study.

We define cancellation as early termination of a project. The PPI database labels as cancelled any project from which the private party has exited by selling or turning over its shares back to government or by ceasing operations. It labels as distressed any project that is in international arbitration or for which
did not induce greater condom use, neither as reported by respondents nor as revealed in an experimental game that our subjects played in health camps. Despite the lack of effect on condom use, we do find significant impacts on a biomarker that proxies for unprotected sex with risky partners. The likelihood of testing positive for Chlamydia, a common STD, decreased by 55 percent in response to treatment for women in our sample (the impact on men is in the same direction but statistically insignificant). This is consistent with the reduction in the number of concurrent partners, and possibly with a more general shift away from risky behaviors.

MTV Shuga worked. The next question is why. To explore the importance of emotional involvement in the narrative, we make use of measures of viewer responses from the communication literature (Green and Brock, 2000; Murphy et al., 2011) that, to the best of our knowledge, have not been used in the economics literature before. In our endline survey we asked a battery of questions on how immersed in the story the respondent was while watching the show, and how much he or she identified with the characters. Using these we construct two indexes, ‘Transportation’and ‘Identification’, and we show that the treatment effects were stronger for viewers that had higher values of these indexes. While these interaction effects 2
# **Real Income Growth Per Capita**

Theory provided various interpretations to the nature of the relation between private savings and income growth. The life-cycle theory of saving and consumption predicts that changes in an economy’s rate of economic growth will affect its aggregate saving rate. In the simplest version of the model, in which young people save for retirement, and old people consume their previouslyaccumulated assets, an increase in the rate of economic growth will unambiguously increase the aggregate saving rate. This is because of the observed increase in lifetime resources (and saving) of younger age groups relative to older age groups. Furthermore, Chow and Lin 1971, among others, supported this hypothesis. However, more complicated and realistic versions of the model yield ambiguous conclusions pertaining to the relationship between saving and growth. For example, young people may have low current income but high lifetime wealth, and may therefore borrow to finance current consumption.

Whether higher growth increases or reduces the aggregate saving rate depends on the respective age-profile of saving, which is an empirical matter and is negatively correlated with age (See Poterba (1994), Paxson (1996), Deaton and Paxson (1997) and Nwachukwu and Egwaikhide (2007)).

Following Modigliani and Brumberg (1954) and Modigliani (1998) the growth in the aggregate growth of real per capita income - not the level - is considered in the proposed setup as a determinant to private savings. The difference log of real per capita income is included in the model.

Real gross private savings and real income are both controlled for population growth - considering both variables in per capita terms. Due to the unavailability of high frequency data on population, the annual population count is used to index both quarterly series.

Accordingly, the nature and magnitude of the relation between growth and savings will be tested using Egypt’s data and provide an explanation to the ongoing debate.

# **Financial Development**

The earlier work of McKinnon (1973) and Shaw (1973) as