rafmacalaba/data-use-mentions-extended huggingface dataset:
, Sayre, and Lima 2021). There has been some research providing advice on the design of QAS and especially standards and monitoring tools for LMICs, including details on case studies and countries’ practices (Raikes, Neuman, and Burton 2019; Raikes, Sayre, and Lima 2021).

Yet there has been very little (to the best of the authors’ knowledge, none to date) research actually using data from QAS to illustrate their potential – or pitfalls – in supporting quality learning for all in LMICs. Although there is information from high-income countries about their QAS, information from LMICs is limited (Anderson et al. 2017; Raikes, Neuman, and Burton 2019). This is partly a data problem; countries’ ministries or agencies responsible for the QAS may release the results of quality assurance efforts, such as school report cards or national reports, but not the microdata nor research validating the QAS itself. Even national reports or databases are relatively rare in LMICs. For instance, only 29% of countries, in a study of early childhood care and education QAS in 14 Sub-Saharan African countries, had their monitoring data reported back to a national database (Raikes, Sayre, and Lima 2021).

This paper investigates the potential ability of QAS to systematically monitor quality and inform action, drawing on data from pre-primary in the Arab Republic of Egypt. The effectiveness of QAS depends on the accuracy and relevance of the data collected through QAS tools used to monitor quality practices within classrooms. Our research questions are:

- (1) How consistently do the tools used within a QAS measure quality?

- (2) What are potential sources or reasons for any inconsistency?

- (3) In light of the degree of measurement error found in measuring quality, how should data from QAS be used to undertake quality enhancement?

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# **Introduction**

This paper is a brief survey of the impact of international trade and trade reform on employment. It focuses mainly on empirical studies that have sought to establish the labor implications of greater trade and trade liberalization. As is revealed by the long bibliography attached to this paper—which represents only a selection from the literature—a huge amount of research has been undertaken on the subject of the relationship between trade, wages and employment. Within this there are numerous excellent literature surveys, many of which review the underlying theory, empirical strategies, methodology, and techniques in some depth.<sup>1</sup> Thus we make no attempt to be comprehensive, and those seeking a more rigorous and detailed discussion of specific papers should refer to these surveys and the papers themselves. Our emphasis is on the broad themes of the literature, with a view to deriving some stylized facts and a list of possible research questions. To keep the paper within reasonable bounds we do not discuss labor economicsoriented literature on labor market institutions, regulation and distortions, the design and effectiveness of possible instruments to facilitate the movement of workers across sectors or employers within sectors, or issues related to the relationship between trade openness and income distribution.<sup>2</sup> As noted by Goldberg and Pavcnik (2004), empirical research to date has offered mixed results regarding the direction and size of the effects of trade liberalization on employment and wages. There are a number of robust stylized facts in terms of outcomes, but debate continues on the impact of trade and trade policy. In part this is because it is hard to obtain a good measure of trade policy, even for OECD countries—the action is mostly on NTBs for which time series data are notoriously difficult to obtain. The weakness in the openness measures that confound the literature on trade and growth are equally problematic here. More fundamentally, trade policy is endogenous—among other things, labor market concerns are one determinant of trade policy,
the poor more because of their greater ability to mobilize resources (Narayan and Nyamwaya, 1996). Despite outbreaks of state initiated tribal violence in recent years, ethnic tension was not mentioned as a problem by people in any of 30 communities across the country.

Similarly, in neighboring Tanzania composed of over 300 ethnic groups, years of socialism and villagization programs have led to a strong sense of national identity which appears to supersede ethnic identity. Tanzania, despite acute poverty and a bankrupt state, has not experienced ethnic violence or polarization. In a participatory poverty assessment conducted in 87 communities across Tanzania, people explicitly linked peace and community economic levels to informal groups with cross-ethnic membership (Narayan, 1997).

In a study on entrepreneurship in Ghana, Barr (1997, 1998) finds that the small, cohesive solidarity networks of the Ghanaian entrepreneurs seem to function as a substitute for formal institutions and markets, while the larger more diverse networks of non-Ghanaian entrepreneurs provide better access to information about markets and technologies.

Several recent studies conducted as part of the World Bank's Local Level Institutions Study (Grootaert and Narayan, 1999) confirm the importance of heterogeneity in group membership and economic outcomes. This pattern of results is found in rural Tanzania (Narayan and Pritchett, 1997) in Indonesia (Grootaert, 1998), and in Burkina Faso (Grootaert, Swamy and Oh, 1999).

Other studies, particularly from Latin America, show time and again that despite high ratings in community solidarity, indigenous communities remain poor with few connections to the powerful within or outside the community. While they may manage to attract government provided basic social infrastructure, this does not result in production opportunities. There is little evidence that indigenous social organizations are providing the foundation for indigenous groups to mobilize either for fundamental rights or for greater access to economic and political participation (Junho Pefia and Lindo-Fuentes, 1998; Gacitua-Mario, 1998). In the absence of outside allies, indigenous social capital of poor communities remains a substitute for the resources and services that
by checking if particular types of households are systematically included or excluded (inclusion and exclusion errors). Additional criteria can be proposed for selecting beneficiaries in these scenarios. An important aspect of this pilot should be to verify the definition of the family that is used as the unit of program eligibility. This can be done by collecting information on the family structure within households. In addition to this, Grama Niladharis, who are responsible for selecting beneficiaries, can be enquired for the definition of family they use.

In addition to these issues, there are practical aspects of program implementation that we do not address here but may be critical in determining program success. A deep assessment of the current implementation process may help in identifying and addressing sources of implementation errors. Designing a proper grievance or appeals mechanism can help reduce potential exclusion errors. For example, smaller households are less likely to be poor and therefore more likely to receive a higher PMT score. However, this may miss smaller vulnerable households like those headed by single parents or widows with small children. A redress process can help address specific cases of exclusion like these. An effective outreach strategy to enroll the intended population can help in reducing exclusion errors. Similarly, designing an effective exit strategy to remove relevant beneficiaries from the program is crucial in order to reduce inclusion errors and to make the program budget‐friendly. Future extensions to our work could also examine the concept of vulnerability threshold, which is set above the targeting threshold and defines a set of households that can be monitored and assisted if needed ‐ i.e. in the event of a shock, the incidence of which can be monitored with rapid phone surveys.

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modeled travel time to places with 50,000 inhabitants circa 2000 (A. Nelson, 2008). (Anderson et al., 2015) find variation in their examination of global data products of cropping systems models. For livestock, we transform the 5 major livestock into international values from livestock products (namely, meat, milk, eggs, honey and wool). The forest (non-wood products, wood-products) components relies on a remote sensing model to estimate forest loss. With regards to the non-timber values, limitation from the sources present two challenges. The estimates use simple averages from the literature that accordingly assume a property of uniformity in the value of a hectare of forest as similar across the world and the sample of forests with literature drawn for the study is representative of the world (Siikam ̈aki, Santiago-Avila,<sup> ́</sup> and Vail, 2015). The fishing model relies on proximity and association with ports or water bodies.<sup>19</sup> Finally, since we do not incorporate any information on hunting, the result is an even distribution across units and time.

Another source of uncertainty is the geographic distribution of the components. Ideally, we would use subnational prices, however it was not feasible, and the results do not reflect this occurrence, including administrative units with higher variation of prices due to the heterogeneity of distinct urban and rural areas.

# **4 Conclusion**

Natural disasters impact both lives and livelihoods and a higher frequency and severity of disasters will likely increase in a changing climate. Socio-economic estimates at the local level inform disaster preparations of the exposure of physical assets and production to natural disasters and have implications for food security. Significant advancement in the spatial allocation of indicators has occurred in the past 10 years such as population (e.g Leyk et al., 2019). The advantages of gridded data as a common spatial unit of integration and the cross-entropy models are clear. These common units allow us to examine within-country
test against endogeneity concerns, given that the informal competition faced by service sector firms is relatively exogenous to manufacturing firms. Table 4 provides the results. They are like the baseline results, both qualitatively and quantitatively.

# 8.3 _Falsification test_

Regression results for the sample of large firms are provided in Table A11 in the Appendix. As expected, the relationship between informal competition and the likelihood of having a quality certificate is weak and statistically insignificant in all the specifications considered. The finding helps bolster our confidence against endogeneity concerns.

# 8.4 _Other regions_

The WBES data that we use covers other regions, including Sub-Saharan Africa, Eastern Europe and Central Asia, East Asia and the Pacific, the Middle East and North Africa, and South Asia. We checked if there is a similar relationship between informal competition and the likelihood of certification by firms in the other regions. Thus, we repeat our baseline estimation first for all the countries in regions other than LAC. Next, we focus on individual regions that have at least 10 countries to ensure adequate variation in our country-year level measure of informal competition. Two regions that satisfy this criterion are Sub-Saharan Africa and Eastern Europe and Central Asia. Regression results are provided in Table 5. For brevity and without any loss of generality, the regression results are shown without any baseline controls (except for country, year, and industry fixed effects) and with all the baseline controls. These results reveal a large, positive, and statistically significant impact of informal competition on the likelihood of obtaining quality certificates in all the regions considered. We caution that some of the other results, like the 28
corresponds to a destination-industry-year, represented as _ist_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Country-industries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

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*_Employment Share of the Industry_**||||||||
|**Agriculture (percent)**|58|49|43|53|20|12|18|
|**T&G (percent)**|4|5|6|5|6|6|6|

_Source:_ Authors’ calculations are based on Cambodia Socio-Economic Surveys and the Sri Lankan 2002 and 2006 Household Income and Expenditure Surveys and the 2002 and 2008 Labor Force Surveys _Note:_ T&G = textiles and garments.

# _4.2. Unit Values_

In both Cambodia and Sri Lanka, unit values show declining trends following the end of the MFA. Sri Lankan apparel export unit prices to the United States declined sharply after the MFAphase-out from $59 to $42 per dozen items over the 2004–08 period (see figure 3). A slight rise in EU unit values over 2004–05 was followed by a moderate decrease (Staritz and Frederick 2011b). In spite of this decline, Sri Lanka’s apparel exports have generally experienced higher unit values than those of other Asian apparel exporter countries, reflecting the higher valueadded production that Sri Lanka specializes in. Unit values of apparel exports from India and Sri Lanka to the EU are higher than those of Asian competitor countries, including Bangladesh, Cambodia, China, Pakistan, and Vietnam (Tewari 2008).

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#### _Source_ : Global Findex database.

_Note:_ Data for Northern Europe, the South Caucasus, and Western Europe are not available.

### b. By country

<!-- Start of picture text --> Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herzegovina<br>Bulgaria<br>Croatia<br>Georgia<br>Kazakhstan<br>Kosovo<br>Kyrgyz Republic<br>North Macedonia<br>Moldova<br>Montenegro<br>Romania<br>Russian Federation<br>Serbia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Pay online Pay in cash when order is delivered<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : The length of the bars represents the percentage of adults aged 15 and above who report buying online.

# Informal Saving and Borrowing Is Prevalent in Developing Economies of ECA

## _Formal Saving_

People save for future expenses and to be prepared to respond to uncertainties. They may save for a large purchase, investment in education or a business, financial needs in old age, or emergency expenses. To meet immediate expenses, they may borrow. Global Findex data show how and why people save and borrow and shed light on their financial resilience to unexpected expenses.

Formal saving is more common in high‐income economies than in developing economies (figure 9). In 2017, 48 percent of the world’s adults reported having saved or set aside money in the previous 12 months. The figure was 72 percent in high‐income economies and 43 percent in developing economies. In high‐income economies, more than 75 percent of savers (55 percent of all adults) save formally at a financial institution. In developing economies
push infrastructure investments affect welfare through an exogenous positive TFP shock in Ethiopia in a spatial general equilibrium model. Vanden Eynde and Wren-Lewis (2021) and Abbasi et al. (2022) use a reduced-form econometric approach and find evidence of strong economic complementarities for roads and electricity in the context of rural India and the SubSaharan Africa region, respectively. None of these papers study the optimality of government infrastructure investment policies and their implications.

Another strand of related literature endogenizes infrastructure investment in quantitative spatial economic models. Fajgelbaum and Schaal (2020) study transport networks to assess optimal investments and inefficiencies in the road networks of European countries. Santamaria (2020) assesses how governments can adjust transportation infrastructure to unexpected economic changes. Both studies, however, exclusively focus on the transportation infrastructure.<sup>3</sup> Arkolakis and Walsh (2023) provide a spatial theory in which a planner affects the production and demand of electricity by choosing the electricity price at each node, and apply it to assess the global impact of the rise of renewable energy. Their framework likewise does not account for investments in other infrastructure types.

The remainder of the paper is organized as follows. Section 2 describes the expansion of the roads and electricity networks in Brazil and the relationship between infrastructure quality and structural change. Section 3 develops the model. Section 4 discusses its calibration. Section 5 shows the quantitative results and main counterfactuals. Section 6 investigates the relationship between optimal infrastructure and political preferences. Section 7 concludes.

# **2 Background and Data**

This section quantitatively describes the road and electricity network expansion in Brazil and the spatial structural transformation process that accompanied the increased infrastructure availability. Our data comes from several sources. The sectoral employment shares and electricity access come from the Integrated Public Use Microdata Series (IPUMS-I, 2019), comprising four waves of individual and household survey data from 1980-2010. We complement these data with the information from Brazil’s national electricity regulatory authority, ANEEL ( _Agência Nacional de Energia Elétrica_ ), and from
has a very moderate e¤ect, usually close to zero and, if negative, smaller (for seven countries out of ten) than the e¤ect of emigration. Not even in a short-run perspective did immigration have a signi...cant negative impact on native wages for European countries. To the contrary emigration did not produce any short-run gains for native wages that may reduce the welfare cost of the long-run losses.

# 6 Conclusions

Immigrants are highly visible to the governments and public in destination countries especially in times of economic anxiety and high unemployment levels. They are also relatively easy to measure by statistical agencies of the host countries through censuses, population and labor force surveys. On the other hand emigrants are much less visible to governments of their birth countries and less known to the general public. There is no mandatory registration for those who leave a country, they are dispersed in many receiving countries and no statistical agency keeps detailed records of all its emigrants and returnees. There is rarely a policy instrument that can a¤ect emigration since freedom of movement allows people to leave any democratic country.

During the last decades emigration has been considered mostly in connection with poor countries and usually in the context of brain drain. There have been studies on the labor market impact of immigration in the destination since the 1970s. However, only very recently and only in connection with the debate on brain drain and brain gain, some economists have began to measure empirically the aggregate income e¤ects of emigration (Beine et.al. 2008). This paper uses a recently constructed database on emigration ‡ows by schooling level between 1990 and 2000, constructed using data across all countries in the world. Using a range of parameters and an aggregate representation of labor markets of receiving countries, we show that residents of Western European countries experienced wage gains, on average, from 22
# **2 Data**

## **2.1 Lockdown data**

Lockdown is a binary variable where one indicates the month the following measures were imposed by trading partners of the Philippines: workplace closure, the closure of public transport, stay at home requirements, restrictions on internal movement, and international travel controls. This definition of lockdowns has been adopted from Majune (2020). Data for these indicators is from the Oxford COVID-19 Government Response Tracker (Hale et al., 2020).

To explain the pattern of lockdown measures, we display the trend of the lockdown Stringency Index for the Philippines and neighboring countries (Thailand, Malaysia, China, Indonesia, and Vietnam) between January 1, 2020, and December 31, 2020, in Figure 1. The index is constructed by re-scaling the maximum ordinal values of nine indicators (one health measure and eight closures and containment policies) to range between 0 and 100. The scores are then averaged to get a composite index for over 180 countries. A score of 0 indicates no restrictions while 100 signals severe implementation of lockdown policies in a country (Hale et al., 2020).

The first lockdown measure in the Philippines was on January 31, 2020, when arrivals of people from some regions were banned. This was followed by the closure of the border, workplaces, public transport, and imposition of stay-at-home requirements and restrictions on internal movement on March 15, 2020, as reflected by a sudden jump in the index, from 34 to 75. The index was 100 on March 22, 2020, indicating maximum implementation of all containment measures. However, it dropped significantly, on May 29, 2020, when workplace and public transport closure, stay at home requirements and restrictions on internal movement were temporarily suspended. The easing of international travel controls started on July 7, 2020. The Philippines had the joint least index with Vietnam by the end of 2020, indicating a significant lax of most containment measures.

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