rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Determinants of Learning Loss**

|Variable|First stage<br>weeks closed|IV learning<br>loss|
|---|---|---|
|Vaccination|-0.301**<br>(0.073)||
|Stringency|1.021*||
||(0.262)||
|Weeks||0.006***<br>(0.002)|
|Constant|-18.723|0.111|
||(14.567)|(0.034)|
|N|41|41|
|R-square|0.409|0.415|
|F-test|13.13|10.98|
|[_p_-value]|0.000|0.002|
|Sargan (overidentification) statistic||0.409|
|[_p_-value]||[0.5223]|

The vaccination rate and the stringency index turn out to be useful instruments. Since we do not have many degrees of freedom, we estimate a very parsimonious equation, explaining weeks closed with the vaccination rate. It turns out that a lower (higher) vaccination rate leads to a longer (shorter) duration of school closures, while a higher stringency index prolongs school closures. Therefore, we can tentatively conclude that the duration of school closures leads to a higher level of learning loss. In the IV estimates, a week of school closures leads to a 0.006 standard deviation increase in learning loss, just slightly higher than the OLS estimate.

# **4. Implications**

In terms of learning loss, it makes little difference if the controls are included; all are insignificant. This suggests that school closures themselves were responsible for learning loss and the severity of that loss was primarily due to how long schools were kept closed. What matters is time spent in school. While pre-COVID learning levels varied considerably across countries, depending on many factors, learning happens if children go to school. Keeping schools open reduces learning losses, even in the poorest countries and in countries with low pre-COVID learning levels. This is especially important because research has found little evidence showing that school closures reduced infection rates.

Students learn less when they are not being taught in school buildings. Duration played a key role. The long-term impact may be increased education gaps, especially between students from high and low socioeconomic status.

The main lesson learned here is that if COVID continues as a low-intensity pandemic, or if a new, similar situation arises, keeping schools open should be a priority, as the evidence shows that the benefits of school closures seemed to have been lower than the cost of learning losses. However, the task at hand is to figure out how to accelerate learning and make up for the lost time. Fortunately, there are several things that work in terms of mitigating learning losses. These include online tutoring programs, which were shown to reduce learning loss significantly in several 8
Table A.5: Technology sophistication in Quality Control

|**Category**|**Manual,**<br>**Visual**<br>**or**<br>**Written Processes**|**Human**<br>**Inspection**<br>**with Computers**|**Statistical**<br>**Process**<br>**Control**|**Automated**<br>**Systems**<br>**for Inspection**|
|---|---|---|---|---|
|**Functionality**|Documenting<br>inspection<br>results,<br>visual<br>checks,<br>manual<br>recording<br>of<br>defects.|Digitizing inspection pro-<br>cesses, capturing photos,<br>real-time data entry.|Analyzing data, statisti-<br>cal process control, gen-<br>erating control charts and<br>reports.|Automated visual inspec-<br>tion,<br>defect<br>detection,<br>measurement, sorting.|
|**Integration**|No integration|Moderate<br>integration<br>with<br>data<br>management<br>systems<br>and<br>reporting<br>tools.|Good<br>integration<br>with<br>data<br>collection<br>systems<br>and reporting tools.|High<br>integration<br>with<br>manufacturing<br>systems,<br>data<br>collection,<br>and<br>reporting tools.|
|**Automation**|No automation|Moderate<br>level<br>of<br>au-<br>tomation<br>for<br>data<br>en-<br>try and reporting; inspec-<br>tions still manual.|High level of automation<br>for data analysis, control<br>chart generation, and re-<br>porting.|High<br>level<br>of<br>automa-<br>tion for real-time inspec-<br>tion,<br>defect<br>detection,<br>and data logging.|
|**Rank**|4|3|2|1|
|**Reason for Ranking**|Lowest sophistication as<br>it lacks automation and<br>integration,<br>relying<br>en-<br>tirely<br>on<br>manual<br>pro-<br>cesses.|Moderately sophisticated<br>with digitized processes<br>but less automated in-<br>spection;<br>integration is<br>less robust.|Highly sophisticated with<br>automated data analysis,<br>good<br>integration<br>with<br>data systems,<br>but less<br>real-time<br>functionality<br>compared to automated<br>systems.|Highest<br>sophistication<br>due to full automation<br>and<br>advanced<br>integra-<br>tion with manufacturing<br>systems<br>for<br>real-time<br>inspection<br>and<br>data<br>logging.|
|**Technology Example**|Checklists<br>and<br>Written<br>Reports|Inspectorio|Minitab|Cognex In-Sight|
|**Average Costs**|Cost of paper and print-<br>ing, varies|Varies|$1,500-$4,000/license|$5,000-$20,000/system|
|**Launch Year**|N/A|2016|1972|2016|
|**ChatGPT ranking**|1|2.5|3|4|
|**Estimated time in a**<br>**task**|5 hours|2.5 hours|1 hour|30 minutes|

Source: The table draws upon various sources of information including desk research, and websites of various companies including Cognex In-Sight, and Minitab, Inspectorio. *CHATGPT estimates the time for inspecting product quality, following similar characteristics of a given firm. The prompt for these estimates and CHATGPT ranking is available in the appendix.
36 important factor to mention that made the concession difficult from the beginning, contributing to its postponement and to lose the window of opportunity was the lack of a "blueprint" or model to follow. In contrast to the other cases, such as teleconmunications, there did no exist an international model or "best practice" to help the team in charge in the design of the concession and regulatory drafts.

As mentioned before, the desirability of water privatization was always weak. Water had high political saliency for Fujimori's core base of support, Lima's poor.<sup>27</sup> The concession would deprive the government of this ability to direct investments to areas with high political payoffs, a factor that has delayed private participation in the management of water elsewhere, in Mexico City for example.

The benefits of the concession to Fujimori's poor constituents in Lima would be further reduced by an increase in prices. The concession allowed up to a 40 percent price increase, which would raise the average residential tariff for water and sewerage from US$0.30 in 1994 to US$0.42 per M<sup>3</sup> . According to World Bank calculations, the average incremental cost for a smaller expansion in the system was US$0.45M<sup>3</sup> (World Bank 1994).28 If this estimate is correct, any private bidder would have been strongly motivated to renegotiate the agreement to win a higher price or lower investment targets than allowed under the. concessions. Although there may have been operational inefficiencies assumed in the World Bank estimate, marginal costs were indeed high in Lima because water was in scarce supply, the required expansion was large and so much of the system had deteriorated and needed replacement. New, deeper wells would have

**27** It is also noteworthy that Lima was not designated a region in the 1993 Constitution enacted by the Fujimori administration, which kept SEDAPAL under the direction of MIPRE rather than transferring it to the municipalities or provinces when responsibility for the other water companies was moved.
# **6 Policy and Program Implications**

Because the extortion of migrants occurs in a distinct location from the environment in which victims are participating politically as deportees, we can distinguish the impact of extortion on civic engagement from potentially confounding factors related to the context in which victimization occurs. We explore extortion’s potentially oppositional psychological and economic effects. On one hand, extortion is a form of victimization which increases fear, and fear is known to depress political engagement. On the other hand, extortion serves as a negative economic shock which increases political participation. We show that extortion is correlated with both a greater likelihood of fear-induced avoidance behaviors in Guatemala and negative assessments of personal economic conditions. Here, higher levels of fear are correlated with lower civic interest, whereas negative economic assessments are correlated with higher interest in civic engagement. Although we find suggestive mediation effects for both pathways, the economic pathway in this case is larger and more robust, leading to an overall positive relationship between extortion and civic action. The results from our analysis of deported migrant data provides insights for policymakers and programming staff working to support displaced persons. We discuss takeaways from our analysis first related to the migration experience itself and then to the reintegration experience after deportation.

It is well-known that migration routes to the United States through Mexico are dangerous; however, our survey work enables us to quantify some of the negative experiences of the migration journey. Of the 1123 Guatemalan deported migrants who agreed to take our baseline survey, 17% experienced extortion during their last journey to the United States (extortion either in Mexico or after crossing the border into the United States). Furthermore, 8% of respondents were held against their will or without their identification documents, and 7% experienced assault. With limited economic opportunity and high violence experienced by would-be migrants living in Northern Triangle countries, large numbers of Guatemalans and other Central Americans will continue to attempt this migration journey despite its dangers. These statistics about migrants’ adverse experiences underscore a need for more sustainable and humanitarian migration policies in receiving countries like the U.S. and Mexico. Such policies may include more pathways for legal migration or the pursuit of asylum, stronger prosecution of actors who engage in abuses against migrants, and economic support to strengthen job opportunities and community safety in home countries.

Nearly 1 in 3 (29%) of the deported migrants we surveyed reported a traumatic experience on their last migration attempt.<sup>8</sup> This suggests that large numbers of U.S.-bound

> 8It is also important to remember that the vast majority of our respondents are male, meaning that our data may under-represent the prevalence and impact of gendered victimization more likely to affect women.

24
coe¢ cients given by � = X�, where X is a matrix of time-invariant risk sharing determinants, and � now is the vector of parameters to be estimated. To avoid simultaneity concerns, the variables in X are measured at the beginning of the sample period, and expressed in logs in order to mitigate the in‡uence of extreme observations.<sup>15</sup> Table 3 reports the estimates of � obtained using di¤erent choices of the X variables. All speci...cations include also a constant, not reported in the table. The results shown correspond to estimates obtained with permanent income innovations constructed using income forecasts inclusive of common factors (that is, based on the model in the last column of Table 1); results using instead AR1-based forecasts are roughly similar but are not reported to save space.

The ...rst column of Table 3 relates the degree of risk sharing to total foreign assets and total foreign liabilities, both as percent of GDP. Both variables carry positive and signi...cant coe¢ cients, suggesting that higher degrees of international ...nancial integration, as measured by gross foreign asset and liability stocks, come along with higher consumption risk sharing.<sup>16</sup> Using the parameter estimates, we can compute the implied risk sharing coe¢ cients of the di¤erent countries as �<sup>^</sup> = X<sup>^</sup> �: As shown in the bottom part of the table, they are centered around 0.47, with the individual coe¢ cients ranging from .097 to 0.903. Further, on average they are higher for industrial than for developing countries, in line with the estimation results reported in Table 2 above. Finally, the last row of Table 3 shows the correlation between these implied risk sharing coe¢ cients and their unrestricted counterparts (shown in the ...rst column of Table B2). For the speci...cation in column 1, the correlation is just 0.31, which suggests that foreign asset and liability stocks account for a signi...cant but relatively modest proportion of the cross-country variation in consumption risk sharing.

Column 2 of Table 3 adds to the total foreign asset and liability stocks a measure of

> 15For a few countries in the sample, the initial observation (corresponding to the year 1972) on some of the foreign assets and liabilities included among the X variables is missing from the Lane-Milesi dataset. In those cases, we use instead the earliest available value. Additionally, for some countries the ratios of FDI assets and/or liabilities to total foreign assets and liabilities contain some zeros in the initial sample years. To allow taking logs, we replace them with very small values (1.e-6).

> 16In reduced-form regressions, Kose et al (2009) obtain a similar result for industrial countries, while Sorensen et al (2007) ...nd that gross foreign assets are positively related to risk sharing among OECD countries, while for gross foreign liabilities the relationship is negative instead.

23
2 maturity of between 5 and 15 years at interest rates between 3 and 5 percent, with reportedly lower rates for “priority projects" and for lending to LDCs.

The politica principle of the CMEA was “equality, sovereignty, and interest.” Unanimity was required on all decisions of common concern. The organs of the CMEA had no supranational executive or legislative mandate. Common decisions of members were merely declarations of intent and were not legally binding unless translated into (mostly bilateral) treaties. The unlitaited veto power the principle gave to every member was circumscribed merely by the “principle of interestedness," which excluded veto rights of countries not directly affected by the particular matter. Nevertheless, the lack of supranationality in procedures tended pc.sistently to reduce the outcome of initiatives to the lowest common denominator; a case in point was the failure of the Soviet initiative during the Khrushchev era to institute an integrated CMEA-wide planning system.

During the four decades after inception, the CMEA developed an elaborate institutional framework for planning and implementing bilateral trade between members, denominated in TR.” This did not create a “common market” with supranational institutions and a common external trade policy. However, the CMEA evolved into a distinct “economic region” in terms of preferential relationships among members codified in bilateral treaties. Agreements on production specialization were often an integral part of trade relations. The framework of the CMEA regime was an outgrowth of central planning, and its rules and procedures differed fundamentally from those of the convertible currency trade regizics of the member countries.

# Il. IMPORTANCE AND PATTERN OF CMEA TRADE

Data on intra-CMEA trade are notoriously deficient and merely indicate orders of magnitude.” Table 1 gives an overview of the structure of global CMEA trade.

The figures demonstrate the weight of intraregional trade for members in 1985. According to these data, 56 percent of total CMEA exports were internal to the region; for the USSR intraregional trade accounted for about one-half the total, and for the Six (in the aggregate and on average) for about 60 percent.

Table 1. Structure of Exports by Trade Areas (1985; US$ billion)

||Areal<br>Country ofOrigin]|—<br>Orcp|ss~«~”:«s<br>LDCs|ea Count<br>Yug.|try ofDestin<br>China|ation<br>~—sUSSR|six|cmMEA ||WORLD|
|---|---|---|---|---|---|---|---|---|
|[OECD<br>——[|9094|2692|6.0|27.0|20.6|15.2|(38.5)|1,247.4|
|LDCsex.Y&C|304.2|142.9|1.9|7.6|10.5|6.8|(17.3)|473.9|
|<br>Yugoslavia|3.5|1.7|na.|0.1|3.4|1.8|(5.2)|10.7|
|China|11.4|13.9|)|n.a.|1.1|1.0|(2.1)|27.3|
|USSR<br>|21.3<br>|15.7<br>|0.2<br>|0.9<br>|na.<br>|40.8<br>|(40.8)<br>|78.9<br>|
|Six|20.4|9.5|1.5|1.5|32.9|17.9|50.8|83.7|
|CMEA|(41.7)|—_(25.2)|(7)|(2.4)|32.9)_—|_—58.7)|(91.6)}|(162.6)|

Source: United Nations.
1 Introduction The economic difficulties triggered by high oil prices during the brief Persian Gulf crisis underline the continuing importance of external developments and adjustment policies in shaping the economic performances of developing countries. It may be recalled that during the seventies, a number of middle-income and semi-industrial countries actually did well after the 1974 oil crisis but had difficulties after the 1979 oil and interest rate shocks. Hindsight tells us that many of these developing economies over-invested and over-borrowed in the mid-seventies but had to contract when faced with constraints in further borrowing after 1979.1 The implementation of adjustment policies in the eighties has led to the problem of slower growth, characterized by a drop of investment as a ratio to GDP.2 A key factor in these experiences is the behavior of investment and its sensitivity to price changes brought about by external shocks and policy changes. Why did investment in some middle-income countries rose so much in the seventies only to have severe debt problems later? Why did investment decline when adjustment policies, such as trade reform, are adopted? These policies, aimed primarily at making investment and growth more efficient and less dependent on foreign resources in the eighties, are constrained by their adverse impact on macro balances and investment activities in the short term. This study attempts to explore this type of investment and growth responses in a middle-income country like the Philippines by using a dynamic general equilibrium analysis.

'These observations were made by several studies such as Balawsa (1986) and Balassa axid McCarthy (1984). 2See, for example, reviews of adjustment lending experiences of the World Bank which contain major _-_ concerns for the recovery of investment and its praductivity in _Adjustment Lending An Evaluation of Ten Years of Experience_ (1988) and _Adjustment Lending Policies for Sustainale Groawth_ (1990).

1
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Policy Research Working Paper 8779

# **Abstract**

This paper presents a new dataset of comparable employment indicators for South Asian countries, constructed from more than 60 primary data sources from 2001 to 2017. The main contribution of the paper is to curate the information provided by individual respondents to censuses and surveys, in a way that is consistent across countries and over time. The usefulness of the dataset is illustrated by conducting a rigorous assessment of employment characteristics, of changes in employment over time, and of the short- and long-run relationship between economic growth and employment growth in South Asia. The exercise shows that agriculture still employs the majority of the working-age population across the region and, except in Sri Lanka, more than half of the employment is self-employment or unpaid family work. The paper also shows that employment rates are generally decreasing in South Asia, and that in some countries female employment rates are falling rapidly. Seasonal growth patterns are shown to affect the composition of employment, while non-seasonal changes in short-run growth affect the overall level of employment. The paper estimates that, in the long run, one percentage point growth of gross domestic product has led on average to a 0.34 percent increase in employment.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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/research. The authors may be contacted at rcmbeyer@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
analyze firm behavior and performance (see www.enterpriseseurveys.org for more details). This is the only multi-country survey of its size and scope and has been used in numerous prior studies.<sup>11</sup> The Enterprise Surveys survey from the universe of eligible firms obtained from the country’s statistical office<sup>12</sup> using stratified random sampling with replacement to generate a sample representative of the whole non-agricultural private economy (so fully government owned firms are excluded from the sampling universe) in the country. The surveys are stratified according to three criteria: _Sector of activity_ (population of industries include manufacturing sectors, construction, services, transport, storage, communications, and computer and related activities), _Firm size_ (the strata include small firms (5-19 employees),<sup>13</sup> medium firms (20-99 employees), and large firms (100 or more employees)), and _Geographical location_ (selected based on centers of economic activity in the country).

While the ES have been produced since 2002, we restrict our sample to surveys administered during 2006-2012 since these provide sampling weights that address the varying probabilities of selection across different strata and are thus indispensable to making assertions about the whole population.<sup>14</sup> For countries that were surveyed twice during this period, we

> 11 The Enterprise Surveys and their precursor, the World Business Environment Survey (WBES) have  been used to investigate a series of questions in finance and economics including the relation between property rights and contracting institutions (e.g. Acemoglu and Johnson ( 2005)),  investment climate and business environment obstacles to growth (e.g. Beck et al. (2005) and Ayyagari et al. (2008)), firm financing patterns (e.g. Beck et al. (2008), Cull and Xu (2005) and Ayyagari et al. (2010)), dispute resolution via courts (e.g. Djankov et al. (2003)), corruption (e.g. Barth et al. (2009)) and tax evasion (Beck, Lin, and Ma, 2014).

> 12 The master list of firms is sometimes obtained from other government agencies such as tax or business licensing authorities. In some cases, the sampling universe is generated from lists maintained by the Chamber of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration.

> 13 We have a few firms in the sample that report having less than 5 employees. All our results are robust to dropping these firms. Our results are also robust to dropping firms that started out as informal enterprises and were formally registered after starting operations.

14 Most surveys contain three sets of weights – strict, median, and weak weights depending on the eligibility criteria used to construct the sample universe. The survey implementation manual recommends the use of median weights for cross-country comparisons which is what we use in this paper.

9