rafmacalaba/datause-encoder-data huggingface dataset:
entire income distribution. For example, Robilliard, Bourguignon, and Robinson (2002) apply a CGE model based on a social accounting matrix with 38 sectors and 15 factors of production to quantify the poverty and inequality impacts of the 1997 financial crisis in Indonesia. The CGE models take into account not only immediate or direct effects but also knock-on effects but they require substantial data. So constructing social accounting matrices (SAM) in countries that lack data requires a significant amount of time, which makes SAMs unsuitable when quick turnaround is essential.

Given the data and time requirements of the combined CGE micro-simulation approach, the tendency is to estimate welfare impacts of the crisis using the output elasticity of poverty method, and the PovStat software. The former uses historical trends of output and poverty to determine the relationship between poverty rates and output growth. Once the relationship is estimated, macroeconomic projections of output can be used to simulate poverty rates. This method is easy to implement and as a result is often used for regional or global poverty simulations. For example, Chen and Ravallion (2008) use this technique for global poverty simulations, and Tiongson, et al. (2009) use it for Eastern European and Central Asian poverty simulations. The main drawbacks of the elasticity method are that only aggregate poverty numbers can be estimated and the model requires an inequality estimate, which is difficult to predict based solely on past information because of the wide variations among crises.

The PovStat software has been adopted in several settings, including estimating the poverty impacts of the Asian Crisis during 1997-98 and the recent economic slowdown in Armenia (World Bank, 2009a) and Bulgaria (World Bank, 2009b); PovStat has four main shortcomings.  First, its capacity to disaggregate within sectors is limited to three sectors agriculture, industry, and services; during the crisis it was observed that some sectors _within_ these three broad classifications were hit harder. For example, in Latvia, trade hotels and construction were the hardest hit. Second, PovStat works well for aggregate poverty/inequality indices but not for disaggregated distributional impacts. Third, PovStat does not distinguish between formal and informal employment, but differences in the way labor laws are implemented across formal and informal sectors may lead to outcome differences across sectors. Fourth, PovStat is not flexible enough for policy simulations in cases where researchers are interested in examining the impact of various policies on poverty outcomes.

3
also expect our results to contribute to the conversation on the costs of the pandemic not only across space, but educational levels. Our findings indicate that the youngest children were the most affected. Given the importance of early childhood and primary education for human capital development, this evidence provides input for policy makers to design interventions that could help mitigate the medium- and long-term consequences of the pandemic ( <mark>Fuchs-Schündeln</mark> _<mark>et al</mark>_ <mark>., 2022)</mark> . Therefore, we hope that this research will be a timely guide for educational policy in Guatemala and other developing countries with similar education systems. While we are unable to study the consequences of the pandemic on learning, documenting that some students were more affected than others is a necessary step to consider when defining recovery policies.

The remainder of this paper is organized as follows. The next section describes trends in educational outcomes in Guatemala before and after the COVID-19 pandemic and introduces the warning index for infection rates developed by the government and used by the Ministry of Education to determine school restrictions on in-person instruction. Section 3 describes the administrative panel data and our selected educational outcomes. Section 4 discusses our identification strategy to estimate the effects of differential exposures to COVID-19. Section 5 presents our findings and tests their robustness. We conclude in Section 6.

# **2. COVID-19 and educational outcome trends in Guatemala**

## _2.1. Changes in educational outcomes before and after the pandemic_

Estimates using Guatemalan administrative records indicate that between-year school dropout increased between 2018 and 2021.<sup>7</sup> While 7.8% of school-age children dropped out in 2019, this

> 7 We define between-year school dropout when a student is observed in one school year but does not appear in the records for the following academic year. This differs from within-year dropout when a student does not finish the current school year. Throughout the remainder of the paper, we refer to between-year dropout solely as “dropout”.

5
- McGuire, Greg and Michael Schuele (2000), ―Restrictiveness of International Trade in Banking Services,‖ in _Impediments to Trade in Services: Measurement and Policy Implications_ , Christopher Findlay and Tony Warren (eds.), London: Routledge.

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- National Bureau of Statistics of Armenia, Gross Domestic Product at Current Prices by Selected Economic Activities for the Years 2001-2006, unpublished data.

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37
assets.<sup>5</sup> The average levels of total liabilities and total debt to assets are comparable to those reported above for a smaller subsample of firms available in Worldscope.

In 2006, utilities and energy report the highest total debt and total liabilities levels followed by construction and transportation. The lowest levels of debt are observed in mining, industry, and trade, and the lowest in tourism and hotels.

As discussed above, while the total levels of debt are fairly low in Jordan, relative to several comparable countries, the levels of short-term debt are relatively higher. The ratio of current liabilities to total liabilities is above 80% on average for the whole sample, and it is highest in industry (almost 90% of total liabilities are current), followed by mining and trade. It is lowest in tourism and hotels and utilities and energy.

**Table 3.  Industry Breakdown of Corporate Vulnerability Ratios, 2006**

||1.<br>Mining|2.<br>Industry|3.<br>General<br>/Services|4.<br>Constru<br>ction|5.<br>Transpor<br>tation|6.<br>Tourism<br>and<br>Hotels|7.<br>Utilities<br>and<br>Energy|8.<br>Financial/<br>Real<br>Estate|All|
|---|---|---|---|---|---|---|---|---|---|
|ROA|3.9%|1.4%|1.2%|2.9%|0.3%|1.3%|2.2%|3.8%|2.3%|
|Quick Ratio|2.6|1.6|3.0|1.3|1.5|6.6|8.7|7.2|4.0|
|Current Liabilities to Total<br>Liabilities|86%|89%|85%|79%|77%|63%|63%|80%|83%|
|Total Liabilities to Total<br>Assets|24%|30%|33%|39%|39%|16%|63%|26%|30%|
|Total Debt (estimated)||||||||||
|Over Total Assets|17%|22%|24%|31%|31%|16%|46%|21%|23%|
|Interest Coverage|8.7|2.8|15.1|10.1|5.4|6.6|8.5|15.4|9.3|
|Interest Coverage Less||||||||||
|than 0.75|9%|25%|24%|22%|30%|13%|0%|22%|22%|
|Uncovered Debt Ratio||||||||||
|(Interest Coverage < .75)|0.5%|19%|21%|27%|3%|7%|0%|24%|18%|
|Altman Z-score|8.0|4.6|4.9|4.4|9.1|5.0|1.1|5.9|5.4|
|NumberofObservations*|11|48|24|9|10|7|4|51|164|

Interest coverage shows the ability to cover interest expenses using the current period earnings. Interest coverage is the highest in financial firms, followed by trade and construction. It is lowest in industry- only 2.8 on average, followed by transportation and tourism and hotels.

> 5 In ASE data there is no line item such as total debt. We have calculated total debt as total liabilities minus accounts payables, which represent trade credit.

8
These leaders overall were more reserved in the implementation of the leftist policies imposed from the central and provincial-level governments. During the Great Leap Forward Movement, the local cadres did not succumb to the calls for farmers to go all in to produce steel in the “backyard furnace,” as did in many leftist regions. Instead, they encouraged local residents to plant trees, and to finish several large-scale civil engineering projects, e.g., constructing ocean-crossing dikes; implementing the drinking water program, and claiming land from sea. These projects greatly facilitated local development, and won local support. Moreover, through self-financing and financing from overseas, the county established a series of small collective and state-owned firms. In 1988, the share of SOEs in Dongshan’s industrial output was only 24.3%, that of collective firms was 36.3%, and that of privately-owned firms was for 36.6%.

A third county is Jing Jiang County which was led by cadres from the YRD faction, and it had strong local guerrilla presence before 1948. In fact, the local guerrilla leaders tended to hold important positions within the county administrative structure, such as County Chief (1949-1958), and party secretary (1972-1976). Before the Cultural Revolution, the cadres from YRD and the local guerrilla force allowed non-state economic activities to continue. Even in 1974, statistical report shows that village-level firms (in reality, most of these village-level firms were privately owned) accounted for 41.1% of the county-level industrial output; and by 1987, this ratio was 80%.

# **4 Data Sources and Descriptive Statistics**

## **4.1 Data Sources**

We now describe our data sources.

**County/Provincial Leaders’ Factional Affiliations and County’s Guerrilla Presence.** Here we rely on information from two primary sources: (1). “History of the Communist Party in Fujian Province, 1926-1987”; (2). “Recollections on Yangtz-River Detachment”. We use these two primary sources to determine whether a county was assigned cadres affiliated with the FA3 or with the YRD.

Moreover, we hand-collected the resume of every member of the Fujian Provincial Communist Party Standing Committee from 1950-1993. We identify if a member belongs to the FA3 faction or the YRD faction based on their working experiences listed on their resumes.

To determine whether a local guerrilla force had strong presence in the counties during the pre-Communist liberation period, we hand check various county gazettes (as of May 1948).

**County-Level Development Performance from 1952-1998.** We examine various measures of development performance at the county level from 1952 to 1998. First, measures related to economic growth and other economic outcomes are gathered from “Statistical Information on 50 Years of Fujian Province” and “Regional

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using data for 24 countries confirm the same result. However all of the above find a positive impact of real depreciation over the longer\run. It is recognized that private and public investment are related. However, there is considerable uncertainty about whether, on balance, increases in total public sector investment raises or lowers private investment (Von Furstenberg and Malkiel 1977). Empirical evidence remains inconclusive. This is because public investment in production and in infrastructure can have opposite effects on private investment. However, it is being increasingly acknowledged that public investment in infrastructure is more likely to be complementary tc private investment because it raises the productivity of private capital. Several studies have examined this empirically (Aschauer 1990, Galbis 1979, Sundararajan and Thakur 1980, Blejer and Khan 1984, Chhibber and Van Wijnbergen 1988, Shafik 1991). Only a few studies do find evidence of a significant complementary relationship. 11 If public investment in infrastricture and private investment are complements, we would expect that the coefficient of adjustment of private investment would beccae larger as the rate of public investment in infrastructure increases, implying a faster response of private investment. Macroeconomic instability is also expected to have an adverse effect on private investment, largely due to the uncertainty it creates in the adjustment process.

> 1 1 Sundararajan and Thakur (1980) found the coefficient of the public sector capital stock in the private investment equation to be statistically insignificant in both countries (India and the Republic of Korea) of their sample. Shafik (1991) confirms this for Egypt.

11
Kishor, Sunita, and Lekha Subaiya. 2008. “Understanding women’s empowerment: a comparative analysis of Demographic and Health Surveys (DHS) data.” DHS Comparative Reports 20, USAID.

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Klein, Elise. 2015. “Women’s Agency and the Psychological Domain: Evidence from the Urban Fringe of Bamako, Mali.” _Feminist Economics_ . 22(1) Knowles, James. 2015. Monitoring and Evaluation Guidelines for Women’s Economic Empowerment Programs.

Laajaj, Rachid, and Karen Macours. 2017. “Measuring Skills in Developing Countries." Working Paper, Paris School of Economics.

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<mark>Locke, E.A., 1968. Toward a theory of task motivation and incentives.</mark> _<mark>Organizational behavior and human performance</mark>_ <mark>,</mark> _<mark>3</mark>_ <mark>(2), pp.157-189.</mark> <mark>Locke, E.A. and Latham, G.P., 2006. New directions in goal-setting theory.</mark> _<mark>Current directions in psychological science</mark>_ <mark>,</mark> _<mark>15</mark>_ <mark>(5), pp.265-268.</mark> <mark>Lundberg, S. and Pollak, R.A., 1993. Separate spheres bargaining and the marriage market.</mark> _<mark>Journal of political Economy</mark>_ <mark>,</mark> _<mark>101</mark>_ <mark>(6), pp.988-1010.</mark> <mark>Luszczynska, A., Scholz, U. and Schwarzer, R., 2005. The general self-efficacy scale: multicultural validation studies.</mark> _<mark>The Journal of psychology</mark>_ <mark>,</mark> _<mark>139</mark>_ <mark>(5), pp.439-457.</mark> <mark>Mackie, G., Moneti, F. and Shakya, H., 2015. “What are Social Norms? How are They Measured?” UNICEF/</mark> University of California, San Diego, Center on Global Justice.

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Malhotra, Anju. 2003. “Conceptualizing and measuring women’s empowerment as a variable in international development.” paper presented at a workshop on “Measuring Empowerment: CrossDisciplinary Perspectives”, Washington, DC, February 2003.

<mark>Mani, A., Mullainathan, S., Shafir, E. and Zhao, J., 2013. Poverty Impedes Cognitive Function.</mark> _<mark>Science</mark>_ <mark>,</mark> _<mark>969</mark>_ <mark>, p.970.</mark> Manski, Charles. 2004. Measuring expectations. _Econometrica_ , 72(5): 1329-1376.

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_Africa’s Macroeconomic Story_ in 2005-06, mainly due to debt relief provided by its Paris Club creditors.<sup>25</sup> Nigeria’s domestic debt has risen from 9.5 to 15 percent of GDP over the past four years, and accounts for almost all of its total public debt.

**Figure 24: Pubic Debt in Selected Countries, 2008 and 2012**

_Percent of GDP_ <!-- Start of picture text --> Domestic debt<br>40<br>2008 2010 2012 External Debt<br>60<br>30<br>40<br>20<br>10 20<br>0 0<br>Mauritius Gambia Malawi Kenya Nigeria Mauritius Gambia Malawi Kenya Nigeria<br><!-- End of picture text --> _Sources:_ Central Bank of Kenya, Nigeria Debt Management Office, IMF Country Report No. 12/129 (Jun. 2012), IMF Country Report No. 13/97 (Apr. 2013), IMF Country Report No. 12/221 (Aug. 2012), IMF _World Economic Outlook_ April 2013 and World Bank _International Debt Statistics_ 2013.

# **_Managing the Risks_**

The development of domestic debt markets provides several potential benefits, but not without risks. Reliable access to a wider range of domestic debt instruments provides a broader set of financing options. The ability to issue debt denominated in domestic currency reduces foreign currency exposures. An active debt issuance program in the domestic market can expand the investor base and help build a benchmark yield curve to facilitate corporate and subnational debt issuance. Domestic currency bonds with long maturities enable institutional investors (pension funds and insurance companies) to manage their liabilities more effectively.

25In 2005 Nigeria reached an agreement with its Paris Club creditors to cancel $20 billion of the $35.9 billion in its external debt outstanding at the time.

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# **Figure 2: Global Convergence in Manufacturing is a bit slower**

<!-- Start of picture text --> 2.5 Convergence in manufacturing productivity<br>1990-2010<br>2<br>China<br>1.5<br>1 y = -0.0416x + 0.8478<br>India Thailand<br>0.5 Malaysia<br>Ethiopia<br>Tanzania<br>Brazil<br>0<br>-0.5<br>-1<br>5 6 7 8 9 10 11 12 13<br>ln(manufacturing VA per worker), early 1990s<br>change in ln(manufacturing VA per worker), early 1990s to late 2000s<br><!-- End of picture text --> Source: World Development Indicators. Ethiopia data from Martins (2014). Labor productivity calculated by the ratio of total sector value-added to total emp in sector. Underlying accounts are in 2005 constant international USD. Values taken from earliest year available from 1990-1993 and latest year available fr Overall conclusions do not change if line is quadratic or cubic in ln(initial VA per worker).

7
10 August. Because the mean differences between contract prices<sup>were not statistically</sup> significant, Gilbert (1997, p.46) concluded that the argument<sup>that producing countries</sup> gain in terms of higher export prices by selling forward<sup>should be judged as "not</sup> proven".

We further compared different futures positions to spot with<sup>the same data. Table</sup> 2 shows that only the averages of the two most remote positions<sup>were significantly</sup> different from spot. Figure 4 plots the difference of the third, fourth,<sup>and fifth</sup> positions against spot; if spot is December, then the third corresponds<sup>to the</sup> following March. The three series are obviously closely<sup>related and the difference</sup> between further and nearby positions can be either positive<sup>or negative. Based on this</sup> analysis and other studies, the evidence for a statistically significant<sup>forward premium</sup> is at best inconclusive.

_Table 2. Cocoa Price Analysis of Variance_

|Position|Average price<br>(£/mt)|Average difference<br>from spot (percent)|
|---|---|---|
|Spot|834.2|0.0|
|Position 2|853.9|2.4|
|Position 3|858.1|2.9<br>|
|Position 4|859.9|3.1<br>|
|Position 5|877.5|5.2|
|Position 6|889.9*|6.7|
|Position 7|899.3**|7.8|

_Source:_ LIFFE monthly data from December 1990 through May 1997.

_*F-statistic for n = 1, 76 significant at IO percent_

** _F-statistic for n= 1, 76 significant at 5 percent_
The high explanatory power of these regressions is even more impressive when one considers the role of measurement error, both in mortality and in the independent variables. Imagine the hypothetical case that one was "explaining" an individual's height with a measure of height taken one minute later. If both measures were completely accurate to the recorded significant digits the regression R-squared would be 1, but if there were random error in the first measurement with variance a,2 then the R-squared would be I - _a,_<sup>_2_</sup> / ( _ay_<sup>_2_</sup> + av<sup>2</sup> ) where ay2 is the true variance of height in the sampled population.

In the case of child mortality we can use a recent compilation of mortality estimates to gauge the degree of measurement error as there are multiple estimates for the same country for the same year (United Nations, 1992). The differences across estimates using different surveys and different methods for the same periods are substantial: the four estimates of child mortality per 1000 in Egypt in 1980 are 203, 167, 171, and 97, the four estimates for Ghana in 1975 are 187, 171, 130 and 125. The average coefficient of variation of these different measurements for a sample of countries with repeated measurements is 0.129 compared to a coefficient of variation for the sample as a whole of 0.880, which would suggest the _maximum_ achievable R-squared, if all the true variation were explained, of 0.978.'° The effect of measurement error in the independent variables has a similar effect in lowering the feasible R-squared." '° We use the coefficient of variation since the mean of the small sample for which we have repeated measurements is much larger (142) than the mean across all countries (87).

" At low levels, measurement error in the dependent and independent variables are roughly additive in lowering the R-squared.

8