rafmacalaba/datause-encoder-data huggingface dataset:
2003). A series of results reports exist for MLA I across 11 African countries of interest (Botswana, Madagascar, Malawi, Mali, Morocco, Mauritius, Niger, Senegal, Tunisia, Uganda and Zambia; see UNESCO, 2000). However, much of the data has not been published. Since microdata is sparse or often unavailable for the MLA and IAEP data, we include these series only for mean scores and for the total population metrics.

<u>The Modern ISATs (mid 1990s onward): In the mid-1990s, the emergence of standardized,</u> psychometrically-robust and relatively consistent ISATs emerged. Below we describe the major ISATs which we use to construct our database.

**TIMSS.** The Trends in International Mathematics and Science Study (TIMSS) is one of the main survey series conducted by the IEA. Five TIMSS rounds have been held to date in Math and Science subjects covering grades 4 and 8. The first, conducted in 1995, covered 45 national educational systems and three groups of students.<sup>4</sup> The second round covered 38 educational systems in 1999, examining pupils from secondary education (grade 8). The third round covered 50 educational systems in 2003, focusing on both primary and secondary education (grades 4 and 8).  In 2007, the fourth survey covered grades 4 and 8 and more than 66 educational systems. In 2011, the survey covered 77 educational systems across grades 4 and 8. The last round was performed in 2015 and covered 63 countries/areas. The precise content of the questionnaires varies but remains systematic across countries.

**PIRLS.** The other dominant IEA survey is the Progress in International Reading Literacy Study (PIRLS). Three rounds of PIRLS have been held to date: in 2001, 2006 and 2011. The PIRLS tests pupils from primary schools in grade 4 in reading proficiency.<sup>5</sup> In 2006, PIRLS included 41 countries/areas, two of which were African countries (Morocco and South Africa), 4 lowermiddle-income countries (Georgia, Indonesia, Moldova, Morocco) and 8 upper-middle-income countries (Bulgaria, Islamic Republic of Iran, Lithuania, Macedonia, Federal Yugoslavian Republic, Romania, Russian Federation, South Africa). The latest round of PIRLS was carried out with TIMSS in 2011 and included 60 countries/areas.

In our database, we use all recent IEA studies across two subjects (mathematics and reading/literacy). We use results from official reports (Harmon et al., 1997; Martin et al., 2000;

> 4 IEA assessments define populations relative to specific grades, while PISA assessments focus on the age of pupils. In IEA studies, three different group of pupils were generally assessed: pupils from grade 4, grade 8 and from the last grade of secondary education. In 1995, two adjacent grades were tested in both primary (3-4) and secondary schools (7-8). In order to obtain comparable trends, we restricted the sample to grades 4 and 8. Some Canadian provinces and states in the United States of America have occasionally taken part in the IEA surveys.

> 5 Similar to TIMSS, pupils from Grade 4 are chosen.

7
Summary statistics can be found in Table 1. Other control variables, namely, population and real GDP per capita (constant 2010 USD), are from the World Bank’s World Development Indicators.

|**Table 1: Summary Statistics - Panel **|**Obs**|**Mean**|**Std.**<br>**Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Firm Purchased Fixed Assets Y/N|26,172|0.42|0.49|0.00|1.00|
|Log of Amount Investment (+1)|26,187|4.24|5.38|0.00|26.33|
|Log of Amount Investment in Land and Buildings<br>(+1)|25,337|1.06|3.23|0.00|26.19|
|Log of Amount Investment in Equipment (+1)|25,712|4.06|5.25|0.00|26.33|
|Central government debt, % of GDP|26,187|39.33|28.14|0.55|336.22|
|Log of real GDP per capita|26,187|7.83|0.92|5.85|9.62|
|Log of age of firm|26,187|2.64|0.75|0.00|5.23|
|Log of Total Population|26,187|16.04|1.25|13.17|19.37|
|Log of size|26,187|2.74|1.14|-1.10|9.82|
|Direct exports 10% or more of sales Y/N|26,187|0.11|0.31|0.00|1.00|
|Foreign ownership Y/N|26,187|0.09|0.28|0.00|1.00|
|Establishment has checking or savings account Y/N|26,187|0.87|0.34|0.00|1.00|

Source: Authors’ calculation from the Enterprise Surveys.

# 2.2 Econometric specification

The following equation is estimated using the panel data set: I I I i 0 1G i 1 2 i (1) cc,ii−1 xx ccii yy ii ii = ββ + ββ IIGGIIGGII + ββ ZZ + ββ ZZ + γγ GG + γγ GG + εε where I I I is a measure of investment by firm at time t; G is the central government debt as a percentage of GDP for country c, lagged by 1 yearii<sup>2</sup> ; Other control variables at the country level (IIGGIIGGII ) ccii include log of real GDP per capita and log of total population. Other control variables at the firm-level ZZ include log of age of firm, log of firm size, whether the firm exports 10 percent or more of sales (direct

> 2 This is to capture pre-existing government debt. Using further lags of GovDebt does not change the findings.

8
# **Table B4. Equivalent Disposable Income.**

Median values, thousands of kronor, 2004 prices.

|**Age**|**1991**|**1998**|**2004**|
|---|---|---|---|
|0 -19|124.0|116.6|141.4|
|20-24|135.5|116.0|138.7|
|25-34|133.2|127.4|155.8|
|35-44|138.9|127.9|154.7|
|45-54|170.1|158.3|180.4|
|55-64|168.2|166.9|203.8|
|65-74|125.2|131.3|151.5|
|75+|94.4|104.7|118.8|
|65+|109.4|116.3|132.9|
|All ages|133.9|128.7|153.7|

Source. Statistics Sweden 67
available evidence on the provinces' experiences to date. The Fondo Fiduciario had balance sheets for only three residual entities both at their inception and at some point later (when asset recovery and liability retirement should have already begun). Unfortunately, the balance sheet data for these three cases do not give any indications as to the terms on which the assets were recovered or liabilities retired. Assets may have been recovered at a rate of pennies on the dollar, while liabilities may have been retired at face value.

**Table 5: Fondo Fiduciario Assistance**

|Bank|Residual<br>Liabilities<br>(millions)|Total Loan<br>from FF<br>(millions)<sup>27</sup>|Undisbursed<br>as of 2/4/97|Debts Paid to<br>B.C.R.A.|Debts Paid to<br>Banco De La<br>Nacion|
|---|---|---|---|---|---|
|Chaco|233.1|78.0|0|78.0|0|
|Entre Rios|N/A.|78.0|0|45.3|0|
|Formosa|244.9|80.0|0|32.2|37.7|
|Jujuy|N/A.|50.0|33.3|16.6|0|
|Mendoza|666.6|160.0|0|89.7|70.3|
|Prv Soc Men|292.1|100.0|0|79.3|20.7|
|Misiones|331.8|78.0|0|20.2|47.6|
|Rio Negro|47.4|80.0|0|32.3|11.5|
|<br>Salta|73.4|50.0|0|16.7|9.5|
|San Juan|175.3|78.0|0|43.7|34.3|
|San Luis|81.8|50.0|0|21.0|0|
|Santa Fe|N/A.|160.0|160.0|N/A.|N/A.|
|Sant del Est|227.3|50.0|33.3|3.0|0.0|
|Tucuman|262.9|80.0|25.0|_53.6_|18.1|

Source: Fondo Fiduciario In many cases the province contracted the owner of the privatized entity to collect residual assets. The private owners receive a higher percentage of face value for recovering lower quality assets (i.e., loans for which re-payment problems have been most chronic). To the extent that residual balance sheets reflect the face value of assets, such contracts make it impossible for the province to receive compensation for the full value of its residual assets. This is not, of course, to imply that these contracts are a bad deal for provinces -- indeed, it is likely that private entities with the appropriate incentives are best equipped to re-

> 27 Fondo Fiduciario loans data are reported in U.S. dollars. Residual liability data are reported in pesos. Since the exchange rate is pegged so that a peso is worth a dollar, this presents little problem.

13
<!-- Start of picture text --> High income Middle income<br>High income High income<br>Middle income Middle income<br>Low income Low income<br>1984 1992 2000 2008 1984 1992 2000 2008<br>Low income<br>High income<br>Middle income<br>Low income<br>1984 1992 2000 2008<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>Corr(GDP−HP) Corr(GDP−HP)<br>0.0 0.0<br>0.6<br>0.4<br>0.2<br>Corr(GDP−HP)<br>0.0<br><!-- End of picture text -->

**Figure 1.** Average GDP co-movement for the three considered income groups. Country-pairs correlation are computed over a time window of 10 years. The figure displays the middle period of the time window.

In figure 2, we focus in the United States and China and observe that those countries experienced a dramatic increase in the correlation between their GDP and the World’s total GDP (above 0.2 in average after 2000).<sup>16</sup> Those increased cross-country correlations are important features of economic fluctuations and it seems relevant to precisely investigate the determinants of those evolution. In the rest of this paper, we attempt to isolate the specific relationship between trade linkages and GDP correlation.

> 16Note that there is also a mechanical effect due to China’s growth: in 1980 China represented only 2.3% of World’s GDP (when accounting for PPP), while the share rose to more than 17% in 2016. For the US, the effect is reverse with the share decreasing by a third between 1980 and 2016.

10
expectation and the possibility of measurement error in expectations, we consider a range between _minimum_ and _desired_ expected standard to be the _latitude of acceptance or expectation_ (Figure ). Households are ‘quite satisfied’ when actual quality falls within this area, i.e. does not differ significantly from household’s expectation. Actual quality beyond the desired expected standard (the upper bound of expectation) yields positive disconfirmation or satisfaction to a household.

This approach can be implemented if data on satisfaction and expectations can be successfully matched with data on actual service delivery. Models based on this approach tend to ignore the difficulty of measuring both objective quality and consumer expectations when it comes to basic services, and do not adequately account for the role of governance in determining satisfaction. Moreover, given that satisfaction with a service can be observed only for a user of the facility, such a model also has to account for the possibility of _sample selection bias_ , related to the decision of a user to opt for a particular service facility.

Typically, two main sources of sample selection bias are self-selection by users into the sample, and non-random sample selection of service delivery facilities by data analysts and survey administrators. Satisfaction surveys often suffer from _both_ these problems and this is true for GDS-2 as well. As discussed in Section III, selection bias in GDS-2 arises from facility data being collected from only the _most frequently used public_ health and education facilities instead of a random sample of _all_ facilities used by households, which creates the possibility of bias arising from self-selection of households into facilities included in the sample. Bias can occur because households choosing the highly frequented public health facilities may systematically differ from users of facilities excluded from the sample, and households choosing 11
households reporting remittances are more likely to stay at school. The largest differences are obtained for Nicaragua, Guatemala and Honduras, where enrollment rates are between 12 and 17 percent higher for recipient households.

**Figure 4. Average Years of Education for Adults (22-65 years old)**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>Jamaica Ecuador Paraguay Peru Mexico Dom. Rep. El Salvador Honduras Nicaragua Guatemala Haiti<br>Country<br>Years of Education<br><!-- End of picture text --> Source: Author’s calculations based on household surveys.

**Figure 5. Differences in School Enrollment Rates for 12-17 years old by Remittances Recipient Status**

<!-- Start of picture text --> 0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Nicaragua Guatemala Honduras Peru Ecuador Haiti El Salvador Paraguay Dom. Rep. Jamaica Mexico<br>-0.05<br>-0.10<br>Country<br>Diff. Recip vs. No Recip.<br><!-- End of picture text --> Source: Author’s calculation based on household surveys.

Of course, some of these differences could be attributed to the fact that households receiving remittances can be systematically different from non-recipient counterparts. It is thus necessary to control for observable demographic differences in order to test the robustness of the estimated effects of remittances on human capital. In addition, to assess the effect of remittances _and migration_ , the human capital of recipients should ideally be compared to households that had similar levels of income prior to migration. In this respect, we take advantage of the fact that 25
otherwise. I also control for _Non-UN Peacekeeping_ , in order to capture whether other peacekeeping forces than the UN were deployed. This variable is coded similarly to the _UN Peacekeeping_ variable. For a description of the data used, see Heldt & Wallensteen (2006). Finally, in line with previous research I control for the type of deal reached, since agreements with power sharing provisions could be more likely to see peace endure (Hartzell & Hoddie, 2003). Hence, the dummy variable _Power Sharing_ was coded 1 if the agreement contained at least one pact concerning the sharing of power, that is, military, territorial or political power, and was otherwise coded 0.<sup>16</sup>

# **Results and Analysis**

From my theoretical argument it was possible to derive five testable implications, and the results from the statistical analysis are presented in Table I and II.<sup>17</sup> In all models, the hazard ratios are reported rather than the coefficients. A hazard ratio above one increases the risk of post-settlement violence, whereas a value below one decreases the risk of post-settlement violence. To exemplify, a hazard ratio of 1.5 means that the risk of peace failing is increased by 50 %, and a hazard ratio of 0.7 indicates that the risk of peace failing is decreased by 30 %.<sup>18</sup> I begin by examining the implications at the settlement level, and the first implication is assessed by looking at the peace duration for the signatories ( _Signatory Peace Duration_ ). The first expectation that was derived from my argument proposes that whether a peace agreement is all-inclusive or not, should not affect the signatories’ commitment to peace. The variable _Exclusive-Multiple_ shows the effect of an agreement that leaves out one or more actors versus a deal that includes all actors, in a multiparty context. The results indicate no statistically significant effect of the variable _Exclusive-Multiple_ on the duration of peace for the signatories (Model 1, Table I). Hence, the result is in line with the theoretical argument that the signatories should have considered that the excluded parties might threaten the peace process, therefore not affecting the signatories’ commitment to peace. The effect even goes in the opposite direction to previous research, which has suggested that all-inclusive

> 16 This is coded in line with the definitions in Walter’s (2002a) war termination dataset (Nilsson, Svensson & Sundberg, 2006).

> 17 For descriptive statistics, see the web appendix.

> 18 All statistical analyses are carried out using Stata 10.0 (StataCorp, 2007).

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# **Does Workfare Work Well?**

# **The Case of the Employment Generation Program for the Poorest in Bangladesh**<sup></sup>

Yoonyoung Cho<sup>†</sup> World Bank and IZA Ummul Ruthbah<sup>‡</sup> University of Dhaka

**Keywords** : Social assistance, workfare, public works, Bangladesh, poverty, consumption **JEL codes** :  I32, O12, I38, O20

>  This paper has been prepared to assess the functioning of the Safety Net Systems for the Poor project supported by the World Bank. The study benefited from generous support by the Department for International Development (DFID) and Korea World Bank Partnership Facility (KWPF). The findings, interpretations, and conclusions expressed here are personal and should not be attributed to the World Bank, its management, its Executive Board of Directors, or any of its member countries. We thank Iffath Sharif, Aneeka Rahman, Ashiq Aziz, Upasana Khadka, Laurent Bossavie, Cem Mete, Dhushyanth Raju, Anastasiya Denisova, Ugo Gentilini, Thomas Bossuroy and seminar participants at the World Bank for their support, feedback, and insights.

> † Corresponding author. Address: 1818 H St. NW, Washington, DC 20433, USA, phone: +1-202-458-7493, email: <u>ycho1@worldbank.org</u>

> ‡ The University of Dhaka, email: <u>uhasanth@yahoo.com</u>
ownership on industry and firm productivity suffer from identification problem because the high growth industries and the better firms attract more foreign ownership.<sup>2</sup> The paper is organized as follows. Section 2 examines the literature on foreign ownership and growth, and discusses foreign direct investment in Korea in recent years. Section 3 presents a theoretical framework explaining foreign acquisitions. Section 4 describes the data. Section 5 examines the theory empirically. Section 6 concludes.

# **2. Foreign Direct Investment and Growth**

Theory suggests that technology diffusion through multinational companies might be an important source of growth for developing countries.<sup>3</sup> There is some empirical evidence that is consistent with the technology transfer argument. Using industry data, Caves (1974) and Globerman (1979) test whether the foreign share of employment contributes to labor productivity in a production function framework. They find evidence of positive spillovers from foreign direct investment (FDI) in Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson (1983) and Blomstrom (1986) find evidence of higher productivity and higher productivity growth in sectors with more FDI. Borensztein et. al. (1998) find evidence of positive spillovers from FDI in a cross section of countries, provided countries have a minimum level of human capital.

These results, however, may be generated by reverse causality because FDI may be determined by the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison (1993) and Aitken and Harrison (1999) find no evidence of positive spillovers in Morocco and 2Aitken and Harrison (1999) argue a similar point. Using plant level data on productivity in Venezuela, they find that foreign ownership is positively correlated with own-firm productivity, but not with productivity growth. They interpret this as evidence that foreigners invest in more productive plants, though they cannot rule out that there are own-firm positive level effects.

3Findlay (1978), Jovanovic and Rob (1989), Grossman and Helpman (1991).

4
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# **Trade-Policy Dynamics: Evidence from**

# **60 Years of U.S.-China Trade**<sup>∗</sup>

George Alessandria<sup>†</sup> , Shafaat Yar Khan,<sup>‡</sup> Armen Khederlarian§, Kim J. Ruhl,<sup>¶</sup> and Joseph B. Steinberg<sup>‖</sup>

**JEL Classifications:** F12, F13, F14

**Keywords:** China Shock, Trade Liberalizations, Trade Policy Uncertainty (TPU), Trade Dynamics

> ∗We thank audiences at the virtual international trade and macro seminar, Armenian Economic Association, SAET meetings, SED, and University of Tokyo.

> †george.alessandria@rochester.edu, University of Rochester and NBER

> ‡sykhan@worldbank.org, World Bank

> §akheder2@ur.rochester.edu, University of Rochester

> ¶ruhl2@wisc.edu, University of Wisconsin–Madison and NBER

> ‖joseph.steinberg@utoronto.ca, University of Toronto
**Table 17 Growth Rates with Principal Component Method**

||(1)|(2)|
|---|---|---|
|Small|‐0.163|‐0.177|
||(0.075)**|(0.145)|
|Medium|‐0.135|‐0.249|
||(0.055)**|(0.103)**|
|Large|‐0.159|‐0.253|
||(0.097)|(0.159)|
|Age|‐0.004|‐0.004|
||(0.002)**|(0.005)|
|Principal Comp without|||
|Collateral|0.06||
||(0.020)***||
|Principal Comp with|||
|Collateral||0.067|
|||(0.029)**|
|Constant|0.304|0.002|
||(0.115)**|(0.191)|
|Obs|419|170|
|R<sup>2</sup>|0.248|0.435|

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Additionally control for foreign and government ownership, export status, 2‐digit industry, and region fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

25
**Table 1:** Descriptive statistics Encuesta Permanente de Hogares (EPH)

||Urb|an|Rur|al|
|---|---|---|---|---|
||Mean|SD|Mean|SD|
|**Income**|||||
|Monthly per capita income, 2017$ PPP<br>|559.7|973.1|335.8|803.8|
|Monthly per capita labor income, 2017$ PPP|425.9|715.6|260.4|763.4|
|Poor (%)|25.8|43.8|43.9|49.6|
|Extremely poor (%)|3.0|17.0|14.5|35.2|
|**Demographics**|||||
|Household size|5.0|2.3|5.3|2.4|
|Age dependency ratio|0.72|0.71|0.95|0.86|
|Female household head (%)|33.8|47.3|25.6|43.6|
|Age household head|47.2|14.2|47.1|14.5|
|Years of schooling household head|8.5|4.6|5.7|3.6|
|Head and spouse only speak Guaraní (%)|15.9|36.6|52.5|49.9|
|**Employment**|||||
|Any HH member in agriculture (%)|23.8|42.6|80.1|39.9|
|Employed (%)|92.9|25.7|96.1|19.3|
|Share of employment in primary sector (%)|3.8|19.2|54.3|49.8|
|Share of employment in secondary sector (%)|20.9|40.7|13.4|34.0|
|Share of employment in tertiary sector (%)|75.3|43.1|32.3|46.8|
|**Assets**|||||
|Household has a car/truck (%)|31.0|46.3|11.9|32.4|
|Household has heating (%)|9.9|29.8|2.1|14.3|
|Household has a microwave (%)|23.4|42.4|6.0|0.238|
|Observations|242,921||187,673||

_Note:_ All calculations are based on the pooled data from the Encuesta Permanente de Hogares (EPH) for 20042019. Individual-level survey weights are applied. Income and labor income is expressed in monthly per capita values at $2017 PPP. Poverty and extreme poverty rates are calculated using Paraguay’s national poverty lines for urban and rural areas, poverty rates show the national aggregate poverty estimate. The age dependency ratio is defined as the number of children aged 0 to 14 and the number of elderly aged above 65, divided by the number of (working-age) adults, aged 15-65. The employment rate is based on all individuals aged 10 or above. Shares of employment by economic sector are based on individuals aged 10 or above who are employed and/or who indicated their type of occupation.

resolution of 0.05° (approximately 5km _×_ 5km grid spacing) (Funk et al., 2014). We use the Google Earth Engine API to download daily temperature and precipitation data, and aggregate raw daily values by calculating geo-spatial averages at the district-level. We produce moving averages for precipitation and temperature for a time bin of 90 days, which we match with survey data based on the exact day of interview. A 90-day, or three-month window is a useful period for capturing short-term weather conditions, as this period is long enough to smooth out daily fluctuations and provide a representative snapshot of seasonal weather patterns, but short enough to reflect the specific weather conditions that individuals and households experience and report in surveys. Although the reference period for income is the last month before the interview, potentially negative effects from adverse weather conditions may not be immediate, so while we do cover the income reference period, we allow for a lead time period of another 9