rafmacalaba/datause-encoder-data huggingface dataset:
therefore part of our country fixed effects and are not included as controls. In the following paragraph, we discuss the control variables that capture aspects of the grievance, resourcemobilization and political-opportunity theories.

Grievance-based approaches to uprisings underscore the role of injustices resulting from for example the distribution of power and resources in a society (Cederman, Weidmann, & Gleditsch, 2011; Gurr 1970). Poverty is often regarded as the most important source of economic grievances; yet, poverty data are insufficiently available for most low-income countries. Therefore, we follow Chenoweth and Ulfelder (2017) and use _infant mortality_ per 1,000 live births (World Bank) as a proxy. In line with most grievance-based models of resistance, our regression model also includes financial control variables that capture the _income level_ ( _GDP_ (ln)), _economic growth_ (GDP growth), _oil rents_ (% of GDP) and _inflation._<sup>9</sup> The effects of _GDP_ and _GDP growth_ are theoretically ambiguous. On the one hand, an increase in _GDP_ tends to imply an increase in living standards and hence, a decrease in grievances and accordingly anti-government sentiments. On the other hand, increases in _GDP_ might also set off modernization to the extent that citizens who are no longer preoccupied with addressing their basic needs take to the streets to demand more civil liberties. Periods of slow economic growth may create anger among citizens affected directly through reduced employment and investment opportunities and indirectly through eroded capacity to finance social safety nets. However, periods of fast economic growth may leave some segments of the population behind, creating anger about lack of opportunities as illustrated by the ‘tunnel effect’.

The effect of oil rents is also theoretically uncertain, as rents not only increase the government’s capacity to provide public services, but also increase citizens’ expectations of the

> 9 The inflation variable contains several outliers. Therefore, we winsorized it at the 1st and 99th percentile. We also estimated regressions with a non-winsorized inflation coefficient, which shows very similar results for the suffering variable. However, in these models the inflation coefficient is unrealistically large.

16
_.05),_ primaril)y attributable to the sharp decline in labor market studies (a reflection of the decline in tracer studies) and the increased emphasis on descriptive input and cost and finance studies in he period 1982-1989.

_Tabtle 6: Distribution of studies by type, 1972-1982 and 1982-1989, as a percent of all studies._

||Educat-<br>ional<br>Inputs|Educat-<br>ional<br>Outputs|Labor<br>Market<br>Studies|Feasib-<br>ibility<br>Studies|Cost<br>ane<br>Finance|MIS<br>and<br>Stat|Other<br>s|
|---|---|---|---|---|---|---|---|
|1972-1982|18.8%|5.6%|40.4%|7.3%|5.6%|9.8%|12.5%|
|1982-1989|24.8%|5.6%|21.2%|8.8%|15.1%|12.2%|12.4%|

24. In contrast there are few distinct trends in distribution of study type over the period 1982-1989, suggesting differences may indeed be due to reporting and classification difficulties. Nevertheless, Table 7 does show a gradual .nove away from broad feasibility **'j** and labor market studies toward a more diverse distribution of study type, with a gradual increase in the proportion of studies investigating educational outcomes.

_Table 7: Annual comparison of distribution of studies by type, 1982-1989, as a percent of all studies within given year._

||Educat-<br>ional<br>Inputs|Educat-<br>ional<br>Outpu-s|Labor<br>Market<br>Studies|Feasib-<br>ib'lity<br>Studies|Cost<br>and<br>Finance|MIS<br>and<br>Stat|Other<br>s|
|---|---|---|---|---|---|---|---|
|1982|27.8|0|24.0|13.0|14.8|11.1|9.3|
|1983|36.0|4.0|20.0|12.0|10.0|12.0|<br>6.0|
|1984|22.9|6.2|14.6|16.7|14.6|8.3|16.7|
|1985|22.9|8.3|22.9|10.4|14.6|12.5|8.3|
|1986|14.3|6.1|34.7|6.1|16.3|6.1|16.3|
|1987|27.6|2.1|19.2|4.3|17.0|21.3|8.5|
|1988|21.3|6.6|19.7|1.6|21.3|8.2|21.3|
|1989|25.9|11.1|14.8|7.4|11.1|18.5|11.1|

**13 incren** **_i_ i Ws. category in 1989 can be explained by the introduction of 'study funda,' with no secifi. allocation of funding. See, for exanVle, Chad CR 1950-CD: Education Rehabilitation Loan and Tunisia LN 3054-TUN: Education pd Trainine Sector Loa.**

9
Table D2: Effect of Ordering of Yes and No Options in ACASI on IPV Questions

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _individual_ q|uestion in the fo|llowing category:|All|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|questions<br>pooled|
|**Panel A. Malawi**||||||
|YES First|-0.01|0.00|0.00|-0.00|-0.00|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|
|NO First mean|0.21|0.13|0.05|0.07|0.12|
|Number of individuals|858|854|855|852|859|
|Observations|5,915|3,385|5,062|2,531|16,893|
|**Panel B. Liberia**||||||
|YES First|-0.01|-0.02|-0.02|-0.02|-0.02|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|NO First mean|0.25|0.19|0.10|0.08|0.17|
|Number of individuals|617|617|617|617|617|
|Observations|4,268|2,446|3,676|1,832|12,222|

Note: Includes only those who are in the ACASI group (FTFI group excluded). Observations at respondentquestion level. Regressions include question-level fixed effects and individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

36
# **The determinants of HIV infection and related sexual behaviors:**

# **Evidence from Lesotho**

**Lucia Corno**

**<u>lucia.corno@unibocconi.it</u>**

**Bocconi University**

**Damien de Walque**<sup>*****</sup>

**<u>ddewalque@worldbank.org</u>**

**Development Research Group The World Bank**

> ***** We thank John Nkonyana, Noosi Nkone, Mahlapane Lekometsa, Mahlape Ramoseme at the Ministry of Health and Social Welfare in Lesotho as well as Charles Annor Frampong Preeti Arora, Elena Bardasi, Louise Fox, T. Mpoy-Kamulayi, Rachel Kline, Oscar Picazo, Elizabeth Sherwood and Ritva Reinikka at the World Bank for useful discussions and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
<!-- Start of picture text --> eX<br>-” of as. 3<br>ie ye ot Aitg t<br>f.-74‘<br>Ate TyCV sei " 7 : a es<br>‘ Ly a y a<br>:<br>aes anA lenage ee eSa & 2 aNd eo hatied<br>i Se»:cosShiakpefy —is WAA2 4 eSSaeedet a yecreh /tS<br>i Bhoaaa ckpretts A> . Tb‘ts "4 Firmsis Clustering & the&eeWEOealt 74OSt+4 TaeVALUErereoriemoreHY<br>. oS<br>ey ra . * Cees >50,000 ae . rg = | eens<br>ae¥ ¥ ‘bzfate?=}“9 esoid a; /> ————————————————“> nonrv* = ——+ — Type ManufacturingWoe= 2 roadsSéteas m=terns ae r NS _—SSS S|“ on HBBBowt— thoPeeere:<br><!-- End of picture text -->
23

**Table 2:** The effect of visa provision on bilateral stocks of migrants.

|Dep var:|Bilateral Sto|ck of Migrants||
|---|---|---|---|
||(1)|(2)|(3)|
|Visa|0.190***|0.057***|0.055***|
||(0.051)|(0.020)|(0.020)|
|RTA|0.879***|0.031|0.036*|
||(0.055)|(0.021)|(0.021)|
|Schenghen|||0.186***<br>(0.039)|
|Distance (ln)|-1.261***<br>(0.026)|||
|Colony|1.915***<br>(0.067)|||
|Language|0.955***<br>(0.070)|||
|Religion|0.510***<br>(0.094)|||
|GDP diference (ln)|0.025|-0.000|-0.002|
||(0.019)|(0.008)|(0.008)|
|Estimator|PPML|PPML|PPML|
|Country-Year Fixed Efects|Yes|Yes|Yes|
|Country-Pair Fixed Efects|No|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|
|Observations|199,514|63,803|63,803|
|Pseudo R-squared|0.823|0.994|0.994|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . In moving from column (1) to (2) the number of observations reduces considerably because country-pair fixed effects perfectly predict the outcome variable of time-invariant bilateral migration stocks, and the concerned observations are dropped. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
To explore the relation between the common factor(s) and the ’push’ variables taken in the literature as indicators of global financial conditions (as in, e.g., Miranda-Agrippino and Rey (2018)), we collect data on several measures of investors’ – risk perceptions the Chicago Board Options Exchange Market Volatility Index (VIX), the Bank of America Merrill Lynch high-yield spread, the Bank of America Below Options-Adjusted Spread, and Moody’s corporate bond yield spread. In addition, we also use the U.S. Federal Funds real interest rate, as well as the real effective exchange rate of the U.S., taken from FRED, and the risk appetite index of Bekaert, Engstrom, and Xu (2017).

To assess the covariates of countries’ factor loadings, we collect information on selected policy and structural indicators of capital account openness, financial depth, stock market capitalization and the exchange rate regime. Following Barrot and Serv ́en (2018), capital account openness is measured by the Chinn-Ito index, and financial depth is measured by domestic credit to the private sector as a percentage of GDP. Stock market capitalization is measured as the total value of all listed shares in a stock market as a percentage of GDP. Following Ghosh, Ostry, Kapan and Qureshi (2015), the exchange rate regime is categorized into three groups, and each is assigned a numeric value so that higher values denote more flexible regimes – i.e., Peg = 1, Intermediate = 2, and Float = 3. Table A2 in the appendix presents the data description, sources and other details.

# **4 Empirical results**

Before proceeding with the estimation of the model, we examine the cross-country comovement of equity returns in the data. The average pairwise correlation of real equity returns equals .51 for the full sample and .67 for the advanced-country sample. A total of 754 of the 780 distinct pairwise correlations in the full country sample are positive, and 652 of them are statistically significant at the 5 percent significance level.<sup>15</sup> In turn, all of the 300 distinct pairwise correlations in the advanced-country sample are positive, and all are significantly different from zero at the 5 percent significance level. Overall, these figures reveal strong international comovement of equity returns, particularly among advanced countries.<sup>16</sup>

> _cos_<sup>_−_1</sup> [ _cos |longi − longj| coslaticostlatj_ + _sinlatisinlatj_ ] where radius is the Earth's radius, and _lat_ and _long_ are, respectively, latitude and longitude for country capitals _i_ and _j_ . The latitude and longitude coordinates for each of the country capitals in our sample were collected from the CEPII database.

> 15We approximate the standard error of a correlation coefficient _r_ by � <u>1</u> _T− −r_ 1<sup>2.</sup>

> 16This result is in line with a large number of studies (e.g., Bekaert and Mehl (2017), Jorda, Schularick, Taylor and Ward (2017), Bekaert, Harvey, Kiguel and Wang (2016), Rey (2013), and Bekaert, Hodrick, and Zhang (2009)) that find significant comovement of asset returns across countries.

10
# **_The spread of the crisis_**

27. What were the channels that allowed the crisis to spread? One channel was money markets. The collapse of Lehman Brothers on September 15, 2008, made the short-term debt that it had issued effectively worthless, triggering panic among the various investors and the funds that held it. This panic resulted in a run on the money market funds that provided lending to the commercial paper market, increased perceptions of default risk, and sowed further panic throughout the global financial system (see also Figure 7). Consequently, commercial banks significantly decreased lending rates following concerted efforts by central banks to inject liquidity into the system.

28. In addition, letters of credit and commercial paper to guarantee that goods in transit between trading partners would be paid for when they reached the final destination were no longer available following the collapse of Lehman Brothers—resulting in the freezing of credit markets (Figure 18).<sup>17</sup> As a result, global trade came to a standstill. At the peak of the crisis in early 2009, exports fell—on a year-to-year basis—by 30 percent in China and Germany, and by 37 percent or even 45 percent in Singapore and Japan, respectively.

**Figure 18: U.S. Commercial Paper Outstanding**

<!-- Start of picture text --> 1400<br>Subprime Crisis<br>1200<br>Lehman Brothers<br>1000<br>800<br>600<br>400<br>200<br>0<br>2001-01 2002-07 2004-01 2005-07 2007-01 2008-07 2010-01<br>Asset backed Non-asset-backed<br>Source: Federal Reserve.<br>US$, billions<br><!-- End of picture text --> 29. Also, while total remittances dropped only marginally, remittances from some groups of workers, in particular migrant workers from Central American countries in the construction

> 17 Roubini and Mihm 2010, p. 118.

21
where F is a generic notation for a probability or cumulative density function given by where �(:) and �(:) are, respectively, the standard normal density and cumulative distribution functions. Note that � 1 and � 2 are themselves unknown parameters to be estimated jointly with � and the �’s. The estimation is done based on Maximum Likelihood with the above probabilities entering into the likelihood function. Assuming that the model is correctly speci...ed, E(vijxit) = 0 and E(�itjxit) = 0, it gives us consistent and asymptotically e¢ cient estimators for the unknown parameters.

There is, however, a problem of identi...cation<sup>12</sup> if we try to estimate the unobserved threshold values jointly with the other unknown parameters. This requires the introduction of normalization constraints in such a way that all the parameters of the model are identi...ed. We circumvent this problem by setting the threshold values exogenously taking the distribution of net sales of livestock into account. In setting the two-threshold values, symmetry is not imposed on the data as transactions costs should not necessarily be symmetric for sales and purchases of livestock.<sup>13</sup> We, therefore, consider net sellers and net buyers separately to ...x the lower and upper limits of the censored observations. Accordingly, we construct di¤erent measures of censored net sales of livestock corresponding to the sample’s 10<sup>th</sup> and 20<sup>th</sup> percentiles. This helps us to compare the parameter estimates at di¤erent censoring points. The unobserved net sales of livestock are thus those within the samples 10<sup>th</sup> and 20<sup>th</sup> percentiles. After generating a censored net sales data with the predetermined threshold values, the parameters are estimated following the procedure of random e¤ects interval data regression technique. In this procedure, the integral over vi is approximated using a Gauss-Hermite quadrature.<sup>14</sup>

# 5 Results

As discussed in the previous section, a random e¤ects interval data regression procedure is adopted to estimate the parameters of the friction model presented in equations (13) and (14). The dependent variable is net sales of livestock (also disaggregated into its di¤erent components) per adult equivalent units converted into monthly observations,

> 12This refers to the case where di¤erent combination of parameter values might lead to the same likelihood value and hence there would be no unique maximum.

> 13Schineller (1997) followed similar approaches to ...x adjustment costs of capital ‡ight and reversals.

> 14See Stata Longitudinal-Data/Panel-Data Reference Manual, Release 13 for a comparison of the likelihood function of the two-threshold Tobit model of this paper with the random-e¤ects interval-data regression model.

17
derive from the broader definition of access to water in the case of Armenia. In Azerbaijan, using the definition of access to a central water supply, the coverage rate is 90 percent, while the HOI is 85 percent. Using the definition based on whether households have access to a water system, Georgia’s coverage rate is 79 percent, and the HOI is 68 percent. These relatively high rates of access are in line with the data of the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP 2017) on household access to improved drinking water among 41 Asia-Pacific countries that show coverage rates in Armenia and Georgia at nearly 100 percent and in Azerbaijan at above 80 percent. All three countries also managed to meet the relevant Millennium Development Goal (WHO and UNICEF 2015).<sup>10</sup> Figure 15: Access to water and sanitation (0-16 years old) a. Armenian b. Azerbaijan

> 10 The notion of coverage rates of the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP 2017) and the World Health Organization and the United Nations Children’s Fund (WHO and UNICEF 2015) is different from the notion here. The definition in the former two sources is the percentage of the population having access to water or sanitation, while the definition here is the percentage of children with access. The definitions of access to water and sanitation differ as well.

28
In addition to the above, there is also likely to be an important liquidity effect of remittance. In the last section, it was noted that average remittance receipts was around Taka 25,000 per transaction. The liquidity effects of such a large amount of cash on household consumption can be substantial, as it enables the household who otherwise have poor access to credit, to finance lumpy expenditures. Furthermore, as noted by Thaler (1990), while “rational” households might well choose to smooth consumption over the entire year, irrespective of how often the remittances were received, this is not guaranteed behavior and consumption could escalate around the time of the remittance receipt.

# _5.2 Estimation method_

The estimation method we use in assessing the impact of migration and remittance on consumption takes into account potential endogeneity of migration status and limitations imposed by data availability. Particularly, we noted in Section 4 that household members themselves undertook the primary initiative to locate the recruitment agent and initiate the process of migration. We also noted the important role of friends and relatives in securing employment and also in financing the upfront cost associated international migration. It is thus likely that households that facilitate migration of its members may be more socially networked than those that do not. Hence, if relevant household characteristics cannot be captured by observable data, estimates of impact based on simple comparisons between migrant and nonmigrant households will be biased if the same unobservable household characteristics that affect the decision to migrate also affect the outcomes considered. Using data collected on immigrants to New Zealand from Tonga, McKenzie et al (forthcoming) show that non-experimental methods all introduce some bias when estimating the impact of migration and the Propensity Score 14
Overall, more than 750 faculty members of economics departments in developing countries were contacted, covering all World regions: Latin America and the Caribbean, Sub-Saharan Africa, East Asia and Pacific, Europe and Central Asia, and South Asia. This way we were able to collect 171 development economics syllabi from developing countries and were able to extract information on 55 additional development economics courses offered in developing countries from the survey responses. Overall this provides information on 145 undergraduate courses in 54 developing countries, and 81 masters courses in 34 developing countries. A list of the universities from which we obtained the information can be found in the Appendix.

Given the difficulties experienced in collecting information on development economics courses, the reader may be concerned about how representative this resulting sample is. We have two comments on this. The first is that given the lack of information on how development economics is taught around the world, the information we have collected is the best currently available. Second, to the extent that there is a bias from collecting data from the top quarter of universities within countries, and that there is selective responsiveness to our requests for data, we believe this bias will be towards sampling from the best courses taught in developing countries. Therefore any gaps identified are likely to be lower bounds for the extent of these gaps in developing country courses overall.

In order to establish a benchmark against which to compare the courses taught in developing countries, we also asked departments and instructors in the United States for development economics syllabi, focusing only on the undergraduate level given the lack of terminal masters degrees in economics in the U.S. We obtained information on 33 courses, comprising of 16 courses from departments classified in the top-20 economics departments in Repec’s rankings, and 17 courses from departments outside the top-20.

# **4. Results**

This section uses the survey and syllabi data to provide a description of development economics as taught in developing countries, and a comparison of this with the content in top-20 and other U.S. undergraduate development classes. The next section will then examine different explanations for the differences which arise.

11