rafmacalaba/data-use-mentions-extended huggingface dataset:
cohort of students who learned the two subjects in English from the beginning of their schooling years; this cohort of students never had the chance to learn either subjects in their native language.<sup>2</sup> The availability of the 2007 and 2011 data sets enables us to differentiate the impact of the switch in the language of instruction and the use of non-native tongue as the language of instruction in schools respectively.

Although the data from TIMSS do not provide specific information on a student’s ethnicity, we can infer whether a student is ethnic Malay or non-ethnic Malay by checking the language spoken at home. In particular, students who speak the language of test at home are ethnic Malay and students who do not speak the language of test at home are non-ethnic Malay and we assume the mother tongue of ethnic Malay students is BM while the mother tongue of non-ethnic Malay students is neither BM nor English.<sup>3</sup> Data from other countries are also extracted from TIMSS to serve as the comparison sample. This sample includes 19 other countries that also participated in the eighth graders’ assessments in 1999, 2003, 2007 and 2011 and did not have any language policy change that affected participating students during the relevant period. These countries are Australia, Chinese Taipei, England, Hong Kong SAR, China, Hungary, Indonesia, the Islamic Republic of Iran, Israel, Italy, Jordan, the Republic of Korea, Lithuania, Morocco, Romania, the Russian Federation, Singapore, Slovenia, and Tunisia.

The outcome variables for this study are students’ mathematics and science test scores. We use a set of correlates of test scores derived from the student, school and teacher background questionnaires as pre-language-policy-change measures. Because not all questions from the

> 2 With the exception of the majority of ethnic Chinese students attending a Chinese National-type school, as they received supplementary classes with Chinese language as the medium of instruction in mathematics and science subjects.

> 3 Appendix B provides technical details on the robustness of
and mutual fund flows out of equity funds and into bond funds.

The rest of the paper is constructed as follows. Section II presents the data used in this paper, and the text analysis we use to construct sentiment measures. Section III presents the empirical framework and key results. Section IV provides further results on the properties of the global news sentiment index. Section V reports extensions and robustness. The last section concludes.

> 3 Our results are orthogonal to the various EPU indexes constructed by Baker, Bloom and Davis (2016). Results available on request.
projects in a country they equalize the marginal product of their interventions across activities, Equation (5) reduces to: If there are no diminishing returns to private capital (i.e., α=0), one additional dollar of MDB-financed government capital therefore raises the marginal product of private capital by ~~,~~ which implies effects are larger when the output elasticity of government capital, _β_ , is large ββ<sup>KK</sup> GG and when government capital is scarce relative to private capital (i.e., when is large). In KK GG practice, there are diminishing returns to capital, and thus α determines the rate at which those diminishing returns set in.

diminishing returns set in. Drawing on cross-country data on government and private capital stocks for and KK GG estimates of β and α from the literature, Chelsky and Kraay (2017) use Equation (6) to estimate that one dollar of MDB-financed activities catalyzes between 0.28 and 0.80 dollars of private investment in low-income countries (where private capital tends to be scarce relative to government capital, and thus is smaller) and between 0.42 and 1.2 dollars of private KK GG investment in emerging markets. Since many observers have argued that the amount of government capital is far from sufficient to meet the SDGs, these estimates suggest that private capital catalyzation, while not negligible, will fall far short of what is necessary.

This is not to argue that the catalyzation of private capital is modest in all contexts, or that the rate has not increased (or is not increasing) due to greater focus on achieving the SDGs.

Moreover, the calibration exercise in Chelsky and Kraay (2017) focuses on private capital catalyzation, rather than on direct and indirect private capital mobilization, both of which may 9
responded faster by having significantly lower delay in domestic closedown by about a month. They also have significantly lower pandemic mortality rate, by 0.9 percentage points, or 43 percent of the mean. They seem to be able to reduce the harm to the vulnerable population. In particular, the marginal effect of the share of elderly on pandemic spread is lower in democratic countries by about 100 percent; that of the share of obese on pandemic mortality rate is roughly zero in democratic countries, in contrast to 0.07 in nondemocratic countries. However, government policy delays appear to be more harmful in democracies. The marginal effect of the delay in domestic closedown on pandemic spread in

> 2 See, for instance, Powers-Riggs’ blog here: Covid-19 is Proving a Boon for Digital Authoritarianism | Center for <u>Strategic and International Studies (csis.org).</u>

4
**Figure 19. Predicted Probability of Moving from Employment to Unemployment (E-U) and from Employment to Out of the Labor Force (E-O) Between 2001 and 2002, by Age and Education in 2001**

<!-- Start of picture text --> EU<br>0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>15-19 20-24 25-34 35-44 45-54 55-ret ret-64 65<br>age<br>Higher Higher02 Secondary Basic<br>EO<br>0.15<br>0.10<br>0.05<br>0.00<br>15-19 20-24 25-34 35-44 45-54 55-ret ret-64 65<br>age<br>Higher Higher02 Secondary Basic<br><!-- End of picture text --> _Notes:_ Gender, ethnicity, employment status, occupation, economic activity and residence are controlled (see Appendix Table 19). Higher02: graduates of higher education institutions in 2002. Postsecondary vocational education merged with general secondary. “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Gender and ethnicity are not important determinants of the probability of becoming unemployed.** However, Appendix Figure 13 shows that females and non-Latvians were significantly more likely to leave the labor force during the 2001-2002 period. With respect to labor force exit, both effects are stronger for youth and the elderly. The difference in probabilities of exiting the labor force is about 0.03 between the sexes and 0.02 between the Latvians and non-Latvians. Note, however, that the 2002 Latvian Labor Force Survey documents that exit from employment to nonparticipation is linked to discouragement in only 7 percent of cases.

**Regional effects.** Appendix Figure 14 compares the probability of making transitions to unemployment across regions. For prime-age workers in the Riga region, Zemgale, and Kurzeme,
>20.0 18.25 1 7.5 2<br>1 5.1 5 6.0<br>10.0 4.0<br>2.0<br>0.0 0.0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>Probability of having online sales<br>Probability of increasing or starting using digital<br>Share of remote workers<br>Probability of investing in digital<br><!-- End of picture text --> Notes: Controls for size, sector, age, country, gender of the owner, and severity of the crisis. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

The descriptive finding above is also confirmed in a regression setting using _PCA 5_ , as the continuous measure for informality (Table 4). Conditional on external attributes (column 1) and initial digital readiness (column 2), the probability of starting to use or increasing the use of digital is substantially lower for informal firms. Yet, there are no significant differences when analyzing heterogeneity by different views of informality (columns 3 and 4) in the use of digital technologies but for other measures of adjustment response (e.g., online sales and investment in digital technologies), parasite firms perform worse.

21
- [36] DUGGAN, M. AND F.M.SCOTT MORTON (2006) “The Distortionary Effects of Government Procurement: Evidence from Medicaid Prescription Drug Purchasing,” _Quarterly Journal of Economics_ 121 (1): 1-30.

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groups. Low, medium, and high skilled workers therefore all seem to experience a reduction in migrant numbers when GDP falls and increase when it rises.

Panels B and C of Table 4 examine the responsiveness of median and mean wages respectively to GDP by skill quartile. Again we cannot reject equality of coefficients across the four skill categories at conventional skill levels and find point estimates which are mostly small in magnitude and statistically insignificant. An exception is the second quartile, in which we see a significant negative coefficient on median wages of -0.31, and a similar-sized, but statistically insignificant coefficient on mean wages. This suggests wages for individuals in this skill range may actually fall when economic conditions at destination improve, although if we control for multiple hypothesis testing by multiplying the p-values by the number of separate outcomegroup results being tested here for wages, then this result also would not be significant.

# **4.4 Does who migrates change over the business cycle?**

An alternative explanation for our results could be that the selection of who migrates is changing over the business cycle. In particular, in a market-clearing model with wages falling in a recession, we could observe in our data a reduction in the quantity of individuals migrating with no change in mean wage paid to migrants if low-skilled, lesser-paid, individuals experience more of a reduction in migrant numbers than higher skilled individuals do during recessions. Indeed Solon et al. (1994) show that such a change in composition leads aggregate wages in the U.S. to be less pro-cyclical than indicated by longitudinal microdata.

We have shown above that our results are robust to controlling for occupational categories, and that we cannot reject that the elasticity of migrant quantities to GDP changes at destination is constant across skill quantiles. Nevertheless, as a further check, we use the Survey of Overseas Filipinos to directly examine whether the observable characteristics of who is migrating varies over the destination business cycle.

The Survey of Overseas Filipinos is an annual survey which asks a nationallyrepresentative sample
education and socioeconomic status (see Appendix). The final aspect of pre-specified heterogeneity was social desirability, measured using a normalized continuous score from the widely used Marlowe-Crowne scale (Crowne and Marlowe, 1960). The large negative program impact on the neighbor social attitude and perceptions index are predominantly driven by individuals with lower scores on the social desirability scale, indicating that they are less likely to suffer from experimenter demand effects. In our view, this lends additional credibility to the negative social cohesion result, and also implies that the study may underestimate the extent of the negative effect on social attitudes, if Jordanian neighbors with higher social desirability tendencies do not truthfully report their possibly even more negative views of Syrian refugees.

# **7 Comparing Forecasts to Estimated Program Impacts**

Comparing experts’ forecasts to estimated the program effects allows us to highlight areas where the study results advanced learning relative to prior expectations. The forecasts focus on the midline and endline measures of the primary outcomes, namely: i) the well-being measures among refugees, and ii) the attitudes and perceptions of Jordanian neighbors towards refugees.

In terms of impacts among refugees, the findings reveal that the actual impacts of the intervention on refugee well-being were generally smaller than the modest positive impacts that experts had anticipated, with the exception of housing quality (Figure 3, Panel A). Notably, statistically significant discrepancies (at over 95% confidence) between the estimated and predicted effects were observed in two areas: housing expenditures and child socio-emotional health. Housing expenditures in the endline survey were notably lower for treatment households compared to control, while forecasts anticipated no such effect. One limitation of the predictions we collected, however, was the fact that we failed to inform the forecasters that the program had mandated that landlords had to maintain stable rent levels for one year following the end of subsidy distribution, and this appears to be the main driver of the non-effect on forecasted housing spending. Thus, some caution is needed in interpreting these effects.

31
)|(0.062)|(0.029)|(0.053)|
|Refugee - Eritrea|-0.008|-0.118<sup>*</sup>|-0.059|-0.146<sup>**</sup>|-0.012|-0.049|
||(0.042)|(0.063)|(0.051)|(0.058)|(0.025)|(0.035)|
|Refugee - Sudanese|-0.030|-0.026|-0.140<sup>***</sup>|-0.234<sup>***</sup>|-0.232<sup>***</sup>|-0.231<sup>***</sup>|
||(0.027)|(0.033)|(0.033)|(0.048)|(0.034)|(0.034)|
|Number of Obs.|2002|2002|2002|2002|2002|2002|

Notes: The dependent variables are participation in paid employment, self-employment, and family farm activities. All regressions include camp fixed effects. Robust standard errors are reported in parentheses. Asterisks indicate statistical significance at 1%<sup>*******</sup> , 5%<sup>******</sup> , and 10%<sup>*****</sup> levels.

Sudanese and South-Sudanese refugees, both male and female, are significantly less likely to be self-employed and participate in family farm activities compared to their hosts. This is partly due to refugees are less likely to have access to land and own their own business (see Table 1).

While we
area-level model in Sri Lanka and slightly less accurate estimates in Tanzania. Generating additional empirical evidence on the relative performance of different types of statistical models is important because in practice their performance depends on the true data generating process, which in practical applications is unknown. Therefore, we compare in the Mexican context the household model estimates with two alternative small area estimation models: a sub-area model and an area-level model.

The sub-area model uses the same type of empirical best prediction model as the household model. However, the unit of analysis is the AGEB and the dependent variable is the estimated poverty rates for each AGEB from the 2014 MCS-ENIGH survey data. These poverty rates are then modeled as a linear function of covariates, with random effects specified at the area level. The inclusion of the area-level random effect allows the model to “borrow strength” from sample data in sampled municipalities, which contain relevant information for non-sampled AGEBs. Covariates are again selected using the plug-in (rigorous) LASSO procedure. The selected AGEBlevel covariates include the predicted shares of extreme and non-poverty from the CNN and the share of pixels classified as buildings at the AGEB level. At the municipal level, selected covariates include extreme poverty; share roads, forest, and grass; share rural; and ten state dummies. These variables explain 28 percent of the variation in AGEB-level poverty rates. The weights adjust both for sample weights and for heteroscedasticity in the dependent variable, due to the differing number of sample observations used to estimate AGEB-level poverty. As in the household model described above, the results are benchmarked to the state-level poverty estimates. The second column of Table A3 presents the coefficients from a post-LASSO OLS regression using the sample data, collapsed to the AGEB (sub-area) level.

In addition, for comparison, we also report results for a simple area-level model as initially described in Fay and Herriot (1979). Candidate predictor variables are aggregated to the municipality level, using 2010 population values from the census as weights. The prediction model is again selected using the
--|---|---|
|2 males + 1 female|3|Upper Rural|9,550|0.46|1|1|1|
|1 male + 2 females|3|Upper Rural|8,900|0.47|0.93|0.99|0.93|
|1 male + 1 child|2|Upper Rural|5,400|0.53|1|1|1|
|1 female + 1 child|2|Upper Rural|4,750|0.54|0.88|0.98|0.88|

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : The calorie requirements considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

11
and whether it is perceived in the same way by both parties. Joint decision making has been found to correlate negatively with IPV in several country studies, including Ebrahim and Atteraya (2019), Friedemann-Sánchez and Lovatón (2012), Gage (2005), Hindin and Adair (2002) and Svec and Andic (2018). This literature primarily focuses on the wife’s perception of the process of decision making and how egalitarian she thinks this process is. While one analysis included both the husband’s and wife’s responses (Zegenhagen et al. 2019), we go beyond this and include covariates that reflect not only their responses, but whether or not they give the same responses. This allows us to explore the direction of disagreement and its asymmetric effect on IPV.

A separate body of interdisciplinary research offers a theoretical explanation for why joint decision making may be associated with lower IPV. In the management, political science and conflict resolution literatures, joint decision making is highlighted as a tool for diffusing responsibility and reducing decisional conflict by sharing risk. For example, in her typology of political blame management strategies, McGraw (1991) features ‘horizontal diffusion of responsibility’, whereby political leaders avoid conflict in the face of a decision gone awry by arguing that their decision was a joint product of a group of individuals. Thompson (1980) discusses the blame-mitigation strategy of emphasizing ‘collective responsibility’. Shared decision making between doctors and patients is also highlighted as a tool for reducing decisional conflict—and — hence, malpractice lawsuits in the medical literature (Hoffmann et al. 2014, Kremer et al. 2007).

Our paper builds on existing work by examining the relationship between joint decision making and physical, sexual and emotional IPV across 12 Sub-Saharan African countries, using nationally representative Demographic and Health Surveys (DHS) for 31,243 couples. We find a strong and significant relationship between joint decision making and all three types of IPV. Compared to joint decision making, sole decision making by the husband
reservations were consistently bundled into a package of reforms implemented by states (e.g., alongside large independent investments in women’s education), the border approach will not be able to differentiate the specific role of the reservations.

We further investigate the channels through which this entrepreneurship flourishes. The heightened start of women-owned establishments is concentrated in industrial sectors in which women have traditionally owned establishments. Equally as important, the greater entrepreneurship is mostly concentrated in household-based enterprises, rather than establishments opening as independent facilities.

We then discuss the potential channels through which this effect occurs, providing both quantitative and qualitative evidence. We do not find much evidence that the entrepreneurship is linked to changes in reporting or to greater access to government-sponsored contracts and business. Likewise, improved financing conditions appear to play a modest role, as the increase in female-owned loan-holding establishments is weaker than the total effect registered and suggests that financing accompanied the increased entry rather than caused it. We then discuss how entry patterns could be consistent with increases at the local level of public goods and infrastructure favored by women (e.g., Pande 2003, Besley et al. 2004, Chattopadhyay and Duflo 2004a,b) or greater aspirations of women towards entrepreneurship (e.g., Beaman et al. 2012). We discuss how these stimuli could have produced the industry patterns observed, to the extent that women entrepreneurs built upon sectors where women have traditionally been active (e.g., Ghani et al. 2013a) and at a household-based scale of operation that was feasible to achieve.

This study contributes to three strands of academic literature. Most directly, our work connects to prior studies of the impact of political reservations for India and women’s economic outcomes. To our knowledge, we are the first in this literature to quantify the entrepreneurship effects in a panel setting that uses state-level variation in implementation timings for identification, with Beaman et al. (2010) providing related evidence using micro-data from West Bengal.<sup>1<
in the early 2000s and following the COVID-19 pandemic. The measure in this paper, however, better reflects monetary policy uncertainty embedded in the Primary Dealers survey or implied volatility of options on one-year swap rates. Notably, monetary policy uncertainty is highest during the GFC in these measures, and it decreases precipitously through 2013 before rising again. There are also peaks in these measures during 1994/95 (bond market turmoil), and 2002/03.

14
0.170)|(0.193)|(0.155)|(0.182)|(0.210)|(0.247)|
|Relative wealth|0.236***|0.213|0.450***|-0.272*|0.233*|0.005|0.384**|-0.097|
||(0.068)|(0.308)|(0.140)|(0.160)|(0.129)|(0.150)|(0.175)|(0.204)|
|MHI-5 score|1.245|4.284|0.973|0.494|0.180|1.528|-1.133|2.749|
||(0.800)|(3.520)|(1.713)|(1.933)|(1.441)|(1.704)|(1.937)|(2.345)|
|**Observations**|1,824|1,824|1,824|1,824|1,824|1,824|949|949|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

67