rafmacalaba/data-use-mentions-extended huggingface dataset:
contemporaneous momentum trading (buying current winners and selling current losers) and lagged momentum trading (buying past winners and selling past losers). Contemporaneous momentum trading is stronger during crises, and stronger for fund investors than for fund managers. Lagged momentum trading, on the other hand, is stronger during non-crisis periods, and stronger for managers. We also find that funds engage in contagion trading, **by** which we mean that they systematically sell assets from one country when asset prices fall in another. This contagion trading is due primarily to underlying investors, not managers.

The paper is organized as follows. The next section outlines our approach to measuring momentum trading and contagion trading. Section III describes our data. Section IV presents our momentum and contagion results. Section V addresses whether return autocorrelation within Latin America can rationalize our section-IV results. Section VI concludes. The appendix provides some related regression-based analysis.

# **H.** Strategies: **Momentum Trading and Contagion Trading**

This section presents our approach to testing whether funds employ momentum and contagion trading strategies. Momentum trading-also called positive feedback trading-is the systematic purchase of stocks that have performed well, and sale of stocks that have performed poorly ("winners" and "losers"). Contagion trading is the selling of assets from one country when asset prices are falling in another. Contagion trading is thus **a** crosscountry phenomenon, in contrast to momentum trading, which is a within-country phenomenon. (This type of cross-country analysis is not possible using recent singlecountry data sets, such as those of Kim and Wei **1999** and Choe, Kho, and Stulz **1999.)**

> First, we review the existing finance literature on momentum trading. Second, we present our approach to testing for momentum trading, an approach that draws from this

> earlier literature. Then we turn to contagion trading, presenting first a brief review of the

> "contagion" literature, followed **by*
from 9 different non-food expenditure categories. Diversity of non-food purchases is calculated in the same way as described above for food purchases. Value of gifted non-food includes all items from 9 non-food categories received as gifts in the past 12 months. Value of all durables acquired includes durables from 9 categories purchased or gifted to the household. Value of durables gifted includes all durables the household received as gifts.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the pre-analysis plan.

45
0.5|-0.4|0.3|
|Car, van and motorcycle drivers|832|Very low|-0.8|-0.2|-0.7|1.5|
|Nursing and midwifery professionals|222|Very low|-0.6|-0.8|-0.7|0.2|
|Other elementary workers|962|Very low|-1.0|-0.3|-1.1|1.5|
|Mobile plant operators|834|Very low|-0.8|-0.3|-1.4|0.8|

_Source_ : Authors’ calculations based SEAD data.

_Notes_ : The factor analysis is based on varimax rotation for orthogonal factors of nine digital, cognitive and socioemotional subsets of skills (three each). Clusters are described in section 6. Darker red and blue colors show higher and lower magnitudes, respectively.

56
increased the volume of bond issuance in European markets, taking advantage of exceptionally low borrowing costs (Figure 16). In an effort to compensate for negative or extremely low interest rates at home, European insurance companies and other institutional investors have also shown increased appetite for higher-yielding investment-grade EMDE debt. In contrast, cross-border lending by European banks to EMDEs has stalled, equity markets underperformed and flows to emerging market bond and equity flows continued to stagnate.

EMDE regions that could benefit the most from low interest rates in the euro area include Europe and Central Asia (which has the largest share of capital inflows supplied by the euro area), followed by Latin America and the Caribbean (through FDI), Sub-Saharan Africa (primarily bank flows), and the Middle East and North Africa (portfolio and bank flows). The share of capital inflows originating in the euro area is significantly lower in East Asia and the Pacific, and South Asia.

> 31 Using cross-country panel data, Arslan and Taskin (2014) and Lane and McQuade (2013) provide evidence of a positive correlation between capital inflows and domestic credit growth.

36
- Lanjouw, P., 1999a, The Rural Non-Farm Sector: A Note on Policy Options, Non-Farm Workshop Background paper, The World Bank.

- Lanjouw, P., 1999b, Rural Non-Agricultural Employment and Poverty in Ecuador, _Economic Development and Cultural Change_ 48(1), 90-122.

- Lanjouw, J. O. & P. Lanjouw, 2001, The Rural Non-Farm Sector: Issues and Evidence from Developing Countries, _Agricultural Economics_ 26, 1-23.

- Leones, J. P. & S. Feldman, 1998, Nonfarm Activity and Rural Household Income: Evidence from Philippine Microdata, _Economic Development and Cultural Change_ 46(4), 789-806.

- Lewis, W. A., 1954, Economic Development with Unlimited Supply of Labour, _The Manchester School of Economic and Social Studies_ 47(3), 139-191.

- McMillan, J., J. Whalley & L. Zhu, 1989, The Impact of China’s Economic Reforms on Agricultural Productivity Growth, _Journal of Political Economy_ 97(4), 781-807.

- Munoz, J. & K. Scott, 2005, Household Surveys and the Millennium Development Goals, _PARIS21 Task Force on Improved Statistical Support for Monitoring Development Goals,_ mimeo.

- National Bureau of Statistics of China, 2003, _China Statistical Yearbook 2003_ (Beijing, Chinese Statistics Press).

- Pyatt, G., C. Chen & J. Fei, 1980, The Distribution of Income by Factor Component, _Quarterly Journal of Economics_ 95(3), 451-473.

- Ravallion, M., 2003, Externalities in Rural Development: Evidence for China, in R. Kanbur and T. Venables (eds), _Spatial Inequality_ (Oxford University Press).

- Ravallion, M. & S. Chen, 1999, When Economic Reform is Faster than Statistical Reform: Measuring and Explaining Inequality in Rural China, _Oxford Bulletin of Economics and Statistics_ 61(1), 33-56.

- Ravallion, M. & S. Chen, 2004, China’
in food and non-alcoholic beverages prices. The largest inflation gap between the bottom and top deciles occurred in 2015, where overall inflation averaged 7.7% but food inflation reached 10.9%. Prices for transportation good and services, increased only by 1.5%.

Table A.1 in the annex shows the contribution of each of the expenditure category to the overall inflation experienced by each of the income deciles for the three latest years with monthly price data available. In 2019, for instance, nearly 75% of the overall inflation borne by the first decile is explained exclusive by price pressures on food and housing expenditures. In contrast, the corresponding figure for the upper decile is 37%.

<!-- Start of picture text --> Figure 4: Contribution of expenditure groups to decile-specific inflation (2011-2020)<br>1st Decile 10th Decile<br>18 18<br>16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Food and non-alcoholic beverages Alcoholic beverages and tobacco Housing, water, electricity, gas etc.<br>Furnishings, household equipment etc. Transport Hotels, cafes and restaurants<br>Others<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK The incidence of inflation in the 2018 crisis shows the opposite pattern. Households from upper parts of the distribution experienced relatively higher inflation (17.4% for the top decile compared to 16% in the 1st decile) largely due to faster price increases in transportation (22%) than in food and non-alcoholic beverages (18%). In the period 2011-2020, increases in food prices explain 43% of the total inflation borne by
In addition to this point estimate, we can also use the confidence interval for the difference in means to provide a 95 confidence for the incidence of illegal migration. Despite sample sizes of over 1,000 households in each case, these confidence intervals are reasonably wide, typically covering 20 percentage points (11 percentage points in the larger Filipino sample). This is one downside of this indirect elicitation approach – if we asked 1,000 individuals a direct question the 95 confidence interval for the proportion would have width at most 6 percent.

Despite the reluctance to ask about illegal migration status in surveys taken at destination, it is possible that reliable answers might be obtained by directly asking sending households in migrant source countries, particularly in cases where illegal migration is commonplace (and hence carries no stigma) and where no contact information for the migrant is collected (limiting the potential for this information to harm the migrant). During a different part of the survey to the list randomization questions, the Moroccan and Ethiopian surveys asked households who reported having a migrant abroad what type of documentation the person had acquired before entering the destination country, with possible options including work visa, student visa, refugee visa, tourist visa, no documents, or other. We code migrants who enter with no documentation or on a tourist visa as illegal migrants, and report the illegal migration rates as obtained via this direct elicitation in the last column of Table 2. The directly reported rates are 39.3 percent for Morocco and 32.6 percent for Ethiopia, which are reasonably close to (and well within the confidence intervals of) our list randomization estimates. This suggests that list randomization is giving us reasonably accurate estimates.

# **4.3 Measuring Illegal Migration Rates for Subgroups**

In addition to knowing the overall (unconditional) incidence of illegal migration, researchers and policymakers will often be interested in knowing more about the characteristics of which types of households are more likely to have illegal migrants. This can be done by simply comparing
country-specific cases, given the wide variety of economic structures in Latin America in terms of the factor intensity and the weight of the industrial, commodities and non-tradable sectors.<sup>16</sup> The regressions incorporating these additional covariates are presented in columns 4-10 of Tables 4.1 and 4.2.<sup>17</sup> The regressions in columns 7 and 8 also include a series of additional control variables to those described before, such as GDP per capita and the real exchange rate against the US dollar (both from World Bank World Development Indicators). These variables are included as a specification robustness test only, since there is no clear theoretically basis to assess their impact on wage skill differentials. The main result shows that after considering these additional variables and specifications, the coefficient on the relative supply of skilled labor in Table 4.1 becomes statistically insignificant. For the case of high-school graduates vs. dropouts, the coefficient on relative supply of workers remains statistically significant (except in column 10), indicating that supply side factors are more relevant to explain the evolution of high-school wage premia rather than tertiary educated (skill) premia.

Regarding the rest of covariates, aggregate unemployment rate has a positive relationship with the two wage differentials, although it is only significantly different from zero in just a few specifications of the skilled wage premium – that in column 8, which incorporates a series of additional macro variables as controls, and that in column 10, which restricts the sample to the nine countries with a long time series (see the note in the respective tables for details). However, including separate rates for skilled and unskilled workers, as in column 6 in Table 4.1, indicates that the unemployment level for unskilled workers does have a positive correlation with the wage skill premium, suggesting that a wage curve-type of effect might depress the remuneration levels of unskilled workers more than that of skilled workers. There is no significant effect for the separate high school and dropout unemployment rate in
surveys is greater when the question is asked nearer to elections.

Table 1 provides further data on reported participation in groups for 18 African countries covered by the most recent round of the Afrobarometer survey around 2008. On average, across all countries, 26 percent report being active members of a group, and 52 percent report frequently attending group meetings. Reported participation does not appear to be strikingly different across the countries classified as “full democracies” by Polity IV, versus those classified as “anocratic”. Indeed, there is significant variation in group participation within these categories across countries. Among the full democracies, the highest ranking (South Africa and Botswana) have active group membership reported by only 16 and 17 percent, compared to a Kenya and a Liberia lower down the “democracy” scale, with
around 0.70, whereas the marginal propensity to consume out of reduction in taxes and tax rebates was only about 0.13 (Feldstein 2009). When separating revenue changes resulting from legislation from other changes, such as those related to changes in prospective economic conditions, it is estimated that a tax cut equivalent to 1 percent of US GDP raises output by about 1 percent within a year, but the magnitude rises in the following periods and reach about 3 percent after three years (Romer and Romer, 2010). A survey of empirical evidence suggests that for the 2008-9 stimuli by the G20, the low set of multipliers was 0.3 for revenue, 0.5 for capital spending, and 0.3 for other spending; the high set of multipliers was 0.6 for revenue, 1.8 for capital spending, and 1.0 for other spending (Spilimbergo, Symansky, and Schindler 2009).<sup>2</sup> <mark>For a sample of 102 developing countries, th</mark> e one-year government spending multiplier resulting from borrowing from official creditors is estimated to be around 0.4 (Kraay, 2012).

**Table 2 Estimates of fiscal multipliers in China and other settings**

|_Entity_|_Period_|_Size of multiplier_|_Source_|
|---|---|---|---|
|China|2008 global crisis|Short term: 0.84<br>Medium term: 1.10|He, Zhang, and Zhang (2009)|
|United States|Since 1990|Government spending: 1.5–2<br>Tax-cut: 0.5–1|Nallari (2010)|
|United States|Since 1980|Tax-reduction and tax-rebate: 0.13|Feldstein (2009)|
|United States|1950-2006|1% within a year, and up to 3% after three years|Romer and Romer (2010)|
|G7
Feenstra et al. (2015), and data on leaders are taken from the Archigos 4.1 data set (described in Goemans et al. 2009).<sup>29</sup> We categorize countries as

> 26 Another potential interpretation is that the turbulence of the leader transitions causes lower growth.

27 That is, the probability of a leader change in year is 1/6 -0.5*growth (recall the growth rate is mean zero). 28 For example, even if endogeneity is 10 times as powerful in the data ( ), the RMSE of the LS leader estimate only increases by 0.06ppts, which is mostly due to biased variance estimates of the leader effect ( ). ββ= −5 29 ⋆ ≡ ln(Y ) − ln(Y ) , where Yt is real GDP per capita (rgdpna/pop in PWT9). If multiple leaders hold office in μμ σσ a year in the same country, we allocate that year to the leader in power for the most days. ii ii ii−1 gg 19
Learning from Developing Country Power Market Experiences: The Case of Colombia Regarding availability, CREG dictates that quarterly availability tests must be made to 5 randomly selected plants from those that have Firm Energy Allocation and in the previous quarter did not meet a certain observed generation level. Such tests may also be performed by order of the CREG on a discretionary basis. Furthermore, for firm energy obligations, ENFICC and related parameters should be audited by the system and market operator (CREG resolution 071, 2006, article 38). However, it has been determined by the Comptroller of the Republic that XM did not comprehensively fulfill its responsibilities regarding the said audits (Contraloría General de la República, 2016).

<!-- Start of picture text --> 1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Scarcity price Pool price<br>COP/kWh<br><!-- End of picture text --> Figure 4-2 Monthly mean pool and scarcity price in Colombia.

Source: Own elaboration based on data collected by the World Bank.

The reliability charge mechanism thus reveals three main problems:

- (i) the agreed pricing mechanism does not work correctly since not all Gencos were ready or willing to supply the firm energy commitments they had been continually paid for, and the scarcity price was temporarily intervened to partially allow recovery of very high variable costs,

27
grade 10, continues to remain low; in 2014, it was 34 percent in community schools compared to 89 percent in institutional schools.<sup>12</sup> According to the national assessment of grade 10 students in 2011, the average achievement level in the five core subjects was only 41 percent (Educational and Developmental Service Centre, 2011). An assessment by Independent Evaluation Group (IEG) of BPEP, the project that introduced the liberal promotion policy, concluded that the policy encouraged children to stay longer in school but “...no means were developed to help the children who were falling behind learn the required material and pass grades or stay in school” (Independent Evaluation Group, 2009).

> 11 Accessed March 15, 2016.

> 12 <u>http://myrepublica.com/society/story/23078/slc-results-2015-declared-with-47-43-pc-pass.html</u> Accessed March 15, 2016.

10
empowerment. Third, electrification enables greater exposure to electronic media, such as television and radio. Improved access to information may broaden horizons about opportunities for women’s economic empowerment, and social and political participation. Fourth, electrification can lead to better education outcomes for girls (Khandker, Barnes and Samad 2012; Khandker et al. 2014; Samad and Zhang 2016 2017). Better education outcomes for girls could have a catalytic effect on almost all dimensions of development, including women’s empowerment over the long term (Duflo 2012).

Using data from the IHDS and a propensity score-weighted method, we find that gaining access to electricity has a positive impact on all four potential enabling factors of empowerment. Moreover, we find that among the four factors, women’s employment and education significantly improve almost all measures of women’s empowerment, while more exposure to electronic media (such as radio and television) is associated with greater financial and reproductive freedom.

The rest of the paper is organized as follows. Section II briefly describes an intra-household bargaining model to explain the causal link between electrification and women’s empowerment. Section III presents the data and descriptive analysis of the observed indicators of women’s empowerment. Section IV constructs latent indexes for women’s empowerment and provides descriptive analysis of the indexes. This section also analyzes the effects of electrification on the empowerment indexes. Section V provides evidence on the causal channels through which electrification enhances empowerment. Section VI concludes.

# **II. Gender, bargaining power and intra-household resource allocation**

How does the adoption of electricity lead to greater women’s empowerment? In this section we first briefly review literature on the bargaining model, which is used to explain intrahousehold resource allocation and bargaining power; and then discuss how electrification contributes to women’s empowerment.

There are two broad types of models for intra-household resource allocation. The first one— the unitary model—considers the household as a collection of individuals, based on the notion 4
R1 for Uganda. Figures 5B and 5C are based on HFPS R1-6 for Ethiopia and Nigeria, R1-4 for Malawi, R1-3 for Uganda. Figure 5C also uses pre-COVID LSMS-ISA surveys for industry of work.

To analyze the medium-run impacts on the labor market, we construct 3 additional indicators. First, oneyear job loss captures whether the respondent was jobless at any point between the start of the pandemic and February/March 2021, based on all available HFPS rounds up to March 2021. The second indicator is joblessness in February/March 2021, which captures whether the respondent was jobless a year after the onset of the pandemic. And finally, we measure the total number of months without work during the first year of the pandemic.<sup>9</sup> As for the early-phase job loss indicator, the one-year indicators are all conditional on the respondent working before the pandemic, as self-reported in the first HFPS round. Although it is less clear to what extent the one-year indicators capture the impacts of the pandemic, as opposed to usual (seasonal) fluctuations in work, they allow us to assess how the labor market developed during the first full year of the pandemic.

An important caveat is that there was substantial attrition in the HFPS in all countries. For the one-year labor market indicators we use only the sub-sample of respondents who participated in all of the HFPS rounds up to March 2021. In this balanced panel, we lose 52 percent of respondents from the early phase job loss sample (i.e., those surveyed in R1 who were working before the pandemic). However, attrition is not significantly related to early-phase job loss or income loss (see Appendix Table A5), which is somewhat reassuring that the one-year labor market indicators are not biased due to selective attrition. We do see that women - particularly women with young children – are less likely to be included in the medium-run

> 8 Unlike in Ethiopia and Nigeria where the surveys were conducted every month, in Malawi and Uganda the surveys were