rafmacalaba/datause-encoder-data huggingface dataset:
and<sup>�</sup> _tD ×_ ̄ _gi ≡ yi,_ � _tmin − yi_ 0 as the overall depth of the decline. Note that _g_ ̄ _i_ is negative by construction. We scale both outcomes by one hundred for readability.

– **Table 3** Average growth rate during decline and total depth

||||_Depende_|_nt variable_|||
|---|---|---|---|---|---|---|
|| ̄_gi_<br>(1)| ̄_gi_<br>(2)| ̄_gi_<br>(3)| ̃_tD ×_ ̄_gi_<br>(4)| ̃_tD ×_ ̄_gi_<br>(5)| ̃_tD ×_ ̄_gi_<br>(6)|
||_Pa_|_nel (a) Stru_|_ctural break_|_s estimated _|_with size = _|_0.10_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)|0.227|0.627**|0.732***|4.151***|5.617***|6.275***|
||(0.190)|(0.238)|(0.217)|(1.486)|(1.351)|(1.581)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.007|-0.017|-0.017|-0.153**|-0.291***|-0.309***|
||(0.013)|(0.011)|(0.016)|(0.074)|(0.092)|(0.100)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|0.002|0.003|0.003|0.048|0.060**|0.053|
||(0.005)|(0.004)|(0.005)|(0.033)|(0.029)|(0.032)|
|US Real Interest Rate|0.082|0.015|0.220|2.773**|2.613**|0.805|
||(0.135)|(0.140)|(0.268)|(1.207)|(1.122)|(2.204)|
|Initial log GDP|0.158|0.413|0.436*|2.076|2.179|2.571|
||(0.261)|(0.310)|(0.240)|(1.482)|(1.796)|(1.766)|
|Region FE|NO|YES|YES|NO|YES|YES|
|Initial Decade FE|NO|NO|YES|NO|NO|YES|
|Spells|54|54|54|54|54|54|
|Log-_L_|-142.247|-137.284|-125.939|-243.640|-241.930|-236.354|
|Adjusted R<sup>2</sup>|-0.064|0.056|0.302|0.232|0.231|0.296|
||_Pa_|_nel (b) Stru_|_ctural break_|_s estimated _|_with size = _|_0.20_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)|0.302*|0.513**|0.481**|4.309***|5.159***|5.016***|
||(0.157)|(0.197)|(0.188)|(1.230)|(1.234)|(1.302)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.002|-0.021**|-0.021*|-0.106*|-0.241***|-0.251***|
||(0.010)|(0.010)|(0.012)|(0.062)|(0.081)|(0.086)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|0.007*|0.009**|0.010**|0.061*|0.074**|0.065**|
||(0.004)|(0.004)|(0.004)|(0.032)|(0.030)|(0.032)|
|US Real Interest Rate|0.017|0.015|0.124|1.806*|1.924**|0.698|
||(0.110)|(0.112)|(0.225)|(0.960)|(0.931)|(1.855)|
|Initial log GDP|0.146|0.270|0.407|1.650|1.668|2.296|
||(0.218)|(0.268)|(0.247)|(1.303)|(1.531)|(1.591)|
|Region FE|NO|YES|YES|NO|YES|YES|
|Initial Decade FE|NO|NO|YES|NO|NO|YES|
|Spells|74|74|74|74|74|74|
|Log-_L_|-189.696|-186.990|-176.881|-331.143|-328.817|-324.852|
|Adjusted R<sup>2</sup>|0.001|0.029|0.200|0.222|0.235|0.256|

_Notes_ : All models include a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

We find only weak evidence of an effect of either executive constraints or fractionalization on the average rate of decline, but effects comparable to those from the duration regressions when we consider the overall depth of declines. Columns (1) and (2) in both panels of Table 3 report the results for the interacted model without and with region fixed effects. Column 24
**Table A11: Impacts of mental health on labor outcomes – Post-traumatic stress**

||**disorder**<br>||
|---|---|---|
|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
||(1)|(2)|
|Post-traumatic stress disorder|-0.035**<br>(0.017)|-0.599**<br>(0.264)|
|_First stage of 2SLS (dependent v_|_ariable is post-traum_|_atic stress disorder)_|
|Trauma experience*Year after<br>arrival|-0.018***<br>(0.005)|-0.019**<br>(0.008)|
|Kleibergen-Paap F stat.|19.070|47.439|
|AR 95-CIs|[-0.202, -0.017]|[-3.882, -0.318]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,621|
|Number of individuals|1,609|1,593|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

43
44 If many of the poor and the “near-poor" claim the benefits, their numbers will overwhelm social assistance offices. This has a further negative effect on targeting: identification of the poor becomes worse. The "failure" errors rise as the proportion of the poor in a given group of people increases. It is relatively easy to identify the poor in a group where poverty is rare. For example, if one takes a group composed only of household with two employed persons and one child, living in a town, poverty in such a group is rare, and those households that happen to be poor are relatively easily identified. The situation is very different when one deals with households where poverty is prevalent. For example, if one takes households with 4 or 5 children where one member is unemployed and who live in rural areas, many of them may be around the poverty line. It then becomes very difficult to tell those who should receive social assistance from those who should not. This is what may be called the “easy discrimination effect": among the people who share some demographic and social characteristics common to the rich, it is easy to target well because the poor stand out.

Using the Polish 1993 data, we have plotted the "failure" error, that is, of not giving social assistance to those who are poor, against the specific group poverty rates. We divide the population into 83 exclusive and exhaustive groups, using as "dividers", the socio-economic group (6 such groups), the place of residence (5 types), and the type of household (3 types: single parents regardless of household size, and small, and large households). This gives a total of 90 groups but since some of them are empty, we end up with 83 groups. For each group we calculate the "failure" to deliver as: Number of poor households without social assistance total number of poor households and the poverty rate in the usual fashion as the number of the poor over the total group population. The results show that the "failure" error is strongly associated with the group poverty rate (see Table 10).

Table 10 Error of "failure" to deliver as function of the poverty rate

**: Highly significant (1%).

If we the extend the findings on the "excess" and "failure" errors to the level of the country as whole, it means that in countries where there are many potential claimants for aid, as in all transition economies, the frequency of both types of errors will be high. In countries where the number of potential claimants is small, errors will be less frequent. The poor will be more easily identified. This outcome does not depend on the intrinsic quality of social assistance
"Infrastructure Coverage and the Poor: A Global Perspective" Komives, Whittington, and Wu connections. 22 Between 40 and 50 percent of the poorest urban households who had access to these three services in their communities had connections in their homes. This is much lower than the coverage rate among the richest urban households, but it does mean that nearly half of urban households with monthly household incomes around US$32 per month (as measured by our consumption aggregate) chose to install water, sewer, or phone service when these services were available to them. The percentage of households with access who connected was highest for electricity service in both urban and rural areas. Sewer, in-house tap, and telephone connection rates follow in that order.<sup>23</sup> These figures do not necessarily imply that households would prefer electricity over other services if they could choose from among all four services (electricity, in-house water, sewer, or telephone). Of the households for whom access information is available on the four services, only 30 percent had access to all four.'<sup>4</sup> Nearly all (98 percent) of these households had electricity, 82 percent had sewer connections, 75 percent had in-house water taps,<sup>25</sup> and 50 percent had phones. Because the LSMS surveys lack information on service prices, it is not possible to determine how differences in the price and connection fees for these services contribute to this outcome.

# **5. Household coverage: a multivariate analysis**

The results presented in sections 3 and 4 demonstrate that infrastructure coverage varies with household income, by country of residence, and between urban and rural areas. In this section we downside, the community data roughly divide households into two groups: those with no possibility of connecting to a network and those who may have a chance of connecting.

22 It is possible that the low connection rates in rural areas is more a reflection of problems with the access data than of the willingness of poor households to connect. Rural communities and primary sampling units cover a larger land area than urban communities. The fact that there is access a particular service somewhere within a rural community does not necessarily mean that it is technically or financially feasible to install a connection at every home in the area. While this is true in urban areas as well, this weakness in the community survey data is especially problematic in rural areas.

23 In Ecuador and Panama, where information on yard taps is available, a greater percentage of urban and rural households of all quantiles chose an in-house or yard tap than a sewer connection (given access to each service).

24 Sewer information is not available for Kazakhstan, and telephone access is not available in Nepal. Therefore, the households with access to all 4 services live in Ecuador, Kyrgyz Republic, and Panama.

**25** Nearly 2500 of the 3000 households with access to all four services live in Ecuador and Panama. In these countries information on yard taps is available. Nearly 95 percent of the 2500 households have an in-house or yard tap.

11
Additional support for a sector specific treatment of productivity where agriculture shows total factor productivity (TFP) growth rates higher than those for manufacturing comes from a recent panel study on sectoral productivity growth in OECD and developing countries.<sup>14</sup> In this study, depending on the estimation method, the average growth rate for agricultural TFP in middle-income developing countries ranges from 1.78 to 2.91 (in % per year).

Other elements of simple dynamics include exogenous growth of labor supply, with skilled labor growing faster than unskilled labor, and investment driven capital accumulation.<sup>15</sup> Equation (17) determines labor supply growth for the skilled workers (unskilled labor supplies are determined in equations (6) and (7)). It simply applies an exogenous assumption about the growth of labor supply, _g_<sup>_ls_</sup> , to the labor supply shift parameter. Equation (18) updates population. Equations (19) and (20) are similar growth equations for land and the sector-specific resource, respectively.

> 14 See Martin and Mitra (1999). 15

> Note that public investment, in this version of the model, has no impact on production technology.

15
**Table 31. Reactions to political risks: Currency restrictions**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0930|-0.0680|||-0.147|-0.108|||
||(0.0647)|(0.0649)|||(0.107)|(0.112)|||
|Export % of sales|-0.000591|0.000566|-0.000315|0.000852|-0.00106|0.000924|-0.000616|0.00143|
||(0.000919)|(0.000900)|(0.000930)|(0.000899)|(0.00149)|(0.00153)|(0.00156)|(0.00158)|
|Share of sourcing via imports|0.000275|0.000886|0.000446|0.000870|0.000554|0.00130|0.000712|0.00121|
||(0.000923)|(0.000916)|(0.000921)|(0.000920)|(0.00151)|(0.00158)|(0.00154)|(0.00161)|
|High-income source country|-0.127|-0.0897|-0.102|-0.0781|-0.207|-0.129|-0.175|-0.126|
||(0.0982)|(0.104)|(0.0949)|(0.0996)|(0.156)|(0.173)|(0.154)|(0.173)|
|>250 employees|-0.0816|-0.0840|-0.0502|-0.0653|-0.108|-0.131|-0.0460|-0.0965|
||(0.0807)|(0.0820)|(0.0827)|(0.0834)|(0.136)|(0.143)|(0.141)|(0.147)|
|Cumulative inv. >US$10M|0.0352|-0.00709|0.0614|0.00735|0.0445|0.00767|0.0958|0.0284|
||(0.0778)|(0.0779)|(0.0773)|(0.0777)|(0.131)|(0.137)|(0.132)|(0.139)|
|Years in country|-0.00284|-0.00369*|-0.00340|-0.00407*|-0.00414|-0.00671*|-0.00510|-0.00716*|
||(0.00210)|(0.00204)|(0.00214)|(0.00212)|(0.00352)|(0.00368)|(0.00372)|(0.00396)|
|Level of foreign ownership (%)|0.000452|0.00142|0.000450|0.00144|0.00112|0.00300|0.00114|0.00300|
||(0.00140)|(0.00134)|(0.00141)|(0.00132)|(0.00225)|(0.00228)|(0.00224)|(0.00231)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.098|0.033|0.117|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Restrictions on your ability to transfer and convert currency]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

48
**Figure 2.5: More expensive imports are driving a negative trade balance.** Trade balance on goods and services (% of GDP), 2000-2021

<!-- Start of picture text --> 0<br>-2<br>-4<br>-6<br>-8<br>-10<br>External balance on goods and services (% of GDP) -12<br>-14<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Development Indicators.

Trade liberalization coincided with some export diversification, with new products reshaping Cambodia’s export basket. Today, moderate and low-complexity products— including apparel, footwear, leather goods, precious metals and stones, and tourism—constitute most of Cambodia’s exports (Figure 2.6). Up to 2008, apparel represented more than half of Cambodian exports, and even though its export value has continued to grow in the last 20 years—from about US$1 billion in 2000 to US$8 billion in 2020—its export share has decreased over time due to increasing participation of other products. Thus, while garments remain Cambodia's main export product, their share has decreased 20 percentage points from about 60 percent to 40 percent between 2000 and 2020.

Growth in the mining sector represents the other side of this coin. The discovery of new mines in recent years stimulated exports of precious metals and stones. Since issuing its first industrial mining license in 2016, Cambodia has significantly increased its export share of precious metals from 0.4 percent in 2000 to 14.2 percent in 2020. Moreover, during the Covid-19 years, the sector reported growth, unlike other industries. Between 2000 and 2020, mining activities grew from US$7 million in exports to US$3 billion.

4 | P a g e
and have been used extensively to improve national account estimates of GDP (e.g. Henderson et al. 2012, Lyu et al. 2018, Chen et al. 2019). Both proxies have also been used to assess the economic impact of major policy measures. Nighttime light intensity, for example, allowed assessing the impact of India’s demonetization in November 2016 (Beyer, Chhabra, Galdo, and Rama 2018, Chodorow-Reich, Gopinath, Mishra, and Narayanan 2020). It is also invaluable to approximate economic activity at the sub-national level, including in India (Gibson, Datt, Murgai, Ravallion 2017, Prakash, Shukla, Bhowmick, and Beyer 2019, Chanda and Kabiraj 2020). In concurrent work, electricity consumption has been shown to have closely tracked economic activity in the United States during the global financial crisis (Cicala 2020a).<sup>4</sup> And it has been employed for an assessment of the economic impact of the COVID-19 pandemic in the European Union (Cicala 2020b, Chen et al. 2020b).<sup>5</sup> In this paper, we first confirm a meaningful relationship between electricity consumption, nighttime light intensity, and economic activity in India. We then propose a new real-time measure of daily economic activity in India at the country and state levels based on daily electricity consumption. We estimate an electricity consumption model based on the day of the week, the week of the year, the temperature, and holidays that explains 90 percent of the variation in India’s electricity consumption. Comparing the actual electricity consumption in 2020 to the one predicted by the model allows us, first, to quantify the economic costs of the COVID-19 pandemic and the national lockdown implemented on March 25, 2020 and, second, to understand different impacts between states. In addition, we use night light intensity to gauge the impacts at the district and city level and to explore their local drivers.

We find a strong impact of the national lockdown on India’s electricity consumption. It dropped on average 28.5 percent in the week after its implementation and was on average still 25.8 percent below normal throughout April. It started recovering in early May and was nearly back to normal between May 23 and May 28, before dropping again at the end of the month to -14.7 percent on May 31.<sup>6</sup> Not all Indian States and Union territories have been affected equally. While electricity consumption halved in some, it did not decline at all in others. It declined more for States and Union territories with a larger manufacturing sector and higher previous short-

> 4While real GDP fell 4.3 percent from peak to trough, weather-adjusted electricity consumption fell around 5 percent (Cicala 2020a).

> 5Energy consumption at the beginning of April 2020 was down by around 10 percent with large differences between countries due to varying impacts of the pandemic and containment measures (Cicala 2020b).

> 6The deviation is statistically significant at the one percent level from March 23 to May 24 and from May 29 to May 31.

2
# **What Really Works to Improve Learning in Developing Countries? An Analysis of Divergent Findings in Systematic Reviews**

David K. Evans World Bank Anna Popova World Bank

## JEL Codes: O15, I21, I28, J13

## Keywords: Education, Impact Evaluation, Human Capital

Acknowledgments: The authors are grateful for comments from Jacobus Cilliers, Katharine Conn, Deon Filmer, Alejandro Ganimian, Peter Holland, Howard White, Jeffery Tanner, and Viticia Thames, and for background materials provided by Katharine Conn, Alejandro Ganimian, and Paul Glewwe. The authors can be contacted at David K. Evans (devans@worldbank.org) and Anna Popova (apopova@worldbank.org).
educational programs that enhance competitiveness and support diversification, by allowing workers (particularly those who lose their jobs in import-competing industries) to “retool” and adjust their skills to those required in the expanding sectors. More generally, there is good analytical and empirical evidence suggesting that trade liberalization has stronger beneficial effects when labor markets are more flexible (see Oslington (2005) and the survey by Hoekman and Winters (2005)).

However, the need for complementary reforms may involve not only the labor market but also the financial sector (see Chang, Kaltani, and Loayza (2009)). In countries with underdeveloped financial sectors, inadequate access to finance—whether to finance shortterm capital needs or physical investment—is a major factor inhibiting exports. Difficulties in assessing the creditworthiness of (and the value of collateral pledged by) small exporting firms, in particular, may constrain access to formal sector loans, with an adverse effect on employment. Again, a well-designed AFT may help to alleviate these constraints.

# **III. IMPLICATIONS FOR TRADE REFORM IN THE CARIBBEAN**

As noted in the introduction, the ongoing debate on AFT programs for developing economies has important implications for Caribbean countries, given the importance of the structural and budgetary constraints that the region faces in attempting to revive its trade reform agenda and take advantage of new opportunities offered by the global trade environment. This section begins by reviewing the relevance of AFT arguments presented earlier; it then discusses some issues that are specific to the region.

# **3.1 Relevance of Aid-for-Trade Arguments**

Consider first the arguments that view AFT as a compensatory scheme. The first two arguments (the need to mitigate revenue losses and avoid cuts in productive expenditure) are highly relevant for Caribbean countries in the present context. Some of these countries rely quite heavily on trade taxes as a source of current revenue. In particular, the six Eastern 15
dedicate considerably more time than men to household chores. The exact opposite is true for paid work (Posel and Grapsa, 2017).

Overall, prime working age women and men work the greatest number of daily hours. Table 3 shows that both men and women in this age group spend more time on market activities than their younger and older counterparts (Pakistan is an exception). This pattern is consistent with higher employment rates for individuals in the 25-44 age group compared to those of individuals in the younger cohorts (Appendix Table A2).

These findings suggest that time use is both gendered and age differentiated. In most countries, we observe an inverted u-shaped relationship between the gender gap in time allocated to market work (men’s time minus women’s time) and age. The amount of time that young men devote to paid work is 1.7 hours greater than the time allocated by young women. Similarly, the gender gap in among prime working age men and women time is 3.4 hours per day, while that for mature adults is 2.7 hours per day. The steep increase in the gender gap when moving from young adulthood to childbearing and peak productive years is explained by a larger increase in the number of working hours for men compared to that for women.

The relationship between the gender gap in time allocated to unpaid domestic work and age resembles a u-shape pattern. The number of hours allocated to this activity increases as men and women move from youth to prime working age and then decreases with maturity (men’s time minus women’s time). These changes are more marked among women and the gender differential is higher for those of prime working age (Panel B of Figure 6). Young women’s unpaid domestic work is 2.3 hours greater than that of young men’s, while the difference increases to 3.9 hours per day among prime working age individuals and decreases again to 2.7 more hours for mature adults.

Men and women of prime working age work more hours in total—in the market or at home—and allocate less time to leisure and personal care than any other age group. These patterns are closely related to lifetime events such as family formation, including marriage and parenthood. Similarly, women of all ages specialize in domestic chores and men specialize in market work, but the degree of specialization varies across age groups. For example, young men allocate fewer hours to market work than their mature adult counterparts and the same if true among women when it comes to unpaid domestic chores.

# _Time Use by Marital Status_

In the analysis that follows, we break down the sample according to sex and three categories of marital status: single or widow/widowed, married or cohabiting, and separated or divorced.<sup>12</sup> The choice of categories was carefully determined based on the classification used in the 19 countries.

> 12 The West Bank and Gaza survey only distinguishes between married and not married people. Not married people are included in the single/widowed category.

13