rafmacalaba/datause-encoder-data huggingface dataset:
the pandemic, and 81 percent only partially.<sup>29</sup> In Indonesia, the increased reliance on government assistance among the bottom 40 (reaching 80 percent in November) may have helped families avoid resorting to the more destructive coping mechanisms. The share of households cutting down on food consumption declined from nearly 70 percent in June to less than 50 percent in November.<sup>30</sup> Yet, other losses -such as those of human capital- may be harder to rebuild.

As the economies in the region recover, unprecedented levels of fiscal stimulus will need to unwind. An informed assessment of which groups have been most affected and the extent to which government actions have mitigated these effects will be critical. Social protection responses originally designed to be of limited duration were often extended as employment and income shocks continued. Going forward, countries will need to consider how to support an inclusive recovery in the face of mounting fiscal pressure.

Finally, the roll-out of the vaccines will play a critical role in achieving a sustained recovery from the pandemic. Countries that have vaccinated their population more rapidly tend to be those with stronger recovery capacity (World Bank 2021c).<sup>31</sup> While vaccination levels are ramping up quickly in many EAP countries such as China, Cambodia, and Malaysia, they remain low in the poorest countries, including Myanmar and PNG, as well as some of the largest, such as Indonesia, Philippines, and Vietnam. Recent data from the region suggests that acceptance is relatively high in most countries, but that timely access to sizeable stocks is proving difficult. In addition, even when countries do manage to procure the needed vaccines, reaching poor and underserved populations may be challenging due to inadequacy in the distribution infrastructure. In addition, there is a risk that poorer households could opt out disproportionately if they are required to pay for vaccinations.

> 29 - <u>Results of Mongolia COVID 19 Household Response Phone Survey (Round 3).</u> January 2021.

> 30 <u>Indonesia High-frequency monitoring of COVID-19 impact (brief for round 4). January 2021.</u>

> 31 According to estimates in that report, 10-percentage point higher vaccination coverage was associated with a one-half of a percentage point higher quarterly gross domestic product growth.

27
Figure 1: Students’ proficiency in locally-managed schools, fifth-grade (2005-2009) <!-- Start of picture text --> Extended winter break Extended winter break<br>Other municipalities Other municipalities<br>Contrafactual Contrafactual<br>220 200<br>210<br>190<br>200<br>190<br>180<br>180<br>170<br>2005 2007 2009 2005 2007 2009<br>(a) Math (b) reading<br>Test score, SAEB scale Test score, SAEB scale<br><!-- End of picture text --> Note: For fifth-graders, the proficiency scale (SAEB scale) ranges from 0 to 350 in reading, and from 0 to 350 in Math. The proficiency for treatment and comparison groups is the average of the proficiency scores of a sample of locally-managed schools in Sao Paulo state, the ones that have the performance of fifth-graders available since 2005. These schools are located in 10 municipalities of the treatment group and 469 municipalities of the comparison group (Table 2). The contrafactual is calculated assuming that the average score of the treatment group would increase at the same pace as that of the comparison group in the absence of the school closures. Because the first year of _Prova Brasil_ assessment was 2005, we cannot test for parallel trends using a longer time frame. Figure A.3 shows the time-trend for grade promotion, repetition, and dropout rates. Source: Data from _Prova Brasil_ /INEP.

Aiming to increase precision and account for potential time-variant confounders, we include a vector of covariates at the school level, as these variables might be correlated with both the decision of the municipality to close its schools and students’ performance in the standardized tests. We use a Lasso ( _Least Absolute Shrinkage and Selection Operator_ ) regression to select the vector of covariates, _Xsmt_<sup>_′_,thatbestpredictthevariationintheschoolaverageproficiency</sup> score (Ahrens et al. (2019)).

The vector of covariates includes dummy variables indicating whether the school has a science and computer lab, sports court, library, and access to the internet; instruction hours per day; the number of students per class; GDP per capita of the municipality; and socioeconomic characteristics of fifth-graders. The vector of socioeconomic variables includes the percentage of mothers with a high school diploma; the percentage of students that have already been retained in one specific grade or dropped out of school; the percentage of white students; the percentage of female students; the percentage of students that work for pay; the percentage of students that previously studied in a private school; the percentage of students who have a computer at 11
||age|migrant|married|cohabiting|divorced|fertility|woman|partner|
|---|---|---|---|---|---|---|---|---|
|active*mine|0.263|0.028|0.025|0.018|-0.003|0.030|-0.036|-0.003|
||(0.510)|(0.042)|(0.027)|(0.029)|(0.017)|(0.115)|(0.031)|(0.030)|

_Note:_ Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age (not column 1), and years of education (not columns 6 and 7). Active is active status of mine in the survey year. *** p<0.01, **p<0.05, *p<0.1.

# **5.1 Employment outcomes**

Using the difference-in-differences approach (equation 1), we estimate results on occupation, child health, and inequality. First, panel A of table 4 indicates that women in active mining communities ( _active*mine_ ) are insignificantly more likely to work in service and sales and less in agriculture, and 1.7 percentage points less likely to work as professionals (statistically significant). There is no change in the likelihood that she is not working. These 5 categories stem from the same occupational variable in the DHS data, and are mutually exclusive. The surveyed individual is told to report their main occupation. The coefficients can therefore be interpreted as relative increases of each specific sector. Women are more likely to earn cash for work, and the likelihood increases by 5.4 percentage points, which is equal to a 6 percent increase.

While the directionality of the occupational outcomes is broadly in line with previous results (Kotsadam and Tolonen, 2016, for 29 African countries, and Benshaul-Tolonen, 2018, for 8 African gold-producing countries), the estimates are largely insignificant, potentially due to a limited sample size. Two categories have positive, albeit insignificant, coefficients: services and manual labor. The (insignificant) estimate for service jobs<sup>9</sup> is equivalent to 6.7% increased employment, and manual labor 10.2%, alongside which the likelihood that a woman earns cash for her work increases with 6%.

For men (panel B of table 4), the estimates point toward an increase in agriculture, services, and professional (all statistically insignificant estimates), but a decreased likelihood of working in manual labor. Results for men in panel B are largely insignificant, and it is worthwhile noting that the sample size is only slightly above 50% than the women sample size due to DHS sampling frame.

**Table 4 OLS estimates women’s and men’s occupation in the DHS individual-level analysis**

|Occupation|
|---|

> 9 Service sector jobs in the wake of structural transformation has been found important in increasing women’s work hours and reduce the gender wage gap (Ngai and Petrongolo, 2017).

17
rural areas important inter-household transfers may not be observed because they take the form of labor input into family farming (Lee and Xiao, 1998).<sup>3</sup> Evidence from the 2005 one percent population sample, which is highlighted in Table 1, shows the sharp differences between rural and urban elderly in their sources of financial support. Where a substantial share of the urban population relies on pension income, fewer than five percent of rural residents over 60 reported that pensions are their most important source of financial support. Indeed, the rural elderly rely significantly more on support from family members, 54 percent, and also quite striking, 38 percent of the rural population over 60 relies primarily on their own labor income for support.

When we examine sources of support by gender in Table 1, we note that family support is relatively more important for women over 60, who tend to live longer, and labor income remains more important for men. Of women over 60, 69 percent report financial support from family members as their most important source of support and only 28 percent report that labor income is most important. By contrast, 49 percent of men over 60 report that their own labor income remains their most important source of support, and only 39 percent report support from family members. When distinguishing the importance of pension by gender in rural areas, a significantly higher share of men (8.1 percent) than women (1.3 percent) report pension income to be their most significant source of financial support. While the gap between men and women would appear to be significant, it reflects historical differences between genders in employment in local government and the military, both of which may provide some pension support.

Neither the nationally representative statistics that we calculate from the 2005 one percent population sample, nor the World Bank Poverty Assessment (Chaudhuri and Datt, 2009)

> 3Using other methods, Cameron and Cobb-Clark (2008) do not find evidence that transfers to parents respond to low parent income in Indonesia.

6
# **Table A14: First stage: Log Refugees at 50km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance) including Kampala district</u>**

|||||||Log of Refu|gees (50km)||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of Distance IV (mean border distance)|9.26140***|9.26348***|8.71557***|8.68045***|||||||||
||(2.09321)|(2.09414)|(2.27924)|(2.30710)|||||||||
|Log of Distance IV (square of mean border<br>distance)|||||5.87456***|5.87569***|5.57536***|5.58210***|||||
||||||(1.35830)|(1.35895)|(1.51200)|(1.53702)|||||
|Log of Distance IV (square-root of mean border<br>distance)|||||||||14.96346***|14.96821***|14.16041***|14.08663***|
||||||||||(3.71555)|(3.71741)|(3.92914)|(3.96700)|
|HH head age||-0.00257|0.00001|-0.00332||-0.00223|0.00064|-0.00103||-0.00277|-0.00031|-0.00385|
|||(0.00339)|(0.00323)|(0.01549)||(0.00339)|(0.00323)|(0.01583)||(0.00345)|(0.00329)|(0.01587)|
|Age squared||0.00003|-0.00000|0.00002||0.00002|-0.00001|-0.00002||0.00003|0.00000|0.00003|
|||(0.00003)|(0.00003)|(0.00016)||(0.00003)|(0.00003)|(0.00017)||(0.00003)|(0.00003)|(0.00017)|
|HH head sex (male)||-0.00417|-0.00603|0.00406||-0.00380|-0.00691|0.01942||-0.00472|-0.00555|-0.01043|
|||(0.01016)|(0.01610)|(0.08027)||(0.01018)|(0.01622)|(0.07669)||(0.01044)|(0.01668)|(0.08533)|
|Household size|||0.00072||||0.00060||||0.00086||
||||(0.00160)||||(0.00154)||||(0.00168)||
|Married monogamously|||-0.10538||||-0.10367||||-0.10768||
||||(0.07847)||||(0.08111)||||(0.07642)||
|Married polygamously|||-0.11569||||-0.11483||||-0.11690||
||||(0.07476)||||(0.07735)||||(0.07251)||
|Separated (divorced/widowed)|||-0.07415||||-0.07261||||-0.07503||
||||(0.07720)||||(0.07977)||||(0.07507)||
|Completed primary sch|||0.00035||||-0.00218||||0.00123||
||||(0.01291)||||(0.01301)||||(0.01300)||
|Commercial farming|||0.00045||||-0.00383||||0.00402||
||||(0.00887)||||(0.01003)||||(0.00848)||
|Wage employment|||-0.01408||||-0.01454||||-0.01507||
||||(0.02163)||||(0.02203)||||(0.02194)||
|Non-agricultural self-employment|||0.00413||||0.00503||||0.00425||

19
# **_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

Galvin, Peter, and Andre Morkel. 2001. “The Effect of Product Modularity on Industry Structure: The Case of the World Bicycle Industry.” _Industry and Innovation_ 8 (1): 31–47.

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———. Forthcoming. “Value Chain Creation and Reorganization: The Growth Path of China’s Mobile Phone Handset Industry.” In _The Dynamics of Local Learning in Global Value Chains; Experiences from East Asia,_ ed. Momoko Kawakami and Timothy Sturgeon, paper 2. Basingstoke, UK: Palgrave Macmillan.

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48
BE indicators at the city-industry level as a proxy for the local BE (Dollar et al. 2005; Hallward-Dreimeier et al. 2006; Aterido et al., 2011; Xu 2011).

We look at several aspects of the business environments that are discussed earlier that might interact with female leadership in affecting productivity. Corruption is measured as Corruption Obstacle (i.e. the extent to which corruption is viewed as an obstacle by firms in the city-industry cell). 17 In particular, the survey asks, “to what extent is corruption an obstacle to the development of the firm?” and the answer is scaled from 0 to 4, where a higher number implies a more severe constraint. To aid interpretation, for this question, we construct a dummy variable of moderate or severe obstacles for that area (i.e., the values of 3 and 4), and call it Corruption Obstacles. We then calculate the city-average of Corruption Obstacles, and view it as the local BE indicator on corruption. To measure access to scarce and critical inputs that might need personal and professional networks, we have several indicators: Power Outage (i.e., city-industry share of firms that experienced a power outage in the survey year); Overdraft (i.e. city-industry share of firms with overdraft facility), which captures access to formal finance; and Trade Credit (i.e. city-industry average of the proportion of total annual sales of goods and services that are paid for after delivery). To capture the relaxation of dependence on personal network and the availability of modern technology, we use Web Intensity (i.e., city-industry share of firms that answer that they use websites to conduct business). To capture competition, we have Informal Competition, which is measured by the share of firms in a city that self-report as competing with informal firms.<sup>18</sup> We in addition control for other key measures of business environment such as _Capacity Agglomeration,_ which captures the concentration of firms within a city that possess high capacity either in terms of technology, management or their ability to adapt to a changing competitive environment.<sup>19</sup> For cultural/institutional environment at the country, we have several groups of variables. First, on female empowerment, we have indicators from the OECD Development

> 17 See Table 2 for definitions and sources of all key variables.

> 18 We do not have other variables capturing competition intensity with formal firms due to the lack of data.

> 19 Following, for example, Clarke _et al_ . (2015), we proxy Capacity Agglomeration by the share of firms in a city that employ more than 50 workers. The use of this proxy is consistent with evidence from many studies which have shown that (reasonably) large firms have stronger capacity than other firms in developing countries.

15
obtained from the central planning problem where the representative household maximizes its expected lifetime utility subject to (3 _._ 1), (3 _._ 2), (3 _._ 3), (3 _._ 4), (3 _._ 5), (3 _._ 6) and (3 _._ 7). The agent chooses _Ct_ , _HCt_ , _HRt_ , _Kt_ +1, _Rt_ +1, _ICt_ , _IRt_ , _Dt_ as well as the time _t_ allocation of capital between the two sectors, _KCt_ and _KRt_ .

b Let _xt_ = _dxt/x_ denote the percentage deviation of _xt_ from its nonstochastic steady state.

The processes that drive the exogenous shocks are given by the following vector autoregressive process

# **4 Stationary Equilibrium and Identification**

The equilibrium in this economy is described by constraints (3.1) and (3.4), the accumulation equations for the stock of R&D, (3.3), and capital, (3.7), and the following optimality conditions: two sectors by assuming a representative agent allocating her time between working in the consumption-good sector, working in the R&D sector and leisure, the results of the next section will still hold. In either case, the VAR analysis that follows does not depend on whether labor is mobile or immobile across sectors.

15
Tarp et al. (2002) appraise the consequences of Vietnam’s shifting import and export patterns and argue that trade and other reforms will not realize their full potential for all Vietnamese households in the absence of deliberately corrective fiscal measures. Further, Le and Winters (2001) argue that there is an imbalance between aid which promotes economic growth and aid which directly targets the poor. They also argue that aid is not regionally directed in a manner conducive to poverty alleviation and is urban-biased.

All of the above observations motivate us to examine the spatial dimension of trade policy incidence and its implications for poverty. Changes in the spatial distribution of poverty have some practical importance as well, because such changes alter the efficient geographical targeting scheme. However, previous studies gave little guidance about how to shift resources in response to a changing macroeconomic environment. In this study, we show which part of the country is least likely to benefit from trade liberalization. In addition to contributing evidence from Vietnam to the more general debate on globalization and poverty, these results provide guidance for those policymakers who want to formulate geographic targeting policies for poverty reduction.

# **3 DATA AND MEASUREMENT**

We combine four different data sets in this study. First, the information required is a socioeconomic data set. We use the VLSS 1997–98 data set, which contains a wide array of microeconomic data, such as information on housing, employment, household enterprises, income and asset holdings. The survey was conducted by Vietnam’s General Statistical Office (GSO). The United Nations Development Programme (UNDP) and the Swedish International Development Agency (SIDA) provided financial assistance whereas the World Bank provided technical assistance. The sample of VLSS 1997–98 is nationally representative and stratified into two groups representing urban and rural areas. The number of households in the sample is 4270 in rural areas and 1730 in urban areas (World Bank, 2001).

Second, we used the 1999 Population and Housing Census. The census was carried out by the GSO with financial and technical support from the United Nations Population 7
We measure performance by the student's score obtained in the tests applied at the beginning of the 4th grade - before the program began- and at the conclusion of the 6th grade, when the program was already in its third year of implementation. The tests were designed and applied by the _Direccion General de Evaluacion_ (DGE) of the _Secretaria de Educacion._ Notice that in the opinion of both the DGE and of the CEE, which conducted the impact evaluation of the program, the Spanish test provides a superior metric. Students' performance in mathematics was very low.

Measured by their scores in Spanish, the performance of students in the experimental group of schools is significantly better in both the rural and native subsamples. As shown in Table 5, before the program, students in native schools in the experimental group were markedly disadvantaged with respect to their peers in the control group. The program eliminated this difference. Students in rural schools were undifferentiated before the program; with the program, those in the experimental group showed significantly higher scores. The percentage change in performance is, on average, three times as large for students in the experimental group. However, in urban areas a retrocession in student's performance was observed probably because bad implementation or the wrong components.

**Table 5. Student's change in performance, 1994.**

||**Befo**|**re (1992)**|**After (1994)**|**Di**|**fference**|
|---|---|---|---|---|---|
||**Students**|**Average test**|**Students Average s core**|**Total**|Percentage|
|Native||||||
|Experimenl|564|14.6|356<br>29.1|13.9|95.3|
|Control|205|23.2|125<br>26.8|4.1|17.7|
|Total - t/test|769|16.9|481<br>28.5|11.4|67.3|
|**Rural**||||||
|Experimernal|645|20.7|421<br>32.9|11.6|56.0|
|Control|208|20.1|128<br>29.7|8.2|40.6|
|Total -ttest|853|20.5|549<br>32.1|10.8|52.5|
|**Urban**||||||
|Experimental|337|26.9|238<br>39.7|12.0|44.5|
|Controi|361|26.9|221<br>44.3|15.9|59.3|
|Total - tttest|698|26.9|459<br>41.9|13.9|51.6|

Source: Own calculations based on PARE's database. Note: Difference respect to control group.

7
<!-- Start of picture text --> 3.50 - -_- --------<br>3.00<br>2504<br>2001<br>Di 1 P50 olyrto rial i<br>PoI ~ modetognodel<br>0 20,000 40,000 60,000 80,000 100,000 120,000 140,000<br>Expenditure (Rp/month)<br><!-- End of picture text --> Figure 3: Relationship between Price of Calories Consumed and Expenditures Suppose one researcher believed the poverty rate was 15 percent and hence began with a reference group of the 15' percentile, while another believed poverty was 30 percent. They then estimate the poverty rate without iterating. Table 1 shows that the resulting poverty rates from the two researchers using exactly the same _method_ on exactly the same _data_ and differing only in their prior (and not unreasonable) beliefs about the appropriate reference group would produce estimates of the poverty rate that differed by 6.7 percentage points (more than 30 percent!).

12