rafmacalaba/datause-encoder-data huggingface dataset:
Table A.7: Robustness checks, alternative definition of undocumented migration. Estimating the effect of migrants’ legal status on hourly wages after return, conditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.085**|-0.018***|-0.035|
||(0.001)|(0.035)|(0.007)|(0.035)|
|Age squared|-0.000***|0.001**|0.000***|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.021***|0.527***|-0.035*|-0.434***|
||(0.004)|(0.121)|(0.021)|(0.105)|
|Primary or preparatory|0.031***|0.438***|0.038*|-0.516***|
||(0.004)|(0.131)|(0.022)|(0.099)|
|Secondary|0.017***|0.361***|0.026|-0.195***|
||(0.003)|(0.108)|(0.018)|(0.075)|
|Rural|0.013***|0.185**|-0.005|-0.042|
||(0.003)|(0.086)|(0.014)|(0.062)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.176*<br>(0.103)|||
|Confict|||0.060***<br>(0.014)||
|Illegal status||||-0.383*<br>(0.205)|
|Observations|14,879|1,955|2,071|718|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Illegal mig.=|1.328; Legal mi|g.=1.742|||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. We use an alternative definition of undocumented migration. We define undocumented migrants as migrants who entered the destination country without having a visa or official document, for countries that required an entry visa, and as those who did not have a work contract for their employment abroad, for countries that had a visa waiver for Egyptians. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for illegal and legal migrants are reported in the last row.

54
|Table Al-i: Variables|||||
|---|---|---|---|---|
|Description|Name|Number<br>of<br>obser-<br>vations|Mean|Std.<br>Dev.|
|Marriage and fertility|||||
|Percent who have never married: women aged 15-19|w15 19nn|94|75.01|14.36|
|Percent who have never married: women aged 20-24|w2024nm|94|33A9|15.23|
|Median age at first marriage: women 20-49|w2049mm|53|17.61|1.506|
|Median age at first marriage: women 25_-49_|w2549mm|82|18.42|1.993|
|Percent who have never had a birth womnen 15-19|wlSl9nb|94|84.34|8.763|
|Percent who have never had a birth: women 20-24|w2024nb|94|40.97|15.003|
|Median age at first birth : women 20-49|w2049mb|30|19.12|0.553|
|Median age at first birth: women 25-49|w2549mb|79|20.41|1.342|
|Education|||||
|Female relative to male:percent of house hold population in school:<br>ages 6-10 (4 observations are either 6-11, 7-10, or 7-12)|enrld|68|0.8908|0.15|
|Female relative to male:percent of household population in school:<br>ages 11-14 (15) (4 observations are either 12-14, 13-15, or 13-16)|enr2d|68|0.8199|0.184|
|Infant and child mortality|||||
|Female relative to male: neonatal mortality|mnrtnnd|<br>66|0.802|0.114|
|Female relative to male: post-neonatal mortality|mrtpnnd|65|1.0011|0.188|
|Female relative to male: infant mortality|mrtlqOd|88|0.8705|0.109|
|Female relative to male: child mortality|nrt4qld|85|1.1365|0.3 17|
|Female relative to male: under-five morality|mrtSqOd|<br>87|0.9422|0.119|
|Vaccinations|||||
|Female relative to male : percent with all vaccinations (i.e. BCG,<br>measles, and three doses of DPT and pol io vaccine) from vaccination<br>card or mother's report: children aged 12 to 23 months (I<br>observation is for 0 to 59 months, I observation is for 12 to 59<br>months)|vacalld|71|0.9679|0.124|
|Female relative to male : percent with no vaccinations|vacnond|61|1.1727<br>Co|0.493<br>ntinued...|

32
disrupted. When the shock takes places in a third source country that is an upstream supplier of inputs, the exporter experiences a production disruption that hurts its exports.<sup>7</sup>

# **2.2 Empirical strategy**

Our empirical strategy is based on the assumption that bilateral trade growth between two countries is affected by supply and demand shocks induced by the health crisis in the exporting country, partner country and in third countries. The negative impact of these shocks is expected to be heterogeneous across sectors, as certain sector characteristics can soften the decline in bilateral export growth induced by the shocks. We estimate difference-in-difference specifications that interact the Covid-19 shocks with sector characteristics. The model is estimated for 28 exporting countries and over 50 trading partners at the ISIC Rev. 3 4-digit level for February to June 2020.<sup>8</sup> The general estimation equation can be expressed as follows: g g i i 1 e e s i i ggh = αα+ ββ eeg ggeegg s sshggo ∗sseeoog gg_oohaaggaaooggeeggaassggaaoo 2 i i 3 e s i + ββ eeaag ppeegg ddeeddaappdd sshggo ∗sseeoog gg oohaaggaaooggeeggaassggaaooi i i + ββ igghaaggdd ooggssppgi ss s sshggo ii mmii ii zzii + ggssg aass ggssggeessgg + γγ + γγ + γγ + γγ + εε where g g i i denotes bilateral annualized growth of exports from country _i_ to partner country _j_ in sector _k_ at time _t_ . The explanatory variables include the supply shock in the exporting country and the ggh demand shock in the partner country interacted with the relevant sector characteristics, e e s i i and i ∗ eeg ggeegg s sshggo i , and the supply shock in third countries, ∗sseeoog gg_oohaaggaaooggeeggaassggaaoo eeaag ppeegg ddeeddaappdd sshggoe s i . ii sseeoog gg_oohaaggaaooggeeggaassggaaoo gghaaggdd ooggssppg ss s sshggo We also include a set of controls, namely exporter-partner-sector ( i ) , exporter-time ( i ) , partner-time ( i ) and sector time ( ) fixed effects to account for potentially omitted variables, while γγ ii γγ i i is the error zzii γγ γγ εε

> 7 Production disruptions could also have trade effects if firms can switch input suppliers. However, switching input suppliers is difficult in the short term, particularly when inputs are customized in GVCs. We therefore expect that a reshaping of value chains in response to a shock is a longer-term process and is more difficult in the period considered in this paper. For an empirical analysis of longer-term effects of natural disasters on GVCs, see Freund, Mattoo, Mulabdic, Ruta (2020).

> 8 In the next section, we use mirror export data to investigate the trade impact of Covid-19 in a larger set of developed and developing countries.

9
# What Do Teachers Know and Do? Does It Matter?

## Evidence from Primary Schools in Africa<sup></sup>

Tessa Bold<sup>a</sup> , Deon Filmer<sup>b</sup> , Gayle Martin<sup>c</sup> , Ezequiel Molina<sup>d</sup> , Christophe Rockmore<sup>e</sup> , Brian Stacy<sup>f</sup> , Jakob Svensson<sup>g</sup> , and Waly Wane<sup>h</sup>

**JEL Classification:** I20; O15; H52

**Keywords:** Education; Teacher Absenteeism; Teacher Performance; Education Policy and Planning; Public Service Delivery

>  Acknowledgements: The authors gratefully acknowledge financial support from The William and Flora Hewlett Foundation and the World Bank, and the partnership with the African Economic Research Consortium. We want to thank, without implicating, Luis Benveniste, David Evans, Owen Ozier and Ritva Reinikka for comments on an earlier draft of this paper.  We would also like to thank a number of people who have contributed to the development of the instruments and the implementation of the SDI surveys, in particular Raihona Atakhodjayeva, Mwangi S. Kimenyi, Shireen Mahdi, Owen Ozier, Obert Pimhidzai, and Jud Shearer— along with numerous collaborators in each of the countries in which SDI surveys have been implemented. Serena Cocciolo provided excellent research assistance to this analysis. We would especially like to thank Ritva Reinikka without whose tireless support over many years this work would not have been possible.

> (a) Corresponding author, IIES, Stockholm University, Email: tessa.bold@iies.su.se; (b) The World Bank; (c) The World Bank; (d) The World Bank; (e) The World Bank; (f) Economic Research Services, US Department of Agriculture; (g) IIES, Stockholm University; (h) The World Bank.

1
_Gender Equality and Economic Development: The Role for ICTs_ 23 that gender equality in education, measured by the female to male ratio of the average years of schooling, also has a statistically positive effect on gender equality in employment. In other words, reduction in gender inequality in education shows signs of reducing gender inequality in the labor market. Lastly, the results also provide some evidence that economic development tends to increase gender equality in employment.

In summary, given existing evidence indicating that improving gender equality in education and employment leads to a higher level of economic development, this paper focuses on reducing gender inequality in education and employment by using ICTs. The paper shows that increasing ICT availability tends to reduce both gender inequality in education and employment. Further, this paper also shows that reducing gender inequality in education may also reduce gender inequality in employment, which may provide another channel for ICTs to improve gender equality in the labor market, thus increasing the economic importance of ICTs in improving gender equality. Lastly, the results show that economic development itself tends to lead to some improvements in gender equality in the labor market. Hence, the use of ICTs to improve gender equality in education and employment may initiate a continuous cycle of positive reinforcing feedback effects between gender equality in employment and economic development, leading to further improvements in both (see Figure 9).
# **C Introduction**

The gender disparity in commuting barriers is pervasive, affecting women in developing and developed nations. Recent OECD findings reveal a substantial gender commuting gap, with men averaging 33.4 minutes per day compared to women’s 21.9 minutes, indicating a 31.1% difference (OECD, 2016). Developing countries exhibit even more pronounced patterns; Indian women, on average, spend a mere 8 minutes daily on employment-related travel, compared to men’s 36 minutes.<sup>22</sup> ILO (2017) reports that lack of transportation decreases women’s probability of participating in the labor market by 16.5 percentage points among developing countries. Given the established inof barriers on women’s labor a natural arises: can fluence commuting supply, inquiry reducing these barriers influence women’s labor supply decisions? Whether policies directly addressing commuting costs would help increase female labor supply or not is far from obvious: social norms regarding household chores and traveling alone also significantly impact female labor supply, as evidenced by prior studies (Fanning Madden, 1981; Turner and Niemeier, 1997; Lee and McDonald, 2003; Abe, 2011; McQuaid and Chen, 2012).

Our study sheds light on the trade-offs women face when commuting cost is reduced by exploiting the rollout of a free busing scheme, the _Pink Slip Program_ , in two states of India. India is a pertinent setting for exploring this issue. There is an overwhelming gender gap in commuting time and modes used. The 2011 Census of India reveals that 30.2 % of women travel to work on foot, and only 24.6 % use any transportation, highlighting the limited access to transportation options for many women. In contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006). Concurrently, female labor force participation is low in India, at a rate of 28.2 % of the working-age population as of 2019, significantly lower than the average of 46 % in low- and middle-income countries.<sup>23</sup> We leverage the _Pink Slip programs’_ state-wide roll-out in Punjab and Tamil Nadu in April and May 2021, respectively. While Delhi also introduced this program in November 2019, data limitations preclude us from including it in our sample.<sup>24</sup> Several media

> 22India’s 2019 National Time-use Survey (https://mospi.gov.in/time-use-survey).

> 23World Bank’s Gender Data portal (https://tinyurl.com/4a8rm5ye). According to the most recent female labor force survey by India’s Ministry of Statistics and Program Implementation, it increased to 37% (http://tinyurl.com/mu2vx7m3).

> 24The CMIE, our data source in this paper, started collecting information on individual time usage after Delhi started implementing the _Pink Slip Program_ . So, we are unable to include Delhi in our sample.

1
properly functioning market. For instance, if the entry of foreign affiliates into downstream sectors creates increased demand for inputs, it may create incentives for indigenous firms to invest in product upgrading, cost-saving technologies or increased capacity, all of which may lead to better performance. The entry of foreign affiliates may also change the market structure and increase the level of competition in a manner similar, for instance, to trade liberalization leading to competitive externalities.

Econometric studies of _intra-industry_ spillovers from FDI are usually unable to distinguish between the various spillover channels. A typical study relates the total factor productivity of indigenous firms to some proxies for FDI presence in the same industry. This means that the empirical results capture both knowledge spillovers and competitive externalities. As noted by Aitken and Harrison (1999), if the increase in competition leads to local firms losing part of their market share and spreading their fixed cost over a smaller market, a negative correlation may be found between FDI presence and the performance of indigenous firms in the short and medium run. In the long run, the weakest performers exit, which then reverses the sign of the correlation. Thus, the conclusions of empirical studies about intraindustry spillovers from FDI depend on whether knowledge spillovers dominate competitive externalities or vice versa. This depends on the host country’s characteristics and the type of FDI it receives.

A study by Aitken and Harrison (1999) based on a panel of more than 4,000 18
3 shows, for rural areas, the main driver for the almost 7 percentage point reduction in poverty was the increase in consumption of food and non-food items, while higher asset ownership and improvement in dwelling characteristics played a minor role. In urban areas, consumption of food and non-food items contributed to a lesser extent to reduce poverty. However, the higher household consumption could not compensate for the increase in average household size, nor the deterioration of dwelling conditions. Further validating the urban results, we see that no change in an individual component of the model would alter the net poverty increase.

**Figure 3: Decomposition of changes in welfare using Urban and Rural models**

<!-- Start of picture text --> 6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>Rural Urban<br>Change in poverty percentage point<br>Assets<br>Consumption Household characteristics Regional dummies Dwelling characteristics Model: Constant Model: Error Net poverty change (p.p.)<br><!-- End of picture text -->

**Note** : Results use a Shapley decomposition on the different components of the prediction of the welfare vector. The different categories include the variables of the urban and rural model independently as shown in Table 3. Model error is the average error across the multiple imputations.

**Source:** IE-LFS 2019-20 and AWMS 2023 (R3).

# Discussion of economic trends in Afghanistan and their effect on poverty levels

Before socioeconomic data stopped being collected, close to half of the population in Afghanistan was living below the national poverty line. As shown in Figure 4, while the national poverty rate in 2019-20 was lower than the 55 percent estimated in 2016-17,<sup>14</sup> poverty trends were showing diverging patterns in urban and rural areas. In urban areas, the observed increase in poverty is the result of the compounding effect of the progressive decline in aid as well as the onset of the COVID-19 crisis, while in rural areas, poverty was on a declining path spurred by strong agricultural production in the 2019 and 2020 seasons.

> 14 World Bank (2018) _<u>Poverty in Afghanistan : Results based on ALCS 2016-17.</u>_ Washington, D.C. : World Bank Group.

15
7 regardless of a rate reduction. An evader might feel that, because of the lower rate, the government will be even more lax in pursuing evaders. Second, rate reduction should not imply any reduction in the provision of public services. Rather, the aim is to reduce rates while increasing the size of the base. In other words, a _revenue neutral_ adjustment is sought. Otherwise, rate reduction may be perceived as a reduction in the benefits from taxes paid. Other things being equal, such a reduction in benefits would increae resistance to paying tax.

21. The probability of detection may be increased in many ways, most of which are within the capabilities of tax administrations in LDCs. The identification of a full tax roll is the most promising. The registration of all individuals, businesses, and properties subject to taxation may be accomplished by door-to-door surveys, by using third party information (import licenses, property transfers, utility connections, motor vehicle registrations, and many other sources), and by integrating all tax records. This requires a unique taxpayer numbering system, which many low-income countries do not have.

22. Detecting under-reporting is as important as detecting non-filing. Under-declaration may be detected by developing a presumptive assessment system for the _hard-to-tax_ and for the selfemployed. A strong audit program is another important protection against under-reporting. Measures for increasing revenue from customs and excise duty include better inspection procedures and the use of an up-to-date import valuation data system. Underlying these proposals should be a stronger training program, computerization, and better advancement possibilities for the tax administration staff.

23. These detection methods will also reduce tax avoidance as taxpayers note the increased probability that _border line_ cases will be rejected. The added cost of preparing an adequate case will also discourage avoidance.

24. An increase in the use of penalties is essential to reducing evasion. In too many lowincome countries the penalty is nominal, or rarely applied. The interest penalty on the underpayment of tax can be below market rates, penalty on under-declaration of imported goods is often minimal, and, though property tax delinquency may be penalized with the seizure of property, this rarely occurs. In some countries court delays create further incentives. The problem is compounded where tax amnesties are common, or where non-compliance is settled out of court.

25. The key to better enforcement is government resolve to impose severe penalties on those - who do not pay. In most instances laws are adequate the problem is with enforcement. One way to cope with court delays is to make the taxpayer remit the assessed amount pending the legal decision. Another approach is to publicly announce the names of delinquents.
_Figure 8: Daily Wage by Education Attainment and Age Group_ <!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59<br>High School & High School & Vocational College<br>No vocational<br>Philippine Peso<br><!-- End of picture text --> Source: _LFS, October 2015, PSA_ 24
# **7.3 Effect on Gender Bias among Voters**

The relative weight that voters assign to leader competence compared to leader gender can increase not just through an increase in the weight that voters assign to competence, but also via a decrease in voters’ gender bias. This could occur if exposure to the DPEP made the average voter less male-biased due to improvements in their educational attainment.<sup>23</sup> Although in Section 7.4 we rule out that our results are driven by the increase in educational attainment of residents in DPEP districts, in this section, we indirectly examine the extent to which the DPEP led to a decline in voters’ gender bias against female politicians. To do so, we investigate the differential effect of the DPEP on women’s win probability in “decisive” versus “indecisive” state elections.

We first define a _constituency_ as having been won decisively if the victory vote margin lies in the highest quartile in the historical distribution of victory margins in all past elections in that constituency. We then define a _state_ election to be a decisive state election if the share of constituencies across the state being won with decisive margins in that election is above the historical median in the distribution of all past elections for that state. Defined in this manner, our “decisive state election” variable seeks to capture the strength of voters’ preference for a certain party in a state. In decisive state elections, candidate gender is less likely to matter to voters than their party affiliation even in the absence of the DPEP. In such states, even a relatively small decline in voters’ gender bias due to the DPEP may be sufficient to increase the probability of a female win. On the other hand, in indecisive state elections, a larger decline in voters’ gender bias is needed to observe an increase in the likelihood of a female candidate winning the election.

In Table A.5, columns (1) and (2) present estimates of program impact on women’s likelihood of winning in indecisive and decisive state elections, respectively. The results reveal that the increase in women’s probability of winning elections due to the DPEP that we have documented thus far occurs _exclusively_ in decisive elections. The coefficient estimate in column (1), although positive, is insignificant, whereas column (2) shows a statistically significant increase of 14.8 p.p. in the probability that women win in a decisive election environment. To the extent that states with decisive and indecisive elections do not differ in terms of the demonstrated positive impact of the DPEP, the heterogeneous effects in columns (1)-(2) capture the influence of DPEP-induced decline in voters’ gender bias on election outcomes.

> 23Alternatively, the DPEP may have increased female voter turnout relative to male voter turnout, which would, under the (reasonable) assumption that female voters tend to be less biased against female leaders, lower the average male-bias among voters. Unfortunately, the SHRUG data does not provide turnout by gender, preventing us from examining this channel directly.

30