rafmacalaba/datause-encoder-data huggingface dataset:
receive upon working, but women whose bargaining power is sufficiently high can flee violent marriages and thus do not face this increase in violence. While it is not possible to definitively rule out other stories that can explain these correlations, I provide evidence that some of the most natural alternative explanations – husband characteristics, women entering the labor force due to economic stress, and psychological backlash – do not explain the results in this paper. The results on age at marriag, though not education, are also inconsistent with a reverse causality story in which lower status women join the labor force to escape domestic violence at home.

These results point to several policy implications for policymakers interested in counteracting domestic violence. While an instrumental violence model can ultimately deliver an optimistic result – women whose outside option improves sufficiently will not face increased violence and may even face decreased violence – women whose bargainis low are at risk for increased violence. Therefore when there is the ing power initially expansion of new jobs in an area, particularly those jobs that hire low-skilled women whose status within the household is likely to be low, policymakers should consider complementary efforts to reduce domestic violence such as information campaigns. If these efforts are successful, communities can reap the benefits of increased female labor force participation without the adverse effects of domestic violence.

# **References**

- S.M. Ahmed. Intimate partner violence against women: Experiences from a womanfocused development programme in Matlab, Bangladesh. _Journal of Health, Population and Nutrition_ , 23(1):95–101, 2011.

- A. Aizer. The gender wage gap and domestic violence. _American Economic Review_ , 100(4): 1847–59, 2010.

- S. Anderson and M. Eswaran. What determines female autonomy? Evidence from Bangladesh. _Journal of Development Economics_ , 90(2):179–191, 2009.

- M. Angelucci. Love on the rocks: Domestic violence and alcohol abuse in rural mexico. _The BE Journal of Economic Analysis & Policy_ , 8(1):43, 2008.

- D. Atkin. Working for the future: Female factory work and child health in mexico. _Unpublished Manuscript, Yale University_ , 2009.

- L.M. Bates, S.R. Schuler, F. Islam, and M.K. Islam. Socioeconomic factors and processes associated with domestic violence in rural bangladesh. _International family planning perspectives_ , pages 190–199, 2004.

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disbursements is only 31.9% (see Table S1.1 and the surrounding text in the supplemental appendix). For the majority of observations, the scaling that is performed in the final step is limited in magnitude and is substantially smaller than if the CRS sectoral disbursements were scaled without any adjustment. For instance, for more than three-quarters of the observations, the CRS disbursements constitute less than half of the aggregate aid. The constructed sectoral disbursements constitute less than half of the aggregate aid for fewer than 10% of observations. Thus, the sectoral allocation of the aid data before scaling is more likely to provide a reasonable reflection of the actual sectoral allocation that one would find if the data were complete. The failure to scale the sectoral disbursements would increase the risk of underestimating the amount of aid received.<sup>12</sup>

# **Empirical model and other data**

First, I consider models that do not distinguish between on- and off-budget sectoral aid: for _i_ = 1 _, . . . , N_ and _t_ = 1 _, . . . , T_ . _SSPit_ denotes recipient government spending on education or health, whereas _SAIDit_ are disbursements that are earmarked for the same sector. **_A_** _it_ and **_X_** _it_ contain other aid variables and control variables that are described below. _λt_ is a set of year dummies, _ηi_ captures country-specific time-invariant effects, and _εit_ is the transient error. Aid and spending variables are expressed as percentages of GDP.<sup>13</sup> High-income countries (2005 GNI per capita of 10726 US$ or more, following World Bank, 2006c) are eliminated from the sample. I begin with a static panel data model similar to that employed by cross-country fungibility studies that utilize information on the intended purpose of aid, particularly Feyzioglu et al. (1998) and Devarajan et al. (2007). This allows for an easier comparison of the results. Later in the paper, I briefly discuss the results from more general models that allow for some dynamics.

I focus on education and health for a number of reasons. First, education and health play a prominent role in the Millennium Development Goals (MDGs). In addition to their importance in the first goal, which involves eradicating extreme poverty and hunger, several other goals explicitly establish targets related to education and health. This suggests that donors have preferences for education and health spending and should be concerned about the extent of fungibility in these sectors. Second, as partially evidenced by their prominent role in the MDGs, there is a widespread belief that better education and health have immediate consequences for human welfare and play important roles in spurring development and alleviating poverty. This belief suggests that the fungibility of aid that is directed toward these sectors may be relevant for the welfare of the population in recipient countries and may influence the overall effectiveness of aid. Third, these areas are rather clearly defined areas of spending, which should increase the definitional overlap between sectoral aid and sectoral spending.

9
Perceptions of women’s performance may also not accurately reflect their actual performance (Beaman et al 2009).

A lab experimental literature presents some related evidence that women either perform worse, or display some different innate attitudes when they are in mixed-gender environments compared to single-gender environments (Booth and Nolen 2012a, 2012b). In contrast, testing our hypotheses required us to set up a large-scale field experiment, because our core idea is that women’s relative performance may change when they move from single-gender or isolated environments (where true innate ability is expressed in private tasks), to a field environment where they have to interact with others to complete the task successfully.

We find that women’s performance drops in relative terms when they are reliant on others in the field. We use micro data on individual interactions collected from around 4,000 farmers to explore alternative hypotheses and underlying mechanisms. It is not simply the case that learning and teaching are different skills and women are better learners than they are teachers. Direct measures of teaching performance indicate that women put more effort into teaching, and farmers they teach experience better yields compared to those in male communicator villages. This does not seem to be about cross-gender frictions in communication either: Nearly half of all maize plots in our sample villages are farmed by women, and the gender gaps remain regardless of whether we examine the responses of other male or female maize farmers.

We do observe that both male and female maize farmers perceive male communicators to be more knowledgeable about agriculture, although by our objective measures those men are no more knowledgeable about the new technology introduced through the experiment. Accordingly, other farmers do not pay as much attention to female communicators. Biases in perception and attention appear to be the most likely underlying causes of the patterns we document.

We also find that adding performance-based incentives encourage communicators to intensify their interactions with both male and female farmers, and eliminates the gap in the socially mediated task. This is not because the incentives lead women to exert differentially more effort in the private task of learning about the technology. In fact, the incentives improve the relative performance of male communicators in the learning task, leaving their knowledge indistinguishable from that of female communicators, and erasing the yield gap. The resulting 4
_winning if and only if_ ∣ _T_ 1 ∣>∣ _T_ 3 ∣ _. Voters’ preferences are defined as follows:_ _It follows that the social ranking induced by the truthful vote s_<sup>∗</sup> ( _R_ ) _over is given by: F_ ( _s_<sup>∗</sup> ( _R_ )) = { _x_ 1 _x_ 2 _,x_ 1 ∗ _x_ 3 _,x_ 2 _x_ 3} _Therefore, the outcomes of the social ranking are the following: x_ 1 _is ranked above x_ 2 _, x_ 2 _is ranked above x_ 3 _, and x_ 1 _and x_ 3 _are socially incomparable. A probabilistic tie-breaking rule can consist of a fair coin toss, which gives an equal probability to either x_ 1 _or x_ 3 _being socially ranked above the other. It may also consist of choosing the candidate that is given priority following a certain criterion (determined, for example, by the alphabetical order of names, the preference of the election chairperson,_<sup>14</sup> _candidate seniority, or a random or non-random order chosen before the vote takes place). Note that because the tie-breaking rule is state-dependent in our model, a different rule may be used for each pair of candidates._

# **3 A ranked-choice election as a strategic form game**

In this section, we model a ranked-choice election as a strategic form game. Before diving into it, we first need to recall some preliminary definitions.

A strategic form game is a triplet ( _N,_ ( _Si_ ) _i_ ∈ _N ,_ ( _ui_ ) _i_ ∈ _N_ ), where _N_ is a finite set of players, _Si_ is a non-empty set of strategies of player _i_ ∈ _N_ , and _ui_ ∶∏<sup>→Risthe</sup> _j_ ∈ _N_<sup>_Sj_</sup> payoff function of player _i_ ∈ _N_ . We denote by _S_ = ∏<sup>thesetofstrategyprofilesand</sup> _j_ ∈ _N_<sup>_Sj_</sup> _s_ = ( _si,s_ − _i_ ) ∈ _S_ the strategy profile where player _i_ chooses the strategy _si_ and the other players choose the strategies _s_ − _i_ .

A strategy profile _s_<sup>∗</sup> ∈ _S_ is a pure strategy Nash equilibrium of a game ( _N,_ ( _Si_ ) _i_ ∈ _N ,_ ( _ui_ ) _i_ ∈ _N_ ) if, for all _i_ ∈ _N_ , _ui_ ( _s_<sup>∗</sup> _i_<sup>_,s_</sup> −<sup>∗</sup> _i_<sup>) ≥</sup><sup>_ui_(</sup><sup>_si,s_</sup> −<sup>∗</sup> _i_<sup>),forall</sup><sup>_si_∈</sup><sup>_Si_.Thatis,forall</sup><sup>_i_∈</sup><sup>_N_,wehave</sup> A strategy profile _s_<sup>∗</sup> ∈ _S_ is Pareto-efficient if there is no other strategy profile _s_ ∈ _S_ such that _ui_ ( _s_ ) ≥ _ui_ ( _s_<sup>∗</sup> ) for all _i_ ∈ _N_ with _ui_ ( _s_ ) > _ui_ ( _s_<sup>∗</sup> ) for some _i_ .

> 14In the U.S. Senate, the tie-breaker is the Vice-President.

11
# **VI. An application to India**

This section illustrates the ideas introduced in the preceding sections of the paper with an application to India using data from the Third National Family Health Survey (NFHS3) for 2005-6. In part, the motivation for using these data is that they have recently been used by Alkire and Seth (2009, 2013) for an analysis of multidimensional poverty in India following the AF methodology, and thus offer a useful point of comparison. The empirical implementation thus closely follows Alkire-Seth definitions of dimensional indicators, cut-offs and weights (as set out in Table 4.1 in Alkire and Seth, 2013).<sup>27</sup> In brief, Alkire-Seth use a slightly modified version of the UNDP MPI framework for India, under which there are 10 unequally-weighted indicators or dimensions, each represented by a binary variable indicating the presence or absence of the particular deprivation for a person. Amongst the 10 indicators, there are two for education with weights of 1/6<sup>th</sup> each, two for health also with weights of 1/6<sup>th</sup> each, and six indicators related to the standard of living each with a weight of 1/18<sup>th</sup> .<sup>28</sup> The cross-dimensional cut-off is set at 1/3; thus, a person is deemed poor if deprived in at By comparis-third of weighted dimensions. With this setup, multidimensional poverty measures ���, �; �� and ���, �; �� are constructed below for ��1/3 and different values of � .<sup>29</sup> The illustration aims to highlight five points as noted in the introduction.

_(i) A significant fraction of multidimensional poverty can be potentially missed by not counting deprivations of those below the cross-dimensional cut-off._ Table 1 reports multidimensional poverty measures both in the aggregate as well as disaggregated by the breadth of dimensional deprivation. The estimates show that about 47% of the Indian population are deprived in one-third or more of the 10 weighed dimensions and are thus deemed to be poor under the dual cut-off approach. However, there is another 41% of the population who also experience deprivations, albeit in less than one-third of weighted dimensions. If these deprivations were to be counted in, multidimensional poverty would be 29% higher, rising from 0.243 (the value of ���, ��1⁄3� ) to 0.314 (the value of ���, �� 1� ). The deprivations of those deemed non-poor by the dual cut-off account for 22 and 10 27
# **2.1** Basic Structure

The urban labor market in developing economies is generally well described as consisting of two sectors, a formal and an informal one, with distinct functioning modes. The segmentation hypothesis has important implications for the functioning of the labor market. For instance, high pay formal sector jobs are often rationed and, despite an excess supply of labor in the low pay informal sector, wages do not adjust. Note that many studies have also suggested that the two segments are highly heterogeneous-in the nature of occupations, in the production mode, in the sources of income, and so on. The view that the informal sector is competitive has received mixed support from the empirical evidence. In fact, in some countries many activities classified in the informal sector present barriers to entry. For instance, access can be conditioned by customs practices and relatively strict unwritten rules. This appears to be the case for many crafting activities in Morocco.

Despite the lack of comprehensive micro-level data on enterprises at the national level, the common view of Morocco's -urban labor market is that of a dual segmented market. Within this framework, there have been attempts to measure the respective size of the two segments. For instance, the civil servants (central government and local governments), workers in public enterprises and those covered by the social security scheme (CNSS) constitute the formal sector. The informal sector corresponds then to the residual.<sup>2</sup> Other studies have shown that the Moroccan urban labor market is highly segmented, and that its functioning is far from competitive.<sup>3</sup> Non-compensating wage differentials between workers with similar productive endowments are pervasive: higher gains are frequently observed when workers (even unskilled ones) are located in particular segments. In fact, despite the existence of excess labor supply, wages in the formal sector do not adjust, and the informal sector is not absorbing all the job seekers. Within this context of restricted labor mobility and specific participation dynamics, the sector of occupation is a key determinant of an individual's remuneration and standard of living. Moreover, surveys show that private firms are willing to pay a premium-as suggested by efficiency wage theories-to secure workers' commitment.

> 2See, for instance, Belghazi (1998) and Touhami (2003).

> 3Empirical tests by El Aynaoui (1998) confirm the prevalence of the segmentation hypothesis.

7
Table 5: Adjusted Budget Deficit and State Debt, 1996-1998

|Adjusted enlarged budget deficit(+)/GDP<br>(percent)"at|03<br>0.3|-. 1<br>-0.1|-.<br>4.5|
|---|---|---|---|
|Net new public borrowing/GDP<br>(percent)b<br>_i57_|76<br>7.6|57|1.<br>181|

Implicit subsidies deducted. Federal government borrowing only. Source: Authors' estimates.

# **Energy Monopolies**

The pricing, taxation and regulation of Russia's vast energy monopolies is not simply a fiscal issue, it is an issue of economy-wide import. Hence, transparency is vital, but this is damaged by the nonpayments system in costly ways, as we shall see below. As noted in the previous section, enterprises received significant subsidies from the Gazprom, the gas monopoly, and RAO UES, its counterpart in electricity.

# _Gazprom_

The typical composition of Gazprom's cash sales is illustrated by its results for 1997.

Table **6:** Gazprom Sales and Cash Collection Ratio, 1997

|Marke<br>|:**Dellveries**|**Price**|**Sales**|**Carcis**|**s rec./Sales**|
|---|---|---|---|---|---|
||**(bi"lion m)**|($1000**c)**|**($ iion)**|**( miim**<br>**o%)**||
|**Europe**|121|88.5|10,707|10,707|100|
|CIS|64|76.8|4,937|2,855|58|
|**Domestic**|301|47|11,536|1,730|15|
|**T tal**|t<br>**4860**0||**20T067,1**<br>n|**80**<br>**15,292**|**56**|

_Source:_ Company data, Customs Committee, Brunswick Warburg, Morgan-Stanley Dean Witter Research, staff calculations From the table, one can calculate that even though domestic shipments exceeded 60 percent of total gas deliveries in 1997, they accounted for only 42 percent of sales, and a mere 11 percent of total cash receipts. Table 7 shows that the average annual implicit subsidy passed on by Gazprom to domestic customers as a result of unrecoverable arrears and inflated NCS prices was about 1.5 percent of GDP per year over 1993-1997, a cumulative total of $26.2 billion.<sup>24</sup> Electric power utilities account for approximately 50 percent of Gazprom's overdue receivables, followed by chemical industry - 7 percent, and ferrous metallurgy - 5 percent.

> 23 We do not include tax offsets as these do not have a direct debt impact (only through pre-emptive tax arrears), although they constitute a subsidy and adversely affect the quality of public spending.

> 24 Pinto, Drebentsov and Morozov (2000). Note that with regulated gas and electricity prices, the energy monopolies cannot in turn inflate their prices for in-kind settlements. Note further that gas prices in Russia are a fraction of those in central Europe, where western prices apply.
# **Winners and Losers from COVID-19: Global Evidence from Google Search**

Kibrom A. Abay, Kibrom Tafere, and Andinet Woldemichael*

**Keywords** : Covid-19, pandemic, coronavirus, Google trends, online search, consumer demand **JEL codes** :  D12, F01, I12, L86.

___________________________________________________________________________

*Kibrom A. Abay:  International Food Policy Research Institute, k.abay@cgiar.org; Kibrom Tafere: World Bank, ktafere@worldbank.org; Andinet Woldemichael: African Development Bank, <u>a.woldemichael@afdb.org. We thank Michael Toman and John Friedman for useful</u> comments.
Table 5– Net Migration Flows by Governorate

|Governorate|Net Migration Flows|Governorate|Net Migration Flows|
|---|---|---|---|
|Port Said|36.5%|Beni Suief|-1.7%|
|Suez|35.7%|Cairo|-2.4%|
|Red Sea|19.4%|Luxor|-2.4%|
|6 October|18.2%|Fayoum|-3.5%|
|Ismailia|17.4%|Beheira|-4.0%|
|North Sinai|9.5%|Gharbeyya|-4.8%|
|Qalubia|8.8%|Aswan|-4.8%|
|Giza|8.6%|Dakahlia|-4.9%|
|Alexandria|7.2%|Minya|-4.9%|
|Helwan|6.2%|Qena|-5.0%|
|Matrouh|4.6%|Menoufia|-5.1%|
|New Valley|1.6%|Sharqia|-5.4%|
|Kafr el-Sheikh|0.3%|Assyut|-6.0%|
|South Sinai|0.0%|Sohag|-6.0%|
|||Damietta|-7.2%|

Source: Authors’ calculations using Labor Force Survey Urban governorates, new cities, Lower Egypt and Frontier governorates are the destinations for most migrants, the majority of whom originated in Cairo and Upper Egypt governorates.

# **Reasons for Migration**

The LFS asks respondents the reasons for migrating, and allows several possible responses: for work, for education, for marriage, to accompany others, or other reasons. Table 6 presents the distribution of migrants, by gender, according to the reason for migrating. The majority of internal migrants (40.4 percent) change localities to accompany someone. Marriage is the reason behind 27.3 percent of migrations, followed by employment (23.36 percent).

While most men migrate to work (45.5 percent) or accompany a migrant (32.2 percent), women migrate because of marriage (45.3 percent) or to accompany a migrant (48.6 percent).

Table 6 – Reasons for Internal Migration by Gender

|Reason for Migration|Male|Female|Total|
|---|---|---|---|
|For Work Only|45.4|1.0|23.4|
|Education|1.7|0.7|1.2|
|Marriage|9.5|45.8|27.6|
|Accompany|32.3|48.7|40.4|
|Others|11.1|3.9|7.5|
|Total|100|100|100|

Source: Authors’ calculations using Labor Force Survey 7
## ONLINE APPENDIX

# **Bring a Friend: Strengthening Women’s Social Networks and Reproductive Autonomy in India**

S Anukriti, Catalina Herrera-Almanza, and Mahesh Karra

# **A Variable**

1. Sought company to visit the ADC from someone: Indicator variable that equals one if a woman asked someone to accompany her to the ADC since September 2018. This variable is not conditional on visiting the ADC.

2. Sought company to visit the ADC from husband or mother-in-law: Indicator variable that equals one if at least one of the individuals that the woman invited to accompany her to the ADC is her husband or her mother-in-law.

3. Sought company to visit ADC from someone other than husband or mother-in-law (non-husband/mother-in-law): Indicator variable that equals one if at least one of the individuals that the woman invited to accompany her to the ADC is neither her husband nor her mother-in-law.

4. Sought company to visit ADC from sister-in-law: Indicator variable that equals one if at least one of the individuals that the woman invited to accompany her to the ADC is her sister-in-law.

5. Sought company to visit ADC from a non-relative: Indicator variable that equals if one of the individuals that the woman invited to accompany her to the ADC is a friend, neighbor, or any other peer that is not her relative.

6. Number of peers sought to visit the ADC: The number of people a woman asked to accompany her to the ADC since September 2018.

7. Visited the ADC for FP services: Indicator variable that equals one if a woman visited the ADC to obtain family planning services since September 2018.

8. Visited any clinic for FP services: Indicator variable that equals one if, since September 2018, the woman visited any health facility to obtain family planning services.

9. Visited the ADC alone: Indicator variable that equals one if a woman went alone to the ADC in all the visits she mentioned since September 2018. This variable equals zero either when the woman went with someone else in at least one FP visit or when she did not visit the ADC.<sup>58</sup>

> 58Phone surveys are included in this variable.

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**Figure 5.1 Average and Maximum Throughput in OECS and Other Caribbean Countries (TEU per hour)**

_Source:_ Authors’ elaboration on data provided by the Florida Shipping Association. _Note:_ TEU = twenty-foot equivalent unit.

# **b. Performance**

**Among the most salient performance issues in OECS are high labor costs and a work philosophy that stems from the break-bulk era.** The size of the gangs in the break-bulk era ports (St. John’s, Roseau, and Long Point) is between two and three times larger than for those newer ports that are structured physically and institutionally to handle containerized ports (Vieux Fort and Campden), despite their shockingly low berth utilization (table 5.3). Another important source of delays and inefficiencies is the practice of inspecting all—that is, 100 percent of—import and export containers rather than drawing a sample to

33