rafmacalaba/datause-encoder-data huggingface dataset:
Table 7: Heterogeneous impacts

|Variables|Skilled|Unskilled|Men|Women|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Panel A: Income_|||||
|T*Post|0.437***|0.264|0.337**|0.612**|
||(0.143)|(0.291)|(0.154)|(0.224)|
|Observations|4,906|1,008|4,216|1,698|
|R-squared|0.636|0.639|0.638|0.655|
|_Panel B: Employment_|||||
|T*Post|0.044**|0.011|0.039**|0.040**|
||(0.017)|(0.021)|(0.018)|(0.020)|
|Observations|15,373|2,299|8,445|9,227|
|R-squared|0.777|0.599|0.792|0.749|

Notes: All regressions include household and individual controls, and district fixed effects. T is a dummy equal to 1 for individuals in the treatment group. Skilled workers are individuals who have completed middle school education or higher. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 30
**Table 3. Oaxaca and Blinder decomposition of the gender gap in monthly wages**

|**Wage**|**All**|**Teenagers**|**Young**|**Prime aged**|
|---|---|---|---|---|
||||**adults**||
|**Overall**||Panel|<br>(a)||
|**Men**|5.971<sup>***</sup>|5.924<sup>***</sup>|5.963<sup>***</sup>|5.992<sup>***</sup>|
|**Women**|5.869<sup>***</sup>|5.865<sup>***</sup>|5.850<sup>***</sup>|5.886<sup>***</sup>|
|**Difference**|0.102<sup>***</sup>|0.059|0.113<sup>***</sup>|0.106<sup>***</sup>|
|**Explained component**|-0.066<sup>***</sup>|0.013|-0.081<sup>**</sup>|-0.060<sup>**</sup>|
|**Unexplained component**|0.168<sup>***</sup>|0.045|0.194<sup>***</sup>|0.166<sup>***</sup>|
|||Panel|(b)||
|**Explained component**|||||
|**Log of weekly hours**|0.012<sup>***</sup>|0.007|0.014<sup>**</sup>|0.022<sup>***</sup>|
|**Human capital**|0.002|-0.013|0.001|-0.005|
|**Self-employment**|0.041<sup>***</sup>|0.082<sup>***</sup>|0.036<sup>***</sup>|0.025<sup>***</sup>|
|**Migration**|-0.005|0.000|-0.003|-0.010|
|**Search method**|-0.018|0.076|-0.027|-0.008|
|**Type of contract**|-0.041<sup>***</sup>|-0.047|-0.022|-0.040<sup>***</sup>|
|**Moonlighting**|0.011<sup>***</sup>||0.014<sup>**</sup>|0.007<sup>*</sup>|
|**Training**|-0.019<sup>**</sup>|-0.016|-0.027<sup>**</sup>|-0.017<sup>*</sup>|
|**Union membership**|0.002|-0.009|0.003|0.005|
|**Firms’ size**|-0.053<sup>***</sup>|-0.066|-0.070<sup>***</sup>|-0.042<sup>**</sup>|
|**Rural area**|0.002|0.000|-0.001|0.004|
|||Panel|(c)||
|**Unexplained component**|||||
|**Log of weekly hours**|0.761<sup>*</sup>|-1.052|-0.017|1.378<sup>**</sup>|
|**Human capital**|0.103|0.300|0.278|0.540|
|**Self-employment**|0.001|0.001|-0.008|0.016|
|**Migration**|0.007|-0.015|-0.005|0.018|
|**Search method**|-0.222<sup>*</sup>|-0.362|-0.556|-0.076|
|**Type of contract**|0.091<sup>***</sup>|-0.034|0.063|0.132<sup>*</sup>|
|**Moonlighting**|0.008<sup>**</sup>||0.010|0.006|
|**Training**|-0.114|0.455|0.000|-0.352|
|**Union membership**|0.022|-0.042|-0.005|0.034|
|**Firms’ size**|0.014|0.296|0.127|-0.097|
|**Rural area**|0.013|-0.023|-0.011|0.013|
|**Constant**|-0.514|0.522|0.318|-1.445<sup>*</sup>|

_Note: * p<.1; ** p<.05; *** p<.01._ _Human capital: General secondary education, Secondary general, Specialized secondary, University, potential work experience, Work and study while at school; Migration: Immigrant with family, Immigrant looking for a job, Immigrant for other reasons; Job search: Search through the educational institution, Search through job fairs, Search through the public employment services, Direct search, Search through the internet, Search answering job advertisements, Search through informal network or friends and relatives; Type of contract: Unlimited, Seasonal; Tax type: no taxes, don’t know whether paid taxes on his/her job; Training type: Apprenticeship, In new technologies, Other; Frms’ size: 5 or less, 5 trough 9, 10 trough 19, 2 or plus._ _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 28
coefficient estimate from our preferred specification indicates that a one standard deviation increase in bailout exposure leads to a 69% increase in the probability that a district experiences the share of non-performing loans (ˆ _γ_ =0.691, s.e.=0 _._ 385), and a 64% increase in the probability that a district experiences an increase in the share of non-performing credit (ˆ _γ_ =0.642, s.e.=0 _._ 355) in the post-program period.

Table VI, columns [3] and [4] verifies that the results on loan performance also hold at the bank-district level using the fixed effects specification described in equation 4.5. The results are again consistent with those found in our preferred specification. Banks that received a greater share of bailout funds are significantly more likely to experience an increase in defaults after the program, and districts in which bank branches were more exposed to the bailout experience a decline in loan performance after the program. It is important to note that this decline in loan performance happens after loans that were in default have been written off and cleared from banks’ books. Given that Table IV columns [1] to [4] show no evidence of new lending, we conclude that the negative effect of the bailout on loan performance is driven by defaults among borrowers that were previously in good standing. Table VI, columns [5] and [6] estimate the size of this effect using bank-district level data and suggest that it is substantial. The point estimates indicate a one standard deviation increase in bailout exposure is associated with a 1.6% increase in the share of non-performing loans (ˆ _γB_ =0.016, s.e.=0 _._ 003) and a 2.4% increase in the share of non-performing credit (ˆ _γB_ =0.024, s.e.=0 _._ 003) over the sample mean.

Taken together, these results point to substantial borrower moral hazard. First, loan performance after the program deteriorates in districts with greater bailout exposure even though banks reallocate credit towards observably less risky districts. Second, because no new lending took place and non-performing loans covered by the program were removed from banks’ books, post-program defaults must be concentrated among borrowers that were previously in good standing (and therefore not covered by the bailout). The program thus 21
major driver of the economy, contributing 40% of GDP in 2005, and employing two-thirds of the workforce.<sup>5</sup><sup>_,_6</sup> A large number of Nepalis have migrated to other countries in search of economic opportunities and remittances constituted 12% of GDP in 2004. Politically, Nepal was a monarchy till 1990, when widespread protests led to the establishment of a multi-party democracy. The first parliamentary elections were held in 1991, and two further general elections were conducted in 1994 and 1999. Even though the Nepalese Congress obtained a majority in the elections of 1991 and 1999, Nepal has faced considerable political instability and governments have been short-lived. Since 1991, there have been 12 governments in as many years.

The Maoist insurgency was started on February 13, 1996, by members of the Communist Party of Nepal-Maoist (CPN-M), with an attack on a police post at Halleri in Rolpa district of Western Nepal. The genesis of the insurgency in the districts of Rolpa and Rukum has been attributed to several factors, including the poverty and general underdevelopment of the area, long-standing grievances against the government (for banning the cultivation of hashish in the 1970s, and the crackdown on CPN-M activists during 1994, among other issues), and a long-standing presence of Communist activists in the area (Gersony, 2003). Since 1996, this “People’s War” has spread to most other districts of Nepal, with 73 out of 75 districts reporting casualties in 2004. The Maoists have carried out several attacks on army barracks, police posts, village council buildings and banks.

The chief objectives of the activist movement were to establish a people’s republic and set up a constituent assembly to draft a new constitution. In particular, this would mean curtailing some or all of the existing powers of the monarchy. The 75 point manifesto released by the Maoists in November 2001 listed several other aims, including the distribution of land to poor and landless people, equal treatment for all castes and languages, and equal rights for women. Despite being named after Chairman Mao, the activists’ activities have been condemned by the Chinese government, and there is no evidence that the activists enjoyed any support from China (Raj, 2004). However, they were believed to have established links with

> 5Nepal Data Profile 2005, the World Bank.

> 62001 Census.

4
<!-- Start of picture text --> How costly are austerity measures in deepening recessions? To what extent<br>does a country’s degree of indebtedness affect the impact of stimulus and<br>austerity? What are the merits of using tax policy relative to expenditure<br>measures in using fiscal policy for stabilization- or debt-reduction purposes?<br>These important policy questions have stimulated a significant revival<br>of the empirical study of the effects of fiscal policy on economic activity.<br>This literature has focused primarily on the United States or other OECD<br>countries.<br>In a recent contribution, Izetzki, Mendoza and Végh (2010) (IMV) as-<br>sembled high-frequency data on government purchases for a large sample<br>of countries. IMV then estimate the effects of government expenditure on<br>output using structural vector autoregressive (SVAR) methods. We extend<br>their evidence along two dimensions. First, we present a new dataset on tax<br>revenues, tax rates, and tax elasticities for a sample of 28 countries, most<br>of which are less-developed. This new dataset allows us to jointly estimate<br>the effects of taxes and government purchases on output using panel time-<br>series econometric methods. Second, using methods introduced by Favero<br>and Giavazzi (2007), we explore the degree to which government indebted-<br>ness affects the impact of fiscal policy.<br>An outline of the paper, and its main findings, can be summarized as<br>follows. In Section 2, we outline the new dataset and discuss the methodology<br>used to calculate average marginal tax rates and tax elasticities. Section 3<br>is a preliminary exploration of the data, where we use panel data methods<br>(OLS and Arellano-Bond GMM estimates) to estimate the effects of tax<br>cuts on economic output. We find that a cut in personal income taxes<br>by 1 percentage point increases GDP by 0.16%. The effect is more<br>substantial in developing countries, where GDP increases by close to<br>0.4% in response to a similar tax cut. Results regarding corporate- and<br>3<br><!-- End of picture text -->
Djibouti is a small country in East Africa with limited economic diversification. In the last two decades, economic growth in Djibouti has been driven by direct foreign investment and public sector-led infrastructure investment, both of which have had limited trickle down effects on job creation and poverty reduction among large portions of the population. Based on the international poverty line for lower-middle-income economies with a daily wage of $3.20 (in USD 2011 PPPs; World Bank, 2019), approximately one-third of Djibouti’s population lives in poverty. The country also suffers from a high level of food insecurity (World Food Program, 2022) and very low human development outcomes compared to other lower middle-income countries. One-third of Djiboutian children are underweight or stunted, and maternal mortality and child mortality are higher than in neighboring and economically comparable countries (World Bank, 2018). Only 60 percent of the population aged 15 years and older is literate, and net enrollment in primary school is only 66 percent (from the 2019 World Development Indicators).

In Djibouti, both women and men have extremely low literacy levels, but the former are less literate than the latter (43 and 60 percent, respectively; World Bank, 2019). Although safety nets exist, their coverage is limited. According to the 2017 Djiboutian Household Survey (Enquête Djiboutienne Auprès des Ménages [EDAM]), only 11 percent of all adult women are employed. Women with some post-primary education are marginally more likely (13 percent) to be working than their peers with less schooling. Working women with more education are also much more likely to be employed within the public sector (73 percent with any post-primary education and 19 percent with only primary education). The same is also true for men. Overall, the public sector employs 78 percent of working adults with at least some senior secondary education (EDAM 2017, authors’ calculation).

Within the poor and highly food insecure context described above, a drought that occurred in late 2011 and into 2012 resulted in significant loss of Djibouti’s population’s livelihood and food security. In response, the government shifted its focus toward developing an emergency response system to provide short-term income support to poor households facing unemployment shocks and high food insecurity, while simultaneously promoting short- and long-term human capital formation. Part of this effort entailed the public works program we study here, which was rolled-out in select poor neighborhoods of Djibouti City first and then successively expanded to other urban and rural areas. The primary target population for this program was all households with pregnant women and mothers of children aged 0-2 years.

In the first part of the program, eligible women from participating households joined community-based child and maternal nutrition sessions. Each session included a maximum of 20 women, was held within walking distance from the participants’ homes, and was led 6
Table 1: The impact of robotization on North-South trade

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1534**|0.4117***|-0.2583***|0.6144***|1.1839***|-0.5695***|
||(0.0690)|(0.1238)|(0.0684)|(0.2156)|(0.3146)|(0.1529)|
|First stage:|||||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||||0.0027***<br>(0.0003)|0.0027***<br>(0.0003)|0.0027***<br>(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6351|0.7738|0.3912|0.6314|0.7655|0.3887|
|Kleibergen-Paap rk Wald F-stat||||111.795|111.795|111.795|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
1. The intensity of screening increases with distance: at the margin, borrower characteristics should improve as distance increases, while the predictions for the average borrower are ambiguous.

2. A pre-loan visit by MFI o¢ cials is evidence of adverse selection: across borrowers the probability of a pre-loan visit is independent of distance.

3. Post-loan monitoring visits by MFI o¢ cials are evidence of moral hazard: ceteris paribus, monitoring is less likely to occur as distance increases.

4. In the moral hazard context: If the informational rent is nondecreasing with distance: rates of default increase with distance, while predictions on monitoring and interest rates are ambiguous. If the informational rent is nonincreasing with distance: monitoring decreases with distance and interest rates increase with distance, while predictions on default are ambiguous.

# 3.1 Data

Data were collected in July 2005. The speci...city of the data is that they both include information on the MFI’s side, and the socio-economic information on clients: this approach allows to record information on clients’ assets, education, and household composition, which are not collected by the MFI. The questionnaire was also especially designed to look at the issue of distance.

The population of reference consists of MFI’s clients in 5 of the 7 regions of Niger. Among the 59 MFIs with an authorization of the Ministry of Finance to o¤er micro...nance services (BCEAO, 2002), 10 were chosen based on availability and regional location. Moreover, as the focus of the present study was the investigation of the impact of distance on MFI performance, sampling of borrowers has been strati...ed by distance. Each sampled MFI branch was asked to sample clients by stratum. Of all the applications received by these 10 MFIs in the last 5 years, 191 loan applications were selected as survey sample. Those applications were requested according to di¤erent distance ranges to individuals and groups. Subsequently, a group and household questionnaire would be administered according to the information provided by the MFI to locate its clients. The ...nal sample consists of 161 clients who have had a ...nancial relationship with 10 MFIs. Statistical weights were applied at the 3 levels of strati...cation: di¤erent regions of 10
# **Appendix S4: Overview of Uganda as Illustrative African economy**

Uganda faces many core challenges common across low-income African subsistence economies. Some key characteristics are described here. These draw from a range of sources, mainly published during the course of the early 2000s, and thus present a thematic overview rather than a precise snapshot at a single point in time. The data also predate Uganda’s recent commencement of oil production.

As of 2012, approximately 35 percent of Uganda’s population still lived below the international extreme poverty line of $1.90 per day in 2011 purchasing power parity terms (World Bank 2017). The vast majority of the country’s poverty is concentrated in rural areas, where most Ugandans are engaged in crop agriculture. Infrastructure is limited. Only approximately 10 percent of households had electricity as of the early 2000s (Okidi et al. 2005). For decades, gross domestic saving rates were extremely low, well below 10 percent of GDP, although they averaged a slightly higher 13.1 percent from 2006-2015 (World Bank 2017). A 1997 Bank of Uganda survey found that fewer than a quarter of rural Ugandans had ever saved and that 85 percent of the other three-quarters cited low income as the primary factor for not doing so (Musinguzi and Smith 2000).

# **i. Staple Food Agriculture**

Uganda’s staple agriculture sector has experienced general long-term stagnation. Figure S4.1 presents trend data for cereal production per capita from 1961 to 2014. From a peak of nearly 180 kilograms per person in 1969, output has been stagnant, at less than 100 kilograms per person, since the early 1980s. Figure S4.2 indicates similar trends for a broader index of food production per capita.

Siriri, Bekunda and Jama (2005) find that yields are typically one-quarter to one-tenth of current potential. This is significantly driven by the low usage of modern farm inputs. Fewer than a third of agricultural households use improved seeds, and only 8 percent use inorganic fertilizer (Okoboi and Barungi 2012). Yet despite the stagnation, Uganda has not become a marked food 57
Policy Research Working Paper 6158

# **Abstract**

This paper addresses the conditions under which donor and non-state actor service provision is likely to undermine or strengthen citizens' legitimating beliefs. On the one hand, citizens may be less likely to support their government with quasi-voluntary compliance when they credit non-state actors or donors for service provision. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens' confidence in their government and their willingness to defer to governmental laws and regulations if citizens believe that the government is essential to leveraging and managing these resources. The author assesses these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows for analysis of associations between donor and non-state actor service provision and the sense of obligation to comply with the tax authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state.

This paper is a product of the Poverty Reduction and Economic Management Departyment, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at asacks@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Lost in Interpretation: Why Spouses Disagree on Who Makes Decisions**

**Sundas Liaqat, Aletheia Donald, Forest Jarvis, Elizaveta Perova and Hillary C. Johnson** r

Keywords: intra-household, measurement, decision-making, gender, Philippines JEL codes: C8, D13, J16, I32 Liaqat: <u>sliaqat@worldbank.org;</u> Donald: <u>adonald@worldbank.org;</u> Jarvis: <u>fjarvis1@worldbank.org;</u> Perova: <u>eperova@worldbank.org; Johnson: hjohnson1@worldbank.org. This paper is a product of the World Bank’s East Asia</u> and Pacific Gender Innovation Lab and Africa Gender Innovation Lab. The authors would like to thank Sonia Akter, Cheryl Doss, Agnes Quisumbing and participants at the Midwest Economic Association Annual Meeting and the Annual Meeting of the Society of the Economics of the Household for valuable comments on earlier versions of this paper. We are grateful to the World Bank Group’s Umbrella Facility for Gender Equality for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.