rafmacalaba/datause-encoder-data huggingface dataset:
- [13] Baland, J.-M., C. Guirkinger, and C. Mali (2011), “Pretending to Be Poor: Borrowing to Escape Forced Solidarity in Cameroon.” _Economic Development and Cultural Change,_ 60(1), 1-16.

- [14] Bandiera, O., R. Burgess, N. Das, S. Gulesci, I. Rasul and M. Sulaiman (2017) “Labor Markets and Poverty in Village Economies”, _Quarterly Journal of Economics_ , 132(2): 811-870.

- [15] Banerjee, A. V., E. Duflo, N. Goldberg, D. Karlan, R. Osei, W. Pariente, J. Shapiro, B. Thuysbaert and C. Udry (2015), “A Multi-faceted Program Causes Lasting Progress for the Very Poor: Evidence from Six Countries,” _Science_ 348: Issue 6236.

- [16] Banerjee, A. V., E. Duflo, R. Chattopadhyay and J. Shapiro (2016), “The Long Term Impacts of a Graduation Program: Evidence from West Bengal,” Working Paper.

- [17] Banerjee, A. V., D. Karlan, R. Osei, H. Trachtman and C. Udry (2018), “Unpacking a Multi-Faceted Program to Build Sustainable Income for the Very Poor,” Working Paper.

- [18] Bardhan, P. (1983), “Labor-Tying in a Poor Agrarian Economy: A Theoretical and Empirical Analysis", _The Quarterly Journal of Economics_ , 98(3): 501-514.

- [19] Bardhan, P. and A. Rudra (1978), “Interlinkage of Land, Labor, and Credit Relations: An Analysis of Village Survey Data in East India", _Economic and Political Weekly_ , 13: 367-84.

- [20] Bardhan, P. and A. Rudra (1981), “Types of Labour Attachment in Agriculture: Results of a Survey in West bengal, 1979", _Oxford Bulletin of Economics and Statistics_ , 43: 89-111.

- [21] Barr, A., M. Dekker, and M. Fafchamps (2012), “Who Shares Risk with Whom under Different Enforcement Mechanisms", _Economic Development and Cultural Change_ , 60(4): 677-704.

- [22] Barr, A. and G. Genicot (2007), “Risk Sharing, Commitment and Information: An Experimental Analysis", _Journal of the European Economic Association_ , 6(6): 11511185.

- [23] Barro, R. (1974), “Are Government Bonds Net Wealth?”, _Journal of Political Economy_ , 82(6): 1095-1117.

- [24] Battaglia, M., S. Gulesci and A. Madestam (2019), “Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts", working paper.

28
presents only half the possible answers. The responses do not allow firms to say that they favor the issue or regulation in question and convey to the respondent that the interviewer believes the answer should be negative. An unbiased response scale, therefore, would include as many positive as negative responses, with "no obstacle" as the middle, indifferent, category. By presenting "not concerned" or "no obstacle" as an endpoint, the positive responses have. in effect, been truncated. Often, however, there is no other obvious way to ask the question as including the positive responses may seem inappropriate. Asking a filter question first can mitigate the bias that these truncated response distributions would otherwise impose.

Sterngold, Warland, and Herrmann (1994) found that including a filter that asks respondents _whether_ they were concerned about an issue before asking them _how concerned_ they were dramatically reduced the percentage of respondents expressing concern. Moreover, the drop in those expressing concern came not just from those who expressed "a little concern," but also among those who were "somewhat" and "very" concerned. The authors concluded that "standard degree-of-concern items may be leading questions that encourage res-pondents to overstate their concerns about issues." Filters and branches could be used effectively in many World Bank surveys, especially when the question itself may bias the respondent's answers. For example, The World Bank often wishes to measure whether institutional features of a country impede firm growth. Such surveys often ask firms to rate regulations or potential production bottlenecks from "no obstacle" to "major obstacle." These questions may suggest to the firm that the interviewer believes that the issues in question are obstacles. In effect, the question leads the respondent to an answer, and thus to biased results. An acceptable, although perhaps unlikely, response is also that the regulations are "very helpful." Presenting these positive responses may make the question seem awkward. Few firms are likely to report that tax inspectors are "very helpful." Not including the positive responses, however, is likely to lead to a strong upward bias in the number of firms reporting that the issue is a problem. World Bank surveys can mitigate this problem by imposing filters on these questions. First ask the firm the binary question of whether the regulation in 30
No, Congolese refugees are not taking jobs away by force. . . The employers should be able to decide who to hire. This is business not charity. . . I definitely prefer to hire refugees. If refugees disappear from here, many of our business will be damaged. It is not only a source of employment, but they also bring other Congolese refugees as customers. They increase our sales. (Alex, Kenyan, Kitengera, Nairobi) The comments from Kenyan hosts in Kitengera show a critical difference between employers and labourers in terms of attitude towards refugees. The literature suggests that hosts are not uniformly affected by the presence of refugees, and that the poorest or most vulnerable within a given community are usually at the highest risk of being negatively impacted (Chambers 1986; Verme and Schuettler 2021). Meanwhile, business owners, like those in Kitengera, may be able to benefit from cheap refugee labour, and take on positive attitudes towards the refugees they interact with.

We observe the same pattern with Ugandan landowners versus non-landowners around the Nakivale settlement. Rural camp contexts often observe heightened resource competition between host and refugees, and in the case of Nakivale there is tension over access to land. The provision of land by the Ugandan government to refugee households is a central underpinning of its self-reliance strategy. Refugees have traditionally been given access to a plot of land upon arrival, which can be used for both consumption and commerce. However, the more than 60% increase in refugee numbers in Nakivale in the years preceding our survey had left the settlement overwhelmed, as the size of the settlement remained the same. As OPM became unable to provide adequate size of land for new arrivals, they instead started locating refugees at the border of the settlement, leading to a 2017 local demonstration against refugees in Nakivale. In our interviews in 2018, villagers living around the settlement expressed concerns about growing tension relating to land access sparked by the influx of refugees. The Ugandan vice chairperson of a church located adjacent to the settlement shared his feeling about the increasing tension over land: Refugees are good people. As long as refugees stay inside the settlement area, we have no problem. But when they come to our land, it is a problem. (Samson, Kazya church EA, Nakivale).

Since most Ugandan villagers living around Nakivale settlement are engaged in farming and animal husbandry that rely upon access to land, the increased occupation of land by refugees has posed a threat to their livelihoods and consequently made the relationship with refugees strained. Our survey data shows differences in opinions between the 70% of Ugandans around Nakivale who own land and the 30% who do not. Whilst across both groups about 8 out of 10 believe refugees are competitors, the landowners are significantly more likely to think of refugees also as economic contributors – 76% agree they are, as opposed to only 63% among hosts without land. This is perhaps explained by the fact that 24
39

**Table 7:  Effect of Listening to Community Radio on Household Education Investments**

||De|pendent variable: Book|purchase|DV: Tuition|
|---|---|---|---|---|
||OLS-<br>Base|OLS-Exclude Peulh,<br>Dendi, HHs w/older<br>or female heads,<br>older respondents|IV for HH<br>listening: Radio<br>access<br>OLS-<br>Base|IV for HH listening:<br>Radio access|
|**Community radio stations**<br>**received by village (#)**|**0.08**<br>**(0.04)**|**0.09**<br>**(0.04)**|**0.03**<br>**(0.24)**||
|**Household listens to at**<br>**least one community radio**<br>**station (IVs: radio access )**|||**1.84**<br>**(0.04)**|**.84**<br>**(0.09)**|
|Private commercial stations|-0.10|-0.11|-0.06||
|received by village|(0.09)|(0.08)|(0.13)||
|Elementary Education –|0.11|0.13|0.14<br>.02|0.04|
|Respondent|(0.14)|(0.14)|(0.09)<br>(0.59)|(0.40)|
|Secondary Education –|0.30|0.29|0.42<br>0.10|0.15|
|Respondent|(0.01)|(0.02)|(0.00)<br>(0.10)|(0.02)|
|Higher Education – Respondent|0.32|0.41|0.78<br>0.09|0.31|
||(0.21)|(0.21)|(0.01)<br>(0.61)|(0.24)|
|Number of children listed|0.11|0.07|0.17<br>.12|0.15|
||(0.09)|(0.38)|(0.04)<br>(0.04)|(0.03)|
|Income is between 27.5K and|0.14|0.14|0.12<br>.001|-0.006|
|44K|(0.01)|(0.04)|(0.09)<br>(.97)|(0.87)|
|Income is over 44K|0.30|0.38|0.23<br>0.08|0.05|
||(0.00)|(0.00)|(0.02)<br>(0.10)|(0.34)|
|Wall - Brick/Semi-rigid|-0.13|-0.22|-0.05<br>-0.01|0.02|
||(0.08)|(0.01)|(0.60)<br>(0.77)|(0.75)|
|Owns a TV|0.39|0.50|0.42<br>-0.10|-0.09|
||(0.00)|(0.00)|(0.01)<br>(0.11)|(0.14)|
|Owns a mobile|0.18|0.12|0.14<br>0.14|0.12|
||(0.01)|(0.20)|(0.09)<br>(0.01)|(0.03)|
|Observations|2,165|1,276|2,165<br>2,165|2,165|
|_R_<sup>_2_</sup>|0.27|0.29|0.10||
|Hansen‘s J-statistic (_p_-value), F-|||.92, 10.2|.68, 5.12|
|<br>statistic<br>**First stage result**|**s (Same f**|**or both IV regressions**|<br>**; robust p-values in paren**|<br>**theses)**|
|Community radio stations received|by village|(#)|.03 (.02)||
|Commercial stations|||-.06 (.003)||
|Religious stations|||.06 (.08)||
|Public stations|||.03 (.30)||
|F-statistic on excluded instruments|||<br>3.3||

Note: ―Household listens to community radio‖ instrumented with access to community stations, commercial stations, religious stations, public stations. Robust _p_ -values, clustered at the village level, reported in parentheses. All specifications include controls for commune fixed effects. Other controls (all are either never or rarely significant) are not reported: access to public or religious radio stations; age and gender of household respondent and of household head; eight different ethnic dummy variables; whether respondent is Islamic or Catholic; number of adults in household and number of household members older than 60; whether head of household is polygamous; whether respondent is single; whether household has cement floors; or more than five rooms, and number of textbooks in the primary school.
and age, we focus on these analytical cuts. In doing so, we update measures presented by Ayyagari et al (2014), hereafter “ADM”, expanding on their results by including additional economies published since their contribution as well as by disaggregating net job growth into gross job creation and job contraction separately. We also consider alternate specifications from Haltiwanger et al (2013), denoted by “HJM”. We find evidence confirming several findings from both studies, showing that these results are markedly sensitive to specifications and the definitions used to classify establishments.

The Enterprise Surveys are a standardized and globally comparable database, providing establishmentlevel data representative of the non-agricultural private sector economy. As such, they cover clearly defined economic sectors, in manufacturing and services, following a stratified survey design and thus are interpreted here using survey weights to account for the varied probability of selection of each establishment. As noted in several studies, including ADM, these data are subject to several caveats, including issues of non-response and data coverage. For instance, by design for practical implementation, the surveys do not cover micro establishments (with fewer than 5 employees) nor do they cover the informal (un-registered) economy. Therefore, these data are likely to underestimate the importance of SMEs. The surveys are also answered voluntarily and therefore they may face some degree of non-response. Nevertheless, the surveys provide a widely comparable data source for most developing economies and they constitute the best approximation for employment dynamics in the aggregate. Annex II provides descriptive statistics of the data used for the results provided below.

# 1. <u>The Share of the Employment Stock: Where are the Jobs?</u>

In 70 of the 117 economies, SMEs, with fewer than 100 employees, account for the majority of employment. On average, this share is 57%, with large establishments accounting for 43%. Once the cutoff is changed to 250 employees, SMEs employ the majority of employees in 104 of 117 economies, with a resulting average of 75% of the stock of employment (columns 1-4 of Table 1). These results are robust to the categorization of establishments using the average employment, and these are shown in columns 5-8. That is, no matter how size is defined, SMEs constitute the majority of employment in the developing world. Columns 9-11, moreover, show that establishments that are ten years or younger account for over two-fifths of employment in the developing world; mature establishments that have been operating for 11 years or more constitute over 60% of the stock of employment, consistent with findings reported by ADM. What is more, in line with their findings, the employment share of SMEs is highest among low-income economies, when compared to their counterparts elsewhere.<sup>25</sup>

> 25 Averages by income group not shown.

14
WPS4063

# Jointness in Bayesian Variable Selection with Applications to Growth Regression

Eduardo Ley The World Bank, Washington DC, U.S.A.

## Mark F.J. Steel Department of Statistics, University of Warwick, U.K.

Abstract. We present a measure of jointness to explore dependence among regressors, in the context of Bayesian model selection. The jointness measure proposed here equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. We illustrate its application in cross-country growth regressions using two datasets from the model-averaging growth literature.

Keywords. Bayesian model averaging; Complements; Model uncertainty; Posterior odds; Substitutes JEL Classification System. C11, C7 World Bank Policy Research Working Paper 4063, November 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Address. E. Ley, World Bank, 1818 H St NW, Washington DC 20433. Email: eduley@gmail.com. M.F.J. Steel, Department of Statistics, University of Warwick, Coventry CV4 7AL, Vox: +44(0)24 7652 3369. U.K. Email: M.F.Steel@stats.warwick.ac.uk
is to identify those features – denoted as _X_ – that offer most predictive power. Some approaches, such as stepwise regression, perform the selection simultaneously with model building, but the two tasks can also be split.

5. _Modelling._ The modelling step involves the development of a supervised learning model _f_ ( _X_ ) that predicts consumption per household member. Below we expand on the PMT approach to variable selection and modelling, and then propose an adjusted algorithm.

6. _Targeting survey & prediction._ Operationalizing a PMT requires the set of variables X to be queried from a list of potential beneficiaries. In addition to the features, this survey may include household characteristics that determine categorical programme eligibility, such as the number of school-age children, verification of household characteristics or identity via inspection of administrative documents. The model _f_ ( _X_ ) is applied to produce consumption estimates for the set of potential beneficiaries. Combining categorical and socio-economic eligibility, the list of beneficiaries is determined either by a ranking of household values (in case of a beneficiary quota) or via an absolute threshold that assigns eligibility if a given household’s income falls below it.

To consider the link between these steps and the design considerations outlined before, note that both cost and verifiability are influenced by the questionnaire design of the targeting survey. The number and detail of questions and the associated verification routines are an important cost lever, especially in urban areas where travel times between households are short for enumerators. In terms of the PMT process shown in fig. 1, verifiability is considered in the pre-selection step. The variable selection aspect of the modelling step also influences costs by shrinking the volume of questions. The implicit policy objective of the PMT algorithm is to achieve the best possible accuracy for a set of pre-selected questions. As such, the emphasis of the standard PMT approach is accuracy, rather than on cost reduction.

The focus of this paper is on modelling, but note that targeting survey design and eligibility determination are also impacted by modelling choices: the targeting survey consists primarily of variables selected, and the predictive model is used to score eligibility. The key statistical challenge in PMT design is model selection, particularly to limit the number of independent variables without sacrificing accuracy. The number of possible variable combinations is enormous in most settings due to the wide extent of the sample population survey. It is computationally infeasible to try out any but a fraction of these combinations within a chosen predictive model, and PMT manages this challenge with the following algorithm.

8
# **1. Introduction**

With the largest snow and ice cover in the world outside the polar regions, the Himalayan region is one of the most important mountain systems in the world and is referred to as the “third pole” (Schild, 2008) and the “water tower of Asia” (Xu et al., 2009). Extending along the northern fringe of the Indian subcontinent, from the bend of the Indus River in the northwest to the Brahmaputra River in the east, the Himalayas directly or indirectly affect lives and livelihood of over 300 million people (Schild, 2008). Through their massive fresh ice reserve, the Himalayas influence flow to thousands of rivers and rivulets that converge into the three main river systems in the region: the Ganges, the Brahmaputra (called Yarlung Zangbo in China), and the Indus (Fig 1).

The Himalayas play a key role on supporting economy of nations like Nepal and Bhutan, which depend heavily on the Himalayas for hydropower, water supply, agriculture, and tourism. For example, Bhutan’s export revenue from hydropower contributed 16.3% of nominal gross domestic product (GDP) or 39% of total exports in 2009/2010 (RMA, 2011). In Nepal, agriculture has remained a key economic sector, contributing about 34% of GDP in 2009 (World Bank, 2011) and employing 93% of the workforce in 2004 (ADB and IFPRI, 2009). Nepal's long term economic development plan centers on hydropower development, although current installed capacity is barely 1.5% of the total 43,000 MW potential. Himalayan States of India and Xizang Province of China also rely on hydropower, tourism, and agriculture for sustaining their economy. All these countries and states have remarkably high potential for hydropower of which only a small fraction has been harnessed. The Government of India released a study (GOI, 2012) showing that the Indian Himalayan states alone have over 70% of India’s hydropower potential in terms of installed capacity greater than 25 MW The Himalayan region holds significant importance in terms of biological (species) richness, biodiversity, socio-cultural diversity, and wealth. The region is one of 34 worldwide “biological hotspots” (i.e., a natural environment with a high biodiversity containing a large number of endangered endemic species) as identified by Conservation International (CI, 2011). The region’s indigenous people consider the Himalayas sacred and look upon them with reverence. In essence, the Himalayas are both pride and necessity of the region.

2
difference-in-differences approach, we opt for estimating a structural gravity model, as explained below, in which bilateral patent applications are related to joint membership in an IPA versus cases where the patent-destination country is in an IPA and the patent-source country is not. A particularly attractive feature of our structural gravity approach, in which we account for unilateral origin- and destination-specific time-varying factors through the use of highdimensional fixed effects, is that we are able to implicitly control for a wide assortment of confounding factors, such as countries’ adoption of and compliance with the TRIPS Agreement. Our approach seeks to distinguish between the impacts on patent flows of mutual IPA membership (within-agreement effects) and those on patent flows from outside IPAs and on patenting from IPA partners to non-member nations (extra-agreement effects).

# **2. Background**

In recent decades, PTAs have evolved greatly in subject matter and intent. Earlier agreements sought primarily to reduce tariffs and other formal barriers to trade, thereby increasing market access between member countries. Beginning in the 1990s they expanded in ambition and depth. The earliest major agreements with enhanced coverage were the North American Free Trade Agreement (NAFTA) and several bilateral accords between the European Free Trade Association (EFTA) and individual countries, such as Estonia, Latvia, and Mexico. This round of trade agreements included rules covering certain regulatory regimes, including IPRs. Not long after, the EU announced its “new trade policy,” increasingly emphasizing regulatory systems, including enhanced standards for patents, copyrights, GIs, and other IPRs in its PTAs with countries in Eastern Europe, the Middle East, Latin America, Canada, and Japan.

NAFTA was among the first multi-country PTAs to set minimum standards for nearly every aspect of IPRs as they existed then. Regarding patents, the agreement established certain TRIPS-Plus rules, including confidentiality for pharmaceutical clinical trials data and extensions in patent length to compensate for administrative delays in granting protection. These provisions, and others, were the foundation on which other TRIPS-Plus patent standards were erected in subsequent PTAs. NAFTA also mandated a minimum copyright length, expanded the types of literary and artistic works (including software) that must be protected, and recognized various neighboring rights. It further required automatic recognition of internationally well-known 3