rafmacalaba/datause-encoder-data huggingface dataset:
those with weaker social networks prior to the conversion are more likely to experience default problems after conversion to individual liability, relative to those who remain under group liability. In sum, as conversions from group to individual liability become more commonplace, we take the first step towards understanding whether and how such conversions work.

The rest of the paper is organized as follows. Section II reviews the literature on group versus individual liability lending programs. Section III presents the experimental design and the administrative and survey data we collected. Section IV presents the empirical strategy and primary results on the impact of group versus individual lending on center and individual performance. Then, section V presents results from three surveys conducted one year after the initial conversion in order to learn more about the mechanism through which changes did or did not occur. Section VI concludes.

# **_II. Literature Review_**

The theoretical literature has focused almost exclusively on comparing the repayment performance under group and individual liability. Ghatak and Guinnane (1999) suggest that group liability can help institutions improve repayment through four channels: (i) ascertaining how risky the borrower is ( _adverse selection_ ), (ii) ensuring that the funds will be used properly ( _ex-ante moral hazard_ ), (iii) ensuring that the borrower tells the truth in case of default about her ability to pay ( _monitoring_ ), (iv) enforcing repayment if the borrower is reluctant to pay ( _voluntary default, or expost moral hazard_ ). Group liability contracts in theory can lead to higher repayment because borrowers have better information about each other’s types, can better monitor each other’s investment, and may be able to impose powerful non-pecuniary social sanctions at low cost.

5
<!-- Start of picture text --> Recommendations for the Monitoging<br>of Poverty and Welfare through a Household Survey<br><!-- End of picture text --> <!-- Start of picture text --> L - I - CILSS Recommendation<br>Content<br>Houcehold Expenditure full detail full detail<br>Household Income full detail income composition based on<br>qualitative questions<br>Basic Needs full detail selected indicators<br>Data Quality<br>Income from non-farm cost-minus-revenue yields to be revised following<br>household enterprises negative incomes experimental survey work<br>Household expenditure mixed reference periods to be revised based on<br>ongoing research<br>Prices insufficient coverage remove from survey - enhanced<br>price collection as<br>part of CPI<br>Community Survey too much rmissing infornmation integrate better into<br>household survey<br>Samole size and design<br>Sample size 1600 households 3000-4000 households<br>Sample design self-weighting unequal representation of<br>regions and groups based on<br>analytic priorities<br>Sample selection flawed listing procedure simplify procedures and<br>improve field supervision<br>Freauencv<br>Monitoring 4 consecutive years undertake once every 4-5 years; 'lighter'<br>monitoring survey in between<br>Panel Data<br>Panel design rotating panel separate panel survey<br><!-- End of picture text --> 19
precarious zone, (e) medium-density precarious zone, (f) high-density precarious zone, and (g) rural area.

While the Kinshasa Survey is prepared with an innovative approach, the use of the survey requires caution against its data quality and comparability. It is extremely challenging to design a sampling survey to collect information about households and individuals that represents Kinshasa, due to the lack of a recent population census. The sampling of the Kinshasa Survey, which is the primary data for this study, was prepared by drawing on satellite imagery. In addition, unlike the 1-2-3 household survey of 2012, the stratification of the 2018 survey explicitly captures precarious areas. Despite such an innovative sampling approach, the Kinshasa Survey has concerns about the data quality because of a problem in the data collection process (see Batana et al. 2021 for detailed descriptions). Considering all these factors, this study does not report any absolute number of population based on the survey and does not make a direct comparison between the Kinshasa Survey and the 1-2-3 household survey.

This note also draws on other existing survey data, such as the 1-2-3 household survey of 2012, Water, Sanitation, and Hygiene (WASH) survey, and Demographic and Health Survey (DHS) 2013–2014, as well as satellite imageries and other spatial datasets, to capture trends in the levels and qualities of services and also identify spatial determinants of service access.

Throughout the paper, we follow the definitions of water and sanitation services as provided by World Health Organization (WHO)/United Nations Children’s Fund (UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation, and Hygiene. To distinguish quality of water and sanitation access, they are classified into the following categories.

In the case of water access, it is classified into (a) improved water; drinking water that includes piped water and protected wells/springs, (b) piped water; water sources coming from public/private taps at home or in neighborhoods and (c) basic water; drinking water from an improved source that is available within 30 minutes for a round trip. These categories are not mutually exclusive. For example, some households have access to improved and basic water (improved water in proximity) while others have access to improved water but not basic water (improved water not in proximity).

Regarding sanitation access, the two categories are a) Limited sanitation; access to improved facilities that are shared with other households, and b) Basic sanitation; the use of improved facilities that are not shared with other households.

5
Incidence of COVID-19 and Connections with Air Pollution Exposure

# 6.2.4 Re-estimation using March 31 confirmed hospital admissions per 100,000 inhabitants

Model 5f in table 7 below, re-estimates Model 2 then uses the covariates in the same specification as Model 5 using confirmed hospital admissions from March 31. This is to evaluate whether the relationship with PM2 _._ 5 is robust to possible measurement error in the confirmed cases. The correlation between confirmed cases per 100,000 inhabitants on March 22 and March 31 hospital admissions is approximately _._ 80, the correlation using March 30 confirmed cases is _._ 88. It is clear from the estimation results that using confirmed admissions instead of cases does not alter the main conclusions. In particular, the parameter estimate for PM2 _._ 5 remains highly significant.

Table 7: Dependent Variable: Confirmed COVID-19 hospital admissions per 100,000 inhabitants.

|**Variable**|**Model 5f**|
|---|---|
|(Intercept)|-256.68***<br>(82.20)|
|Population density|-2.48<br>|
||(1.82)|
|Share from 14 to 24|1.55|
||(1.24)|
|Share from 25 to 44|2.25*|
||(1.20)|
|Share from 45 to 64|0.40|
||(0.84)|
|Share above 65|3.18***<br>(1.15)|
|Share of unmarried|2.63***|
||(0.74)|
|Share of single households|-1.90***<br>(0.48)|
|Share of non-western immigrants|-0.47<br>(0.36)|
|Share of water surface|14.89**<br>(7.49)|
|Share with long-term illness|0.88|
||(0.55)|
|Mean PM2_._5|3.74***<br>(1.33)|
|_λ_|-0.10<br>(0.17)|
|_ρ_|0.68***<br>(0.09)|
|R2|0.54|
|AICc|3242.22|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 21
impacting the ability of the private sector to generate growth and employment. This subtle change in message was better appreciated by both government and the private sector, resulting in a general consensus that the telecommunications sector needed to be reformed. As a result, the government took the decision to privatize ZAMTEL and liberalize the international gateways, resulting in a significant decline in the cost of telecommunications.

The energy sector is similarly a good example of how PE analysis has been used at the sector level to change policy dialogue and bring changes in the sector. During the early 1980s, Zambia enjoyed 100% surplus electricity capacity, mainly due to lower than expected growth of the mining sector. However, the increased demand for electricity from the expanding copper mining sector in recent years has transformed the surplus into a deficit. This increased demand has been coupled with increasing domestic demands for electricity in urban areas. Zambians have long enjoyed the lowest tariffs in subSaharan Africa – less than 2 US cents per kwh till 2008 and significantly below the cost of new generation, estimated at about 8 cents/kwh. This resulted in the only 22% of the population having access to electricity, with only about 3% in rural areas.

Several studies have historically been carried out on the institutional reforms needed in the energy sector to improve performance, attract private sector investment and address the challenges of rural electrification. A commercialization program was agreed between ZESCO and the government within the framework of the HIPC and MDRI initiatives backed by cancellation of US$2.7 billion in government debt. The objective and spirit of this commercialization plan was **“** enforcement of improved performance of ZESCO via its management, board and the Energy Regulation Board (ERB) **”** and included a list of factors needed for sustained good performance, one of which was autonomy of the management. To help achieve this, the government committed to ensuring independence of the ZESCO Board and ERB, as well as freedom of both from political interference. A Cost of Service Study supported by the Bank in 2007 identified a number of performance related issues within ZESCO and a series of performance benchmarks were agreed between the ERB and Zesco. These were intended to address public concerns about performance related services and provided a multiyear incentive tariff framework based on the use of self-enforcing incentives. Implementation of the recommendations of these studies was historically less than satisfactory, as the recommendations often ignored the political realities confronting the sector. In particular, it was felt that any tariff increase in the sector would directly impact the relatively prosperous urban population that wielded considerable political influence.

Informed by a deeper analysis of the political economy, the Bank dialogue shifted significantly to accommodate a more balanced and nuanced approach between the push for efficiency and the need to 12
**Table A7** Impact of Treatments on Female Time Use

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|**In the past day, hours spent...**|||||
|Sleeping|||||
|treatment x post|-0.349|0.052|8.936|5,182|
||(0.234)|(0.202)|||
|treatment x post x Goal|-0.162<br>||||
||(0.266)||||
|In school|||||
|treatment x post|0.448|-0.154|3.970|5,182|
||(0.335)|(0.303)|||
|treatment x post x Goal|0.149<br>||||
||(0.416)||||
|Studying|||||
|treatment x post|0.285**|-0.132|0.750|5,182|
||(0.127)|(0.116)|||
|treatment x post x Goal|0.159<br>||||
||(0.173)||||
|On sports/ leisure|||||
|treatment x post|0.157|0.197|1.238|5,182|
||(0.147)|(0.142)|||
|treatment x post x Goal|-0.295<br>||||
||(0.206)||||
|Doing domestic tasks|||||
|treatment x post|-0.267|0.404*|3.341|5,182|
||(0.242)|(0.231)|||
|treatment x post x Goal|-0.131<br>(0.292)||||
|Working on family farm|||||
|treatment x post|-0.017|0.009|0.156|5,182|
||(0.067)|(0.047)|||
|treatment x post x Goal|-0.112||||
||(0.074)||||
|On income generating activities|||||
|treatment x post|-0.004|-0.326|0.835|5,182|
||(0.219)|(0.231)|||
|treatment x post x Goal|0.267||||
||(0.294)||||
|Praying|||||
|treatment x post|-0.024|-0.395*|0.695|5,182|
||(0.187)|(0.209)|||
|treatment x post x Goal|0.292<br>(0.269)||||
|With boyfriend|||||
|treatment x post|-0.147***|-0.015|0.212|5,182|
||(0.055)|(0.049)|||
|treatment x post x Goal|0.059||||
||(0.057)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. See notes for Table 2 on table structure and control variables. The outcomes are the hours the female reports spending in each category on the day prior to survey. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel.

66
Table 11. Cross-border Tax Effects: FAI vs. FDI

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var. is I/K corresponding to:|FAI|FDI|FAI|FDI|
|Rate Diferential|1.738***|0.354|||
||(0.508)|(0.647)|||
|Non-Host Component|||2.010***|0.178|
||||(0.619)|(0.587)|
|Host Component|||-1.648***|-0.814|
||||(0.520)|(0.623)|
|Host KF-METR|-0.003|-0.018*|-0.003|-0.017*|
||(0.005)|(0.009)|(0.005)|(0.009)|
|Constant|-48.299***|-26.619*|-47.749***|-28.415**|
||(10.644)|(14.407)|(10.575)|(14.487)|
|Observations|12,532|17,442|12,532|17,442|
|Pseudo-_R_<sup>2</sup>|0.129|0.157|0.130|0.159|
|Controls:|Host, Parent<br>Bilateral|Host, Parent<br>Bilateral|Host, Parent<br>Bilateral|Parent,<br>Bilateral|
|FEs:|Year|Year|Year|Host-Year,|
||Parent|Parent|Parent|Parent|

_Notes:_ All specifications are estimated by PPML and include parent country and year fixed effects along with the following characteristics of the multinational firm’s host and parent country: GDP (in log), capital account openness index, trade openness, inflation rate, government expenditure share, and exchange rates. Standard errors, in parentheses, ** * are clustered by host. ***, and indicate significance at the 1%, 5% and 10% levels respectively.

59
obstacle, minor obstacle, or moderate obstacle (Tax rates are a major obstacle). Note that even through SMEs and large firms may face similar tax rates, the burden they impose to SMEs vs large firms may be different. The data source is ES.

Apart from taxes, there may be differences in enforcement or in the ability of the firms to deal with corruption, power outages, and other such factors. We account for the overall quality of the business environment by using the average severity level (on a 0–4 scale) of the following as obstacles for the firms’ operations in the pre-pandemic period: electricity, tax administration, obtaining licenses and permits, corruption, and labor laws (Obstacles severity). The data source is ES.

The capacity of the firms to adapt existing products, delivery methods, and operating methods (such as work from home) or introduce new ones may impact their performance during the pandemic (see Clampit et al. 2022). We capture such effects through firms’ innovation and R&D activity prior to the pandemic. That is, we use a dummy variable equal to 1 if the firm spent on R&D activity during the three years prior to the year the ES was administered and 0 otherwise, and a dummy variable equal to 1 if the firm introduced a new product during the three years prior to the year the ES was administered and 0 otherwise. Both variables are for the pre-pandemic period and taken from the ES. We show that our results do not change if we use adjustments made by the firm after the outbreak of the pandemic in product, process, and delivery.

<mark>We consider the firm’s legal status as a possible factor influencing its performance. A few studies have shown that business legal forms may differ with respect to risk-sharing and risklimiting possibilities, which in turn impact firms’ ability to cope with external shocks (see Doś et al. 2022). To this end, we use two dummy variables for the pre-pandemic period: one for sole</mark> 16
- 15

domestic manufacturers for their imports of parts and components for domestic peoduction.

The data in this paper are reasonably close to those<sup>in</sup> Berlinski (1985) who analyzed the percentage of duty<sup>exempt imports in</sup> Argentina and other tiembers of the LAIA. Berlinski estimated that 44 percent of Argentine imports from non-LAIA countries<sup>in 1979 were</sup> exempted from tariff payments and that the higher rate<sup>of exemption</sup> applied to intermediate and capital goods.

# 11.2 Fiscal Impact of Tariff Exemptions

The data contained in Table 2 allow some estimation of<sup>the</sup> fiscal cost of tariff exemptions. Given an average nominal tariff rate of 27-28 percent which together with the ten percent<sup>surcharge</sup> introduced in 1985 raises the average to 37-38 percent,3/<sup>and given a</sup> realized tariff rate of only 20.5 percent, the foregone<sup>revenue in</sup> 1986 can be estimated at approximately 775-822 million dollars4/ . As far as the relative contribution of the various promotion<sup>schemes</sup> discussed earlier to the total loss of revenues is concerned,<sup>a recent</sup> analysis was conducted at the Ministry of Economy<sup>for 1987. The</sup> results appear in Table 3.

- 3/ In October 1987 tariff rates were increased again<sup>by 5 percent</sup> across the board.

- 4/ This estimate is partial because it ignores the effect<sup>of various</sup> tax exemptions on firms' activity and hence<sup>on the tax base for</sup> those taxes that firms do actually pay.
16 whole prior to implementation; this is as it should be if the effects we are estimating are causal relationships. There are a few effects for specific age groups, but there are very few.

Table 5 and to a lesser extent Figure 2 suggest that among the sample as a whole UC reduced the likelihood of being too ill to work. The effect is most pronounced for those over 65, though there is a perceptible effect too among those aged 50-64. Among the over-65s, the effect is sizeable – the numbers suggest that among this age group UC may have reduced the probability of someone being too ill to work by as much as 5-6 percentage points. For the sample as a whole, the effect is much smaller – between 0.5 and 0.7 of a percentage point.

Whether the sample impact is small or large is hard to say without a yardstick to compare it against. The VF provides one yardstick. The VF was not a health intervention per se but by raising living standards of rural residents it can reasonably be expected to have had some health effects. Table 5 suggests that any health effects that the VF had were a good deal smaller than those of UC – on average, the effect of the VF on self-reported health was around one quarter that of the UC scheme.

# VI. **CONCLUSIONS**

Our analysis exploits the phased rollout of the Thai UC policy to estimate its effects on health, specifically on the probability of a person reporting themselves as too sick to work. We control for province and quarterly effects, as well as multiple individual- and household-level variables. We also estimate the effects on our self-reported health limitations variable of the Thai Village Fund (VF) scheme which began to be rolled out
Figure 4. Dynamic treatment effects on average wage reported by the firm <!-- Start of picture text --> Soft letter Strong letter<br>Filing period Filing period<br>.15<br>.15<br>.1<br>.1<br>.05<br>.05<br>0<br>0<br>Marginal effect on avg. wage (w.r.t control) Marginal effect on avg. wage (w.r.t control)<br>-.05<br>-.05<br>Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> Source: Authors' calculations using historical tax declaration records. Coefficients from random effects model plotted.

# Considering heterogeneity of impacts based on sector of activity

Beyond the differential impact of the notification letters due to characteristics of the firm in terms of whether the owners themselves were explicitly targeted for suspicion of under-declaration, an important factor to consider in this context is the sector of economic activity of the firms subject to these notifications. Some industries would have been deemed as essential (e.g., food services) and others as non-essential (e.g., entertainment venues), and thus influence the operational capacity of firms and with it (a) access to postal mail and (b) the ability to retain employees. How these factors influence reactions to strategic notifications is seemingly ambiguous: on the one hand, essential businesses (such as grocery stores) that would have stayed open and not necessarily limited capacity would have for the most part “received the message” (employers and employees), but have been too busy to react. On the other hand, non-essential businesses (such as travel agencies) were much less likely to get the message in the first place, but also potentially more sensitive to the messages because of the uncertainty of their business and operational capacity in the short- and medium-term.

While our data only permits us to look at very aggregate indicators of the type of economic activity, it nonetheless illustrates the heterogeneity of impacts based on firm characteristics. The estimation of equation (2) above by aggregated sector of economic activity (professional services, wholesale/retail trade, 16
17

# In the long-run, markups are defined as follows:

# **Equation** 5

p _-ca_ = 0.93 _(p*1_ - _p,)_ -12.09 Ap The long-nin results confirm the influence of foreign prices, as an increase in foreign prices is almost translated into a one-for-one increase in markups. In the long-run, the general level of inflation is non-neutral; for each 10 % decline in the quarterly inflation rate, markups in the service sector will double. This relative large effect partially arises from the slow correction error mechanism in the prices of services (captured by the coefficient a5) and partially from the weak contribution of wages and foreign prices to the short-run dynamics.

## _Commerce_

The short-run dynamic of inflation in commerce is influenced positively by the variations in wages as labor is the main input used in this sector (Table 5). In contrast, the variations in the prices of foreign goods do not play a significant role, but this was expected for a non-traded sector. The inflation rate is also affected by the changes in demand pressures as discussed below.

The coefficients associated with the variations in demand are consistent with the collusive market model. As discussed earlier for the industrial sector, this outcome may be the result of imperfect competition, though the evidence at hand is sketchy for this sector. The large number of retail companies suggests a high degree of competition 18, but the presence of few distributors and wholesalers may explain the collusive behavior captured by the econometric analysis. For example, in the case of diary products such as pasteurized milk and cream, two companies (Sancor and Mastellone) control about 67 % of the production and distribution. Clearly, additional work would be necessary in this area.

> 18 In 1994, the structure of the retail commerce sector was the following: 35 percent of total production by supermarkets bigger than 350 sq.m. 30 percent bv supermarkets smaller than 350 sq. m., and 35 percent by small stores (source: Alpha (1994)). The recent evidence shows a strong process of concentration in this sector, in line with the market structure in industrialized countries.