rafmacalaba/datause-encoder-data huggingface dataset:
Simulations were run wherein different road segments along the East Road would be hypothetically impacted by the 3-, 10-, and 30-year rain events according to the overall lengths described in Table 6 above. To do so, an automated bootstrapping method was used to calculate 100 possible outcomes for segmentlevel impact (i.e., which set of segments might be disrupted) across the entire East Road. This methodology is described further in Annex 2.

For each run of the simulation, the west-most disrupted road segment was identified. With each simulation, the population within the buffer zones to the _east_ of this point was counted as the population cut off from access to the main market or hospital at Auki, respectively. From this number, the number of people living within a 3-km distance of the disrupted segment for markets and 10-km distance for hospitals were excluded, since we assume that they can just walk across (as depicted in Figure 12). The final estimated populations affected by a given rain event and road upgrading alternative are averages of the model-run outcomes for this type of exercise. The per-event affected populations for the 3-km are detailed in Table 7 and for the 10-km in Table 8.

Table 7. Affected population in 3-km buffer zone, per single rain event

|Rain Event<br>Frequency|Design<br>Scenario|Total Affected<br>Length(km)|Affected Population<br>(Max)|Affected Population<br> (Min)|Affected Population<br>(Average)|
|---|---|---|---|---|---|
||SQ|1.653|8,635|7,991|8,428|
|3-year Rain|MD|0|0|0|0|
|(10 times)|HD|0|0|0|0|
||SLS|0.399|8,635|6,448|7,890|
||SQ|2.84|8,635|8,336|8,532|
|10-year Rain|MD|0.727|8,635|7,992|8,449|
|(3 times)|HD|0.367|8,635|6,220|8,219|
||SLS|1.099|8,635|7,953|8,403|
||SQ|4.584|8,638|8,378|8,588|
|30-year Rain|MD|2.471|8,638|8,336|8,577|
|(1 time)|HD|1.932|8,638|8,336|8,554|
||SLS|2.950|8,638|8,259|8,562|

Table 8. Affected population in 10-km buffer zone, per single rain event

|Rain Event<br>Frequency|Design<br>Scenario|Total Affected<br>Length(km)|Affected Population<br>(Max)|Affected Population<br> (Min)|Affected Population<br>(Average)|
|---|---|---|---|---|---|
||SQ|1.653|22,947|21,283|22,400|
|3-year Rain|MD|0|0|0|0|
|(10 times)|HD|0|0|0|0|
||SLS|0.399|22,947|16,387|20,900|
||SQ|2.84|22,947|22,170|22,671|
|10-year Rain|MD|0.727|22,947|21,286|22,458|
|(3 times)|HD|0.367|22,947|15,518|21,826|
||SLS|1.099|22,947|21,169|22,343|
||SQ|4.584|22,949|22,268|22,822|
|30-year Rain|MD|2.471|22,949|22,170|22,791|
|(1 time)|HD|1.932|22,949|22,170|22,730|
||SLS|2.950|22,949|22,025|22,754|

22
33 Fig. 2.13. Ratio of moves to new and previously occupied units in Moscow (% of mover households, by year) <!-- Start of picture text --> Newly constructed Previously occupied<br>4 -4-<br>3.5 3.5 3.2<br>3 3 -<br>2.5 2.5<br>1.9 ~ ~ ~ ~ ~ .5 1<br>12 2 -1.5<br>1 ~~~~~~~~~~~~~1.6<br>2 179 1.84 29519 94 1s<br>05 0.5<br>0 0<br>1993 1994 1996 1993 1994 1995<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996.

Fig. 2.14. Methods of obtaining housing in the primary and secondary housing markets in Moscow (% of mover households in 1993-1995.) <!-- Start of picture text --> 7 -<br>6 _<br>5 _<br>4 - 3.4 / owaiting list<br>3/ _exchange<br>H purchase<br>2-<br>0<br>New housing Previously occupied<br>housing<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996 The means of solving the housing problem by income group are presented in Fig. 2.15. The poor tenants displayed low mobility. It is noteworthy that among them, relatively few households (2.3% as against an average of 4%/) received housing through the waiting list system. Clearly the poorest people on the waiting list do not have preferential access to free housing, which is indicative of a poor targeting program. A large share of middle-income families demonstrated a very moderate capacity for improving their housing conditions by using both market and non-market methods in relatively equal measure. And only the top income group was marked by a large volume of apartment purchases and exchanges. It is also noteworthy that 3.8% of the households in the top income category managed to get free housing through the waiting list.
- 15 -

specific and expected to reflect the average project value for bidders. For road contracts, the total length of roads to be constructed and/or rehabilitated and the number of lanes of those roads are included. The type of work is also distinguished by new construction, rehabilitation and upgrading. For electricity contracts, the size of installed capacity in MW and the capacity of transmission lines—in terms of both voltage (kV) and length (km)—are measured. For the water- and sewage-sector contracts, similarly, the proxies of treatment capacity (million m<sup>3</sup> per day) and total network length (km) are introduced to control for heterogeneity across projects.

Since two sets of project data assisted by different organizations are merged, the donor dummy variable is supposed to capture the systematic difference in selected projects.<sup>9</sup> Finally, the project location dummy variables are also included in _Xt_ .

Our (dis)aggregation is still imperfect in the sense that different types of components are pooled together, especially in the electricity and water sectors. However, one advantage of our data treatment is that we can remain sufficient observations, whence ensuring robustness of estimates.

We are also allowed to infer the cost of an “average” infrastructure contract by pooling various but necessary components in each sector. Technically, we will evaluate the estimated bid function at the mean values of variables included. Any infrastructure project is a complex compound made up of various elements. This is an inherent and important feature of infrastructure projects. From the practitioner point of view, it is of little interest how much a specific type of equipment—for instance, a power generating turbine—costs. Such information may be publicly available on a list of manufacturing prices. Instead, an important

> 9 In fact, the two data are different. In our sample, first, the Japanese aid data concentrates on 5–6 East and Southeast Asian countries. On the other hand, the World Bank data cover the rest of the world, such as Middle East and Africa. This is our “intended” selection bias to cover the whole developing world. Second, the Japanese data are only available for the contract amount greater than one billion Japanese yen (roughly 9 million U.S. dollars). The World Bank data includes many small contracts, which may be less than 1 million U.S. dollars. Hence, the donor dummy variable may capture such systematic differences in projects and contracts assisted by the two donors.
# **Rwanda**

||**Prevalence among**<br>|**females 15-24(%)**<br>|**Spectrum**|**estimates**|**Indicator 1.6 -**<br>|
|---|---|---|---|---|---|
||**National Antenatal**|**National Demographic**|||**HIV prevalence**|
||**Sentinel Surveillance**<br>**Surveys **|<br>**and Health or AIDS**<br>**Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|<br>**in young people**<br>**15-24(%)**|
|**1990**|-|-|0.92|-|-|
|**1991**|-|-|0.96|-|-|
|**1992**|-|-|0.87|-|-|
|**1993**|-|-|0.76|-|-|
|**1994**|-|-|0.67|-|-|
|**1995**|-|-|0.60|-|-|
|**1996**|-|-|0.57|-|-|
|**1997**|-|-|0.51|-|-|
|**1998**|-|-|0.45|-|-|
|**1999**|-|-|0.39|-|-|
|**2000**|-|-|0.34|-|-|
|**2001**|-|-|0.27|-|-|
|**2002**|-|-|0.25|-|-|
|**2003**|-|-|0.23|-|-|
|**2004**|-|-|0.22|10|-|
|**2005**|-|1.5|0.21|29|-|
|**2006**|-|-|0.18|52|-|
|**2007**|-|-|0.17|71|3.5|
|**2008**|-|-|0.18|-|-|
|**2009**|-|-|0.16|77|3.7|
|**2010**|-|1.5|0.13|84|-|
|**2011**|-|-|0.13|82|1.86|
|**2012**|-|-|0.13|87|-|

|National DHS or AIS data sources:<br>**Study reference**|Rwanda DHS 2005 and<br>**Incidence rate/100**<br>**personyears**|2010/2011<br>**Time frame of study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|
|Braunstein et al., Sex Transm Dis 2011|3.5 (2006-2007),<br>2.1(2007-2008)|2006-2008|Female sex workers|-|

# **South Africa**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.15|-|
|**1991**|-|-|0.30|-|
|**1992**|-|-|0.55|-|
|**1993**|-|-|0.93|-|
|**1994**|-|-|1.41|-|
|**1995**|-|-|1.93|-|
|**1996**|-|-|2.40|-|
|**1997**|-|-|2.72|-|
|**1998**|-|-|2.85|-|
|**1999**|-|-|2.81|-|
|**2000**|-|-|2.67|-|
|**2001**|23.1|-|2.49|-|
|**2002**|23.5|-|2.26|-|
|**2003**|24.8|-|2.09|-|
|**2004**|25.1|-|1.96|4|
|**2005**|24.9|16.9|1.85|15|
|**2006**|22.4|-|1.79|21|
|**2007**|22.1|-|1.73|28|
|**2008**|21.7|13.9|1.72|-|
|**2009**|21.7|-|1.75|42|
|**2010**|21.8|-|1.78|57|
|**2011**|20.5|-|1.72|66|
|**2012**|-|-|1.37|80|

<u>ANC data sources: The 2011 National Antenatal Sentinel HIV & Syphilis Prevalence Survey in South Africa, The 2010</u> National Antenatal Sentinel HIV & Syphilis Prevalence Survey in South Africa <u>National DHS or AIS data sources: South African National HIV Prevalence, HIV Incidence, Behaviour and Communication</u> Survey 2005 and 2008 60
# **References**

Abowd, J.M., Creecy, R.H. and Kramarz, F. (2002). Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data. Longitudinal EmployerHousehold Dynamics Technical Papers 2002-06, Center for Economic Studies, U.S. Census Bureau.

- Ahn, S. and Fessler, J.A. (2003). Standard errors of mean, variance, and standard deviation estimators. Technical report, University of Michigan, EECS Department Paper.

Andrews, M.J., Gill, L., Schank, T. and Upward, R. (2008). High wage workers and low wage firms: negative assortative matching or limited mobility bias? _Journal of the Royal Statistical Society: Series A (Statistics in Society)_ , 171(3):673–697.

- —— (2012). High wage workers match with high wage firms: Clear evidence of the effects of limited mobility bias. _Economics Letters_ , 117(3):824–827.

- Azam, M. and Kingdon, G.G. (2015). Assessing teacher quality in India. _Journal of Development Economics_ , 117:74–83.

Bates, D.M. (2010). _lme4: Mixed-Effects Modeling with R_ . New York: Springer.

Behrman, J.R. and Birdsall, N. (1983). The quality of schooling: quantity alone is misleading. _The American Economic Review_ , 73(5):928–946.

- Behrman, J.R., Gaviria, A. and Székely, M. (2001). Intergenerational mobility in Latin America. _Economía_ , 2(1):1–31.

- Behrman, J.R., Rosenzweig, M.R., and Taubman, P. (1994). Endowments and the allocation of schooling in the family and in the marriage market: the twins experiment. _Journal of Political Economy_ , 102(6): 1131-1174.

- Behrman, J.R. and Rosenzweig, M.R. (2004). Returns to birthweight. _Review of Economics and Statistics_ , 86(2):586–601.

- Björklund, A. and Salvanes, K.G. (2011). Education and family background: Mechanisms and policies. In E. Hanushek, S. Machin and L. Wößmann (Eds.), _Handbook of the Economics of Education_ , volume 3, chapter 3, pp. 201–247. Elsevier.

- Carneiro, P. (2008).  Equality of opportunity and educational achievement in Portugal. _Portuguese Economic Journal_ , 7(1):17–41.

- Chirwa, E.D., Griffiths, P.L., Maleta, K., Norris, S.A. and Cameron, N. (2014). Multilevel modelling of longitudinal child growth data from the Birth-to-Twenty Cohort: a comparison of growth models. _Annals of Human Biology_ , 41(2):168–179.

- Combes, P.P., Duranton, G. and Gobillon, L. (2008). Spatial wage disparities: Sorting matters! _Journal of Urban Economics_ , 63(2):723–742.

- Corak, M. (2013). Income inequality, equality of opportunity, and intergenerational mobility. _Journal of Economic Perspectives_ , 27(3): 79-102.

Correia, S. (2017). reghdfe: Stata module for linear and instrumental-variable/GMM 26
Government support programs such as partial credit guarantees (PCGs) or credit lines can be useful, especially if combined with technical assistance for financial institutions to develop new SME lending technologies. However, more often than not these schemes are underutilized and can even distort the market. International experience with such schemes suggests that they only work when banking systems are sound and if there is a level playing field between different types of institutions so that the PCG can encourage competition in the market. Incorporating international experience and undertaking a careful review of the terms and conditions including testing the design with the private sector is therefore important before launching a new scheme.

The remainder of the paper is organized as follows. Section 2 explains the data and the methodology used. Section 3 summarizes the data collected from commercial banks while section 4 discusses the determinants of bank’s involvement with SMEs. Section 5 concludes.

# **2. Data and methodology**

The data was collected through surveys of commercial banks in five Sub-Saharan African countries, Kenya, Nigeria, Rwanda, South Africa, and Tanzania, to better understand their involvement with SMEs and their business models for serving this segment. All surveys were completed between 2010 and 2012.<sup>2</sup> In each country, the majority of commercial banks were surveyed and interviews were held with those that responded to the questionnaires. The surveys are comparable to those used in previous World Bank studies aimed at better understanding banks’ involvement with SMEs (World Bank, 2007a; World Bank, 2007b; Beck et al, 2008; Stephanou and Rodriguez, 2008; De la Torre et al, 2010; Rocha et al, 2010), but a greater effort was made to also analyze the demand side of SME finance in more depth in Nigeria, Rwanda, and South Africa. The commercial bank surveys were complemented with interviews with alternative providers of SME finance such as microfinance institutions, development banks, private equity funds, and leasing companies. During the qualitative interviews, discussions were typically held with those directly responsible for the SME business.<sup>3</sup> Data on the involvement of banks with SMEs was collected through a tailored questionnaire designed to address four broad areas: (i) the extent of the banks’ involvement with

> 2 Fuchs et al (2011), Berg et al (2012), and Abdel Aziz and Berg (2012) analyze the information collected in South Africa, Nigeria, and Rwanda, respectively.

> 3 To increase the willingness to share information, it was stressed during the interview process that all data provided by the banks would remain anonymous and be presented only in an aggregated fashion.

4
economy on the downside of the iuzuets Curve, tbus increasing their political voice. Others argue the contrary, and that government intervenion was extensive -- although local -- even in pre-industrial societies. This debate should tell us much about attitudes<sup>0owards</sup> poverty and how their evolution might be expected in the Third world if they obey the same<sup>historical</sup> laws. There is, of course, the role of policy<sup>to consider.</sup> Section TV will have quite a bit to say about this issue, in particular 19th century experience with what Michael Lipton (1976) calls the urban bias, and what Theodore Schultz (1964) and others sincc have called the squeeze agriculture policy. These and other policies matter, and most l9th contury industrializers appear to have adopted policies surprisingly similar to the ones which the Third World bas adopted, policies that poverty-orient **e** d conomists have consistently criticized.

Having suggested the main ways in which early industrialization might increase poverty, or at least inhibit its eradicaton, a central issue should be emphasized. It is not economic growth per se that is sometimes said to make the poor poorer, but ratber the processes and policies that are associated with various growth regimes that matter. These pr"e&sses may themselves be regarded as facilitating industrializati **o** rn as its joint product. Having said as much, it becomes clear that the kind<sup>of arguments</sup> sometimes put foward by the optimists are simply not relevant to the claims put foward by the pessimists. For example, in a recent survey for the World Bank Gary Fields (1989) compares changes in the poverty share between Third World countries exhibiting rapid growth and those exhibiting stagnation. Be concludes tentatively that the poor did better during periods of rapid growth. Similarly, the Council of Economic Advisors "discovered' in 1964 that the share in poverty declined by more in periods of rapid American growth. But no one ever suggested that the poor do better when an economy is stagnating than

-9-
2 find empirical patterns suggesting that the willingness to reduce another’s material payoff – either for the sake of achieving more equality or for the sake of being ahead – is stronger among individuals belonging to high castes than among those belonging to low castes.

The last finding (based on Hoff and Pandey 2007) is of particular interest because individuals’ caste status can be considered as exogenous – individuals cannot freely select into different castes but inherit the caste status of their parents.<sup>2</sup> For this reason, and because we control for demographic variables such as education, land ownership, and house type, our finding may represent the impact of a caste culture that put extreme emphasis on the superiority of the high castes and the inferiority of the low castes.<sup>3</sup> It seems quite plausible that caste culture contributes to social preferences such as the motive to be superior in terms of material payoff even when this comes at a cost to equality and efficiency.

# **I. The Role of Spite in Cooperation and Punishment**

In symmetric public goods experiments, players have the same endowment and the same material payoff functions. Free-riders earn a higher material payoff than cooperators in this setting. If a punishment opportunity is introduced such that individuals are first informed about other group members’ contributions to the public good and can subsequently target punishment to specific individuals, many cooperators punish the free riders (Fehr and Gächter 2002). Such punishment occurs despite the fact that the punisher has to pay for sanctioning others. However, there is also evidence indicating that some free-riders punish the cooperators. For example, in a symmetric one-shot public goods experiment with a punishment opportunity (Armin Falk, Fehr, and Fischbacher 2005), roughly 13% of the subjects free ride and punish the cooperators. Interestingly, this punishment occurs only if the cost for the punisher is smaller than the cost for the punished subject, i.e., only if the free riders can increase the difference in material payoffs between themselves and the punished subjects. If instead, every $1 invested in punishment reduces the punished subject’s income by $1, spiteful punishment completely vanishes. This

> 2 Also, the assignment of different social groups and individuals to the two extreme ends of the caste hierarchy generally occurred so long ago (at least a millennium, possibly two) that any cultural differences across castes at the extreme ends of the caste hierarchy that may have determined that assignment should have by now been erased unless the differences were reinforced by the socioeconomic regime in which the caste groups have lived.

> 3 The Indian caste system represents an extreme form of social hierarchy. High castes traditionally could command forced labor from low caste individuals. Low castes are those groups that have been denied basic rights and were subject to the traditional practice of untouchability.
# **Table 2: Regression results – Bottom 20**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|B20|B20|B20|B20|B20|B20|B20|B20|B20|B20|B20|
|Initial income level||-0.0557***|-0.0734***|-0.0551**|-0.0699***|-0.0691***|-0.0588**|-0.0704***|-0.0610**|-0.0411|-0.114***|
|||(0.0206)|(0.0225)|(0.0277)|(0.0231)|(0.0215)|(0.0237)|(0.0226)|(0.0235)|(0.0287)|(0.0428)|
|Δ Mean income|1.005***|0.936***|0.919***|0.896***|0.899***|0.871***|0.977***|0.907***|0.910***|0.977***|0.888***|
||(0.0871)|(0.0994)|(0.0939)|(0.111)|(0.130)|(0.114)|(0.125)|(0.0965)|(0.0985)|(0.116)|(0.149)|
|Life Expectancy|||0.00654***||||||||0.00388*|
||||(0.00178)||||||||(0.00198)|
|School enrollment||||0.000483|||||||0.000876*|
|(gross)||||(0.000374)|||||||(0.000501)|
|Inflation|||||-2.61e-05||||||4.22e-05|
||||||(4.05e-05)||||||(4.43e-05)|
|Inflation variation||||||-3.54e-05***|||||-1.75e-05**|
|||||||(8.23e-06)|||||(7.03e-06)|
|Credit/GDP|||||||-2.26e-05||||-0.000569**|
||||||||(0.000196)||||(0.000255)|
|Population 15-64||||||||0.00741***|||0.00754***|
|||||||||(0.00225)|||(0.00253)|
|Connectivity|||||||||0.000552***||0.000371*|
||||||||||(0.000176)||(0.000187)|
|Democratic||||||||||0.00400|0.000355|
|Accountability||||||||||(0.00519)|(0.00558)|
|Constant|0.000637|0.376***|0.0551|0.327*|0.477***|0.470***|0.398**|0.0276|0.397**|0.263|-0.0113|
||(0.00147)|(0.140)|(0.146)|(0.180)|(0.158)|(0.146)|(0.156)|(0.157)|(0.157)|(0.188)|(0.237)|
|Observations|299|299|298|272|265|277|281|299|291|255|207|
|R-squared|0.605|0.632|0.655|0.597|0.600|0.622|0.575|0.653|0.641|0.599|0.628|
|<br>Number of countries|117|117|116|111|105|111|114|117|115|98|85|

FE Regression. Cluster-robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
in Kordofan and Darfur. As seen previously, though both crops are primarily grown for subsistence, on average only one-fifth of a household’s millet production is sold, compared to, on average, one-third of production for households growing sorghum. Millet ranks as the most subsistent of all crops grown. The greater subsistence requirement of millet means that households that rely on it for their own caloric needs may take greater efforts to ensure that it is cultivated efficiently, and sufficient is produced. Due to the relative poverty of subsistence households, this may be one reason why we observe a positive relationship between millet yield and poverty.

Figure 36: Sorghum and Millet Yield and Poverty by State, 2014/15

### (a) Sorghum

<!-- Start of picture text --> 80%<br>70%<br>Central Darfur<br>60% South Kordufan<br>South Darfur<br>50%<br>North Kordufan<br>40% West Kordufan West Darfur<br>East Darfur<br>30% White Nile Red Sea<br>North Darfur<br>20% Al-Gadarif<br>Kassala<br>Sinnar<br>10% Al-Gezira<br>0%<br>0 0.5 1 1.5 2 2.5 3 3.5 4<br>Yield (100 kg/acre)<br>Poverty Rate<br><!-- End of picture text -->

### (b) Millet

<!-- Start of picture text --> 70%<br>60% West Darfur<br>South Kordufan Central Darfur<br>50% North Kordufan South Darfur<br>40% East Darfur<br>North Darfur<br>30% West Kordufan<br>20%<br>10%<br>0%<br>0 0.5 1 1.5 2 2.5<br>Yield (100 kg/acre)<br>Poverty Rate<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

# Productivity and Farm Characteristics

## Irrigation

Of the four main crops grown in Sudan, yields of sorghum, groundnut, and sesame are higher when grown on irrigated lands (Figure 37). For millet, only a statistically insignificant increase is observed, though this may be due to the very uncommon occurrence of millet being grown on irrigated lands. For 38