rafmacalaba/datause-encoder-data huggingface dataset:
household received an RDP house from the government. Based on this, Table 7 presents the distribution of RDP beneficiaries ranked by market income deciles. Note that the distribution of RDP households is relatively flat with relatively small shares in the poorest and richest deciles. Note that since houses have been built since 1995, people who have them so long might be today in a higher decile, but 10 or 20 years ago could have been in lower deciles.

|Table 7. South Africa. Distrib<br>RDP hou<br>(Percent bymarket|ution of benefic<br>sing<br>income decile)|iaries of|
|---|---|---|
|Households<br>lives in<br>RDP|Household<br>has an RDP<br>house<br>elsewhere|Total<br>RDP|
|Poorest decile<br>6.3%|9.5%|6.7%|
|2<br>10.5%|9.5%|10.4%|
|3<br>12.6%|8.8%|12.2%|
|4<br>13.6%|9.0%|13.1%|
|5<br>14.2%|10.4%|13.8%|
|6<br>13.8%|10.2%|13.4%|
|7<br>11.9%|9.9%|11.7%|
|8<br>10.9%|14.2%|11.2%|
|9<br>5.0%|10.6%|5.6%|
|Richest decile<br>1.3%|7.9%|2.0%|
|Total<br>100%<br>Source: Own estimates based on IES|100%<br>2010/2011.|100%|

# **Progressivity of Social Spending as a Whole**

How progressive is social spending overall? As we saw above, cash transfers, education spending and health spending as a whole are strongly progressive with cash transfers taking the lead. The only categories of social spending that were progressive only in relative terms were spending on college and university education.

Remember that spending concentration curves for a <mark>2</mark> transfer targeted to the poor are above the 45 degree <mark>3</mark> diagonal, if they are progressive in absolute terms, when the share of benefits going to the bottom <mark>4</mark> deciles is higher than the share of market income of <mark>5</mark> those deciles. In figure 17A the bottom 40 percent of <mark>6</mark> the income distribution receives about 59 percent of <mark>7</mark> spending on direct transfers and 43 percent of <mark>8</mark> spending on in-kind transfers, but accounted for <mark>9</mark> about 1.6 percent of total market income across <mark>Richest decile</mark> deciles. The results thus confirm that when combined, direct transfers (cash transfers and FBS) <mark>Total</mark> are progressive in absolute terms. In-kind transfers in the form of education and health services are also progressive in absolute terms but not to the extent of direct transfers (figure 17B).

Figure 17. South Africa: Concentration curves of benefits, 2010 <u>(Share of market income)</u> <!-- Start of picture text --> A. Direct transfers B. In-kind transfers<br>1 1<br>0.8 0.8<br>0.6<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Cumulative Proportion of Population<br>Cumulative Proportion of Population<br>45 Degree Line Market Income<br>45 Degree Line Market Income<br>Direct Transfer In-kind Education Transfer<br>Direct Transfers In-Kind Transfers In-kind Health Transfer<br>Cumulative Proportion of Benefit Cumulative Proportion of Benefits<br><!-- End of picture text --> <mark>Source: Own estimates for South Africa based on IES 2010/11.</mark> 27
Table 5: Spillover Effects using distance - II

||Mean<br>(1)|Median<br>(2)|Coefficients<br>(3)|
|---|---|---|---|
|Treatment (T)|||0.037<br>[0.031]|
|Number of treated women within 0-200 radius|40.40|35.00|0.007***<br>[0.001]|
|Number of treated women within 200-400 radius|20.47|16.00|0.009***<br>[0.002]|
|Number of treated women within 400-600 radius|11.91|4.00|0.007***<br>[0.002]|
|Number of treated women within 600-800 radius|15.50|6.00|0.005**<br>[0.002]|
|Number of treated women within 800-1000 radius|11.08|1.00|0.005*<br>[0.003]|
|Number of treated women within 1000-1,200 radius|7.88|0.00|0.002<br>[0.003]|
|Number of women within 0-200 radius|56.78|47.00|-0.002**<br>[0.001]|
|Number of women within 200-400 radius|29.84|24.00|-0.004***<br>[0.001]|
|Number of women within 400-600 radius|18.33|7.00|-0.005***<br>[0.001]|
|Number of women within 600-800 radius|21.28|8.00|-0.003*<br>[0.002]|
|Number of women within 800-1000 radius|15.81|4.00|-0.004*<br>[0.002]|
|Number of women within 1000-1,200 radius|11.50|0.00|0.000<br>[0.002]|
|Observations|||2,637|
|R-squared|||0.234|

Note: The dependent variable takes the value 1 if a woman reports having voted in the February 2008 elections and had a verifiable ink mark on her thumb. In column 1, the mean of each variable is reported. Column 2 reports the coefficient along with the standard errors in parentheses below the coefficient, clustered at the gepgraphic cluster level. The symbols *, **, *** represent significance at the 10, 5 and 1 % respectively. All specifications include village fixed effects and the following controls: number of registered female voters, whether woman has a NIC or CNIC, level of schooling, age, zaat status, TV access, number of children between 0-5, total land owned, index of mobility and whether woman seeks advise from "Pir".
with probability one third and J$10,000 or J$15,000 with probability two thirds. In this case, JBDC was better prepared to pre-announce the dates of the training and to commit to those dates. This was reflected in overall higher attendance levels as we show below.

In order to implement the randomization, we asked each participant to randomly pick a card where a code was written. As they picked the card, they exited the room and went to another room where they were told the treatment condition that corresponded to that card. In this case, all treatments were framed as an scholarship for the course.<sup>13</sup> Half of the participants assigned to a positive price were also randomly allocated to a credit option, as in the BDM design, where they were allowed to pay in 3 installments (before the first, sixth and eight class, respectively).

Column 4 of Table 1 shows that random allocation to offered prices worked well. Table A3 shows that balance was also achieved across treatments (credit vs no credit).

# **3.5 Training Implementation**

Entrepreneurs who purchased the course were invited to attend training. For the BDM sample, the training started in December 2017 and lasted for 10 weeks. Seven groups were formed in 6 different locations. For the TIOLI sample, there were two training periods, one from March to May 2018 (8 groups in 4 locations), and a second one from May to July 2018 (3 groups in Kingston).

# **3.6 Data**

We rely on three sources of data: baseline data, administrative data on payment and attendance, and follow-up data. The baseline survey was conducted either on the phone or online on a rolling basis between June 2017 and May 2018. Attendance and payment data were recorded by JBDC.

We conducted a phone follow-up survey with all entrepreneurs who attended demonstration sessions from late September 2018 to February 2019. We hired a private local survey firm with international experience, and as we expected, given the low trust levels and high incidence of violence, attrition was an important problem. From the BDM sample, 302 (66%) answered the survey, while from the TIOLI sample 266 (71%) did. Compared to other phone surveys, these response rates are not low. However, they limit the range of

> 13 For example, respondents were told “You won a scholarship of 75%, you are invited to take the course at a price of J$5,000. You need to pay this amount within the next 2 weeks to be admitted into the course.”

14
modeled travel time to places with 50,000 inhabitants circa 2000 (A. Nelson, 2008). (Anderson et al., 2015) find variation in their examination of global data products of cropping systems models. For livestock, we transform the 5 major livestock into international values from livestock products (namely, meat, milk, eggs, honey and wool). The forest (non-wood products, wood-products) components relies on a remote sensing model to estimate forest loss. With regards to the non-timber values, limitation from the sources present two challenges. The estimates use simple averages from the literature that accordingly assume a property of uniformity in the value of a hectare of forest as similar across the world and the sample of forests with literature drawn for the study is representative of the world (Siikam ̈aki, Santiago-Avila,<sup> ́</sup> and Vail, 2015). The fishing model relies on proximity and association with ports or water bodies.<sup>19</sup> Finally, since we do not incorporate any information on hunting, the result is an even distribution across units and time.

Another source of uncertainty is the geographic distribution of the components. Ideally, we would use subnational prices, however it was not feasible, and the results do not reflect this occurrence, including administrative units with higher variation of prices due to the heterogeneity of distinct urban and rural areas.

# **4 Conclusion**

Natural disasters impact both lives and livelihoods and a higher frequency and severity of disasters will likely increase in a changing climate. Socio-economic estimates at the local level inform disaster preparations of the exposure of physical assets and production to natural disasters and have implications for food security. Significant advancement in the spatial allocation of indicators has occurred in the past 10 years such as population (e.g Leyk et al., 2019). The advantages of gridded data as a common spatial unit of integration and the cross-entropy models are clear. These common units allow us to examine within-country characteristics, especially in the case of spatial data that do not conform with each other such as administrative boundaries and natural hazards to inform analyses with local estimates. We present a novel data set that disaggregates the national and regional accounts of the agriculture sector across areas as a result of a model where we use ancillary data including satellite data. This allows us to estimate especially in countries that have a relatively higher share of agricultural activity in the entire economy. Then, we examine the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, where nearly 1.2 billion people live, and find an estimated US$432 billion of agricultural GDP in 2010.

> 19The freshwater case does not account for any variation, whereas the marine port locations incorporate variation on vessel holds.

25
2.2 Can you please tell me your age group?

- 1 Under 16

- 2 16 – 20

- 3 21 – 25

- 4 26 – 35

- 5 36 – 45

- 6 46 – 55

- 7 56 – 65

- 8 66 or over

- 2.3 What is your marital status?

- 1 Married

- 2 Living with domestic partner (all suggestions on the questionnaire are from Estanislao) 3 Single

- 4 Separated

- 5 Widowed

- 6 Divorced

- 2.4 How many people do you share your house with?

- 2.5 What is your religion? _[Options and codes to be filled in as locally appropriate]_

- 1

- 2

- 3

- 4

- 5

- 2.6 In terms of your ethnicity, do you consider yourself ...? _[Options and codes to be filled in as locally appropriate]_

- 1

- 2

- 3

- 4

- 5

- 2.7 (If appropriate) Do you belong to a particular tribe? _[Options and codes to be filled in as locally appropriate]_

- 1

- 2

- 3

89
# **Appendix 2:  Data Sources**

# **Production**

The input-output (IO) matrix is taken from the social accounting matrix (SAM) developed in Debowicz et al (2013), where we use the tab PSAM2C to derive the matrix representing 2010 technology. The IO matrix is 50x50, but in order to simplify computations we have aggregated the matrix to 27x27, where row and column 27 represent a single aggregate import. Sectoral value-added functions are assumed to be Cobb-Douglas, with shares of the 5 different types of capital, urban and rural labor, and land giving the weights in the valued added function. These are also derived from PSAM2C. We have not directly estimated sectoral investment functions. Rather, we have assumed that the investment function, as in equation (2) is uniform across the 5 types of sectoral capital and is given by the value-added function for the construction industry, taken from the SAM. Rates of investment will differ across capital types because of the differing rates of return to capital in different sectors. Finally, the coefficients �, � representing the tax evasion and credit rationing in equations (3), (4) are taken to be uniform across sectors and are determined from calibration. For this exercise, we have assumed that �= 0, that is, no credit rationing.

# **Consumption**

For the utility function described in equation (5), Appendix 3, we use weights derived from the SAM, tab EH. This gives shares of consumption for each household type, and we assume Cobb-Douglas utilities for each household. Factor allocations for each household category are taken from PSAM2C, which allocates various types of capital, labor and land across households. We aggregate land into a single type, and labor is aggregated into urban and rural.

40
# **Figure 4: Share of Renters, Mortgage Owners and Outright Owners, 2007**

<!-- Start of picture text --> Austria Belgium Cyprus Czech Republic Denmark<br>.8 .8 .8 .8 .8<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>Estonia Finland France Greece Hungary<br>.8 .8 .8 .8 .8<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>Ireland Italy Latvia Lithuania Luxembourg<br>.8 .8 .8 .8 .8<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>Netherlands Poland Portugal Slovak Republic Slovenia<br>.8 .8 .8 .8 .8<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>Spain Sweden United Kingdom<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>Renters Owners - Mortgage Outright Owners<br><!-- End of picture text --> Source: SILC - EU Statistics on Income and Living Conditions, 2007 17
sorting and the extent to which intermediate cities in India are becoming more attractive for manufacturing plants. We study the impact for the sector’s performance through measures of average labor productivity and total factor productivity (TFP).

Our work exploits several forms of variation to identify the effects. First, our data include two surveys before and after the upgrades, which allows us to exploit pre-post variation for the GQ upgrades. Second, we use GIS software to code how far districts are from the GQ network. Throughout this paper, we measure effects for nodal districts in the GQ network, but we do not ascribe a causal interpretation to these effects because the GQ upgrades were in large part designed to improve the connections of these hubs and the GQ upgrade decision may have been endogenous to the growth prospects of these hubs. Instead, our key focus is on non-nodal districts that are very close to the GQ network compared to those that are farther away. We specifically compare non-nodal districts 0-10 km from the GQ network to districts 10-50 km away (and in some specifications with additional concentric rings to 200 km away). Additional sources of variation come from the sequence in which districts were upgraded, differences in industry traits within the manufacturing sector, and differences in the traits of non-nodal districts 0-10 km from the GQ network.

We find generally positive effects of the GQ upgrades on the organized manufacturing sector. Panel estimations find substantial growth in entry rates in non-nodal districts within 10 km of the GQ network after the GQ upgrades. These patterns are absent in districts 10-50 km away, and the data suggest that there might have even been declines in entry rates in districts farther away (perhaps indicative of a more substantial shift of activity towards the GQ network due to the improved connectivity). Heightened entry rates are evident in districts where the GQ project upgraded existing highways and where the GQ project constructed new highways where none existed before.

Beyond entry rates, we find positive but statistically insignificant impacts for the total level of manufacturing activity across all districts within 10 km of the GQ network. There is greater heterogeneity on this dimension, however, with the construction of new highways being associated with aggregate activity gains. As we discuss, the limited impact for upgrades of existing highways for total activity could be due to the fact that our sample end point in 2007 is not sufficiently removed from the GQ upgrades— such that we can observe the marginal entry decisions but not the long-term consequences of these decisions for business locations. The differences could also be due to higher exit rates for incumbents in the area. While we can observe new firms with our data, we are unfortunately limited with respect to measuring the exit margin.

3
_Table 14: Dependent variable – The firm is green_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|GOB|0.0610|0.0403|-0.028|-0.070|-0.058|-0.106|-0.028|-0.097|
||(0.1203)|(0.1207)|(0.269)|(0.282)|(0.285)|(0.300)|(0.342)|(0.357)|
|GOBx(1-IQ)|||0.217|0.275|0.290|0.367|0.247|0.382|
||||(0.414)|(0.458)|(0.461)|(0.511)|(0.661)|(0.711)|
|ROA||0.0558||0.062||0.063*||0.061|
|||(0.0367)||(0.038)||(0.038)||(0.038)|
|LTA||0.0097||0.001||-0.001||0.005|
|||(0.0389)||(0.043)||(0.042)||(0.040)|
|ln(SIZE)|0.2724***|0.2713***|0.274***|0.272***|0.274***|0.272***|0.273***|0.272***|
||(0.0502)|(0.0501)|(0.050)|(0.050)|(0.050)|(0.050)|(0.050)|(0.050)|
|ln(AGE)|0.0732|0.0731|0.072|0.072|0.072|0.072|0.073|0.073|
||(0.0506)|(0.0506)|(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|
|SOE|-0.0418|0.0116|-0.068|-0.012|-0.089|-0.036|-0.066|-0.019|
||(0.2735)|(0.2817)|(0.273)|(0.282)|(0.273)|(0.279)|(0.267)|(0.273)|
|FEM|0.0597|0.0616|0.056|0.058|0.056|0.058|0.058|0.060|
||(0.1049)|(0.1048)|(0.106)|(0.106)|(0.106)|(0.106)|(0.105)|(0.105)|
|Const|-0.8822***|-1.0625***|-0.897***|-1.025***|-0.926***|-1.052***|-0.912***|-1.085***|
||(0.2274)|(0.3754)|(0.221)|(0.396)|(0.218)|(0.378)|(0.215)|(0.365)|
|N. Obs|9,802|9,802|9,802|9,802|9,802|9,802|9,802|9,802|
|R2|0.1480|0.1499|0.148|0.151|0.149|0.151|0.148|0.150|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule o|f Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

31
**Table 7: Summary of Empirical Literature on Regulatory Governance for the Power Sector**

|Study|Period|Countries|Findings|
|---|---|---|---|
|**Studies that co**<br>|**mpare formal**<br>|**and informal regul**<br>|**atory practice**<br>|
|Correa et al.,<br>2006|Early 2000s|Brazil|Correlation between scores on formal attributes of the regulatory system and informal operation of the<br>regulatory system is relatively low, at 0.7, for several infrastructure regulators in Brazil (including energy).|
|Andres et al.,<br>2007|Early 2000s|Latin America<br>(various)|Correlation between formal and informal scores for power sector regulators in several Latin American<br>countries dimensions of regulatory autonomy, transparency and accountability are generally very low,<br>between 0.3 and 0.4.|
|Gillardi and<br>Maggetti,<br>2010|Mid 2000s|Western Europe<br>(various)|Weakly positive, but statistically insignificant, relationship between formal measures of regulatory<br>independence and those that capture actual informal regulatory practice. Formal independence may be<br>higher or lower than informal independence dependingon individual cases.|
|Hanretty and<br>Koop, 2010|Mid 2000s|Western Europe<br>(various)|Find a statistically significant positive correlation between formal measures of regulatory independence and<br>those that capture actual informal regulatory practice.|
|**Studies that m**<br>|**easure indepe**<br>|**ndence of regulator**<br>|**s**<br>|
|World Bank,|Mid 2000s|Sub‐Saharan|Electricity regulators score better than those in other infrastructure sectors on autonomy, transparency and|
|2010||Africa (various)|accountability, but still only score around 50% against best practices in these areas overall.|
|Pargal et al.,<br>2014|2010s|<br>Indian States<br>(various)|<br>Most State Electricity Regulatory Commissions have not achieved true autonomy from State Governments,<br>lack necessary resources, do not create adequate mechanisms for transparency and accountability.|
|**Studies that co**<br>|**mpare efficac**|<br>**y of regulation betw**|<br>**eenprivate and state‐owned utilities**<br>|
|Vagliasindi,<br>2013|2000s|Nigeria,<br>Ukraine|State‐owned enterprises are less responsive than private ones to regulatory incentives for loss reduction<br>and investment.|
|Khanna, 2013|2000s|South Africa,<br>Mozambique|Performance contracts are not effective in delivering performance improvements for state‐owned utilities.|
|Mountain,<br>2014|2000s|Australia|State‐owned network service providers are less responsive to than privately owned companies to the<br>efficiencyincentives embodied inprice capregulation.|
|Mirrlees‐<br>Black,2014|2000s|Australia|State‐owned network service providers tend to invest more heavily than privately owned companies under<br>the same regulatoryregime and are more likelyto end‐upwith excess capacity.|
|The World<br>Bank,2010|2000s|24 Sub‐Saharan<br>African countries|Based on cross‐country analysis, state‐owned utilities with performance contracts do not perform any<br>better than those without,unless independent auditingis included in the contractual framework.|

1
# **TableA11. Factors affecting the non-poor household's probability to live in the GMA** **<u>and, once in the GMA, the probability to participate in the VAG/CRB</u>**

|||Probitmode|lsfortheh|ousehold's proba<br>Participate|bility to<br>in the|
|---|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CR|B<sup>_a_</sup>|
|||(1)||(2)||
|park2|Kafue dummy variable|-1.014|***|-0.366||
|||(0.341)||(0.257)||
|park3|Lower Zambezi dummy|-0.532||-0.340|**|
|||(0.340)||(0.149)||
|park4|Luangwa dummy|-0.434||-0.323|**|
|||(0.350)||(0.132)||
|hage|Age of the household head in years|-0.005||-0.002||
|||(0.004)||(0.008)||
|fhhh|Female-headed household dummy|0.255|**|-0.092||
|||(0.120)||(0.184)||
|maxedu|Education of the most educated household<br>|-0.039|**|0.051|**|
||member in years|(0.019)||(0.022)||
|c0to14|Number of children below 15 years|-0.045|*|0.032||
|||(0.024)||(0.037)||
|f15to60|Number of female members 15-60 years old|0.023||-0.059||
|||(0.050)||(0.100)||
|m15to60|Number of male members 15-60 years old|0.085|*|0.065||
|||(0.044)||(0.089)||
|m61plus|Number of members older than 60 years|<br>-0.109||<br>0.045||
|||(0.097)||(0.218)||
|kroad|Distance to the nearest main road in km|0.006||0.010|***|
|||(0.005)||(0.002)||
|kbsch|Distance to the nearest basic school in km|0.006||-0.011||
|||(0.009)||(0.010)||
|kheal|Distance to the nearets health centre in km|-0.004||0.015|***|
|||(0.005)||(0.004)||
|vacast|Value of consumer durable assets in ZMK|-0.020|**|-0.005||
|||(0.008)||(0.019)||
|dcoop|Participation in cooperatives dummy|-0.353|***|0.466|***|
|||(0.114)||(0.178)||
|nproj|Number of projects in the community|0.044|**|0.065|***|
|||(0.020)||(0.019)||
|dcfnd|CRB funded dummy variable|<br>0.612|***|<br>0.572|***|
|||(0.172)||(0.174)||
|npart|Number of participants in the CRB/VAG|<br>0.083||<br>0.189|***|
|||(0.054)||(0.024)||
|cons|Intercept|0.734|**|-2.438|***|
|||(0.325)||(0.311)||

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

26