rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> A. B. C=A+B D. E. F=D+E<br>Reinsurance Reinsurance Total reinsurance Reinsurance Reinsurance _ | Total reinsurance<br>original portf. original portf. portf. updated portf. updated portf. updated portf.<br>Motor vehicle liability insurance 0 460 460 10,000 253 10,253<br>Other motor insurance 0 247 247 2,500 136 2,636<br>Marine, aviation and transport insurance 2,500 240 2,740 2,500 452 2,952<br>Fire and other damage to property insurance 4,500 4,500 7,500 0 7,500<br>Fire non natural catastrophe per risk 1,530 1,530 1,559 1,559<br>Fire natural catastrophe per event (EQ) 722 722 670 670<br>General liability insurance 0 222 222 2,500 122 2,622<br><!-- End of picture text -->

||A.<br>Reinsurance<br>original portf.|B.<br>Reinsurance<br>original portf.|C=A+B<br>Total reinsurance<br>portf.|D.<br>Reinsurance<br>updated portf.|E.<br>Reinsurance_ <br>updated portf.|F=D+E<br>| Total reinsurance<br>updated portf.|
|---|---|---|---|---|---|---|
|Motorvehicle liability insurance|0|460|460|10,000|253|10,253|
|Othermotor insurance|0|247|247|2,500|136|2,636|
|Marine, aviation and transport insurance|2,500|240|2,740|2,500|452|2,952|
|Fire and otherdamage to property insurance|4,500||4,500|7,500|0|7,500|
|Fire non natural catastrophe per risk||1,530|1,530||1,559|1,559|
|Fire natural catastrophe per event (EQ)<br>||722<br>|722<br>||670<br>|670<br>|
|General liability insurance<br><br>|0<br><br>|222<br>|222<br>|2,500<br><br><br>|122<br><br>|2,622<br>|
|[Tota<br>CT|oo—|en|taza=~|]<br>25000<br>|sat|2atga|

<!-- Start of picture text --> G. H. I. J=A*(1-G-H) |K=D*(1-G-1)<br>[%] Expected Reinsurance Reinsurance P &Lstatement | P & L statement<br>Line of business ultimate loss ratio commission commission costs of prop RI | costs of prop RI<br>original portf. updated portf. original portf. updated portf.<br>Motor vehicle liability insurance 70% nil. 18% 0 1,200<br>Other motor insurance 60% mil. 27% 0 325<br>Marine, aviation and transport insurance 80% 10% 9% 250 275<br>Fire and other damage to property insurance 70% 20% 18% 450 900<br>General liability insurance 60% nil. 27% 0 325<br>tot 080s<br><!-- End of picture text -->
unorganized sector declines from 2001 to 2006. Our panel is unfortunately too short to identify whether this decline is a short-term fluctuation (similar to 2000 to 2005 declines in manufacturing in Table 1a) or part of a long-term trend.

Looking at the relative growth of female- and male-owned businesses, one sees larger increases for female-owned businesses. This parallels what we observe in the manufacturing sector. However, the differences in the services sector are not very strong. Moreover, they are somewhat obscured by the even larger increases in organizational categories where we do not observe the gender of the business owner (e.g., cooperatives). Excluding these other categories, women-owned businesses account for 27.7% of the net growth in the services sector unorganized employment, well above their baseline share. Thus, while we see elements in the services sector of increased women’s business ownership contributing to the persistence of small-scale businesses, the pattern is only suggestive and substantially weaker than in manufacturing.

# **4. Variation across Districts and Industries**

We turn now to describing traits of places and industries that correlate with increases in female business ownership in the unorganized sector. As a reference, App. Table 1a-1b lists the states that are in our sample, the change in their organized sector shares, and the changes in their unorganized sector shares. App. Tables 2a-2b provide statistics by two-digit NIC industry in manufacturing and services, respectively.

Table 3a shows correlations of unorganized sector changes with district-level traits taken from the 1991 and 2001 Population Censuses. We show traits from the district level to maximize the number of observations and the granularity in local conditions. Column headers indicate sector and time period, and district traits are measured at the start of each time period. We consider the univariate correlation to several factors that have been found important for India’s spatial development by prior work. Population and population density are natural baselines. We next model the district’s age structure as the ratio of working age population to non-working age population. This ratio relates to the demographic dividend often discussed in the Indian context. We also consider the share of the district population in a scheduled caste/tribe and the female labor force participation rate (e.g., Iyer et al. 2011, Klapper and Parker 2011).

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# **Table 12:  Main Determinants of Spending for Rural Roads**

|_Dependentv_|_ariable: Rel_<br>|_ease_|_dfundsforf_<br>|_eede_|_r roads_<br>|_maint_|_enance in 20_<br>|_06(p_|_er capita) _<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**||**(2)**||**(3)**||**(4)**||**(5)**||**(6)**||**(7)**||
|Road|1.44||1.2||-5.08||||||||2.93||
|condition|_3.11_||_3.14_||_5.18_||||||||_3.44_||
|Network<br>|**4.71E+05**|******|**4.74E+05**|******|||**4.85E+05**|******|||||**4.81E+05**|**|
|length<br>per capita|_5.19E+04_||_5.17E+04_||||_4.71E+04_||||||_5.37E+04_||
|Number of<br>|3.89E+06||||||||**1.86E+07**|******|||5.38E+06||
|constituents<br>per capita|_4.29E+06_||||||||_6.37E+06_||||_4.36E+06_||
|Number of<br>|||4.23E+06||||||||**2.00E+07**|******|||
|NRM<br>constituents<br>per capita|||_5.19E+06_||||||||_7.88E+06_||||
|A|0.01||0.01||||||||||0.01||
|rea|_0.01_||_0.01_||||||||||_0.01_||
|Poverty|||||||||||||2.07||
|rate|||||||||||||_2.23_||
|Ctt|-3.25||5.46||**738.64**|******|97.7||**431.67**|******|**488.78**|******|-127.08||
|onsan|_101.35_||_99.79_||_97.04_||_62.46_||_92.12_||_83.47_||_174.94_||
|# of obs.|55||55||55||55||55||55||52||
|R^2|0.68||0.68||0.02||0.66||0.14||0.11||0.70||

Notes: (**) implies significance at the 5 percent level and (*) at the 10 percent level. Standard error is reported in italics.

On the link between spending in roads and agricultural potential, results are similar: despite statements, agricultural potential<sup>29</sup> does not appear to be a major consideration when allocating the road maintenance budget in Uganda.<sup>30</sup> Using the 2006 figures of the amount of money released to the districts under the heading of Road Maintenance Conditional Grants, a simple correlation test was run with the agricultural potential data. The results show that there is no correlation between the agricultural output of a district and the amount of road grants received: 0.05 for the correlation coefficient between coffee potential and road grants 0.05; -0.02 between cotton potential

> 29 See annex 7 for details on the methodology.

> 30 The DUCARIP does not address the agricultural potential subject nor mention agricultural potential as a factor determining road intervention. However it does say “Implementation of DUCARIP will entail close collaboration between MOWT and MFPED. Programs supported by Development Partners like PMA (Plan for Modernization of Agriculture) will require liaison and coordination with other stakeholders, e.g. Ministry of Agricultural...” (“The Ten-Year District, Urban and Community Access Roads Investment Plan (Draft),” Ministry of Works and Transport , 2008, page 23

> http://www.roadfund.ug/Resources%20Files/DUCARIP-FinalDraft-19.02.2008.pdf

34
well-being due to reduced stress about multitasking while caring for children and more mental bandwidth. Thus, we predict increases in time spent on income generating activities and increased earnings as primary outcomes. In addition, we anticipate improvements in self-reported well-being and financial resilience as secondary outcomes due to improvements in women’s employment and earnings.

**Effects of childcare on women’s decision-making autonomy, gender attitudes, and intrahousehold dynamics.** Our anticipated effects on these dimensions of women’s empowerment are theoretically ambiguous. Under a collective model of household decision-making, affordable childcare could increase women’s employment and relative earnings, thereby increasing women’s intrahousehold bargaining power and empowerment (Hiller and Tour ́e, 2021). Alternatively, the theory of gender deviance neutralization predicts that women may feel compelled to perform stereotypical gender roles within the home in order to neutralize the act of ceding some of their childcare responsibilities to a childcare center (Atkinson and Boles, 1984; Greenstein, 2000). Thus, any potential improvements in women’s empowerment resulting from increases in bargaining power might be offset by women engaging in behaviors to emphasize their traditional female roles and conform to prevailing gender norms. The overall effect of childcare on women’s decision-making autonomy, gender attitudes, and intrahousehold dynamics in this context is therefore an empirical question.<sup>6</sup>

**Effects of childcare on child development.** We hypothesize that children of public works participants in sites with the childcare intervention will spend more time in childcare centers. The resulting impact on child development depends on the relative quality of the alternative childcare options. Given that caregivers in these community-based childcare centers are trained and supervised, we anticipate that there could be improved early childhood development for enrolled children due to increased stimulation by trained childcare providers and increased access to age-appropriate toys and learning materials. Conversely, if children would instead have stayed at home with an engaged caregiver, then it is possible that receiving individualized attention at home would be more beneficial than the care received in a group setting.

> 6In contrast to predominant American expectations on maternal practice, anthropological research on motherhood ideals among urban Asante market traders in neighboring Ghana (Clark, 1999) and an ongoing complementary qualitative study with a sample of urban mothers in Burkina Faso suggest that parenting norms in this context emphasize meeting children’s resource needs, rather than necessitating mothers spend time with them.

9
## **Figure 5: Sector of employment prior to arrival in Chad of newly registered refugees**

<!-- Start of picture text --> Other 2<br>Self-employed 8<br>Informally employed 1<br>Formally employed 1<br>Agriculture/Livestock/Fishing 87<br>0 25 50 75 100<br>Percent<br><!-- End of picture text -->

## Source: UNHCR December 2023.

In view of the likeness of recently arrived refugees and those who have stayed in Chad longer, the remainder of this paper extends the description of Sudanese refugees staying in Chad as captured by the ECOSIT IV survey, with a particular focus on differences between better and worse off refugee households.

# 4. Poverty, refugee income and assistance

When analyzing the potential fiscal burden refugees might present, poverty measurement takes on particular relevance, for at least three reasons.

First, the poverty line, the yardstick against which consumption is compared to determine whether a person is poor, presents an objective standard for the minimum level of consumption needed to assure a dignified life. In a global context, the International Poverty Line is used to cost Global Basic Needs (World Bank and UNHCR 2024). As this note focuses on Chad, the national poverty line of FCFA 684 per refugee per day is used as benchmark.<sup>7</sup> Second, a straightforward way to estimate income earned by poor refugees is by deducting the (preassistance) poverty gap<sup>8</sup> from the poverty line. This difference reflects the average amount poor households consume net of humanitarian assistance and is a good proxy for the income earned by poor

> 7 Typically, poverty lines consist of two parts. The first part covers a food allowance needed for adequate nutrition based on a minimum caloric intake (2,400 calories in Chad) using a locally relevant basket of goods. The second part covers a non-food allowance reflecting the cost of essential non-food items such as clothing, shelter and private expenses on health and education (Ravallion 1998). Those individuals who consume less than the poverty line are considered poor.

> 8 The poverty gap is calculated as = ~~(~~ 1) ∑ =1 − ) where _N_ is the total population, _H_ is the total population of poor <mark>refugees (who are living at or below the poverty line</mark> gg NN HHii (zz _z_ yy ) ii <mark>and</mark> _<mark>y</mark> i_ <mark>the c</mark> onsumption <mark>of the poor individual</mark> _<mark>i</mark>_ <mark>.</mark>

8
within-student across-subject comparisons. In addition, we build on Metzler and Woessmann’s (2012) proposal to use contemporaneous within-student within-teacher acrosssubject variation to identify impacts. This approach, initially applied by Melzer and Woessmann (2012) to Peruvian data, was subsequently implemented by Bietenbeck et al. (2017) and Hanushek et al. (2018) in multi-country settings (in 13 Sub-Saharan African countries spanning years 2000 to 2007 in the former and 31 countries in the latter).

In order to obtain consistent estimates of the impact of teacher content knowledge using a contemporaneous specification one needs to assume either (i) that only contemporaneous teacher’s knowledge matters to the production of current achievement or (ii) that there is no correlation between the current teacher’s knowledge and past teachers’ knowledge (Todd and Wolpin 2003). We relax both assumptions by exploiting the fact that we have data for both the current and previous teacher. As a result, our approach is more in line with the value-added approach where, as a rough approximation, prior achievement is used as a sufficient statistic for the history of prior teacher inputs.

# _2.2 Data_

We use data from the Service Delivery Indicators (SDI)—an ongoing Africa-wide program that aims to collect informative and standardized measures of what primary teachers know, what they do, and what they have to work with. The SDI program—piloted in Tanzania and Senegal in 2010 (Bold et al. 2010, 2011)—grew out of concern about poor learning outcomes observed in various student tests, as well as evident shortcomings in school-level service delivery in fast-expanding education systems.

To date, the SDI program has collected data from a total of seven countries (eight surveys): Kenya (2012), Mozambique (2014), Nigeria (2013), Senegal (2010), Tanzania (2010, 2014), Togo (2013), and Uganda (2013).<sup>6</sup> In each country, representative surveys of between 150 and 760 schools were implemented using a multistage, cluster-sampling design. Primary schools with at least one fourth-grade class formed the sampling frame. The samples were designed to provide representative estimates for teacher effort, knowledge, and skills in public primary schools, broken down by urban and rural location. For four of the six non-pilot surveys, representative data were also collected for private primary schools. Across the eight surveys, the SDI collected data on 2,600 schools, over 21,000 teachers and 24,000 students.<sup>7</sup>

> 6 The survey in Nigeria covered only four states (Anambra, Bauci, Ekiti, Niger).  The data for Nigeria are therefore not nationally representative, but representative of these four states.  For simplicity we nevertheless refer to this data set as Nigeria.

> 7 See Bold et al. (2017) for details of the sample.

5
The results have included a remarkable increase in the quantity of FDI inflows, rising from the levels around $2 billion per year in the late 1980s, to around $4 billion during the early 1990s and averaging $10 billion per year since 1994. But even these dramatic increases in amounts are perhaps less important than the changes in quality, as most of the additional FDI during the last five years has created world-class facilities that are competitive in price and quality with others all over the world, and that therefore can, and do, include Mexico as parts of the global strategies of multinational corporations. The days of high-cost, old technology factories producing for Mexican consumers only are gone, as are the days of manufactured exports consisting mostly of simple, low-value-added products based on cheap Mexican labor. Mexico today is more and more integrated in world-class production linkages, and this includes not only foreign-invested companies but also a growing number of purely Mexican firms that have also taken advantage of the changed policy framework.

**Table 1**

Foreign Direct Investment in Mexico and Policy Changes: 1989-97 ($ bn or percentages)

|||App<br>Sha|roximate<br>res (%)|Approxi|mate Shares|Estimated<br>Shares (%)|Policy Changes|
|---|---|---|---|---|---|---|---|
||$ FDI<br>Inflow<br>s|M&A|Greenfiel<br>d|Tradable<br>s|Non-<br>Tradable<br>s|of<br>globalizing<br>FDI||
|1989|3.0|10<br>I<br>l<br>__|90<br>I<br>I|40|60<br>l|30<br>______|NewFDIregulations<br>- elimination of<br>51% Mexican majority in mostsectors|
|1990|2.6|10|90|40|60<br>:____<br>_|40<br>___________<br>____tourism,|Privatization of telephones, mining, steel,<br>airlines|
|1991|4.8|20|80|40|60|50|NAFTA negotiations begin|
|1992|4.4|20|80|40|60|60<br>=___=_____|Privatization of banks, financial system<br>_l_<br>reforms|
|1993|4.4|30|70<br>____<br>_|40<br>___<br> <br>_________|60<br>__________|60<br>_____________|Inmminent<br>NAFTA approval, new FDI<br>law codifyfing the 1989 regulations, more<br> privatizations|
|1994|11.0|30|70|40|60|70|NAFTA approval, relaxed FDI<br>restrictions|
|_1995_|9.5|50|50|80|20|80|Economic crisis; deregulation in gas,<br>electricity|
|1996|7.6|50|50|85|15|90|Banking system opening to FDI[|
|1997|12.1|60|40|85|15|90|Long distance telephone,<br>communications deregulation|

Source: Bank of Mexico on FDI flows and FIAS' estimates on shares.

20
- 33 -

# Table 12b. Impact of AGOA: Inputs Sourced from the United States

|||**Mauritius**|||
|---|---|---|---|---|
|ParameterValues||GrowthinExports (re<br>(inpercen|lativeto2000)<br>t)||
|||2001-2004<br>_Withruleoforigin_||2004-2008|
|Lowerlimitof<br>expected costpush|Totaleffect|Effectifgeography<br>hadbeenneutral|Totaleffect|Effectifgeography<br>hadbeen neutral|
|a=0.38,e=1|3.1|4.8|-33.3|-24.4|
|a=0.38,e=2|7.8|8.4|-50.4|-39.3|
|a=0.38,e=5|17.3|19.1|-69.4|-52.9|
|||_Withoutruleoforigin_|||
|_e_=1|35.5|n.a.|-18.4|n.a.|
|_e_=2|53.6|n.a.|-21.1|n.a.|
|_e_=5|112.3|n.a.|+18.3|n.a.|

||**Madagasca**|**r**||
|---|---|---|---|
||GrowthinEx|ports relativeto2000,|(in percent)|
|Parametervalues|2001-2004|2005-|2008|
|||Totaleffect|Effect had<br>geographybeen<br>neutral|
|Lower limitofcostpush||||
|a=0.38,e=1|29.5|-20.1|-13.1|
|a=0.38,e=2|44.8|-25.6|-19.3|
|a=0.38,e=5|92.3|-39.1|-26.6|
||_Withoutruleof_|_origin_||
|_e_=1|Sameasabove|-3.2|n.a.|
|_e=2_|Sameasabove|-6.7|n.a.|
|_e_=_5_|Same asabove|+5.6|n.a.|

Source: Authors' calculations.
Table 4.

Relationship between Revenue performance and State Share, 1995-1997

||||**State Share**||
|---|---|---|---|---|
|||100% State<br>Owned|More than 50%<br>State Owned*|Less than 20%<br>State Owned|
|**1997**|Revenue per Employee|31,700|43,864|95,931|
||Number of Enterprises|596|265|5,939|
|**1996**|Revenue per Employee|32,528|47,455|91,353|
||Number of Enterprises|648|257|4,112|
|**1995**|Revenue per Employee|32,163|38,088|81,420|
||Number of Enterprises|958|239|4,259|

*- This group does not include 100% state owned companies. Figures are in 1995 Litas.

Table 5.

Distribution of Enterprises by Types of Ownership and Years of Privatization

||**1995**|**1996**|**1997**|
|---|---|---|---|
|Fully State Owned|618|380|326|
|Privatized in 1996|-|238|238|
|**Mean of Private Share Ownership**|-|_0.80_|_0.82_|
|_Std of Private Share Ownership_|_-_|_0.31_|_0.30_|
|Privatized in 1997|-|-|54|
|**Mean of Private Share Ownership**|-|-|0.76|
|_Std of Private Share Ownership_|-|-|_0.33_|
|_TOTAL_|618|618|618|

35
and thus reduce poverty and seasonal food deprivation. On the other hand, irrigation helps enhance farm productivity and thus food consumption by 3.1 percents. This in turn reduces food poverty by 1.4 percentage points. Public investments on physical infrastructure such as rural electrification and irrigation have substantive payoffs in poverty reduction.38 The presence of agricultural banks does not seem to have any significant effect on consumption and poverty. But micro-credit programs, such as the Grameen Bank, help increase total consumption (by 4.4 percent) and hence, reduce both moderate poverty (by 4.5 percentage points) and extreme poverty (by 5.3 percentage points).<sup>39</sup> The Food for Work (FFW) program contributed substantially to overall per capita total consumption (by 6.9 percents) and food consumption (by 7.5 percents) with consequent reduction of moderate poverty (by 7.3 percentage points), food poverty (by 3.2 percentage points), and extreme poverty (by 8.1 percentage points). The FFW program is thus most appropriate for addressing seasonality both in income and consumption as well as containing seasonal and overall poverty. The Vulnerable Group Feeding (VGF) program also increases per capita total consumption and food consumption by 2.7 percents and 4.4 percents, respectively. This in turn reduces moderate poverty, food poverty and extreme poverty by 2.9, 2.0 and 3.4 percentage points respectively.

Do some of these programs have differential effect during the lean season on consumption and poverty? To demonstrate whether this is the case, the monga dummy is interacted with program variables such as Grameen Bank, FFW and VGF (results are not

> 38 Rural road expansion is another public investment that may have beneficial effects on poverty. However, HIES data does not provide information on rural roads.  But given that rural electrification or Grameen Bank expansion follows rural road expansion, the effects of electrification or Grameen Bank captures in part impact of roads.

> 39 Given the estimated effect, this means 1.0 percent annual rate of extreme poverty reduction due to Grameen Bank with its village coverage as low as 23 percent in 2005. This finding is not different from the average estimated impact of micro-credit on village-level poverty using household-level panel analysis (see Khandker 2005).

33