rafmacalaba/datause-encoder-data huggingface dataset:
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# Table 18: Brazil - Simulated Outcomes for Children In Female Headed Households, 1984

(number or percent)

|||R|ecife|||Sb|o Paulo|||Por|to Ale|gre|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|.tcome by<br>lldren's Age<br>Status|All|FIH|_FHl_|Poverty<br>Effect|All|FHH|FHH|Poverty<br>Effect|All|FiH|FHl|Poverty<br>Effect|
|Age 7-9|||||||||||||
|-notinschool|14|23|19|_0.5_|8|8|7|-|10|.12|11|0.6|
|Age 10-14|||||||||||||
|-not in school|14|22|17|0.7|8|12|11|0.2|12|18|16|0.4|
|-in the labor force|7|10|7|0.8|8|12|11|0.3|9|16|14|0.2|
|hildren Aged 10-14<br>In the Labor Force<br>-not in school|56|69|65|0.3|36|42|40|0.2|66|67|70|-|

## Source: author's abulations

otes: FHH' is the simulated outcome for children in female-headed households.

Poverty effect is the proportion of the difference between households explained by the difference in means. For example, 0.5 implies tha 50 percent of the difference In means Is attributable to the lower income of PHHS.
The data were sourced from the National Health Information System (NHIS). This system provides monthly data at the health center level on the quantity of each contraceptive method distributed within their designated catchment area by the Burundian government as well as the number of CHW referrals to health center facilities. An important limitation of the NHIS data is the difficulty of distinguishing between true zeros and missing values when a health center reports no data for a contraceptive method in a given month. To partly address this issue, we classify observations as missing if all contraceptive methods show zero quantities for a given health center and month. This classification applies to approximately 5% of our sample.

The primary outcome of our study is the quantity of SP injections reported by health centers in the NHIS data. As secondary outcomes, we employ the quantities of relevant alternative contraceptive methods to investigate potential substitution effects.

# **Study Sample**

Table 1 summarizes key information on the 138 study health centers, revealing the limited capacity of health centers and low contraceptive utilization (column 1). Panel A shows that only 38 health centers (28%) had HPO availability. Among these health centers, each had only one HPO, indicating very limited in-home administration capacity before the CHW intervention rollout. On average, there were 16 CHWs serving a population of 15,775 within the health centers’ catchment areas. Of this population, an estimated 3,739 are women of childbearing age, translating to roughly 230 women per CHW.

Panel B presents the baseline number of SP injections administered per month within the catchment area of health centers as well the overall quantity of all contraceptives dispensed, including injectables (SP and DMPA-IM), condoms, birth control pills, emergency contraceptive pills, implants, and intra-uterine devices. It is worth noting that this measure should be interpreted with caution given the varying protection duration across contraceptive methods. A detailed breakdown by each contraceptive method is provided in Table A1. Given the estimated average of 3,739 women of childbearing age residing in the catchment area served by each health center, these figures suggest low contraceptive coverage in our study areas. According to Burundi Institute of Statistics and Economic Studies (2017), the weighted average contraceptive 10
between managerial ability and inverse scale efficiency is -0.589 (p<0.0001); and between farm size and managerial ability is 0.686 (p<O.0001).

Results obtained from the analysis are mixed, and clearly demonstrates the complexity of the issues involved. A whole range of farm sizes seems to be scale efficient (figure 5), depending on how farmers organize their specific variable and fixed input nix, as well as the combination of outputs they produce. In this respect a number of relatively small farms are scale efficient, although there seems to be a bias towards a larger number of relatively small farms being scale inefficient. On the other hand, the relationship between scale efficiency and managerial ability seems to be much stronger. This emphasizes the importance of management: results support the notion that better managers operate on larger farms than less skilled Managers.24 In this respect, Groenewald (1991) is of the opinion that "returns to management" is a more appropriate concep **t** han returns to scale.

# _Econometric Estimation of the Farm Size-Efficiency Relationship_

Another appropriate way in which to measure relative efficiency of small and large farms is to investigate the difference in profits, net of the cost of family labor, per unit of capital invested Binswanger _et al,_ 1993). The following test of the farm size-productivity relationship, proposed (and discussed) by Binswanger, _et al_ (1993), was used to take most of the methodological considerations into account:

# _P/K = g (OP, OW, H, Z) with expected signs g, <0; g2 >0; and g3 >0;_

_(1)_ where K is assets, L is labor, P is private or social profits net of private or social cost of family labor, OP is operated area or value of operated land, OW is owned area or value of owned land, H is the number of household workers, and Z is a vector of exogenous land quality, distance from infrastructure, and land improvement variables. Also, g, should be negative because of rising supervision costs; g2 should be positive because ownership provides better access to credit; and g3 should be positive because family members have incentive to work and can supervise. Equation 1 does not describe a casual relationship, but a multiple correlation.

In addition to this specification, managerial ability of the farmer (M) is an important explanatory variable which needs to be considered. This clearly also impacts on efficiency (see results of DEA analysis) and because it varies for different farms, it should also be included as part of the multiple correlation depicted by equation 1.

> 24 This result is consistent with earlier findings reported by Van Schalkwyk, _et al_ (1993) for Vaalharts, and Sartorius von Bach and van Zyl (1992) for the Aberfeldy district in the Eastern Free State.

36
> declines may not materialize). They are also lowered for textile products and to a lesser extent clothing. Among the farm products, consumer prices are raised slightly for livestock products, somewhat more for grains, and significantly for cotton (plant-based fibres). Producer prices are down more for farm products than for most other products except autos though, and farm output is down for all but cotton and meat. Moreover, feedgrain exports shrink by three-quarters and cotton exports by half with the abolition of export subsidies. The difference in the effects on production and consumption shown in Table 9 reveal that China's food, feed and fibre selfsufficiency will be reduced at least slightly by these reforms. But the extent is really quite minor: the trade balance column in Table 9 suggests that for all agricultural and food products, net imports would be greater because of the remaining accession reforms by only $3.96 billion per year by 2007 (in 1997 US dollars), which represents only 1 per cent of total imports.

> The above results depend as always on the assumptions in the model. To check the sensitivity of some of those assumptions, three alternative scenarios were run to compare their results with those in the base accession scenario: greater agricultural protection cuts; removing negative agricultural protection; and removing the wedge between farm and non-farm wages.

_Alternative scenario 1: greater agricultural protection cuts_

> What if the grain, sugar and cotton NRPs were to drop to the in-quota tariff levels shown in Table 6, for example? An additional GTAP simulation was run and the differences for factor rewards are not huge in aggregate but they would be in the direction of worsening income inequality: unskilled farm wages would fall 0.9 instead of 0.7 per cent and rewards to farm land would fall 6.4 instead of _5.5_ per cent on the one hand, while on the other non-farm wages would rise 1.4 instead of 1.2 per cent for the unskilled and 1.0 instead of 0.8 per cent for skilled workers (Table 10). These changes would attract only another million workers from farms, given the assumed impediments of off-farm migration. But while agricultural incomes would be lower, farm household income would not fall if at least 60 per cent of its income came from wages of non-farm unskilled labor (see row 8 of Table 10). Domestic production of grains, sugar and cotton would be less though, and domestic consumption greater, so self-sufficiency in those products would be slightly lower. Even so, net imports of all food and agricultural products would be greater by only $1.5 billion per year by 2007 ($5.43 instead of $3.96 billion). Such an import increase would be within the tariff rate quotas for those items with the possible exception

13
E t.

B Even before privatizadon, the banks acted independently and were de-facto the most marketoriented sector in Russia. With the mass privatization program for enterprises, most banks are now majority privately owned.

Table 2: Assets of Russia Banks (as of July 1, 1993)

||Total Asset|s|
|---|---|---|
||Rb billon|% of GNP|
|Specialized Banks<br>of which:<br>|14.366<br>|12.1<br>|
|Vneshtorgbank|7,597|5.4|
|Rosselkhozbank|3,984|3.4|
|Sborbank|1,926|1.6|
|Promstrolbank|1,273|1.1|
|20 Largest Commercial Bunks|14,479|12.6|
|Total Assets Banking System|41,304|34.9|
|TotalCredit to Non-Banks<br>(notofinterbank credit)|17,697|16.4|

Many enterprises have switched their accounts to the more successful banks and have oftn also become shareholders. Ownership in 'zero" banks is usually fairly concentrated, with the five largest shareholders controlling as much as half of the shares.' Ownership of "spin-off' banks is more diversified, with the largest shareholder accounting for 5-10 percent of equity, and the largest five for about 25 percent.

The new banks act as corporate treasurers to their owners and customers. Many enterprises continue to rely on their banks to monitor their own financial conditions and to prepare financial statements.7 In addition, banks provide payments services, deposit accounts, short-term working capital loans, etc. Some sixty banks had general foreign exchange licenses in 1993 and served as correspondent banls to handle foreign exchange transacdons for enterprises and other banks.
government contributions, and relative sector priorities.

These problems typify work on the poverty-environment nexus, which is complicated by uncertainty over appropriate rules for the allocation of program budgets. Within countries, program administrators often use indicators such as poverty incidence to assign regional priorities. This approach provides equal budgetary allotments for all poor households if they are uniformly distributed across regions. However, recent poverty-mapping studies in Asia, Africa and Latin America have shown that this is often far from the case (Simler and Nhate, 2002; Dasgupta, et al., 2003; Lanjouw, et al., 1998). Even if budgetary allotments are equal, resource-constrained program planners have to confront difficult tradeoffs among multiple objectives: the number and proportion of poor households actually served; the average quality of services; and the associated degree of improvement in environmental quality.

The allocation problem may be compounded by regional differences in administrative costs. In this paper, we consider three spatial factors that affect these costs. The first is the settlement density of poor households. For program administrators and technical assistance workers, average transport times and costs are significantly lower in densely-populated areas. Such areas also benefit from scale economies in projects that improve community-level services (e.g., sanitation or access to safe water). The second spatial factor is poverty incidence. Areas with high poverty incidence have relatively low screening costs, because they have lower risks of benefits "leakage" to higher-income households. The third factor is overall population density, which affects monitoring and enforcement costs for natural resource conservation and pollution management programs.

How should poverty-environment programs accommodate these diverse concerns, and what are the implications for the use of priority indicators such as poverty incidence or the rate 3
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 20: log Non-Formal Spell Length (Tobit) - Job Separations Originating in Tradable Sector - 1995-2005 <!-- Start of picture text --> 0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>-7<br><!-- End of picture text --> Each point reflects an individual Tobit regression coefficient where observations represent individual spells, and the dependent variable is the log spell length in years, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text) in the worker’s region at the beginning of the nonformal spell, with various worker-level controls and pre-trend control (described in the text). Negative estimates imply longer non-formal spell lengths for workers losing formal employment in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

59
**Table 4.** Probit model estimations including all hazards.

||Br|azil|Dominica|n Republic|Ec|uador|El Sa|lvador|Hon|duras|M|exico||Peru|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|
|TH index Flood|0.105|0.012|0.000|0.000|-0.032|-0.006|-0.025|-0.005|-0.036|-0.008|0.025|0.005|-0.018|-0.003|
||[0.056]*|[0.006]*|[0.042]|[0.006]|[0.042]|[0.008]|[0.017]|[0.003]|[0.032]|[0.007]|[0.013]*|[0.003]*|[0.023]|[0.004]|
|Number of Floods|-0.014|-0.002|-0.086|-0.012|0.018|0.003|-0.089|-0.017|-0.039|-0.008|0.021|0.004|-0.009|-0.001|
||[0.005]***|[0.001]***|[0.084]|[0.012]|[0.022]|[0.004]|[0.031]***|[0.006]***|[0.019]**|[0.004]**|[0.008]**|[0.002]**|[0.005]*|[0.001]*|
|TH index Landslides|-0.333|-0.038|0.063|0.009|-0.026|-0.005|-0.010|-0.002|0.027|0.006|-0.012|-0.002|-0.074|-0.012|
||[0.158]**|[0.018]**|[0.067]|[0.009]|[0.049]|[0.009]|[0.023]|[0.004]|[0.044]|[0.010]|[0.017]|[0.003]|[0.024]***|<br>[0.004]***|
|Number of Landslides|0.231|0.026|||0.171|0.032|-0.130|-0.025|||-0.099|-0.019|0.047|0.008|
||[0.064]***|[0.007]***|||[0.085]**|[0.016]**|[0.124]|[0.024]|||[0.019]***|[0.004]***|[0.020]**|[0.003]**|
|TH index Earthquake|0.079|0.009|||0.068|0.013|||0.105|0.023|-0.010|-0.002|-0.022|-0.004|
||[0.092]|[0.011]|||[0.074]|[0.014]|||[0.123]|[0.027]|[0.023]|[0.005]|[0.040]|[0.006]|
|Number of Earthquakes|-0.546<br>[0.201]***|-0.062<br>[0.023]***|0.878<br>[0.294]***|0.125<br>[0.042]***|-0.109<br>[0.057]*|-0.021<br>[0.011]*|0.765<br>[0.168]***|0.146<br>[0.032]***|0.019<br>[0.056]|0.004<br>[0.012]|-0.036<br>[0.021]*|-0.007<br>[0.004]*|-0.022<br>[0.017]|-0.003<br>[0.003]|
|TH index Cyclones|||||||||||0.158<br>[0.029]***|0.031<br>[0.006]***|||
|Number of Cyclones|||0.059<br>[0.050]|0.008<br>[0.007]|||0.300<br>[0.074]***|0.057<br>[0.014]***|0.039<br>[0.023]*|0.009<br>[0.005]*|0.003<br>[0.003]|0.001<br>[0.001]|||
|TH index Drought|0.065|0.007|||||||0.012|0.003|-0.049|-0.010|-0.002|-0.000|
||[0.083]|[0.009]|||||||[0.145]|[0.032]|[0.022]**|[0.004]**|[0.024]|[0.004]|
|Number of Droughts|0.062|0.007|||-0.028|-0.005|0.962|0.183|0.032|0.007|-0.023|-0.004|-0.030|-0.005|
||[0.020]***|[0.002]***|||[0.094]|[0.018]|[0.212]***|[0.040]***|[0.030]|[0.007]|[0.034]|[0.007]|[0.045]|[0.007]|
|Controlling by exposure<br>Clli b i|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|ontrong y copng|||||||||||||||
|mechanisms|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controlling byregion|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by districts in brackets (by states in Brazil and Ecuador). Source: authors' calculations based on ThinkHazard! EM-DAT, and SEDLAC database.

13
Ethnic polarization and the duration of civil wars distance across groups using the strength of the sentiment of identity or political relevance, then there is an important endogeneity problem. At the end we will be explaining conflict using conflict as the explanatory variable, because the sentiment of identity is high when there is conflict. Fourth, as argued by Montalvo and Reynal-Querol (2002) and Duclos, Esteban and Ray (2004), there are many interesting instances in which individuals are interested only in the dichotomous perception Us/They.

Figure 1 shows that the choice of indicator is important. We use the measures of ethnic fractionalization and ethnic polarization from Montalvo and Reynal-Querol (2002, 2005). Ethnic polarization and ethnic fractionalization exhibit a nonlinear relationship. For low levels of fractionalization the correlation between ethnic fractionalization and polarization is positive and high. In particular, we know that when there are only two ethnic groups ethnic polarization is two times ethnic fractionalization. That is the reason why the slope of the line is 1/2 for ethnic polarization up to 0.4. However, for the medium range the correlation is zero, and, for high levels of fractionalization the correlation with polarization is negative.

# _4. Ethnic polarization and the duration of civil wars: some theoretical considerations_

Let’s consider a country with two social groups. The size of each group is known with some measurement error<sup>6</sup> . Imagine that something (for instance the discovery of natural resources) ignites a conflict between both groups<sup>7</sup> . Assume that the parties will fight as long as the probability of winning of one of the groups is not significantly higher, in statistical terms, than the probability of the other. Once the conflict begins, troops clash in initial “battles”. In each of them some information about the size of each group is revealed and the variance of the posterior distribution is reduced. The theoretical duration is defined as the number of “battles” needed for the conflict to end. Given the size of the groups the higher the initial variance of the measurement error the higher will be the duration conditional on the conflict having started. In addition, the higher the degree of polarization, the longer the duration of the war.

> 6 We can also assume that the size of the groups is known, but the proportion of fighters that can be attracted by each group is a random variable function of the size of the group. In principle, there is no need to introduce asymmetric information.

> 7 In this paper we are not concerned with the onset of civil wars but with the duration conditional on the onset.

9
and Huizinga (1998) points to large benefits of judicial efficiency on the reduction of banks’ net interest income, independently from any notion of market power. This may hide different levels of judicial effectiveness across developed and developing countries where typically banking markets are more concentrated and judges could more likely be captive of the economic power. Additional empirical evidence may therefore signal to what extent judicial reform and competition policies should go hand in hand in order to effectively bring forward an effective reduction of the cost of credit in emerging economies. From this perspective our paper is closely related to the paper by DemirgucKunt, Laeven, and Levine (2003), that uses net interest margins as a measure of the bank inefficiency and shows that countries with better institutions (including better property rights) have lower margins and to the paper by Claessens, Demirguc-Kunt, and Huizinga (2001) that investigates the role of foreign banks in a cross-country study and shows that foreign bank entry reduces net interest margins.

The paper proceeds as follows. Section 2 presents the criteria followed in the construction of different measures of lending spreads, describes the explanatory variables and provides some descriptive statistics. Section 3 presents the estimation methodology and illustrates the main empirical results. Section 4 concludes.

# **2. Methodology and Data**

# _2.1 Description of bank lending spread variables._

Our empirical analysis is based on a cross section of bank lending spreads and assesses the sensitivity of such spreads to the quality of the legal system and to a number of other country-specific variables. The dependent variable is represented by the difference 4
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

# **Modeling changes in the number of retirees**

Changes in the number of old age pensioners were modeled by using the flow method (see APPENDIX D for details) via setting the number of new retirees by age and gender as percentage of previous year’s contributors. These percentages show the probability of an active employee of a particular age and gender to retire in the next year.

As mentioned in Section III.A, the available data on the flows of retirees were not segregated by beneficiary category and could only partially be used for deriving the retirement pattern for new old age pensioners. In accordance with the current law, it was assumed that individuals retire between age 45 and 61. Based on the available data for PSPF and data for some other pension schemes with early retirement arrangements, the retirement probabilities were assumed to increase with age as shown in Figure 42 and Figure 43. Same retirement pattern was used for both genders in both sub-schemes. Applying such retirement rates essentially results in the average retirement age varying within quite a narrow corridor between 53 and 54 for both genders and in both subschemes.

**Figure 42: Age specific retirement probabilities* for civil servants**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61<br>Age<br><!-- End of picture text -->

**Notes** : * Percentage of the respective age and gender cohort of the covered population **Source** : WB calculations based on actual data

The second beneficiary category – survivors – was modeled in a more aggregated way. As there was no reason to believe that the number of survivors as a share of the total covered population will significantly change over time, it was assumed to remain at the current level of 2.7% for civil servants and 0.3% for teachers. This assumption can be reconsidered in further simulations.

The projected number of beneficiaries in each of the modeled sub-schemes is plotted in Figure 43.

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