rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1:  Evidence on the Validity of the Identification Potentail Differences Between Heavy Rainfall and Other Areas**

||Heavy R|ainfall||||
|---|---|---|---|---|---|
||No|Yes|Difference|t-stat|p-value|
|**Migration**||||||
|Share of female in household|0.487|0.480|-0.007|1.110|0.270|
|Head is female|0.117|0.128|0.011|0.850|0.400|
|Migration in response to shocks*|0.042|0.024|-0.018|1.350|0.180|
|**Health**||||||
|Chronic Illness*|0.146|0.149|0.003|0.680|0.500|
|Sick/injured duringlast 30days*|0.204|0.205|0.000|0.070|0.940|
|**Risk Aversion**||||||
|Precautionary Grain Stock*|60.556|59.139|-1.417|0.060|0.950|
|Land rental*|0.261|0.279|0.018|0.410|0.700|
|**Payments Without Receipt**||||||
|Propensity to Pay Without Receipt|0.398|0.409|-0.011|-0.62|0.540|
|Amount Paid Without Receipt|271|182|89|1.40|0.160|
|**Falsification Test: Effects of Heavy Rainfa**|**ll on Corruption in La**<br>**Propensit**|**rge Cities**<br>**y to Pay**|**Amount o**|**f Bribe**|**Income**|
|Heavy Rainfall Dummy|0.102|0.099|0.078|0.082|0.33|
|**( t Statistic)**|**(0.18)**|**(0.18)**|**(0.35)**|**(0.37)**|**(0.92)**|
|Income|No|Yes|No|Yes||
|Number of Observations|753|753|246|246|753|

Note: *Data source is Household Income and Expenditure survey (2010)
# **2. The Governance Database**

We have compiled a large number of governance measures from a variety of sources into a governance database. These different sources produce indicators of a wide range of concepts relating to govemance, using different techniques, and covering different sets of countries. Table 1 presents the sources of governance data included in the database. These include intemational organizations, political and business risk rating agencies, think tanks, and non-governmental organizations. In this section, we provide an overview of some of the key features of these sources. Appendix 1 presents a detailed description of each of these sources.

We classify sources of governance data along two dimensions: (1) according to their unatureW, i.e. the techniques they employ to measure governance; and (2) according to their "coverage", i.e. the extent to which the set of countries covered by each indicator is representative of the world as a whole.

With respec **t** o their "nature", sources are either polls of experts or cross-country surveys of firm managers or citizens in general. Measures based on polls of experts represent consensus ratings agreed upon by a small number of country, sector, and regional experts convened by the reporting organization. Typically, country analysts produce an initial assessment for each country based on publicly-available information and their direct knowledge of the country. For most of the sources we report, these assessments are guided by a checklist of specific issues which analysts take into consideration when providing their initial ratings. For all sources where we were able to obtain this information, we report the checklists underlying the ratings. These initial ratings are then reviewed by a panel of regional and sectoral experts, who determine the final rating for each country. In addition to providing quality control on the initial assessments, the purpose of this final stage is to improve the cross-country comparability of the ratings by ensuring that countries are benchmarked appropriately.

Indicators based on surveys present averages by country of the responses of a large number of respondents to a variety of questions relating to governance. Typically, survey respondents are asked to rate aspects of governance on a categorical scale. The sampling frame of the surveys we use varies. Some sources focus on the opinions of 4
# **Hidden Treasures in the Comoros:**

**The Impact of Inter-Island Connectivity Improvement on Agricultural Production**

Atsushi Iimi<sup>¶</sup> Africa Infrastructure, Transport Global Practice World Bank Group Key words: Agricultural production; Market accessibility; Inter-island connectivity; Value chain analysis; Instrumental variable tobit regression.

JEL classification: C34, C36, O13, R41

> ¶ Corresponding author.
children, and their trends closely align in both treated and control districts, both before and after the policy implementation. Lastly, it is important to note that the short period between policy implementation and the start of data collection limits the analysis of the pretreatment period.

To address this limitation, we use HBS data to extend the pre-treatment period, even though we only have data for 14 regions in total. In Appendix Figure 7, the employment and coverage rates are shown separately for fast-implementing and slow-implementing regions. Before the policy implementation, the employment rate of women with young children and the coverage rate were both higher in the control regions compared to the treated regions. This discrepancy is largely due to the inclusion of Tashkent, the capital city, in the control group. Therefore, in Appendix Figure 8, we exclude Tashkent and re-analyze the data, showing that both the control and treated regions followed a relatively similar trend before the policy implementation. Over time, the employment rate of women in treated regions gradually caught up with that of women in control regions. It is also important to note that during the Covid-19 pandemic in 2020, when kindergartens were frequently closed, the employment rate of women with young children declined in control regions where coverage rates were higher.

Measuring the treatment at the regional level proved to be suboptimal, as district-level measurements revealed that all regions, except for Tashkent city, included both treated and untreated districts. As an alternative, we compared the employment rates of respondents living in households with young children to those living in households without young children (Appendix Figure 9). This comparison was particularly relevant as households with young children were the ones most likely to be impacted by the policy. From the graph, it can be observed that the employment rate of male respondents living with young children was slightly higher than those living without young children during all study periods. Although both groups of women had lower labor supply compared to men before the policy implementation, women living in households without young children had noticeably higher employment rates than women living with young children. After the policy implementation, this discrepancy decreased over time.

Further, we provide descriptive characteristics of our sample women for treated and not treated regions before and after the implementation of the policy in Appendix Table 2. As we can see from the table, on average women living in districts where preschool coverage increase is below the median are relatively similar to women who are living in districts with above median coverage increase in terms of individual and household characteristics. Before the implementation of the policy in nontreated 11
|Median<br>percentage<br>reduction<br>in cve in<br>sampled<br>areas|62.8%|59.7%|68.3%|60.6%|64.0%|
|---|---|---|---|---|---|

Figure 6: Cve for direct (Horvitz-Thompson approximation) Vs. cve for model-based estimates for sampled areas by country.

<!-- Start of picture text --> Burkina Faso Chad Guinea<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1<br>Mali Niger<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1<br>CV of direct estimates<br>CV of model estimates<br><!-- End of picture text --> The results in Table 4 show a large reduction in the median cve of the model-based estimates relative to direct estimates. Figure 6 shows reductions in the cve for all but a few target areas. The large efficiency gains from the use of model-based estimates are possibly moderately overestimated. In real data evaluations (e.g., Masaki et al., 2022 and Newhouse et al., 2022), coverage rates of confidence intervals produced by using parametric bootstrap MSE estimates are somewhat below the nominal 95%. Nevertheless, even considering this, we expect the model-based estimates to be more efficient than direct estimates.

As a further comparison between model-based and direct estimates, we consider a goodness of fit statistic at the target area level, following Brown et al. (2001). The statistic is based on computing Z scores defined as follows, 19
- De la Croix, D. and M. Doepke (2004), ”Private versus public education when di¤erential fertility matters”, Journal of Development Economics, 73(2): 607629.

- Docquier, F., O. Faye, P. Pestieau (2008), "Is migration a good substitute for education subsidies?", Journal of Development Economics 86, 263-276.

- Docquier, F., B. L. Lowell and A. Marfouk (2007), "A Gendered Assessment of the Brain Drain," IZA Discussion Papers 3235, Institute for the Study of Labor (IZA).

- Ebanks, E., P.M. George, C. E. Nobbe (1975), “Emigration and Fertility Decline: The Case of Barbados.”Demography 12(3): 431-445.

- Farber, S.C. and B.S. Lee (1984), “Fertility Adaptation of Rural-to-Urban Migrant Women: A Method of Estimation Applied to Korean Women.”Demography 21(3): 339-345.

- Fargues, Ph. (2007), "The demographic bene...t of international migration: a hypothesis and its application to Middle Eastern and North African countries", in: Ozden, C. and M. Schi¤ (eds), International migration, economic development and policy, World Bank and Palgrave Macmillan: Washington DC.

- Freedman, R. and D. P. Slesinger (1961), “Fertility Di¤erentials for the Indigenous Non-Farm Population of the United States.” Population Studies 15(2): 161-173.

- Galor, O. and A. Mountford (2006), ”Trade and the great divergence: the family connection”. American Economic Review, 96(2): 299-303.

- Goldberg, D. (1959), “The Fertility of Two-Generation Urbanites.”Population Studies 12(3): 214-222.

- Goldberg, D. (1960), “Another Look at the Indianapolis Fertility Data.”Milbank Memorial Fund Quarterly 38(1):23-36.

- Goldstein, S. (1978) “Migration and Fertility in Thailand, 1960-1970.” Canadian Studies in Population 5: 167-180.

- Goldstein, S. and A. Goldstein (1981), “The Impact of Migration on Fertility: An ‘Own Children’Analysis for Thailand.”Population Studies 35(2): 265-284.

- Gould, D.M. (1994), “Immigrant Links to the Home Country: Empirical Implications for U.S. Bilateral Trade Flows,”Review of Economics and Statistics, 76: 302-16.

35
# **Annex 1: Notes on methodology**

This annex provides detailed notes to the methodology described in section 2.

## A1.1 Methodology used in the AICD

The AICD estimated the overall annual costs of underpricing, T&D losses, collection losses, and overstaffing as follows: Underpricing= kWh billed× (unit historical cost−overstaffing− efficient average tariff); T&D losses= kWh billed× unit historical cost× (%loss−10%)/(100% −10%) ; Collection losses= kWh billed× effective average tariff× (100% −% bill collection); and Overstaffing= {413 −(no. of customers per employee)}/413 × annual salary bill , where 413 is the benchmark number of customers per employee in developing countries. The results obtained were then divided by utilities’ revenues and GDP to show their respective shares (as percent of revenue and percent of GDP).

Opex was taken from utility financial statements, while capex was calculated based on new replacement value of existing assets. This approach is used to reflect the reality that many, if not the majority, of capital investments are financed by the state, and therefore investment costs and associated debt servicing are not reflected on utility financial statements.

While the AICD carried out modeling to develop and cost a long-term expansion plan for each country with and without regional trade, the study did not use these modeling results to compute the supply cost. Instead, the AICD took the existing assets in the base year (typically 2005), and made assumptions about overnight investment costs to estimate capex. For power generation, the specific assumptions used for costs and years of economic life can be found in annex 7 of Briceño-Garmendia and Shkaratan (2011). The unit costs for coal, gas, and oil-fired power generation were the same in every country, but location-specific for hydropower. There was no distinction in capex between diesel and fuel oil generation. The capex associated with T&D consisted of two components. The first had to do with T&D infrastructure, and the second to do with connecting customers. For the latter, a simple rule of US$500 per connection was assumed, multiplied by the total number of MV and LV customers. To annualize capex, a discount rate of 10 percent was used. Unit historical costs and effective average tariffs were calculated using the formulae below: Unit historical cost Effective average tariff ≅ Effective residential tariff for monthly consumption of 100 kWh.

To improve estimations, the methodology used in the present study departs from that used in the AICD:

- The AICD divided the annualized capex by kWh generated and multiplied by kWh billed to arrive at total capex. This does not yield the total benchmark capex because T&D losses are embedded

70
program. With stable policies and favorable climatic conditions the sector grew 2 percent annually during this period. Production declined in 2007 due to an unprecedented drought.

**<u>Table 2.7. Agricultural value-added: growth and share, 1968-2007</u>**

||GDP series<br><br>|
|---|---|
|Period/year|1987 base<br>1998 base|
|**Average annual g**|**rowth in agriculture (percent)**|
|**1968-2006**|**1.31**|
|1968-1979|1.37|
|1979-1989|0.57|
|1989-1999|1.61|
|**1998-2006**|**0.88**<br>**1.23**|
|**1998-2007**|**0.24**|
|2002-2006|2.00<br>2.04|
|2002-2007|0.10|
|**Share of agricultu**|**re in GDP (percent)**|
|1968|33.3|
|1978|23.4|
|1988|18.2|
|1998|13.7<br>12.1|
|2006|11.0<br>9.7|
|2007|8.6|

_Source: calculated from TurkSTAT (2008)_ Share of agriculture in GDP declined drastically by roughly 20 to 25 percentage points, due mostly to higher growth in the rest of the economy. Recent population census in 2007 indicated that population in Turkey grew by 1.9 percent per year during the same period.

The structure of the agricultural production in Turkey has many of the characteristics of a developing economy. The share of agriculture in total employment is still around 27 percent. The average land and herd size per farm household are small. Farms in Turkey are generally familyowned, small, and fragmented. The average cultivated area per holding was about 5.2 ha in 1991, and it increased to about 6 ha in 2001. About 85 percent of holdings, on 41 percent of the land, were smaller than 10 ha. 15 percent of holdings were from 10 to 50 ha, and they cultivated almost half of the cultivated land (TurkSTAT, 1994, 2004b). The average size increases from west toward southeast due to the climate and fertility differences. According to the agricultural surveys, the proportion of the irrigated land in total cultivated land increased from 14 percent in 16
rural poverty reduction (55% of the total), 2.3% to urban, and 1.6% to the population shift effect (Ravallion et al., 2007).

For the “$1 a day” line, we find that the urban share of the poor is rising over time, from 19% in 1993 to 25% in 2002. The ratio of urban poverty incidence to total poverty incidence has also risen with urbanization, implying that the poor have been urbanizing faster (in proportionate terms) than the population as a whole.

Using the “$2 a day” line, we find a slightly higher share of the poor living in urban areas, but that this share has been rising at a slower pace than for the $1 a day line. There is also a sign of a deceleration in the urbanization of poverty using the $2 line (Ravallion et al., 2007).

There are notable differences across regions in the urbanization of poverty. In 2002, the rural headcount index for East Asia was nine times higher than the urban index, but only 16% higher in South Asia, the region with the lowest relative difference in poverty rates between the two sectors. The contrast between China and India is particularly striking. We find that poverty incidence in urban China in 2002 was barely 4% of the rural rate, while it was 90% for India. Urban poverty incidence in China is unusually low relative to rural, though problems in the available data for China (notably in the fact that recent migrants to urban areas are undercounted in the urban surveys) are probably leading us to underestimate the urban share of the poor in that country.<sup>19</sup> We find that the urban share of the poor is lowest in East Asia (6.6% of the $1 a day poor lived in urban areas in 2002), due in large part to China. The urban share of the poor is highest in Latin America, where 59% of the $1 a day poor, and 66% of the $2 a day poor lived in urban areas in 2002. This is the only region in which more of the “$1 a day” poor live in urban than rural areas (the switch occurred in the mid-1990s).

South Asia and SSA turn out to be the regions with highest urbanization of poverty at <u>given</u> overall urbanization, due to their relatively high urban poverty rates relative to rural; these are also the regions with the highest overall poverty rates (Ravallion et al., 2007). In 2002,

> 19 For further discussion see Ravallion and Chen (2007).

14
**Table A5 – Relationship between drivers of vaccine hesitancy and being unwillingness to get vaccinated**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Trust vaccine|-<br>0.522***|||||-0.404***|
||(0.0288)|||||(0.0376)|
|Social norms||-0.443***||||-0.181***|
|||(0.0297)||||(0.0378)|
|Concern about COVID|||0.0102|||0.0206|
||||(0.0264)|||(0.0245)|
|Been vaccinated||||-0.0717**||-0.0755***|
|||||(0.0233)||(0.0217)|
|Trust internet|||||0.0624|-0.000163|
||||||(0.0442)|(0.0408)|
|Male|-0.0390|-0.0721*|-0.0742*|-0.0787*|-0.0759*|-0.0496|
||(0.0291)|(0.0297)|(0.0315)|(0.0314)|(0.0315)|(0.0289)|
|Under 35 years|-0.00586|-0.0175|-0.0260|-0.0247|-0.0308|-0.00270|
||(0.0221)|(0.0226)|(0.0240)|(0.0239)|(0.0241)|(0.0221)|
|Rural|-0.00892|0.00578|-0.00602|-0.0100|-0.00703|-0.00331|
||(0.0358)|(0.0368)|(0.0391)|(0.0388)|(0.0389)|(0.0357)|
|Complete Secondary School|0.00731|0.0182|0.0264|0.0278|0.0237|0.00906|
||(0.0230)|(0.0235)|(0.0249)|(0.0249)|(0.0250)|(0.0229)|
|Richest 20%|0.0171|-<br>0.0000312|-0.00815|-0.00658|-0.0139|0.0182|
||(0.0284)|(0.0290)|(0.0308)|(0.0307)|(0.0309)|(0.0284)|
|Constant|0.621***|0.626***|0.560***|0.610***|0.569***|0.656***|
||(0.0454)|(0.0466)|(0.0548)|(0.0507)|(0.0491)|(0.0510)|
|Observations|1843|1843|1843|1843|1843|1843|

Note: This chart is based on answers to question 8, 10, 13, 29 and 30 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

43
giving them incentives to select workers that they do not think will overstay and not to be complicit in any overstaying.

An additional interesting feature in reducing the role of overstay is the role of reputation and competition for places in the program. This occurs both at the source country level, and at the village level within countries. Since employers have a range of potential countries to recruit from, the Pacific kick-start states are acutely aware of the need to build a reputation for reliable, dependable workers. This is made explicit in the Inter-Agency Understandings, for example, the 2009 IAU for Vanuatu states “The New Zealand Government has zero tolerance for overstaying under the RSE Policy...RSEs are unlikely to seek to recruit further workers from Vanuatu if they experience negative consequences for non-compliance relating to Vanuatu workers.” This then translates into consequences for selection within countries. For example, in Samoa the Prime Minister announced that as punishment for any shameful behavior by RSE workers while in New Zealand, the worker’s whole village would be banned from taking part in the program for two to four years (Duggan, 2013). The consequence is that there is social pressure not to jeopardize the future possibilities for others by overstaying and thereby creating a negative reputation for one’s community.

The result has been very low overstay rates, as seen in Table 1. Overstay rates have averaged under 1 percent for the first six seasons. NZDoL (2010) attributes the overstaying in the first two years largely to a couple of isolated cases in which workers had disputes with employers about insufficient work being available.

**Table 1: RSE Overstay rates by Financial**

**Year**

|**Financialyear**|**Overstay rate(%)**|
|---|---|
|2007/08|0.47|
|2008/09|0.62|
|2009/10|1.25|
|2010/11|1.02|
|2011/12|1.03|
|2012/13|0.70|
|**Overall first sixyears**|0.86|

Source: Data provided by New Zealand Ministry of Business, Innovation and Employment. Note that cases which are not resolved in one fiscal year are carried over to the next fiscal year, so these rates are upper bounds for the number of stayers.

10
RIF is regressed on the covariates of interest for the given model. The entire procedure is bootstrapped to ensure consistent estimation of the associated standard errors.

# **Appendix References:**

Firpo, S., N. Fortin, and T. Lemieux, “Unconditional Quantile Regression,” _Econometrica_ , 77(3), 953-973, 2009.

Frisch, R. and F. Waugh, “Partial Time Regressions as Compared with Individual Trends,” _Econometrica, 1(4), 387–401, 1933._ Hampel, F. R., “The Influence Curve and Its Role in Robust Estimation,” _Journal of the American Statistical Association_ , 60, 383–393, 1974.

Koenker, R. and G. Bassett, “Regression Quantiles,” _Econometrica_ , 46(1), 33–50, 1978.

45
Learning from Developing Country Power Market Experiences: The Case of Peru Peru has historically lacked adequate power transmission capacity and has recently suffered from constraints on gas transport (see also section 4.2). Electricity transmission remained very weak until mandatory transmission planning and competitive bidding for new transmission projects was introduced. Supply restrictions and price spikes occurred during 2008 due to constraints on gas transport capacity from Camisea towards new generators.

Investments in power generation grew from US$ 235 million(m) in 2005 to US$ 1,829m in 2015 (22.8% per annum in real terms, inflation adjusted to 2015). A significant hike in generation investment occurred during 2011, when investment more than doubled from US$ 607m to US$ 1,307m. Generation investment remained at relatively high levels between 2010 and 2015, reaching about 70% of total investments in power infrastructure.

Investments in transmission infrastructure grew from US$ 35m in 2005 to US$ 341m in 2015 (29.6% per annum in real terms, inflation adjusted to 2015). Transmission investments quadrupled during 2009 to US$ 281m and reached a maximum of US$ 485m during 2012. Transmission investments grew from 5% of total investments in power infrastructure in 2005 to 13% in 2015.

# 5.3. Sustainability

The Peruvian power market has increased its reliance on fossil-fuels by substituting hydro-based power generation with gas-fired power generation supplied by domestic gas production, primarily from Camisea gas fields (see Figure 5-6 and section 2.2). Indeed, hydro supplied about 90% of the Peruvian electricity needs in the early 2000s. Since then, gas-fired generation increased its share of electricity supply from 17% in 2005 to 45% in 2015, while hydro generation lowered its share of electricity supply from 74% to 50% over the same period. Thermal generation (gas, coal and oil) accounted for 41% of power generation in 2017.

<!-- Start of picture text --> 100%<br>90%<br>26% 25%<br>32%<br>80% 39% 37% 41% 42% 44% 46% 48% 48% 50% 41%<br>70%<br>60%<br>50%<br>40%<br>74% 75%<br>68%<br>30% 61% 63% 58% 58% 56% 53% 50% 50% 48% 57%<br>20%<br>10%<br>0%<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Hydro Thermal Other<br><!-- End of picture text --> Figure 5-6 Evolution of technology-wise share of power generation in Peru.

Source: COES 21