rafmacalaba/datause-encoder-data huggingface dataset:
24

- _Budgeting procedure-this_ should incorporate a system for projecting the likely costs of the programme from one year to another, and should identify how any shortfall or surplus of subsidy funds should be dealt with.

- _Selection procedure-this_ should establish the routes through which applications can be made, and the way in which they will be screened, as well as the process for reaching a final decision regarding eligibility for the subsidy.

- _Payment procedure-this_ should clarify the way in which subsidy beneficiaries will be billed, and establish how and when funds will be transferred from the government to the concessionaire.

# **_Budgeting_**

As noted above, in the Panama case, the legal framework states that the funding for the subsidy programme must be explicitly identified in the budget of the Ministry of Health. Using the simulation model developed in the subsidy study, it was possible to project the levels of expenditure associated with different subsidy design parameters.

While direct water consumption subsidies to the 'social cases' had historically cost the treasury some US$3m per year. The projected financial costs of the new scheme were substantially lower. In the worst-case assumption of a US$3 monthly subsidy to each of the 17,500 households in extreme poverty, the total annual expenditure would amount to only US$630,000.

On the other hand, providing sewerage connection subsidies of US$750 to all 17,500 of the poorest households would cost over US$13m. Nevertheless, assuming that the allocation of subsidy funds to the sector remained constant at US$3m, connections for this entire group could be funded within a six-year period.

While it is relatively straightforward to predict how many people are likely to meet the eligibility criteria, the actual demand for subsidy funds depends critically on other factors. These include the degree of public interest in applying for the scheme, as well as the extent to which non-eligible households succeed in getting through the screening process, both of which are much harder to predict.

Owing to the possibility of escalating demand, it was considered advisable to place a ceiling on the number of subsidies awarded each year in order to ensure that budgetary restrictions are met. This also provides the opportunity to review whether the screening mechanisms are functioning adequately in weeding out ineligible applicants.

Ultimately, if public funds are not adequate to cover legitimate demand for the subsidy, it may be necessary to reconsider the size and scope of the subsidy programme, or the sources of finance. For this reason, among others, it is important to build in regular reviews of the design parameters of the subsidy scheme.
- 49 -

# **III.** MITIGATION, SELF INSURANCE &VULNERABILITY REDUCTION MEASURES

_The natural risk exposure of Caribbean economies implies that even while exploiting the insurance mechanism in the most cost effective manner, this will still result in higher relative premiums compared to other regions in the world. Besides the advantages of risk pooling (discussed in chapter VI) which allows diversification of risk and better leverages the available capital, another major source of both premium and risk reduction, consists of physical measures to reduce the structural vulnerability of properties and critical assets. This chapter shows how very modest investments in mitigation such as roof straps to protect against hurricane force winds can reduce the loss exposure substantially, sometimes by as much as 50%. Such measures need to be accompanied by regulatory enforcement of building codes as well as insurance-based incentives which can be achieved through the standardization of data on construction code ratings and property risk characteristics. These resources can also be harnessed to develop publicly sponsored quasi insurance schemes for vulnerable low income communities, in order to instill a framework and awareness regarding the costs and benefits of both physical and financial risk management tools._
only 7 percent of the staff weeks and days of lodging required for typical HIES data collection although it requires a slightly higher transportation cost—110 percent of the transportation costs required for HIES data collection. The transportation cost is slightly higher than that of HIES because consumption and nonconsumption data need to be collected by different teams. Although the total cost saving depends on costs of lodging, staff time, and transportation, it is likely that the cost of data collection for Subsample Imputation is likely to be much lower than for HIES data collection.

These results are encouraging for the Subsample Imputation approach. But, it is also worth noting that all the analyses were conducted for a specific time period, for a specific country, that is, Bangladesh in 2000 and 2005. In a different time period, or for a different country, the superiority of the Subsample Imputation approach might disappear. However, this paper provides an analytical framework to determine the magnitude of cost savings and statistical advantages of Subsample Imputation over other approaches. Furthermore, to implement the Subsample Imputation approach requires a modeling exercise every year, the burden of which cannot be underestimated in some developing countries where technical and analytical capacity of statistics bureau is often severely limited.

Finally, this is not the first evaluation of the statistical precision and cost-effectiveness of the hybrid approach. Fujii and van der Weide (2013) studied the hybrid approach in a quite general environment. For the most part, these two papers are mainly complementary and reinforce the evidence of the hybrid approach’s usefulness. However, there are differences: first, this paper compares the existing survey-tosurvey imputation approach, which is identical to Full Sample Imputation, with the new hybrid approach. Fujii and van der Weide (2013) focus on derivation of properties of statistics estimated from the hybrid approach. Second, one potential shortcoming of the hybrid approach is that it needs to conduct price adjustments and update poverty lines using a limited number of unit values available in a subsample. Practically, this is an important challenge since it might expand the margin of error in poverty estimates. This paper studied the effect of limited sample size, while Fujii and van der Weide (2013) did not. Third, this paper conducts all analyses using data from Bangladesh 2000 and 2005, while the results from Fujii and van der Weide (2013) are general. All in all, these two papers fill each other’s gaps and together build a strong case for a hybrid survey approach.

26
<!-- Start of picture text --> Average Prices, Sausage II<br>(December 1991 -- Aupust 1992)<br>300-<br>250 ................<br>.................................... ........... ; ;;;.........<br>K ^ -~~~~~~~~~~~~0-,0-0 *<br>...................' ...... .... .....................<br>10o ... ...............................................................................................................<br>15.<br>50- ;.<br>*<br>Dac931 01/283 02111 02/25 03'31 04/14 04/28 05/12 060)2 06/16 0W 30' 07/14 07/283 08/11 08/2M5<br>D ate<br>-M arket P?rice State P?rice<br><!-- End of picture text --> _35_
contributor to stunting inequalities is income inequality. By contrast, inequalities in water and sanitation, and in parents' education do not appear to account for large shares of stunting inequality in Vietnam in the years in question. The results suggest that the main drivers of _rising_ inequalities in stunting between 1993 and 1998 are increased inequalities at the community level and changes related to income (rising income inequality but also an increase in the income elasticity of malnutrition). With better data one could try to "drill down" further to see how much of the changes at the community level were related to health sector changes.

**Figure 17. Unpacking the causes of inequalities** in **child stunting, Vietnam**

<!-- Start of picture text --> 0.02 -<br>0.00<br>g t-002 -0.02 - _ X ' M X C 2 * 29 Community factors<br>*00 E 5Parents height<br>M Parents' schooling<br>8 -0.06 - | . | E3 Drinking water & sanitation<br>o Child's demographics<br>9 -0.08-<br>-0.10 Household income<br>-0. 10<br>-0.12<br>-0.14 __<br>1993 1998 Change<br><!-- End of picture text -->

_Source:_ Wagstaff, van Doorslaer, and Watanabe (2001).

# **8.** Conclusions

The 42-country World Bank study, which provided the starting point of the paper, reveals significant correlations between inequalities in different indicators of child healthmortality, malnutrition, diarrhoea, and ARI. The data also reveal large international differences in the degree of inequalities in child health outcomes. A simple stylized theoretical model rationalizing the health-income relationship predicts that these intercountry differences could reflect, at the minimum differences in average incomes, differences in income inequality, and differences in the degree of public financing of health care (since this reduces the price of health care at the point of use). Surprisingly, the data show only very weak-and statistically insignificant-associations between inequality in under-five mortality (the inequality variable used throughout) on the one hand and income inequality and the proportion of health spending financed publicly on the other. By contrast, there was a strong and significant positive relationship between mortality inequality and income per capitaricher countries have higher relative inequalities in child mortality between the poor and better-off. Evidence from trends in health inequalities showed that health inequalities have _36_
# **IV.2 Quantitative analysis**

The quantitative analysis consists in validating the model and conducting counterfactual experiments to assess the implications of changes in the distortions stemming from the business environment and the detection probability.

## **IV.2.1 Validation of the model**

We simulate the model for 30 African and Latin American countries using the calibrated parameters described above. For each country, the model predicts the level of tax evasion and output per worker. Figure 3 plots GDP per worker from the model against GDP per worker from the World Bank’s World Development Indicators (WDI). The reported value of the GDP per worker is normalized by the United States levels in both the model and the data. Each circle represents one country and corresponds to the correlation of output per worker between the simulated data and the WDI data. The straight line is obtained from an OLS regression between the model and the data. As it can be seen, the predicted values of output per worker in the model are positively correlated with the data. The coefficient is statistically significant at the 1% level, and the regression coefficient is 4.58. Similarly, Figure 4 plots the level of tax evasion from the model and the data. The predicted values of tax evasion are highly correlated with the WBES data. The regression coefficient is 0.92 and is statistically significant at the 1% level. Focusing on the R-squared, the model explains 49% of the variation of the GDP per worker and 35% of the dispersion of tax evasion among African and Latin American countries.

23
officially registered, has recorded over one million new contracts per year since 2006. Roughly one-third of this number are “new hires” or first deployments overseas, and the remaining twothirds are “rehires” or new work contracts for workers who are already overseas or who have previously been overseas. Within the country, international labor migration rates are highest in areas closest to major cities like Manila (the capital) and Cebu. An open question is why individuals in some outlying provinces – such as Sorsogon, which is more than 12 hours by bus from the capital – typically have substantially lower rates of international labor migration despite facing larger income gaps between home and abroad.

The baseline survey and intervention for the Sorsogon experiment was completed in mid2010, with roughly 5,000 households in the sample. Randomization was at the household level. The experimental conditions were as follows: a control group; a group offered information on typical wages in common overseas work destinations, on procedures for applying for overseas work, and on the typical fixed costs involved in overseas labor migration; and a group offered assistance in applying for overseas work (in addition to the information offered to the previous treatment group). Households in the “assistance” treatment were offered access to and assistance with uploading information on themselves into a job-seekers’ website that recruitment agencies in Manila could then use to search for suitable candidates to fill overseas job openings. These interventions are intended to test the relative importance of various types of information and transactions costs in explaining the low incidence of international labor migration from outlying areas of the Philippines.

Among the sample of individuals are enrolled in the job-seekers’ website database, a follow-on randomization will be implemented, in collaboration with a local microfinance institution, intended to shed light on the importance of credit constraints as a migration barrier. Prospective migrants in the database will be randomly allocated into the following groups: a group offered a small loan to cover the costs of job search (mainly costs of travel to and lodging in Manila to attend job interviews); a group offered a larger loan to cover costs of travel overseas once a job offer is obtained; a group offered both types of loans; and a control group offered no loan products. Estimated effects of these loan treatments should reveal the relative importance of credit constraints at two different stages of the international labor migration process (initial job search vs. overseas travel once a job offer is obtained).

20
# **Appendix 1: Balance Checks**

## **Individual Variables**

|||**Provinc**<br>**Mass**|**e level inter**<br>**media treat**|**vention**<br>**ment**|||**Commun**|**ity treatme**|**nt sample**|**District level**|**intervention**|<br>**School**|**component**|**sample**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contro|l mean|Treatm|ent mean||Contr|ol mean|Treatm|ent mean||Contr|ol mean|Treatm|ent mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|Average number of children<br>under 5 per HH|707|1.459|717|1.398|-0.061|707|1.46|763|1.409|0.051|684|1.493|705|1.435|0.058|
|Average HH size|707|5|717|5.359|**-0.359***|707|5|763|5.05|-0.05|684|6.26|705|6.04|0.22|
|Gender (M ale): HH head|707|0.908|717|0.898|-0.1|707|0.908|763|0.927|-0.019|684|0.905|705|0.929|-0.024|
|Age: HH head|706|35.39|713|37.96|**-2.97*****|706|35.392|762|35.757|-0.365|683|38.269|702|38.47|-0.201|
|Percentage of HH heads that<br>ever attended school|706|0.954|710|0.952|-0.002|706|0.955|763|0.969|-0.014|682|0.959|704|0.97|-0.011|
|Highest educational level<br>achieved: HH head||||||||||||||||
|Primary|672|0.382|674|0.445|-0.063|672|0.382|735|0.41|-0.028|649|0.473|677|0.411|0.062|
|Secondary|672|0.522|674|0.439|**0.083****|672|0.522|735|0.49|0.032|649|0.414|677|0.484|**-0.07***|
|Trade school|672|0|674|0|-|672|0.052|735|0.061|-0.009|649|0.049|677|0.04|0.009|
|University|672|0.052|674|0.051|0.001|672|0.043|735|0.039|0.004|649|0.062|677|0.065|-0.003|
|Gender (M ale): other HH||||||||||||||||
|members|2831|0.37|3126|0.383|-0.013|2831|0.371|3092|0.379|-0.008|3600|0.406|3555|0.416|-0.01|
|Age: other HH members|2827|14.18|3123|15|**-0.82***|2827|14.184|3088|14.776|-0.592|3594|13.609|3549|13.689|-0.08|
|Percentage of other HH<br>members that ever attended||||||||||||||||
|school|1783|0.933|2106|0.935|0.002|1783|0.934|1993|0.963|**-0.029****|2557|0.958|2515|0.957|0.001|
|Highest educational level<br>achieved: other HH members||||||||||||||||
|Kindergarten|1652|0.078|1941|0.062|**-0.016***|1652|0.078|1911|0.061|**0.017***|2434|0.096|2391|0.081|0.015|
|Primary|1652|0.518|1941|0.536|-0.018|1652|0.518|1911|0.507|0.011|2434|0.605|2391|0.622|-0.017|
|Secondary|1652|0.35|1941|0.347|0.003|1652|0.35|1911|0.38|-0.03|2434|0.272|2391|0.269|0.003|
|Trade school|1652|0.039|1941|0.029|0.01|1652|0.039|1911|0.038|0.001|2434|0.014|2391|0.018|-0.004|
|University|1652|0.013|1941|0.024|**0.011****|1652|0.014|1911|0.015|-0.001|2434|0.014|2391|0.01|0.004|
|Percentage of teenagers that<br>spent time on:|||||||||||||||0|
|School|710|0.961|819|0.927|0.034|710|0.962|695|0.965|-0.003|1393|0.933|1375|0.974|-0.041|
|Studying|710|0.973|819|0.958|0.015|710|0.973|695|0.967|0.006|1393|0.953|1375|0.978|-0.025|
|Children care|710|0.728|819|0.71|0.018|711|0.729|695|0.683|0.046|1395|0.72|1375|0.723|-0.003|
|Homework|711|0.713|819|0.752|-0.039|711|0.713|695|0.722|-0.009|1395|0.691|1375|0.699|-0.008|
|Paid work|711|0.016|819|0.019|-0.003|711|0.017|695|0.01|0.007|1395|0.014|1375|0.007|0.007|
|Unpaid work|711|0.091|819|0.188|**-0.097****|711|0.091|695|0.056|0.035|1395|0.08|1375|0.094|-0.014|

39
Policy Research Working Paper 10930

# **Abstract**

This paper presents comprehensive findings on the relationship between economic growth and poverty. Using a first-difference model applied to data from more than 80 countries spanning over 20 years, the paper investigates how changes in gross domestic product affect the Multidimensional Poverty Index and its subcomponents, considering variations in income level, region, and resource dependency. The analysis confirms that economic growth generally reduces the Multidimensional Poverty Index, although the magnitude of the effect varies significantly. It is less pronounced in low-income countries, Sub-Saharan Africa, Latin America and the Caribbean, and resource-dependent countries. The paper disaggregates gross domestic product growth by its dimensions, revealing that growth driven by total factor productivity, consumption, and sustainable growth significantly decreases the Multidimensional Poverty Index. In contrast, factors such as human capital development, capital deepening, investment, government spending, exports, and imports show ambiguous effects on the Multidimensional Poverty Index. These findings suggest that the effectiveness of these factors depends on country-level conditions. Given the clearer positive impact of total factor productivity, consumption, and sustainable growth on reducing multidimensional poverty, policy makers should prioritize strategies that promote these types of growth to fight poverty, especially in contexts where the effects of other growth contributors are uncertain or not well understood.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fmulangu@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Source: The author's calculations based on the national BOPs._ _Note: The overallfreight factor is the ratio of freight costs (defined as the sum offreight debit and credit) to merchandize value (the sum of merchandize debit and credit) in the balance ofpayments._ The freight factors in the South Caucasus are high though not impossibly high by international standards. For instance, the following countries had similar or higher freight factors in 1995 (in percent): Bahrain - 14.5, Kuwait - 13.8, Bulgaria - 9.7, Poland - 9.3, Ukraine - 8.42. The freight factor in each Caucasus country is, however, much higher than in the EU, where it stands at 1.5 percent. The restrictions placed on the movement of goods explain in part the generally high freight factor in the FSU; other significant factors include high cost of transporting goods due to inefficient transportation systems and long distances between trading hubs.

The gradual easement of the blockades over the last five to seven years can be attributed to:

- Increased political stability in Georgia, which has allowed it to become a major transit route for Armenian exports and imports;

- Increased cooperation with Iran, which has increased its role as a transit country;

- Optimization of trade flows and routes due to the improved efficiency of regional trade, gained trading experience, and the increased knowledge of markets and trading partners.

The early assumptions of a prolonged and severe economic blockade made a few years ago had led to some costly, uneconomic decisions. One such example is the construction of a large freight terminal at Zvartnots airport (Yerevan), which was financed by an EBRD loan in the amount of US$22.8 million. The demand for air cargo transportation, which was based on the assumption about a strict international blockade of Armenia, has turned out to be significantly lower than the initial estimate. As such, the terminal currently operates at below 20 percent capacity.

Nevertheless, closed borders and blockades still inflict economic losses and lead to a suboptimal geography of trade in the region.

# **2.3. Main Commodity Flows**

The main commodity flows among Armenia, Azerbaijan, and Georgia and their main trading partners, Turkey, Russia, and Iran, are presented in Table 4.

> 2 Source: _A Review of Statistics on Trade Flows in Services_ (1997). Note that oil exporters in the samnple have very high freight factors due to the high costs of the transport of oil to distant markets.

11