rafmacalaba/data-use-mentions-extended huggingface dataset:
_32_ <!-- Start of picture text --> Civil Service Pension Program<br>Projected Fund Size<br>10 1.50%<br>1 00%<br>6<br>.....Fund Size as%o O 0.50%<br>0 0.00%<br>1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045<br><!-- End of picture text --> Notes: Left axis is in trillion of Rupiah; Right axis is in percent of GDP. Sources: Staff calculations based on Taspen's data.

# **Options for Reform**

Unless corrective measures are taken now, the resulting budgetary burden would be difficult to manage. Among the options to be considered are: First, Taspen needs to produce better investment results, which could be accomplished by resisting political pressures and using external investment advisors.

Second, limits on administrative expenses incurred by Taspen (relative to contributions and assets) need to be established, monitored and disclosed. These limits should be set on the basis of international experience.

Third, the civil service pension plan should provide the same vesting and portability of benefits as is required of approved private pensions. This consistency would facilitate labor mobility between the private and public sectors.

Fourth, pension benefits should be based on the average salary for the final three to five years of service, not on the salary of the final month.

Fifth, the cost-of-living adjustment should be done yearly to protect the purchasing power of the retirees. The adjustment should be restricted to the change in a price index, and not to wages. The current practice of indexing pensions to wages is not fiscally sustainable.

Sixth, as with other plans, the retirement age should be raised to at least 60.
and Rural Affairs and Ministry of Finance, 2021a).<sup>5</sup> Chinese fuel subsidies are determined ex post in that the amount of subsidy awarded for fishing in year _t_ depends on the policy in year _t_ + 1. For example, the fuel subsidies vessels received in 2015 depended on 2015 fishing activities and the parameters of the policy in 2016. We exclude 2015 data from our primary analysis because the policy we evaluate was publicly announced in 2016; some vessels might not have known about the new policy when choosing how much to fish in 2015 (Ministry of Agriculture, 2016). We include data from 2020 in our primary analysis because the 20162020 formula was used by most provinces to award subsidies that year.<sup>6</sup> We obtain similar results when we repeat our analysis with data between 2015 and 2020, and data between 2016 and 2019 (Section 5.3).

During our period of analysis (2016-2020), the fuel subsidy received by vessel _i_ of gear (fishing method) type _j_ in year _t_ is given by _Subsidyijt_ = _SubsidyStandardt×SubsidyFactorj×SubsidizedEnginePowerij×SubsidyDaysit_ (1) The subsidy standard is the same for all vessels but changes each year; controlling for year fixed effects does not change our results (Section 5.3). The subsidy factor is a constant that depends on the vessel’s gear; there are 10 possible gears (Appendix B). The discontinuities in subsidy amount occur because of the subsidized engine power term, which we explain when we introduce our empirical strategy in Section 4. The particular thresholds at which discontinuities occur derive from the government’s goal for firms to scrap their vessels and rebuild them in standardized sizes.<sup>7</sup> We subset the data in our primary analysis

> 5China ended explicit fuel subsidies from 2021 and replaced them with a “compliance award” based on the compliance rating scores of distant water fishing companies (Ministry of Agriculture and Rural Affairs and Ministry of
but decreases significantly as diet diversity increases. Wasting tends to reflect recent factors (e.g. seasonal hunger or epidemics) more sensitively than does stunting. The diet diversity variable may reflect circumstances at time of collection of anthropometric data, while calorie availability represents an annual average.

Women’s and children’s overweight is positively associated with per capita calorie supply (unsurprisingly) and also with dietary diversity (MDD and MDD-W), although not significantly. This latter finding is consistent with Herforth et al. (2020) who cite two previous studies noting the positive association between dietary energy intake with dietary diversity. The coefficients on the GDRS sub-scores (NCD-Protect and NCD-Risk) have the expected sign for women’s overweight but are not significant. There are regional differences, in particular the Southeast Asia region has significantly higher stunting and wasting for children and low BMI for women, controlling for other factors (compared to the Africa region). The Eastern Mediterranean region has significantly more overweight both in children and women, controlling for other variables (compared to the Africa region). The Gini coefficient, a measure of inequality, is not significant in any of the regressions. The effect of calorie supply per capita is non-linear and has the strongest impact at the lower levels of calorie availability.

Results for hypertension and diabetes are mixed, perhaps because the data disadvantages mentioned above are more severe (less consistency of date, lack of age cohort data, and inconsistencies in the population covered, e.g. dependent variables are for all adults whereas some of the independent variables are only for women, etc.). Hypertension and diabetes both go down as ln(per capita calorie supply) increases and (results not presented) average level of hypertension is highest in low income countries and decreases consistently from low to lower-middle to upper-middle to high income countries. In theory this variable is supposed to 12
same dependent variable and add to the regressions a dummy for a survey year. Note that the 2003 TLSS survey did not include questions on the respondent’s ethnicity or education of his/her parents. Therefore the pooled regressions include only variables that are found in both datasets such as “war cohort”, war cohort interacted with residence in conflict area and rural residence. All regressions include fixed effects at the raion level (69 groups). Tables 2.1 and 2.2 report results of the OLS regressions based on the pooled samples of 2003 and 2007 TLSS data for men and women, respectively.

The results from regressions on the pooled sample of 2003 and 2007 data for men (Table 2.1, Col. 3) indicate that there was some catch-up in the completion of basic education by individuals born in 197619
20 upper four of the five blocks are explicitly subsidized, the largest unit subsidy reserved for the block covering 801 to 2,000 kWh a month.

Subsidized lifeline rates are limited only to those consuming less than the cap in nine countries (Benin, Cameroon, Cabo Verde, Gabon, Ghana, Mozambique, Nigeria, São Tomé and Príncipe, and Togo), shifting households to tariffs for the next tier for the entire consumption if the cap is exceeded. This has the same effect as volume‐differentiated tariffs in Gabon, Mozambique, and Nigeria. Not having access to the subsidy by exceeding the limit by even 1 kWh makes it more difficult for the poor when the size of the block size is relatively small (say less than 50 kWh). The median increase in the effective unit energy charge for consuming more than the limit on the first block is 68 percent. There is large variation across countries, however, ranging from 4 percent in The Gambia and 7 percent in Senegal to 340 percent in Madagascar to 450 percent in Kenya and Zimbabwe. In Mali, pre‐paid customers do not lose access to the low rates for the first two blocks and tax exemption even if they consume more than 100 kWh, but post‐paid customers lose both benefits if 100 kWh is exceeded.

Table A.6 in the annex provides additional information, including the number of schedules and blocks and the type of tariffs (increasing block or volume‐differentiated) in each of the 39 countries. Among the 22 countries with household survey data, only Rwanda had a single tariff schedule with one block and no fixed charges. All other countries had more than one schedule, more than one block, or both, making back‐calculation of kWh consumed from spending on electricity not possible.<sup>9</sup> Increasing block tariffs were the most common tariff type, but there are several with volume‐differentiated tariffs, of which three have volume‐differentiated tariffs only between the first and second blocks
# **3 Determinants of destination choice**

## **3.1 The extended Todaro model**

The previous analysis has highlighted at least two stylized facts, which are not in line with the basic Todaro (1969) model. First, urban welfare levels clearly dominate rural welfare levels, across the whole distribution. This is true when we look at data on those who migrate to these locations in the KHDS, or on all those residing there in the census data. In the census, for example, Table 3 shows the constructed wealth index in the rural areas going from -0.99 at the 10th percentile to 0.29 at the 90th percentile, while in cities these same percentiles span 0.33 to 2.73: even the lowest city welfare level, measured this way, outperforms that of the highest rural ones. Similarly, KHDS shows that moves to towns or cities clearly outperform not moving or moves to rural areas.

These facts are problematic for the original Todaro model, as the model requires rural wages to equate the expected city wages in equilibrium. Kanbur et al. (2019) extend the model by introducing a cost to moving for each migrant (financial, social, psychological,...). The equilibrium condition now requires that rural wages plus migration costs need to equal the expected urban wage, so that there is a range of costs that can make the Todaro model fit the stylized facts.

Second, both KHDS and the census show that the economy is not dualistic, but displays a clear gradient from villages, over towns to cities. Thus, the second extension the authors make to the Todaro model is the introduction of multiple destinations.

The model highlights the role of a number of key variables when it comes to destination choice: the cost of moving and the destination wages. The next section introduces these variables into a regression framework to assess their relative importance and their interaction with poverty, education and age.

We use distance to destination as
# **5) Scaling up business training**

Classroom-based business training programs typically provide training to 15 to 40 small businesses at a time. Developing countries have hundreds of thousands, or millions of such small firms – for example, the Mexican Economic Census enumerated 4.1 million firms with 0 to 10 workers (INEGI, 2016). The big challenge for policy makers is then how to provide good quality business training services at a scale that can reach vast numbers of firms while not being prohibitively expensive for the government. I see three (potentially complimentary) approaches for achieving this goal.

# _Approach 1: Charge firms for training and develop the market for business services_

A first general approach to scaling is to develop and use the market for business service provision and having firms pay at least part of the costs of training. Many governments and NGOs have offered business training for free or for a nominal fee. Charging for training can make such programs more financially sustainable, and may be a way of selecting entrepreneurs who value the training more and who will exert more effort. However, it may also prevent poorer and liquidityconstrained entrepreneurs from getting assistance. Recent work by Maffioli et al. (2020) in Jamaica explores these issues by randomly varying the price charged for training, enabling them to assess how the demand for training varies with price, and to investigate whether those who pay more for training attend training more regularly. Their business training course consisted of 40 hours divided into ten lessons of four hours each, and cost $150 to provide. They find demand falls sharply as price increases: 76 percent attend at least one class when it is offered for free, 65 percent when they have to pay a token fee of 5% ($7.50), 29 percent when charged half of the cost ($75), and only 11 percent when charged the full cost. Offering the opportunity to pay in three installments did not change willingness to pay. Poorer and smaller businesses are less likely
Policy Research Working Paper 6297

# **Abstract**

Drawing on Public Expenditure and Financial Accountability assessment scores from 118 countries, this paper provides the first comparative analysis of public financial management performance in small Pacific Island Countries (PICs). It applies a Tobit regression model across the full cross-country sample of Public Expenditure and Financial Accountability scores and country variables to identify potential causes for the observed underperformance of Pacific Island countries relative to other countries of similar income. First, the analysis finds small population size to be negatively correlated with Public Expenditure and Financial Accountability scores, with the “population penalty” faced by small Pacific Island countries sufficient to explain observed underperformance. Second, through application of a new capacity index of Public Expenditure and Financial Accountability dimensions, it finds strong evidence in support of the hypothesis that small population size impacts scores through the imposition of capacity constraints: with a limited pool of human capital, small countries face severe and permanent challenges in accessing an adequate range and depth of technical skills to fulfill all functions assessed through the Public Expenditure and Financial Accountability framework. These findings suggest that approaches to strengthening public financial management in small Pacific Island countries should involve: i) careful prioritization of public financial management capacity toward areas that represent binding constraints to development; ii) adoption of public financial management systems that can function within inherent and binding capacity constraints, rather than wholesale adoption of “best practice” imported systems; and iii) consideration of options for accessing external capacity to support public financial management systems on a long-term basis, from regional agencies, the private sector, or donors.

This paper is a product of the Poverty Reduction and Economic Management Unit, East Asia and the Pacific Region. 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://econ.worldbank. org. The author may be contacted at thaque2@worldbank.org.
not reject the over-identi...cation restrictions.

Table 9 reports alternative speci...cations of the allocative e¢ ciency regressions. Overall the results uphold the conclusions drawn from the baseline speci...cations of Table 8 columns (3) and (4). The MTBF and MTPF coe¢ cient estimates maintain their prior patterns, although again the di¤erence transform model in column (3) performs somewhat poorly. The covariates openness and con‡ict do not display any signi...cant e¤ects on health spending volatility.

# 4.3 Technical E¢ ciency

Table 10 presents the baseline regressions for technical e¢ ciency, measured as e¢ ciency scores from a stochastic frontier model of health delivery; see equations (2), (3), and Table 4. As expected, this indicator of ...scal performance is much more persistent than the previous ones. The FE-OLS bracketing range, based on columns (1) and (2), climbs to 0.858�0.999, indicating strong persistence. National health delivery is a complex system that may take decades to fully internalize the bene...ts of a given reform. The MTEF coe¢ cient estimates are all negative, small and far from statistically signi...cant. Hypothesis (3) above predicts that the top-phase MTEF (the MTPF) increases technical e¢ ciency.

Columns (3) and (4) present the D-GMM estimates of the MTEF e¤ects. The internal instruments are the second lag of technical e¢ ciency and the second and third lags of the MTEF indicators. Compared to the previous two columns, the estimated MTEF e¤ects turn positive and increase in magnitude. The speci...cation with external instruments is more precise. The MTPF e¤ect is 1.015 and statistically signi...cant at the 10 percent level. The lack of precision should be expected since the within-country variation in technical e
(0.06)|(0.21)|
|**CS-Other**|-0.06*||-0.03|0.01|-0.04|
||(0.03)||(0.03)|(0.02)|(0.07)|
|**First Stage F Statistic**||107||||
|**Noise Controls**|Yes|Yes|Yes|Yes|Yes|
|**Project Controls**|Yes|Yes|Yes|Yes|Yes|
|**Fixed Effects**|Project Type,<br>Sector|Project Type,<br>Sector|Project Type, Sector|Project Type,<br>Sector|Project Type,<br>Sector|
|**Observations (clusters)**|2,463 (18)|2463|2,463|2,463|2,463|

**Notes:** *** denotes significance at 1%, ** at 5%, and * at 10% level. Robust standard errors are in parentheses, and are clustered by organization in Column 1. All columns report OLS estimates. In Columns 1 and 3, the dependent variable is a dummy variable that takes the value 1 if the project is fully completed and 0 otherwise. In Column 2, the dependent variable is the monitoring management index defined using baseline data. In Column 4, the dependent variable is a dummy variable that takes the value 1 if the project is initiated and 0 otherwise. In Column 5, the dependent variable is an index of project completion (that is a continuous measure between zero and one). Project Type fixed effects relate to whether the primary classification of the project is 'Advocacy and Policy Development', 'Financial & Budget Management', 'ICT Management and Research', 'Monitoring, Training and Personnel Management',
Bank (2024, Forthcoming).

Central Bureau of Statistics (CBS). "Report on the Nepal labour force survey 2017/18." (2019).

Chakravarty, Shubha, Mattias Lundberg, Plamen Nikolov, and Juliane Zenker. "Vocational training programs and youth labor market outcomes: Evidence from Nepal." _Journal of Development Economics_ 136 (2019): 71-110.

Chicoine, Luke. "Free primary education, fertility, and women’s access to the labor market: Evidence from Ethiopia." _The World Bank Economic Review_ 35, no. 2 (2021): 480-498.

Crowne, Douglas P., and David Marlowe. "A new scale of social desirability independent of psychopathology." _Journal of consulting psychology_ 24, no. 4 (1960): 349.

Cislaghi, Ben, and Lori Heise. " Measuring Gender-related Social Norms: Report of a Meeting, Baltimore Maryland.” Learning Group on Social Norms and Gender-based Violence of the _London School of Hygiene and Tropical Medicine_ " (2016): 44.

Deshpande, Ashwini, and Naila Kabeer. "Norms that matter: Exploring the distribution of women’s work between income generation, expenditure-saving and unpaid domestic responsibilities in India." _World Development_ 174 (2024): 106435.

Deshpande, Ashwini, and Jitendra Singh. “Dropping out, being pushed out or can’t get in? Decoding declining labour force participation of Indian women.” No. 14639. IZA Discussion Papers, 2021.

Dhar, Diva, Tarun Jain, and Seema Jayachandran. "Reshaping adolescents' gender attitudes: Evidence from a school-based experiment in India." American economic review 112, no. 3 (2022): 899-927.

Field, Erica, and Kate Vyborny. "Women's mobility and labor supply: experimental evidence from Pakistan." _Asian Development Bank Economics Working Paper Series_ 655 (2022).

Gauri, Varun, Tasmia Rahman, and Iman Kalyan Sen. "Measuring social norms about female labor force participation in Jordan."
a short talk about the potential of technology use in education. Students in t <mark>he cross-designed information treatment group received the returns to schooling information at the close of the workshop.</mark> <mark>Endline student testing was conducted during workshops in April 2012, just before students sat the JS2 end of year exam All students completed a self-administered survey and were given 45minute tests on</mark> reading comprehension and ability (as was done at baseline), math, and non-verbal intelligence.

<mark>The Math test included publicly released items from various years of the Trends in International Mathematics and Science study (TIMMS) Grade 4 test.</mark> The timed non-verbal intelligence test was based on Raven’s Progressive Matrices, which assesses general intelligence or “general cognitive ability” by measuring the ability to form perceptual relations and to reason by analogy (Raven 2003).

Students were asked to bring their eReaders to the endline workshops, and information on device use and status was collected through the self-administered student survey, direct observation of the device, and extraction of use information from the device.

> 12 Nearly half of this “attrition” is due to class repetition. See Table 1.1.

> 13 Using information from a set of sampled workers from an investment climate survey, we generate information on the returns to schooling for completion of primary, junior secondary, senior secondary school and tertiary schooling.

6
in 2004. Under full trade and subsidy liberalization, global cotton markets would change dramatically: the value of production would fall by one-third or more than $5 billion per year in high-income countries (mostly in the US), and the value of their exports would fall by $3.6 billion. The world totals would hardly change though, as developing country output and exports of cotton would expand by about the same amounts, with Sub-Saharan Africa enjoying more of that gain than any other region (Table 14). Indeed cotton is so important in Sub-Saharan Africa minus South Africa that it contributes one-quarter of that region’s net gain in agricultural value added from full liberalization.<sup>8</sup> The bottom line, therefore, is that if all current distortions to world trade in merchandise were phased out over the rest of this decade then, according to the latest GTAP database and the Linkage model, by 2015 developing country agricultural production, employment and real net income would be greater than without such reform. And it would be greater in most regions within the developing country group, the exceptions being South Asia and Eastern Europe where import tariffs are higher for agricultural than non-agricultural goods. This does not _necessarily_ mean that if each individual developing country were to unilaterally liberalize we would find farmers benefiting in all cases except in South Asia and Eastern Europe; but if we were to run those many individual model simulations we may well get that result also, since, as is apparent from Table 3, terms of trade effects of reforms by others are usually dominated by efficiency gains from own reforms except for the least-distorted economies.

> 8 If full cotton liberalization was included in the Doha package, it would nearly double both the net welfare gain and the boost in net farm income in the SSA region. Furthermore, the share of all developing countries in global cotton exports would be 85 percent instead of 56 percent in 2015 – all of which vindicates the efforts to ensure cotton receives specific and substantial attention in the