rafmacalaba/datause-encoder-data huggingface dataset:
_Decentralization: The Way Forward for Rural Development?_ line with the governments' proclaimed aims of food security and poverty reduction. While revenues were raised and prices stabilized, highly inefficient instruments of intervention were chosen that were not costeffective.

The ideological rationale for high rates of agricultural taxation was grounded in the widely-held view that the best way to accelerate economic growth was through urban growth and encouragement of rural-urban migration. These views have been thoroughly discredited and many developing countries have reduced levels of agricultural taxation. Nevertheless, rural areas still face the challenge of mobiliAing sufficient resources to fund the components of RD.

# **Inadequate Resource Mobilization**

It is difficult to mobilize sufficient resources in rural areas to finance the components of RD. Raising taxes is more difficult in rural than urban locations, because taxation reduces already low levels of rural income. Even where this is not the case, the tax base is usually small. Also, due to a higher level of self-sufficiency in rural communities, trade in taxable goods and services is often limited.

In any case, most of the taxes proposed for rural areas do not possess the desirable characteristics of an appropriate local tax as identified in the optimal taxation literature. Bird and Wallich (1993) have argued that a local tax should be characterized by: (i) the presence of a fixed tax base to permit location-specific variation in tax rates; (ii) a visible tax base to ensure accountability; (iii) a perception of the tax as being fair; (iv) stable tax yields that are not eroded by inflation; and (v) tax revenues that are adequate to meet needs as they expand. Despite these difficulties, efforts have been made to raise rural taxes. Three main types of taxes have been used:

**Agricultural Commnodity Taxes.** In the second half of the twentieth century, export taxes on agricultural commodities have been the single most important source of government revenue from agriculture and rural areas. As noted in the Schiff and Vald6s (1992) study, commodity taxes contributed an average of ten

6
Finally, not all experiences have been the same. Initial conditions, policies, political economies, resource endowments, and presence of conflict have all influenced growth outcomes. A significant number of countries in SSA have sustained annual growth rates of more than 5 percent for more than a decade. The growth experiences are also quite diverse, with a few countries experiencing consistent long-term growth, a few experiencing long-term stagnation, and the majority experiencing some growth, especially between 1960 and 1973 and between 1995 and 2008 (World Bank, 2007).

Overall, there are significant differences in Chinese and African growth rates (Chart 1). China’s growth has relied on agricultural reforms, and subsequently, a growing investment and capital formation coupled with a phenomenal trade surplus (Chart 2). At the same time, there was not a significant increase in external assistance or indebtedness. The state sector has reduced in size and the private sector in China has grown steadily over the years and has become one of the big drivers of productivity growth and employment generation. Currently, it accounts for more than 50 percent of GDP. Moreover, China’s rapid rise in savings compared to investment has translated into a strong current account surplus as well as a rise in central bank reserves. Household savings have reached more than 40 percent of national disposable income. At the same time, this has been possible with a containment of inflation (Chart 3). Finally, it is important to note that rural growth has played a big role in poverty reduction in China.<sup>1</sup> By contrast, African growth has been more elusive and less sustained and has been influenced by commodity price booms and busts. While reforms have been significant, they have not generated the same payoff as in China. While macroeconomic stabilization has been reached to a large extent, the effects on growth have not materialized as strongly as anticipated. The Washington consensus has not been able to reverse African growth stagnation. Moreover, there has not been a strong structural shift from the government to the private sector in terms of

> 1 Ravallion and Chen (2007) find that rural economic growth had a much higher poverty impact than urban economic growth during the 1981-2004 period.

7
# **1. Introduction**

A large literature in economics, human biology and medicine has been devoted to understanding the effects of early childhood conditions on outcomes later in life. Research has consistently shown that the month of birth is an important predictor of health outcomes, morbidity and mortality. But, so far, no convincing theories have been proposed to explain this association.

Most of the studies showing correlation between months of birth and children’s health outcomes have been restricted to high-income settings. The correlation between an individual’s height and his/her month or season of birth was documented by Weber _et al_ . (1998), for Australia; Kihlbom and Johansson (2004) for Sweden; Shephard _et al_ . (1979) and Koscinski _et al_ . (2004), for Poland. Early exposure to cold conditions was reported to be associated with higher weight during adulthood in England (Phillips and Young, 2000). Van Hanswijck de Jonge _et al_ . (2003), using data from the United States, found that birth weight and early infancy weight gain varied by season and were modified by ethnicity. Tanaka _et al_ . (2007) showed that in Japan the height and weight of schoolchildren were influenced by their months and seasons of birth. We are unaware of any study that directly investigated variations in children’s health by season-of-birth in the context of poor developing countries.

In this paper, we use data from three rounds of the India National Family Health Survey (NFHS) to: (i) quantify the effects of the months of birth on children’s anthropometrics, and (ii) test four hypotheses explaining the relationship between the months of birth and children’s health outcomes. We find that Indian children born during the monsoon months have worse health outcomes than children born during the fallwinter months. The ‘month-of-birth’ effect is shown to be present in all three waves of the NFHS and persists after controlling for a wide set of observable and unobservable characteristics. The effect is large: the differences in weight and height among children born during the monsoons and other children are comparable to the differences in health outcomes between children born to illiterate mothers and mothers who had completed primary education.

2
Policy Research Working Paper 10924

# **Abstract**

Education and skills are two key determinants of earning potential, with sector specialization significantly influencing earnings. This study examines the drivers behind training choices in two high-paying sectors: information and communications technology (ICT) and energy. Drawing on data from 2,528 individuals seeking vocational training in Côte d’Ivoire, we find that a majority (72% of men and 51% of women) aspire to train in ICT or energy. For both genders, higher levels of education and larger professional networks are positively correlated with selecting training in these high-paying sectors. For women, previous training in similar fields strongly predicts their training choices, highlighting path dependency. Additionally, women benefit more from male role models, which significantly increase their likelihood of choosing a training in ICT or energy. Women with greater agency are also more likely to opt for training in these sectors. Conversely, women holding more traditional views on specific household responsibilities are less likely to choose high-paying sector training.

This paper is a product of the Gender Innovation Lab, Africa 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://www.worldbank.org/prwp. The authors may be contacted at mzenaki@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
|**Level of Data Aggregation**<br>**Table 4. Foreign Direct Invesme**|**nt - Growth in t**<br>**Region-Region**|**he Value of In**<br>**Country-**|**ternational I**<br>**Region**|**nvestment Po**<br>**Co**|**sitions: Rob**<br>**untry-Country**|**ustness**<br>|
|---|---|---|---|---|---|---|
|**Estimation Method**|**OLS**|**OLS**|**PPML**|**OLS**|**PPM**|**L**|
|**Dependent Variable**|**Log(Value)**|**Log(Value)**|**Value**|**Log(Value)**|**Value**|**Value**|
|North-to-North Trend|0.060 ***<br>(0.011)|0.054 ***<br>(0.005)|0.053 ***<br>(0.004)|0.062 ***<br>(0.005)|0.054 ***<br>(0.005)|0.053 ***<br>(0.005)|
|North-to-South Trend|0.056 ***|0.090 ***|0.050 ***|0.067 ***|0.053 ***|0.049 ***|
||(0.007)|(0.012)|(0.006)|(0.005)|(0.009)|(0.008)|
|South-to-North Trend|0.061 ***|0.078 ***|0.048 ***|0.079 ***|0.044 ***|0.047 ***|
||(0.017)|(0.012)|(0.007)|(0.006)|(0.008)|(0.008)|
|South-to-South Trend|0.099 ***|0.141 ***|0.081 ***|0.125 ***|0.100 ***|0.079 ***|
||(0.014)|(0.009)|(0.003)|(0.005)|(0.004)|(0.004)|
|**Wald Tests on Trend Coefficients:**|||||||
|1. South-to-South = North-to-North|0.039 **|0.087 ***|0.027 ***|0.062 ***|0.047 ***|0.027 ***|
|2. South Destination = North Destination|0.017|0.050 ***|0.015 ***|0.025 ***|0.028 ***|0.014 **|
|3. South Source = North Source|0.022 *|0.037 ***|0.013 **|0.037 ***|0.019 ***|0.012 **|
|**Fixed Effects**|R-R|C-R|C-R|C-C|R-R|C-C|
|**R-R Fixed Effects x Post-2008 Dummy**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Number of Observations**<br>Note: This table shows the trends in the value<br>squares (OLS) and Poisson pseudo maximum<br>region (C-R), and country-to-country (C-C). <br>between R-R dummies and a dummy that equ<br>pairs, respectively. *, **, and *** represent stat|72<br>of foreign direct inv<br> likelihood (PPML)<br>The regressions inc<br>als 1 after 2008. For<br>istical significance at|9,934<br>estment (FDI) p<br>. There are three <br>lude R-R, C-R, o<br>the C-R and the<br>10%, 5%, and 1|11,637<br>ositions for 200<br> levels of data a<br>r C-C fixed ef<br>C-C regression<br>% level, respecti|86,410<br>1-18. The estimat<br>ggregation: regio<br>fects. Regression<br>s, standard errors<br>vely.|288,579<br>ion methods are<br>n-to-region (R-R<br>s also include t<br>are clustered by|125,180<br>ordinary least<br>), country-to-<br>he interaction<br>C-R and C-C|
change in the _x_ characteristics. However, the estimates shown under Model 3 in Table 3.4 show that we have similar, or even somewhat better, results.

For further comparison, we also provide estimates using MI methods in Table 3.5. Two imputation models are used, one is the normal linear regression and the other the predictive mean matching, which offer the closest corresponding modeling options to the normal linear regression model and the empirical distribution of the error terms model provided in Table 4. Estimation results suggest that both methods, in particular the predictive mean matching method, work reasonably well (using our preferred Model 3). In fact, the predictive mean matching method may offer even slightly better estimates than those in Table 4. However, there are two limitations with MI methods: the variance for MI estimates are larger than those offered in Table 4, and it takes more computer time to obtain these estimates.<sup>29</sup> It can be useful to briefly discuss estimation results based on proxy means testing. As discussed earlier, proxy means testing would likely provide biased poverty estimates (Proposition 2). The intuition behind this result is that predicted poverty does not appropriately adjust for the error term, and is based on the deterministic part of Equation 1 only (see Equation 5). Indeed, estimation results (Table 3.6) show that the estimated poverty rates are much lower than the true poverty rates, with the difference ranging around 5 percentage points for Model 3 in both years.

# **IV.3. Spatial Targeting with Poverty Map**

While tracking poverty trends over time requires the temporal imputation of poverty, monitoring poverty on a larger and more disaggregated scale would require the spatial imputation of poverty. Government agencies in richer countries such as the U.S. Census Bureau regularly rely

> 29 Furthermore, as earlier discussed, these estimates are based on Model 3, which is selected using the method proposed in Dang _et al._ (2017). These results are also consistent with MI estimation results using household survey data from Jordan offered in this study.

27
Table 11: OLS regression of impacts on real income growth under different settings, on initial characteristics of cells and changes in trade costs.

|||Real incomegrowth<br>||
|---|---|---|---|
||Armington|Monopolistic<br>Competition|IRS and Labor<br>Mobility|
||(1)|(2)|(3)|
|Share of Employment|‐0.00923<sup>*</sup>|0.00612|0.0459<sup>*</sup>|
|in Manufacturing|(0.00506)|(0.00643)|(0.0245)|
|Decline Export|0.512<sup>***</sup>|0.901<sup>***</sup>|2.628<sup>***</sup>|
|Trade Cost|(0.0653)|(0.0830)|(0.316)|
|Decline Import|0.356<sup>***</sup>|0.301<sup>***</sup>|0.236<sup>***</sup>|
|Trade Cost|(0.0173)|(0.0220)|(0.0838)|
|Constant|0.491<sup>**</sup>|‐0.268|‐3.216<sup>***</sup>|
||(0.241)|(0.307)|(1.167)|
|Observations|52|52|52|
|Adjusted_R_<sup>2</sup>|0.941|0.898|0.629|

OLS regression with all variables expressed as percentages. Standard errors in parentheses * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01

# **5. The BRI: Infrastructure improvements and border effects**

A large component of transport times between countries in Central Asia and with the rest of the world is the time spent at borders. De Soyres et al. (2018) produced separate estimates of changes in border delays based on reductions in importing and exporting times from the ‘trading across borders’ section of the World Bank’s Doing Business Database.<sup>10</sup> Table 12 combines these estimates with those of the preceding section to give combined reductions in transport time. The table indicates that the estimated reduction in border times yield greater average time saving than do the physical investments alone (the simple average of numbers in table 12 is a 39% reduction in travel times, as compared to a 15% average reduction in the previous experiment, the average of numbers in table 4). Furthermore, there are considerable differences in the geographical pattern of these changes. For example, reduced border delays bring Kyrgyzstan very large reductions in transport times, with many falling by over 60%. China‐3 has lower travel times with Pakistan and improved access to the rest of the world through Gwadar.

> 10  For any border, the data on “Border Compliance” and the total delay is assumed to be the sum of export time from the exporting country and the import time from the importing country. Documentary compliance is not included as it does not relate to travel time.  All data are available at http://www.doingbusiness.org/data/exploretopics/trading‐across‐borders.

24
Policy Research Working Paper 6404

# **Abstract**

This paper addresses whether microcredit participants in Bangladesh are trapped in poverty and debt, as many critics have argued in recent years. Analysis of data from a long panel survey over a 20-year period confirms this is not the case, although numerous participants have been with microcredit programs for many years. The results of the analysis suggest that participants derive a variety of benefits from microcredit: It helps them to earn income and consume more, accumulate assets, invest in children’s schooling, and be lifted out of poverty. This is not to say that non-participants have failed to progress over the same period. Both participants and non-participants have gained as the economy has grown; however, the rates of poverty reduction have been higher for participants. Testing the net effect of microcredit programs requires applying an econometric method that controls for why some households participated and others did not, conditional on their initial characteristics. In addition, the method must control for time-varying, unobserved heterogeneity that affects everyone over time, albeit in possibly different ways. The paper’s econometric estimates show significant welfare gains resulting from microcredit participation, especially for women. They also show that the accrued benefits of borrowing outweigh accumulated debt. As a result, households’ net worth has increased, and both poverty and the debt-asset ratio have declined.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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 skhandker@worldbank.org.

_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
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda new scheme pick up fast while pension payments decrease more slowly than in Reform I.1 (in fact, pension payments continue growing over the first three decades). The impact on government expenditure is presented in Figure 26 and Figure 27.

**Figure 26: Pure DC reforms make Government expenditure for civil servants much lower than in baseline scenario***

<!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>ref I.1A ref I.2 base<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : * Comparison of Government expenditure for civil servants – Reform I.1A, I.2 with baseline (% of GDP).

**Source** : WB calculations based on PROST.

- 59 -
Table 5: Decision Making Power over Large Purchases - Correlation with Perceived Norms, conditional on Personal Beliefs

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Female in HH<br>has all the<br>power<br>(Yes=1/No=0)|Male in HH has<br>all the power<br>(Yes=1/No=0)|Joint decision<br>making with<br>spouse<br>(Yes=1/No=0)|No power at all<br>(Yes=1/No=0)|Female in HH<br>has all the<br>power<br>(Yes=1/No=0)|Male in HH has<br>all the power<br>(Yes=1/No=0)|Joint decision<br>making with<br>spouse<br>(Yes=1/No=0)|No power at all<br>(Yes=1/No=0)|
|||**_Gender Equ_**|**_ality Norm_**|||**_Male Breadw_**|**_inner Norm_**||
|Female|0.15***|-0.15***|0.03|0.05***|0.11***|-0.08***|-0.03***|-0.02***|
||[0.02]|[0.02]|[0.02]|[0.02]|[0.01]|[0.01]|[0.01]|[0.01]|
|Personal Belief:Agree(No=0/Yes=1)|-0.01|-0.08***|0.08***|-0.00|0.02***|0.09***|-0.08***|-0.01**|
||[0.01]|[0.01]|[0.01]|[0.01]|[0.00]|[0.01]|[0.01]|[0.01]|
|Personal Belief:Agree*Female|-0.03*|0.06***|-0.05**|-0.03**|0.04***|-0.07***|0.02*|0.05***|
||[0.01]|[0.02]|[0.02]|[0.02]|[0.01]|[0.01]|[0.01]|[0.01]|
|Community Norms (0/1)|0.01|-0.05***|0.06***|-0.01|-0.01|0.00|-0.01|-0.00|
||[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|
|Community Norms (0/1)*Female|-0.00|0.03**|-0.03|-0.04***|0.02**|0.00|-0.01|0.01|
||[0.01]|[0.02]|[0.02]|[0.02]|[0.01]|[0.01]|[0.02]|[0.01]|
|Constant|0.26***|0.54***|0.11**|0.00|0.22***|0.42***|0.28***|0.04|
||[0.03]|[0.04]|[0.05]|[0.04]|[0.02]|[0.03]|[0.04]|[0.04]|
|Observations|25183|25183|25183|25183|36433|36433|36433|36433|
|Adjusted R-squared|0.06|0.09|0.22|0.25|0.06|0.10|0.21|0.21|
|Mean Female|0.15|0.07|0.57|0.19|0.17|0.08|0.56|0.19|
|Mean Male|0.06|0.18|0.54|0.18|0.07|0.21|0.52|0.18|
|PersBelief+Fem(P-value)|0.01|0.03|0.02|0.01|0.00|0.00|0.00|0.00|
|ComNorms+Fem(P-value)|0.68|0.06|0.08|0.00|0.10|0.34|0.20|0.42|
|Mean VIF|4.50|4.50|4.50|4.50|3.21|3.21|3.21|3.21|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|YES|YES|
**Figure A.6. Predicted Extreme Poverty Rates Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>2<br>16<br>1.5<br>14<br>1<br>12<br>.5<br>10<br>0 8<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>23<br>12<br>22<br>11<br>21<br>10<br>9 20<br>8 19<br>7 18<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>30<br>2.5<br>25 2<br>1.5<br>20<br>1<br>15 .5<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true extreme poverty rates. Dashed lines represent the true extreme poverty rates. Extreme poverty line is defined as US$1.25 (2011 PPP) per day per capita in Bangladesh and Nigeria and as half of the national poverty line in Viet Nam and Ethiopia. National food poverty lines are used as extreme poverty line in Malawi and Tanzania. Dotted lines represent confidence intervals of the true extreme poverty rates. Estimates are obtained using the normal linear regression models.

63