rafmacalaba/datause-encoder-data huggingface dataset:
The first column of Table 4 reports the<sup>resultsof this specification when X</sup> contains only the share of trade<sup>in GDP.TheOLS andIV estimates replicate those in</sup> our previous paper. We find a positive<sup>effectof trade on growthat thisdecadal</sup> frequency, with a coefficient that is<sup>economicallysensible andhas a t-statistic greater</sup> than 2. In the first-stage regressions,<sup>we findthat while only initial incomeisa significant</sup> predictor of growth, both initial income<sup>andinitial trade aresignificant predictors of trade.</sup> This suggests some concerns about<sup>the strength of the identification.The diagnostic</sup> statistics for the presence of weak instruments<sup>paint a somewhatmixedpicture as well.</sup> The Shea partial R-squared for growth<sup>is very small,while the partialR-squaredfor trade</sup> is a respectable 0.25. On the other<sup>hand,theStock-Yogo test statistic for the nullof</sup> weak instruments is large enough<sup>that wecanreject thenull.The bottom panel reports</sup> conventional Wald-based confidence<sup>intervalsas well asintervals based on the S-</sup> statistic.<sup>9</sup> While the confidence interval<sup>for the coefficient oninitial incomeis very large,</sup> the confidence interval for the coefficient<sup>on tradeisessentially thesameas that based</sup> on the usual Wald statistic. This gives<sup>us someconfidence in the robustnessof this</sup> basic finding on the effects of trade<sup>on growthat decadal frequencies,even thoughthe</sup> coefficient on initial income appears<sup>to be less well identified.</sup> The middle panel of Table 4 reports<sup>results addingone of the five time-varying</sup> measures of institutional quality we considered<sup>in our previous paper.Following Clague,</sup> Keefer, Knack and Olson (1999) we<sup>proxy for the strength of property rights protection</sup> using what they refer to as "contract-intensive<sup>money",i.e.the share of currencyin</sup> circulation held inside the banking system.<sup>In themiddlepanel of Table 4, we enter this</sup> variable and treat it as endogenous.<sup>The top part of the table again replicatesthe results</sup> of our previous paper. We find that<sup>the coefficienton tradedoes notchange very much</sup> relative to the previous case, while<sup>the measureof institutional quality enters negatively</sup> with a very large standard error. However,<sup>the diagnostic statistics for the presenceof</sup> weak instruments strongly point to identification<sup>problemsin this specification.The Shea</sup> partial R-squared statistics are tiny,<sup>as is theminimumeigenvalueof the concentration</sup> matrix. Not surprisingly, in this case<sup>confidence intervals based on the S-statistic cover</sup> 9 In this section we report only the<sup>confidence intervalsbased onthe S-statisticbecause of its</sup> convenient property that projection<sup>methods are not required.This is most useful in the middle</sup> specification of Table 4 where we have<sup>threeendogenous variables,andensuresthat the results</sup> are comparable across these three specifications.

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Policy Research Working Paper 8529

# **Abstract**

Substantial work has demonstrated that early nutrition and home environments, including the degree to which children receive cognitive stimulation and emotional support from parents, play a profound role in influencing early childhood development. Yet, less work has documented the joint influences of parenting and nutrition status on child development among children in the preschool years living in low-income countries. Using panel data on parenting, nutrition status, and early developmental outcomes of about 7,000 Cambodian preschool-age children, this paper demonstrates that inequities in early development associated with family wealth are evident at the start of preschool and increase over time. A significant share of these inequalities can be explained by differences in parental stimulation and early nutrition status. Better educated parents engage in better parental activities that stimulate children’s development. However, the positive association between parental activities and child outcomes is particularly strong for non-stunted children, and parental activities can only explain about 8–14 percent of the cognitive gap between the lowest and highest wealth quintiles. The results highlight the need for integrated interventions that address both parenting and early nutrition, also suggesting that parenting interventions for the most disadvantaged families should be carefully designed and evaluated to ensure maximum effectiveness.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted via jberkes@diw.de or dfilmer@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
among existing firms. Conversely, a poor business environment can cause firms to leave the province and bias the negative effect of the overall PCI on firm productivity. The enterprise survey does not provide information about the establishment year of the firms so we cannot control for the entry of firms into a province. We side-step the difficulty of modelling a firm’s location choice by testing for the results in a balanced panel instead. The results are presented in Table 7. The effect of the overall PCI index is very similar to the baseline results, suggesting that our results above are not driven by firms’ location choices.

**Table 7: Regression results with balanced panel**

||Labor productivity|Log(LP TFP)|
|---|---|---|
|PCI overall index|0.329**|0.005**|
||(0.151)|(0.002)|
|Share of large firms in province|-0.171|0.002**|
||(0.117)|(0.001)|
|Capital intensity ln(K/L)|2.505|-0.178***|
||(1.660)|(0.040)|
|Firm size (ln employment)|-21.912***|-0.067|
||(4.097)|(0.061)|
|Constant|92.644***|4.588***|
||(16.183)|(0.400)|
|Observations|541,000|128,176|
|R-squared|0.028|0.076|
|Adjusted R-squared|0.0275|0.0738|
|N-cluster (province level)|63|63|
|Number of firms|135,250|32,044|
|Sector-year Fixed effects|Y|Y|
|Firm fixed effects|Y|Y|

Second, we perform a falsification test by examining the firms that are located in SEZs. This test is based on the assumption that firms located in SEZs are subject to a separate, and typically preferential, set of centralized regulations that are unlikely to be affected by the quality of provincial bureaucrats. The results are presented in Table 8. They show that the overall PCI, while having a negative effect on productivity, is not statistically significant. The results in this sub-sample are consistent with the hypothesis that the overall PCI do not have any significant impact on productivity these firms.

**Table 8: Regression results for firms in SEZs**

||(1)<br>Laborproductivity|(2)<br>Log(LP TFP)|
|---|---|---|
|PCI overall index|-0.475|-0.004|
||(0.288)|(0.003)|
|Share of large firms in province|-0.015|0.002**|
||(0.105)|(0.001)|

16
the pertinence of the question. The results also warn against overinterpretation of the static finding that poverty rates are higher in rural areas and secondary towns than in metropoles, and call for deeper reflection about the optimality of ongoing urbanization processes, especially in areas where urban concentration is already high (as in Sub-Saharan Africa). In depth, comparative historical country case studies, including about the political economy of urban concentration, as well as theoretical and empirical analyses to better unpack the channels will prove extremely valuable.

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# The Distributional Impact of Fiscal Policy in South Africa<sup>1</sup>

Gabriela Inchauste<sup></sup> , Nora Lustig<sup>ϯ</sup> , Mashekwa Maboshe<sup>*</sup> , Catriona Purfield<sup></sup> , and Ingrid Woolard<sup>*</sup>

**JEL classification** : H22, I38, D31

**Keywords** : fiscal policy, fiscal incidence, social spending, inequality, poverty, taxes, South Africa

> 1 This paper is part of the project “The Distributional Impact of Fiscal Policy,” a joint initiative of the World Bank and the Department of Economics at Tulane University to implement its diagnostic tool, the Commitment to Equity Assessment (CEQ) to six middle income and developing countries.  Led by Nora Lustig since 2008, the CEQ is a project of the Center for Inter-American Policy and Research (CIPR) and the Department of Economics at Tulane University, the Center for Global Development and the Inter-American Dialogue. The authors are grateful to Asad Alam (Country Director, World Bank) for guidance, Precious Zikhali for help with poverty measurement and assistance with ensuring we had the latest administrative and fiscal data, and Jon Jellema for help with estimating the indirect effects of indirect taxes. The authors want to express their appreciation for the excellent research assistantship provided by Sean Higgins (Tulane University) and Catherine Lee (World Bank), as well as Jacob Edelson, Nicole Florack, Xinghao Gong and David Roberts.  The authors would like to thank Servaas Van der Berg (University of Stellebosch), Blanca Moreno-Dodson (World Bank), Samuel Freije-Rodriguez (World Bank), Jorge Martinez-Vazquez (Georgia State University), and David Coady (Fiscal Affairs Department, International Monetary Fund) who served as peer reviewers for this work. Last but not least, we would like to thank Michael Sachs and Ian Stuart of South Africa’s National Treasury who facilitated this work and provided early feedback, as well as participants in a workshop on the preliminary findings held in Pretoria in May 2014.

>  The World Bank.ϯTulane University * University of Cape Town.
**Chart 2: Price of Water by Type of Service Provider**

<!-- Start of picture text --> $11.00<br>$10.00<br>Public Privat e<br>$8.00<br>Piped Unit ized<br>$6.32<br>$6.00<br>$4.00<br>$3.60<br>Based on data from 47 countries<br>Average<br>and 93 locations based on the<br>literature review<br>St d. Deviat ion<br>$2.00<br>$0.79 $0.86 $0.76<br>$0.34 $0.45<br>$0.00 $0.02 $0.17<br>Public Utilities Private Netw orks Point-source Vendors Tanker Trucks Carters<br>Mobile Distributors<br><!-- End of picture text --> Chart 2 above indicates that price of water increases as economies of scale in production and sales decrease. Likewise, as unit size or volume of sales decrease, prices go up. Carters, which have the smallest sales volume, charge the highest unit price per m<sup>3</sup> of water— as much as $11.00/ m<sup>3</sup> or more. Thus, the ability of the service provider to amortize capital costs as well as operating inefficiencies (such as fuel, bribes and salaries) over larger volumes is the key to improving affordability.

On average, water from piped network operators (PNO) costs 1.5 times more than that from a formal utility, whereas, water from a point source (PS) costs, on average, up to 4.5 times the utility and, finally, water from the mobile distributors (MD) can costs up to 12 times more than the utility. Taking into account the role of local conditions in establishing prices, this information provides a useful baseline. Further study of factors influencing SPSP prices by geographic location and understanding how these factors contribute towards such pricing is necessary.

The range of prices by geographic location, as seen in the Chart 3 below, also confirms that local conditions play a key role in determining prices. Factors like: distance from the source, seasonal fluctuations in water availability, economic conditions, physical terrain, 26
initial range of variation. For the parameters that determine transitions in and out of the social security we did some simulations to understand their influence on the steady state distribution of the cohort. On this basis we defined initial values and also imposed the constraint 0<1 so that the probability of keeping a job is always higher than the probability of finding one (which is consistent with the data reviewed in Section 2). Finally, for the probability of working when retired we used as a starting reference the average derived from the household survey.

For some of the parameters the economic model puts restrictions on their range of variation, hence we apply left or right censoring. In all cases, judgment is involved in setting the variance of the distributions so that there is enough variation to explore larger regions of the parameter space, but not too much that it would delay convergence (see MacKay, 2003). The initial distributions of the model parameters for the 8 sequences are presented in Table 1.

To compute the ratio _d_ for each  i we proceed as follows. First we use **C** b(a,m|Md(.|  ,ξ), **Е** ) with m=1,000 to compute the probabilities that at various ages _a_ an individual of the cohort would be in various states _e_ . We define these probabilities by p(a,e) and calculate them by simply counting the number of individuals in state _e_ at age _a_ and then dividing by _m_ . Then, the probability that the data would have been generated by  is given by the multinomial distribution:

where Na is the number of individuals of age _a_ who were sampled from the population, s(a,e) the share of these individuals that is in state e (which come from the pseudo sample), and Fa the number of possible combinations of individuals across states. Because we are only interested in likelihood ratios, the sample size is normalized to 1 so that (4) becomes the Dirichlet distribution with parameters s(a,e).<sup>10</sup> The ratio d is then given by: where the normalizing constants for the distributions are dropped from both the numerator and denominator. Then taking logs we obtain: ' ' ' log  _d_  <mark></mark> _s_  _a_ , _e_  log  _p_  _a_ , _e_ | **θ**  log  _p_  _a_ , _e_ | **θ** _s_  log  _f_  **θ** _s_ | **θ**  log  _f_  **θ** | **θ** _s_ , (6) _a e_ 10 The Dirichlet distribution is a Bayesian prior of the parameters of the Multinomial distribution. It gives the likelihood of the probabilities p(a,e) given the shares of each cohort in each state.

16
and Economic Successn Unequal Chances: Family Background and Economic Success, pages 201–247.

- Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N., and Verashchagina, A. (2008). The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends. _The B.E. Journal of Economic Analysis & Policy_ , 7(2):1–48.

- Iversen, V., Krishna, A., and Sen, K. (2019). Beyond poverty escapes _−_ social mobility in developing countries: A review article. _World Bank Research Observer_ , 34(2):239–273.

- Jayachandran, S. (2015). The Roots of Gender Inequality in Developing Countries. _Annual Review of Economics_ , 7(1):63–88.

- Jensen, R. (2010). The (Perceived) Returns to Education and the Demand for Schooling. _The Quarterly Journal of Economics_ , 125(2):515–548.

- Jensen, R. (2012). Do Labor Market Opportunities Affect Young Women’s Work and Family Decisions? Experimental Evidence from India. _The Quarterly Journal of Economics_ , 127(2):753–792.

- Kingdon, G. G. (2007). The progress of school education in India. _Oxford Review of Economic Policy_ , 23(2):168–195.

- Knight, J., Sicular, T., and Yue, X. (2011). Educational Inequality in China: The Intergenerational Dimension. University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 201113, University of Western Ontario.

- Luke, N. (2019). Gender and Social Mobility: Exploring gender attitudes and womens labour force participation. WIDER Working Paper Series 2019-108, UNU-WIDER.

- Maitra, P. and Sharma, A. (2010). Parents and children: Education across generations in India. Technical report.

- Mazumder, B. (2014). Black White Differences in Intergenerational Economic Mobility in the U.S. _Economic Perspectives_ , (Q I):1–18.

- Mogstad, M. (2017). The Human Capital Approach to Intergenerational Mobility. _Journal of Political Economy_ , 125(6):1862–1868.

- Narayan, A., der Weide, R. V., Cojocaru, A., Lakner, C., Redaelli, S., Mahler, D. G., Ramasubbaiah, R. G. N., and Thewissen, S. (2018). _Fair Progress? Economic Mobility_

37
# **Figure 7 Impact of taxes on progressivity**

A. Scatterplot of tax revenue on the share of direct taxes in total taxes B. Scatterplot of direct tax share on prosperity gap <!-- Start of picture text --> Percent of total tax revenue Factor<br>80 7<br>70 6<br>5<br>60<br>4<br>50<br>3<br>40<br>2<br>30 1<br>20 0<br>0 10 20 30 40 50 20 40 60 80<br>Tax to GDP Direct tax (percent of total tax revenue)<br><!-- End of picture text -->

- C. Predicted prosperity gap and progressivity

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>10 20 30 40 50 60 70 80 90<br>Direct tax (percent of total tax)<br><!-- End of picture text --> _Sources:_ International Monetary Fund; Authors’ calculations _Note:_ A.B. Binscatter with 50 bins where observations along the vertical and horizontal axis are partitioned and then binned into one single point.

# **6.3 Government spending volatility and uncertainty**

economy or contribute to higher growth volatility. Government spending may be more volatile in countries with lower tax revenue. These economies may also have less opportunity to borrow undermining their ability to stabilize the business cycle and implement effective countercyclical policy.

The literature on economic and policy volatility and growth presents a compelling case for the negative impact of volatility on economic growth. Ramey and Ramey (1995) demonstrated through data from 92 countries that higher volatility is correlated with lower growth rates. Their findings 14
2000, 2004 and 2006. Participation is defined as one if an individual reported being engaged in the activity in either their primary or secondary occupations and is expressed in percentages.<sup>9</sup> As can be seen in Table 1, the labor force participation rates of rural individuals aged 16 through 70 fell from 89 to 82% for men, and from 86 to 73% for women between 1997 and 2006.<sup>10</sup> Work-related migration increased substantially for both men and women. The male migration rate tripled from 7.8 to 24.5%. However, despite a similar threefold increase (5.6 to 14.9%), the female migration rate remains much lower than men’s. The unconditional gender gap in the migration rate increased over time from about 2% in 1997 to almost 10% nine years later.

The percent of men and women engaging in agricultural self-employment are similar on average. Both declined from around 76 to around 56% over the period. The percentages working locally for a wage declined somewhat between the late 1990s and mid 2000s to 23% for men and 16% for women. There appears to be a slight increasing trend in the percent of women employed in family businesses ─ from 10% in 1997 to about 12% in 2006.

Migrants are the youngest among the four labor categories, and female migrants are on average younger than male migrants. Those self-employed in agriculture are the oldest and least educated workers while those employed in local wage work have the highest levels of education. It is also clear that women have less education than men across all employment categories. The profile of workers across activities is consistent with that found in other studies based on different data sources (for example, Zhao 1999).

Averages across men and women as presented in Table 1 mask the fact that gender differences in labor allocation to activities vary substantially across age groups. These are also changing over time. As shown in Figure 1, the very youngest women have, at least since 1996, had a higher migration rate than the youngest men. But women in general are less likely to migrate than men, and the gender gap in migration widens

> 9 There is no minimum time requirement for being recorded as a participant and participation in on-farm and off-farm rural employment activities are not mutually exclusive.  As a result, participation across the four activities does not add up to one hundred percent for each year.

> 10  These trends are confirmed in the census.  Based on the 0.966% sample of the 2005 Chinese Census, the rural labor force participation rate in 2005 is 84.2% for men (aged 16-70) and 71.3% for women (aged 1670). According to the 1‰ sample of 2000 Chinese Census, the rate is 87.7% for men and 76.7% for women in 2000. We thank Meiyan Wang in IPLE-CASS for providing the information.

10
<!-- Start of picture text --> Figure 2. Distribution of total mangrove area and distribution<br>of total area protected by mangroves by region<br>Sub‐Saharan Africa<br>South Asia<br>Latin America & Caribbean<br>East Asia & Pacific<br>0% 10% 20% 30% 40% 50% 60%<br>Distrbution of total area protected by mangroves Distribution of total mangroves area<br><!-- End of picture text --> Source: Figure 1 and authors estimates described in the text.

For the top 10 countries with mangroves listed in Table 1, estimates of area that would be subject to storm surge if there were no mangroves and the reduction in surge area due to the presence of mangroves are summarized in Table 2. (Similar estimates for all countries are listed in Annex 2). It should be noted that extensive mangrove coverage does not always result in wide coastal protection. Although most of the countries with extensive mangroves benefit from significant reductions in storm surge that can be attributed to their mangrove forests; there are several notable exceptions where mangroves reduce the inundation area by less than 15 percent. Papua‐New Guinea (7 percent), Bangladesh (10 percent) and República Bolivariana de Venezuela (14 percent) are illustrative examples.

11
Table 1: Individual outcomes in Bangladesh by current employment status and past migration experience

|Employment status|Emp<br>(with paid|loyer<br> employees)|Self-e<br>(without p|mployed<br>aid employees)|Wage e|mployee|
|---|---|---|---|---|---|---|
|Migration experience|Non-mig|Returnee|Non-mig|Returnee|Non-mig|Returnee|
|Percentage of employment sta-|5.5%|9.5%|22.6%|59.2%|71.9%|31.3%|
|tus by migration experience|||||||
|Duration of migration|-|6.4|-|6.7|-|5.1|
|Unexpected return|-|0.24|-|0.22|-|0.30|
|Net revenue/Income ($)|4524|6319|1693|1753|1612|1597|
|Sector: Manufacture|0.25|0.02|0.14|0.005|0.30|0.06|
|Sector: Construction|0.04|0.09|0.02|0.02|0.14|0.35|
|Sector: Transportation|0.04|0.15|0.20|0.30|0.20|0.22|
|Sector: Retail/hotel/resturaunt|0.60|0.73|0.56|0.67|0.12|0.33|
|Sector: Other services|0.06|0.02|0.08|0.01|0.23|0.04|
|Number of employees|4.4|3.0|||||
|Employees: 1-2|0.71|0.76|||||
|Employees: 3-5|0.20|0.16|||||
|Employees: 6+|0.09|0.08|||||

_Note:_ The sample is restricted to males aged 18-59 who are employed in non-agricultural sectors. Net revenue and income are inflation-adjusted and converted to 2016 constant dollar. _Source:_ Non-migrant data is from the Household Income and Expenditure Survey 2016/17, while data on returnees is from the Bangladesh Return Migrant Survey 2018/19.

9