rafmacalaba/datause-encoder-data huggingface dataset:
Table 4. Effects on gender norms attitudes, experiment 1

||||**Panel A: Men**<br>|||
|---|---|---|---|---|---|
||Both<br>parents|Father<br>involvement|During<br>breastfeeding|First<br>months|Who earn<br>less|
|Information (M1)|-0.018|-0.003|-0.021|-0.014|-0.000|
||(0.026)|(0.018)|(0.029)|(0.041)|(0.042)|
|Observations|549|550|551|546|513|
|R-squared|0.030|0.017|0.056|0.105|0.090|
|Control mean|0.904|0.952|0.875|0.563|0.434|
|||**P**<br>|**anel B: Women**<br>|||
||Both<br>parents|Father<br>involvement|During<br>breastfeeding|First<br>months|Who earn<br>less|
|Information (M1)|0.031|0.012|0.006|-0.083**|-0.005|
||(0.022)|(0.017)|(0.031)|(0.037)|(0.036)|
|Observations|660|660|655|654|623|
|R-squared|0.030|0.041|0.043|0.129|0.083|
|Control mean|0.898|0.943|0.807|0.512|0.317|

Notes: The table shows the results of regressing the variables regarding agreement with gender norm attitudes on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,221 individuals in experiment 1 (554 men and 667 women). Source: Post-intervention survey.

Table 5. Effects on intention to use parental leave and effective use, experiment 1

||||**Panel A:**<br>|**Men**<br>||
|---|---|---|---|---|---|
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Information|0.002|-0.008|0.007|0.011|0.003|
|(M1)|(0.028)|(0.038)|(0.010)|(0.037)|(0.003)|
|Observations|537|528|528|528|554|
|R-squared|0.095|0.047|0.065|0.045|0.039|
|Control mean|0.873|0.742|0.0115|0.223|0|
||||**Panel B: W**<br>|**omen**<br>||
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Information|0.013|0.014|-0.018|0.006|0.011|
|(M1)|(0.030)|(0.036)|(0.036)|(0.006)|(0.011)|
|Observations|654|649|649|649|666|
|R-squared|0.069|0.065|0.064|0.018|0.037|
|Control mean|0.811|0.683|0.311|0.00305|0.012|

Notes: The table shows the results of regressing the variables regarding intention to use parental leave on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,221 individuals in experiment 1 (554 men and 667 women). Source: Postintervention survey.

# _4.2. Effects on recent parents_

In this subsection we report the results of the intervention for individuals in experiment 2, that is, formal workers who recently were parents and are benefiting from maternal and paternal leave. Recall that for this sample of participants there is no pure control group (i.e., individuals who did not receive a message). Thus, 17
of trade, mainly GDP, distance and remoteness, are controlled for. The gravity model is presented in detail below.

# **3.1. Derivation of the gravity equation with infrastructure and landlockedness**

We use the gravity model as a simple and efficient tool to predict the volume of bilateral trade and to assess the burden of landlockedness (see surveys proposed by Evenett and Keller, 1998, Anderson and van Wincoop, 2004, and Feenstra, 2004). We adopt the popular version of the gravity model currently applied when aggregating trade between economies with an assumed specialization in differentiated products. As shown by Deardoff (1998), as well as Anderson and Van Wincoop (2003, 2004), the utility maximization of an identical (over countries) CES utility function yields the following expression for bilateral imports, where _M ijt_ is the CIF (cost, insurance and freight) value of the aggregate merchandise trade _Y_ flow imported by country i from exporter j; _i_ ( _j_ ) _t_ is the gross domestic product of country i (j) in _t_ ; _Ywt_ is the world income in _t_ , _θ_ ijt is bilateral transport costs; _σ_ is the elasticity of substitution in the CES utility function; and _Rit_ , _Rjt_ can be interpreted as “multilateral trade resistance” or “remoteness” indices. Following Helliwell (1998), Brun et al. (2005), and Carrère (2006), the remoteness index is defined as the weighted distance to all trading partners of country _i_ :10 Positive signs are expected: for a given distance _Dij_ , the more remote a pair of countries is from the rest of the world, the more they will trade with each other.

The estimation of equation (1) raised issues related to the estimation of bilateral transport costs, _θ_ ijt. In the standard implementation used, among others, by Baier and Bergstrand (2002) or by Anderson and Van Wincoop (2003), transport costs include distance ( _D_ ij) along with a vector of dummy variables for common borders ( _B_ ij, equal to 1 if countries _i_ and _j_ share a common border, otherwise 0) and landlockedness ( _L_ i(j), equal to 1 if country i (j) has no direct sea access, otherwise 0). Nevertheless, Limao and Venables (2001) emphasized that distance explains only 10% of the change in the transport costs. Poor road infrastructure

> 10 For a detailed discussion on this approximation see Brun et al. (2005) or Carrère (2006).

13
# **Structural Transformation and Productivity Growth in Africa: Uganda in the 2000s**

**By**

_Sabin Ahmed_<sup>_*_</sup> _, Taye Mengistae**, Yutaka Yoshino ** and Albert G. Zeufack**_<sup>_1_</sup>

**JEL Codes:  O49, O14, O55, O10**

**Key Words: Economic Growth and Aggregate Productivity; Industrialization; Manufacturing and Service Industries; Economy Wide Country Studies; Economic Development**

> 1 A version of this paper was presented at the CSAE 2015 Conference on Economic Development in Africa, St. Catherine’s College, University of Oxford, during 22 ‐24 March 2015. This version reflects comments received from participants and discussants at that conference and staff of the World Bank’s Country Management Unit for Uganda. *George Washington University; email: sahmed1@worldbank.org ; sabahmed@gwmail.gwu.edu ; ** World Bank; email: tmengistae@worldbank.org,  yyoshino@worldbank.org, <u>Azeufack@worldbank.org.</u>
program could be readily calculated from the simulated distribution of net incomes. However, this ignores the fact that participants are unlikely to have been idle in the absence of the program, but would have found some sort of work, possibly in the form of casual “odd jobs.” Ignoring participants’ forgone incomes will clearly lead to an overestimation of the poverty reducing impact of the program.

# **3. Data and descriptive results**

We use the October 2001 and October 2002 rounds of the _Encuesta Permanente de Hogares_ (EPH) done by the Government of Argentina’s Statistical Institute (INDEC). The survey is only done in large urban areas (representative of 70% of the total population).

Information is collected on employment, incomes, education and household demographics. A subset of the sample is linked as a panel, with approximately one third of the sampled households in 2001 being re-interviewed in 2002. For the purpose of this study, a special module on _Jefes_ participation was administered in October 2002 to those adult members for whom _Jefes_ was not the main occupation. (The existing survey was deemed adequate for those for whom _Jefes_ was the primary occupation.) Before turning to the household-level analysis it is of obvious interest to see how the grossed-up aggregate participation rate in _Jefes_ from the EPH compares to the administrative data on aggregate registrations. This is complicated by the fact that, while the _Jefes_ program had national coverage, the EPH sample frame excludes 30% of the population. The Appendix presents calculations of how the EPH participation aggregate compares to the administrative records corresponding to the EPH sample frame. By our preferred method (based on the applicants’ places of residence) the grossed-up EPH count of _Jefes_ participants accounts for 91% of the administrative aggregate. This is a significant discrepancy at the 5% level, though just 9
assets and pension assets, in millions of Chilean pesos.<sup>38</sup> A potential concern with regard to sample restrictions is whether we might disproportionately be excluding poorer households, who are the target of the pension policies we are evaluating. As table 2 shows, the distributions are very similar, except in the right tail of the earnings distribution. The estimation sample contains a slightly smaller proportion of high earning individuals households, which is unlikely to affect our conclusions.

The rest of sample characteristics are also very similar before and after sample restrictions. Two thirds of the sample are couples, and most of the single households are women. Women are a lot less likely than men to be working (31.9 percent versus 73.5 percent). Among workers, women are slightly less likely to be working in the formal sector than men. The high average age of the sample (51.5 for men and 50.9 for women) is due to the fact that the estimation only incorporates workers over 35 years of age at the time of the first survey (2004).

# **6 Estimation results**

Model parameters are estimated by the Method of Simulated Moments (MSM). This method was chosen, in part, because it more easily accommodates missing state variables than does simulated maximum likelihood, which would require numerical integration over all possible missing state variable values. Details of the estimation procedure and standard error computation are provided in section A.2.

This section first discusses the estimated values obtained for key parameters of interest and the model’s within-sample fit. Then, we describe the out-of-sample pension reform predictions and discuss how they compare to reform impacts estimated in other studies.

## **6.1 Parameter estimates**

Tables 3 and 4 provide the parameter estimates and associated standard errors governing preferences and labor market opportunities. The main parameters governing preferences for work and saving align with estimates reported in the literature. The coefficient of risk

> 38The responses for each type of asset holding (housing, cars, etc.) have been top coded at 1 percent to reduce the effect of outliers/miscoding.

22
24

# 7. _PoioofIL LZicW

As the Bank has beconme more involved in finaing legal services under legal reform projects, several issues have emerged. Locting quailied legIl advisers to assist on reform progams has proven to be a challenge. Lawyers in private practice do not always have a policy perective which may be needed to prepare comprehensive law reforms. Similaly, acadenics may not be as onnecd to prcl s involved in pleentin of reform progams.

The great majority of potental bidders for lega svis comng frm outide the country in quesdon have so far been law fims based in the United Stas or the United iGngdom, or branches of these offices. While ihese are igly qualified fims, ofken with world-wid **e** xpetse, concenttin of he serce has made it diffict to meet divesty objectives under the Bank's procurement guiddines. TIZs has resulted in some cases in a bias in sweral countries owards leg advice based on United Stats or United Kingdom models, which are not always sitable for civil law juidicos resenting the bulk of Bank borvws carying out law rform prgrams. Law firms or lg adviss from developing countis who are _irsted_ in wdang ouide ter home countries on law reform progrms are very few.

Pardculy in the cae of cmprehenive law reform progams, the issue of conflict of ntst has come up. Law firms adviing the goverment an broad gal refms have uque opportunities to be involved in the design, draftdng and impent of laws. This knowledge is highly maktable to pnvate ivestor and others ivolved with the legal systm. The Bank has genealy imposed restrici on law firms prhibing thm om taking dLients i the me country wher the finn is executing lBank-finnced legal atnc Legal senices are very expensive as they are nonmaly based on the ates of intemaonal law firms. The Bank has ncouged governmts to negotate more fiavorble rates based on longer commitments and less senior but qualfied lawyers. Ihe Bank has also _awuged_ joint participaton by law firms and univtes or public insitutes to keep costs down an prvide a mix of acdemic and practical eprence.
majority of countries across the world, especially developing countries, face challenges in collecting poverty data. High-quality consumption surveys that are comparable for forcibly displaced persons and their hosts are and will remain in limited supply, given the cost and challenges associated with these types of surveys. In the meantime, cross-survey imputation methods can provide a second-best alternative that can potentially save time and money.

<mark>This study combined</mark> survey and census-type data on refugees to estimate welfare for <mark>refugees in Chad. W</mark> e showed how different sets of variables as well as different sources of data fare in the identification of poo <mark>r households, in particular, how well the set of variables available in the ProGres database can predict poverty. In a second step, the paper estimated the accuracy of the current U</mark> NHCR/WFP/CNARR <mark>targeting strategy and compared it with the targeting strategy based on imputed consumption in the light of international evidence.</mark> The results suggest that the set of variables available in ProGres accurately predicts the welfare rates for different poverty lines. Adding variables related to asset and animal ownership provides predictions that are very close to the ones with only the variables available in the ProGres data set. These results are especially promising, as the UNHCR ProGres data are available in most refugee locations where the UNHCR runs the registration system, and thus these methods are replicable in many settings of forcibly displaced persons.

The <mark>current targeting strategy that is used for food, livelihoods, and cash-based assistance, despite its simplicity, is rather accurate when compared with the existing international evidence. Th</mark> e targeting errors resulting from the current <mark>UNHCR/WFP/CNARR targeting strategy for a poverty rate of 25% are in the same error range as other targeting methods around the world, as reported in Coady et al. (2004). We also showed that the existing targeting method can be improved by imputing consumption using the methodology proposed in this paper.</mark> 16
Policy Research Working Paper 5657

# **Abstract**

Using a novel dataset with transactions level exports data from four African countries (Malawi, Mali, Senegal and Tanzania), this paper uncovers evidence of a high degree of experimentation at the extensive margin associated with low survival rates, consistent with high and middle income country evidence. Consequently, the authors focus on the questions of what determines success and survival beyond the first year and find that survival probability rises with the number of firms exporting the same product to the same destination from the same country, pointing towards the existence of cross-firm synergies. Accordingly the evidence is consistent with the hypothesis that those synergies may be driven by information spillovers. More intuitively and consistently with multi-product firms models, the analysis also finds that firms more diversified in terms of products, but even more in terms of markets, are more likely to be successful and survive beyond the first year.

This paper is a joint product of the Finance and Private Sector Development Department, Africa Region; and the International Trade Department, Poverty Reduction and Economic Management Network. 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 authors may be contacted at Liacovone@worldbank.org, f.g.rauch@lse.ac.uk, ocadot@worldbank.org, mpierola@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
WPS3592

# **Which Inequality Matters?**

# **Growth Evidence Based on Small Area Welfare Estimates in Uganda**

## **Youdi Schipper and Johannes G. Hoogeveen**<sup>*****</sup>

## **Abstract**

Existing empirical studies on the relation between inequality and growth have been criticized for their focus on income inequality and their use of cross-country data sets. This paper uses two sets of small area welfare estimates – often referred to as poverty maps – to estimate a model of rural per capita expenditure growth for Uganda between 1992 and 1999. We estimate the growth effects of expenditure and education inequality while controlling for other factors such as initial levels of expenditure and human capital, family characteristics and unobserved spatial heterogeneity. We correct standard errors to ref lect the uncertainty due to the fact that we use estimates rather than observations. We find that per capita expenditure growth in rural Uganda is affected positively by the level of education as well as by the degree of education inequality. Expenditure inequality does not have a significant impact on growth.

## World Bank Policy Research Working Paper 3592, May 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developm ent 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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._

> *Schipper is with the Vrije Universiteit Amsterdam and consultant to the World Bank. Hoogeveen is with the World Bank. Please send correspondence to both yschipper@feweb.vu.nl and jhoogeveen@worldbank.org. We are grateful to the Uganda Bureau of Statistics, Entebbe, for their help with the provision of the survey and census data. For useful comments and other forms of help we would like to thank Chris Elbers and seminar participants at the Vrije Universiteit.

1
||**Ta**|**ble A2: E**<br>**[**|**for ea**<br>**ffect of 20**<br>**Population**|**ch decile o**<br>**11 PPPs o**<br>**share with**|**f African dis**<br>**n Entire Af**<br>**2011 PPP]**|**tribution(**<br>**rican Dist**<br>**‐ [Populati**|**percentage **<br>**ribution**<br>**on share w**|**points)**<br>**ith 2005 P**|**PP]**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Poorest**|**Decile 2**|**Decile 3**|**Decile 4**|**Decile 5**|**Decile 6**|**Decile 7**|**Decile 8**|**Decile 9**|**Richest**|
|Congo, Dem. Rep.|12.1|0.2|‐2.8|‐1.9|‐3.8|‐1.0|0.0|‐1.9|‐0.9|‐0.9|
|Liberia|0.6|0.3|0.0|‐0.1|‐0.2|‐0.2|‐0.1|‐0.1|‐0.1|0.0|
|Madagascar|‐3.3|‐0.9|0.7|0.9|0.6|0.9|0.3|0.0|0.3|0.3|
|Burundi|‐0.1|‐0.5|0.2|0.1|0.1|0.1|0.0|0.1|0.0|0.0|
|Tanzania|1.9|1.5|0.7|1.3|‐0.8|‐0.7|‐1.9|‐0.7|‐1.3|0.0|
|Malawi|‐0.3|0.0|0.0|0.2|0.0|0.0|0.3|0.0|0.0|‐0.2|
|Mozambique|1.4|1.2|1.1|0.3|‐0.4|‐0.7|‐0.3|‐1.8|0.0|‐0.7|
|Rwanda|‐0.1|0.0|0.2|0.2|0.0|0.0|‐0.3|0.0|0.2|‐0.2|
|Ginuea‐Bissau|0.0|0.0|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Lesotho|0.1|0.1|0.0|0.0|0.0|0.0|0.0|0.0|‐0.1|‐0.1|
|Burkina Faso|0.2|1.4|0.3|0.7|0.2|0.2|‐0.7|‐0.9|‐0.7|‐0.7|
|Niger|0.2|0.2|1.2|0.2|0.4|0.0|‐0.2|‐1.2|‐0.7|‐0.2|
|Central African Republic|0.1|0.1|0.0|0.1|0.0|‐0.1|0.0|‐0.1|‐0.1|‐0.1|
|Nigeria|‐10.0|‐2.1|‐2.2|‐2.5|2.2|2.3|2.9|4.9|2.5|2.4|
|Uganda|0.5|1.1|0.5|0.0|0.5|‐0.5|0.1|‐0.5|‐0.5|‐1.0|
|Togo|0.0|0.1|0.1|0.1|0.0|0.1|‐0.1|‐0.1|‐0.1|‐0.1|
|Mali|‐0.2|0.0|0.2|0.0|0.0|0.2|‐0.2|0.0|0.0|0.0|
|Zambia|‐1.9|‐0.4|0.2|0.2|‐0.2|0.4|0.4|0.2|0.6|0.4|
|São Tomé and Principe|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|South Sudan|‐0.2|‐0.3|0.2|‐0.1|0.1|0.0|0.0|0.1|0.0|0.1|
|Guinea|0.0|‐0.2|‐0.3|0.2|‐0.2|0.2|‐0.1|0.2|0.2|0.2|
|Swaziland|0.0|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|‐0.1|
|Côte d'Ivoire|0.0|‐0.3|‐0.3|0.0|0.0|0.0|0.1|0.0|0.0|0.6|
|Angola|‐0.6|‐1.5|‐0.6|0.0|‐0.6|‐0.6|0.1|0.9|1.2|1.8|
|Cameroon|0.0|0.0|0.0|0.3|0.0|0.0|0.1|0.3|‐0.6|0.0|
|Mauritania|‐0.1|0.1|0.0|0.0|0.0|0.0|0.1|0.0|‐0.1|0.0|
|Ghana|‐0.4|0.0|‐0.4|0.0|0.0|‐0.4|‐0.3|0.4|0.4|0.7|
|Sudan|0.0|‐0.5|0.0|‐1.1|0.0|0.0|‐0.4|0.0|0.6|1.6|
|Namibia|0.0|0.0|0.0|0.1|0.0|0.0|0.0|0.0|0.0|‐0.1|
|Cabo Verde|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.1|
|Botswana|0.0|0.0|0.0|0.1|0.0|0.1|0.1|0.0|0.0|‐0.2|
|Mauritius|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.1|‐0.1|
|South Africa|0.0|0.0|0.8|0.8|2.3|0.0|0.1|0.0|‐0.8|‐3.3|
|Seychelles|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Total<br>_Note: Countries are sorted ac_|0.0<br>_cording to the_|0.0<br>_ir mean co_|0.0<br>_nsumption e_|0.0<br>_xpenditurep _|0.0<br>_er capita in 2_|0.0<br>_011 PPPs._|0.0|0.0|0.0|0.0|

28
6 exclusion is of sufficient importance to greatly weaken the utiltiy of available trade and production series for many of the uses for which they were intended.

# m. Alternative Definitions of Manufactured Goods

A review of previous studies and sources of empirical information on the composition and value of manufactures production or trade shows that at least five different defnitions for these products have been used (see Table 1 for a description of their major characteristics). In terms of the Standard International Trade Classification (SITC) system, one set often centers on a "core group" of products classified in SITC _5_ through 8 (less SITC 68-nonferrfus metals) with varous other items either added or deleted. For example, the World Bank in the World Development Report 1987 (WDR87) used a definition that covers SlTC _5_ to 8 less 68 (nonferrous metals). Also excluded were textile yarn and thread, cotton fabrics, special textile fabrics and pearls and precious stones, although in most studies the Bank defned manufactures as all products in SITC S to 8 less 68 (see Table 1).9 UNCTAD employed a defnition of manufactures for compiling trade data that excluded ferrous metals (SITC 67) as well as the nonferrous metal group from the core group of products.'° A limited number of studies included the nonferrous metals products (SlTC 68), thereby defring manufactures as all items classified in SITC groups _5_ though 8.

9 World Bank (1988, p. xi). These and other defnitions of manufactures often encounter major problems with the SITC system which does not adequately distinguish between prmary and fabricated goods. For example, SITC 667.2 does not separate raw uncut diamonds (a primary good) and cut and polished diamonds (which could properly be considered a manufacture). UNIDO and the World Bank have excluded SITC 667 due to the preponderance of uncut gems in many African countries' exports (see, for example, the data on Sierra Leone in Table 2). This exclusion drops cut and polished diamonds which constitute a major component of Israel's manufactures trade (Table 2).

'° See, for erample, UNCrAD's Handbook on Internationa Trde and evelopment Sta_ for other years. UNCrAD classified SITC 67 and 68 in a separate "ore and metals" group which included unprocessed raw ores as well as some highly fabricated finished metals products.