rafmacalaba/datause-encoder-data huggingface dataset:
context, the government adopted a Poverty Reduction Strategy (PRS) in 2003. The strategy is designed to cut the number of poor by half by 2015 through strong and sustainable economic growth. Cameroon reached the Completion Point in May 2006, after three full years of implementation of the 2003 PRS. This achievement signals the satisfaction of Cameroon’s development partners with the implementation of this strategy.

How much poverty reduction has this improved policy environment brought about? Preliminary analysis by the National Statistical Office based on the most recent household survey (2007) indicates that the overall incidence of poverty is still around 40 percent, about the same level as in 2001. The Gini index of inequality seems to have dropped a couple of percentage points from 41 percent in 2001 to 39 percent in 2007. These observations raise some interesting evaluative questions. To what extent has growth been pro-poor in Cameroon? What are the proximate causes of observed variations (over time and across socioeconomic groups) in the distribution of economic welfare?

The purpose of this paper is to use available household level data, particularly the 2001 and 2007 surveys, to try to answer these questions using counterfactual decomposition of changes in the distribution of economic welfare. To put things into perspective, we present in section 2 a profile of growth, inequality and poverty for the period 1996-2007. In that section we use the Shapley decomposition to explain variations in poverty in terms of changes in _per capita_ expenditure and changes in inequality.

In section 3 we explain the Oaxaca-Blinder decomposition framework and use it to identify sources of variation in the distribution of economic welfare. In general, this decomposition technique can be used to study group differences in any continuous and unbounded outcome variable. For policymaking purposes, we need to understand the nature of the changes in the distribution of welfare associated with the process of economic growth. While the Shapley decomposition limits this understanding to changes in mean welfare and inequality, the generalized Oaxaca-Blinder decomposition as implementation of a full PRSP for one year; (3) implementation of structural and social reforms agreed upon at the Decision Point.

6
example, perform worse in math exams and demonstrate lower interest in mathematical careers (Spencer, Steele, and Quinn 1999; Kiefer and Sekaquaptewa 2006). In the Netherlands, even when boys and girls may demonstrate similar levels of academic ability, boys tend to choose more academically prestigious tracks (Buser, Niederle, and Oosterbeek 2014).

Internalized beliefs and biases, however, are difficult to measure using standard survey techniques either because individuals are unaware of the biases they harbor, or because self-reported biases often conform to what the respondent thinks the listener would like to hear. In this study, we use a tool developed by psychologists to measure implicit biases, the Implicit Association Test (Greenwald et al, 1998). For women who were implicitly biased _against_ associating women with professional attributes at baseline, the likelihood that the program induced switching into the ICT sector was more than two times as large than for unbiased women. This suggests that training programs have the potential to overcome self-defeating biases that reinforce occupational segregation, even when they do not explicitly set out to do so. In this particular case, we hypothesize that training to work in the ICT sector changed women’s ability to imagine themselves as professionals in ICT.

While job training programs typically focus primarily on the transfer of skills, our results are consistent with a small empirical literature which suggests that job training also offers a potential opportunity to shift beliefs and norms about the appropriate sectors for men and women to work in, and thus reduce occupational segregation (Campos et. al. 2015). Nopo, Robles and Saavedra (2008) examine the impact of a vocational training program in Peru which explicitly sought to reduce occupational segregation by encouraging females to enter male-dominated occupations. Using propensity score matching, the authors find that 18 months after the end of training, women were 15 percent more likely to be employed and had a 93 percent increase in earnings, while the program reduced occupational segregation by 30 percent.

Globally, occupational segregation is a phenomenon common to both high- and low-income settings. Blau and Kahn (2016), for example, find that observed gender differences in occupations and industries are the most important factors underlying the gender wage gap in the United States. In Sri Lanka, once industry is taken into account, female ownership loses its power to explain gender performance differences among small business owners (de Mel et al., 2009). More generally, the World Development Report, using data from 33 low and middle-income countries 4
be very different to that of a middle or upper income person with a very different frame of reference. This may be correlated with certain attributes of the household, such as if rural or more isolated households may overstate their welfare given that they have a limited experience or exposure to higher living standards with which to judge their own economic standing.

We have provided various tests for bias due to such heterogeneity in individual scales. The tests entailed adding vignettes of hypothetical households to a national household survey for Tajikistan in 2007. Respondents placed these vignettes on the same six-step ladder used to report their own subjective economic welfare. In our first test for this bias, we do find some significant covariates for vignette responses amongst the regressors commonly used to explain subjective welfare. However, the effects are neither very strong nor consistent across different vignettes.

In the second and third tests, we explore the extent of bias due to the frame of reference effect by embedding vignette rankings by households into standard regressions for subjective welfare. We do this in two ways: we include vignettes among the covariates and, to address endogeneity concerns with this approach, we also recalibrate the self-assessments of welfare to accord with the heterogeneity in scales revealed by the vignette responses. The striking finding of these further tests is that the bias is negligible with respect to the “income effect” on subjective welfare as well as other covariates. Based on this study, the concerns that past uses of subjective economic welfare data are compromised by systematic differences in the meaning given to the scales used appear to be unwarranted.

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Central America, Mexico, and the Dominican Republic using a panel data set over 1970 to 2003. She finds that controlling for financial development in the analysis strengthens the positive impact of remittances on growth and concludes that financial development potentially leads to better use of remittances, thus boosting growth. Neither study, however, investigates the impact of remittances on financial development. Our paper contributes to the literature by directly addressing this issue, exploring the impact of remittances on bank deposits and credit to the private sector.

# **II. Empirical methodology and data**

We empirically examine the relationship between financial development and remittances by estimating a number of variants of equation (1), depending on the assumptions made about the error term and the exogeneity of remittances.

’ _FD_ i,t= β 1 _Rem_ i,t + β 2 _X_ i,t + α i + ui,t (1) where _i_ refers to the country and _t_ refers to the time period from 1975 to 2003. However, data for the complete time period are not available for all countries and countries are only included if at least five years of data are available. A complete list of countries and time periods is given in Appendix 1. Table 1 provides definitions and sources for each of the variables in our estimations, while Table 2 presents descriptive statistics.

_FD_ , financial development, refers either to the ratio of bank credit to the private sector or the share of bank deposits expressed as a percentage of GDP.<sup>6</sup> These are the standard measures of financial depth used in the literature (e.g., King and Levine, 1993). Data to construct these ratios come from the _International Financial Statistics_ (IMF) and the _World Development Indicators_ (World Bank). As shown in Table 2, there is considerable variation in financial 8
42 Graph 16 Stock Prices, Capital Flows and Domestic Credit 1980<sup>**-**</sup> 2001

# PERU

<!-- Start of picture text --> 0.6.<br>0.4.<br>0.2.<br>0.0.<br>-0.2 B0 82 84 86 88 90 92 94 96 98 00<br>- Log of stock price index In real terms |<br>2.4E+07*<br>2.OEi07.<br>1.6E+~07.<br>1.2E+07-<br>8.0E408 ~ ~ ~ ~ ~ ~~~~~~~~~-0<br>4.OE+08<br>0.OE+00<br>80 82 84 86 88 90 92 94 96 98 00<br>- Domesfic credi in real terms<br><!-- End of picture text -->

<!-- Start of picture text --> 2-<br>I<br>0-<br>-1-<br>-2 80 82 84 86 88 90 92 94 96 98<br>Capital flows|<br>120.<br>80.<br>40.<br>0<br>4<br>-80 '<br>80 82 84 86 88 90 92 94 96 9800<br>- Real domesfic credit growth<br><!-- End of picture text -->
# <u>Table 8: Openness, Technological Innovation and the Demand for Skills: Robustness Manufacturing Sample</u>

||Manufacturing|Low Tech<br>Manufacturing|High Tech<br>Manufacturing|
|---|---|---|---|
||(1)|(2)|(3)|
|Exporter|-0.0653*|0.0471|-0.229***|
||[0.0388]|[0.0505]|[0.0599]|
|Foreign Ownership|0.209***|0.107*|0.276***|
||[0.0414]|[0.0550]|[0.0628]|
|Technological Innnovation|0.265***|0.215***|0.329***|
||[0.0342]|[0.0431]|[0.0551]|
|Baseline Firm Characteristics?|Yes|Yes|Yes|
|Country-Industry-Size Fixed Effects?|Yes|Yes|Yes|
|Observations|5,976|3,391|2,585|
|Rsquared|0.277|0.284|0.238|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Dependent variable is the logarithm of the share of workforce with more than the secondary education. Columns (1) through (3) take the baseline specification (in column 5 of table 5) and run it again for all the firms in the manufacturing sectors (column 1), manufacturing low technology sectors (column 2) and for the manufacturing high technology sectors (column 3). All the variables are defined in table A1 in the appendix.
# **VI – References**

Ahrens, A., Hansen, C.B., Schaffer, M.E. 2018. lasso2: Program for lasso, square-root lasso, elastic net, ridge, adaptive lasso and post-estimation OLS.

- Bedi, T., A. Coudouel and K. Simler, (2007) _More than a pretty picture: using poverty maps to design better policies and interventions._ Washington DC: The World Bank Group.

- Elbers C., J. O. Lanjouw and P. Lanjouw (2003) “Micro-Level Estimation of Poverty and Inequality”. _Econometrica_ 71(1): 355–364.

- Fay, R. and R. Herriot. (1979) “Estimates of income for small places: an application of James-Stein procedures to census data.” _Journal of the American Statistical Association_ 74 (1979): 269–277.

- Goodman, S., Ben Yishay, A., Runfola, D., 2016. Overview of the geo Framework. AidData. Available online at geo.aiddata.org. DOI: 10.13140/RG.2.2.28363.59686

- Heeringa, Steven G., Brady T. West, and Patricia A. Berglund. _Applied survey data analysis_ . Chapman and Hall/CRC, 2017.

- Molina I. and Rao J. (2010) “Small area estimation of poverty indicators.” _Canadian Journal of Statistics_ , 38(3), 369-385.

- National Research Council. _National Patterns of R&D Resources: Future Directions for Content and Methods: Summary of a Workshop_ . National Academies Press, 2013.

- Office for Official Publications of the European Communities, Luxembourg.  Eurostat (2013). Handbook on precision requirements and variance estimation for ESS household surveys

Rao, J. N. K. (2003) _Small Area Estimation._ 1st ed. Wiley-Interscience.

- Statistical Office of the Republic of Kazakhstan (2015) “Income and Living conditions in the Republic of Kazakhstan – 2013”. Final Report. Belgrade, Republic of Kazakhstan: Statistical Office

- Kolenikov, Stanislav. "Resampling variance estimation for complex survey data." _The Stata Journal_ 10.2 (2010): 165-199.

- Wolter, Kirk. _Introduction to variance estimation_ . Springer Science & Business Media, 2007.

- World Bank (2012) “Pilot Study of Small Area Poverty Estimation Methods for the New Member States of the European Union,” Report prepared for the Scientific Steering Committee of the World Bank and European Commission Project on Small Area Poverty Estimation, Washington, DC: The World Bank Group.

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that these lost profits are significant in volume, then much would be achieved in terms of underwriting traditional worries about differential pricing.

Empirical analysis of the above kind would, moreover, be interesting in relation to recent economic analysis that shows that in poor countries characterized by great disparities of income (e.g., South Africa and India), companies that seek to maximize their profits on a product will rationally charge a high price for it so that only the most affluent segment of citizens can afford it (Flynn, Hollis, and Palmedo 2009). The real reason as to why the for-profit pharmaceutical sector is adverse to charging lower prices for their products in low-income countries (with a substantial affluent class) as compared to high-income countries is perhaps not so much grounded in worries about parallel trade as in the knowledge that profits can be maximized in low-income country settings by charging a high price for the product. Showing that the impact of parallel trade only has a limited negative effect on the size of profits made on affluent markets will lend support to this hypothesis.

Lastly, but perhaps most importantly, it would be useful to have additional empirical work done on the question of what the correlation is between strong IPRs (and the enforcement of such rights) and the volume of innovation. Sector studies in the pharmaceutical and agricultural/seed production sectors would be particularly useful together with studies that examine possible correlations in developing countries and developed ones to see if the overall correlation pattern is the same in both regions. Some empirical work has already been done on the overall correlation issue, but further work would be welcome.<sup>33</sup> As mentioned in section 9, it

> 33 A few examples of this kind of work should be mentioned here. Léger concludes that strengthened Intellectual Property Protection (IPP) is an incentive for innovation. IPP has, however, a very limited effect in developing countries compared to industrial ones (Léger 2006). Lanjouw argues that longer product patents encourage innovation in the developed world, (due to the ability to employ monopolistic pricing schemes) and that strengthening IPRs do encourage R&D and innovation (Lanjouw 2005). Finally, Park argues that the enforcement of IPRs indirectly increases productivity by increasing investment in R&D, (due to greater returns). In the more

42
deregulation. As we discuss below, we control for the interaction of branch deregulation with a unit banking indicator, the small bank share, the small firm share and population dispersion, each of which we measure in the year before deregulation. Data on the share of small firms and banks are from Kroszner and Strahan (1999). Data on population dispersion are from the U.S. Census Bureau.

# **1.4. Methodology**

We use a differences-in-differences specification to assess the relationship between branch deregulation and income distribution, based on the following regression set-up: where Ys,t is a measure of income distribution in state s in year t, α and β are vectors of state and year fixed-effects, Xs,t is a set of time-varying, state-level variables and ε is the error term. The variable of interest is Ds,t, a dummy variable that takes on the value one the year after state s deregulates. The coefficient, γ , therefore indicates the impact of branch deregulation on income distribution. A positive and significant γ suggests that deregulation exerts a positive effect on the degree of income inequality, while a negative and significant γ indicates that deregulation pushed income inequality lower. Consistent with past work on branch deregulation, we drop the year of deregulation. In total, we have data for 48 states plus the District of Columbia, over 30 years, minus 37 state-years in which deregulation occurred. Thus, 1,433 state-year observations serve as the basis for our analysis.

The differences-in differences estimation technique allows us to control for omitted variables. We include time-specific dummy variables to control for economy-wide shocks that might drive income distribution over time, such as business cycles, long-term trends in income 11
# **4.** **<u>Conclusions</u>**

Using a comprehensive cross-country firm-level data set, we examine how institutions and financial development affect firms’ access to external finance, and unbundle the roles of contracting institutions and property rights institutions. We pay particular attention to how micro enterprises and SMEs are affected by institutions and financial development relative to large firms. We document that MSMEs but especially micro firms had significantly lower access to external finance. Overall, we do not find firms’ access to external finance to be facilitated by financial development, nor by contracting institutions. In stark contrast, it is positively and strongly associated with property rights institutions. We also find evidence that measurement error in institutional indicators is likely to be important: after correcting for such error, the roles of property rights institutions become even more prominent. In contrast, correcting for measurement error does not alter our conclusion on the lack of importance of contracting institutions. Among property rights institutions, Executive Constraints and Minority Shareholders—two measures pertaining to limitations on the power of political and economic elites— prove to be particularly important for facilitating external finance. We also find that, overall, property rights institutions tend to facilitate the relative access to external finance of micro firms and SMEs more than contracting institutions or financial development. However, a specific aspect of contracting institutions, auditing standards and quality (i.e., WEF Auditing Quality), proves to be particularly important for leveling access to finance across sizes.

Our findings suggest that particular attention should be given to the financing plight of micro firms, which seem to have much more difficulty in obtaining external financing, particularly from formal sources. Furthermore, institutions -- especially property rights institutions -- appear to be more important than financial depth in facilitating firms’ external finance. For leveling the playing field by firm size in access to finance, both property rights institutions and contracting institutions that facilitate the information environment are essential. Our results offer complementary micro-level evidence to support Acemoglu and Johnson (2005) in asserting the importance of property rights institutions over contracting institutions, and limitations on the power of political or economic elites. Our results suggest that an important channel of institutions (and mainly property rights institutions) affecting economic development is through firms’ access to external finance.

23
most advanced of all corridors included in their study in terms of prices and efficiency of services; this is mainly because of an unregulated transport market.

Figure 6 compares transport prices with the Logistics Perception Index (LPI)<sup>11</sup> for some countries as well as African regions. Compared with other countries, such as France and the United States, transport prices in Africa are more expensive and provide a lower quality of service, as measured by the LPI. The Central African region is an extreme case of high prices associated with low quality.

**Figure 6**

**Transport Services in Africa: Quality and Cost**

<!-- Start of picture text --> 12<br>Central Africa<br>y = -1.7571x + 12.366<br>10<br>R 2 = 0.4826<br>East Africa<br>8 Poland<br>West Africa<br>6 Southern Africa Germany<br>SpainFranceUSA<br>4<br>2<br>1.5 2.0 2.5 3.0 3.5 4.0 4.5<br>Transport Quality<br>(LPI)<br>(in US cents per tkm)<br>Average transport price<br><!-- End of picture text --> _Source_ : Teravaninthorn and Raballand (2008).

Building on Wilson et al.’s (2003) methodology, Njinkeu, Wilson, and Powo-Fosso (2008) analyze the impact of reform along four categories of trade facilitation efforts: port efficiency, customs environment, regulatory environment, and services infrastructure. Using a gravity model, they find that the port and service infrastructures are the primary factors that tend to expand intra-African trade.

> 11  The LPI is a measure of perceptions of the logistics environment of 140 countries along seven areas:  i) efficiency of the clearance process by customs and other border agencies,  ii) quality of transport and information technology infrastructure for logistics, iii) ease and affordability of arranging international shipments, iv)  competence of the local logistics industry, v) ability to track and trace international shipments, vi) domestic logistics costs, and vii) timeliness of shipments in reaching destination.  It allows comparison across countries and regions. It is based on a yearly survey of international freight forwarders. The survey uses an anonymous, Web-based questionnaire that asks professionals in several logistics service companies worldwide to evaluate their country of residence, as well as eight countries they are dealing with, on seven logistics dimensions. Country performance in these areas was evaluated using a 5-point scale (1 for the lowest score, 5 for the highest). The LPI is a weighted average of these measures constructed using principal component analysis in order to improve the confidence intervals.

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