rafmacalaba/datause-encoder-data huggingface dataset:
23

**Figure 15:** The Relationship between Productivity (log TFPQ) and Distortions (log TFPR)

<!-- Start of picture text --> 3<br>2<br>1<br>0<br>-1<br>log_tfpr = 0.609 + 0.44518 log_tfpq R 2 = 41.8%<br>-2 n = 7412 RMSE = 0.599<br>-4 -2 0 2<br>log TFPQ<br>log TFPR<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>log TFPR<br><!-- End of picture text --> Source: Authors’ calculations.

At higher productivity levels, the capital wedge is also higher, suggesting a systematic link between the capital wedge and productivity (figure 16). The positive correlation of 0.1951, which is significant at the 99% level, supports the conjecture that productive firms appear to face higher capital distortions compared to less productive ones.

The log output wedge has a positive and significant correlation with physical productivity (correlation =0.5224, p-value=0.001), suggesting that the more productive firms also appear to face higher output distortions (figure 17). Examples of output distortions include preferential market access or preferential income tax incentives given to certain firms. Such market distortions will result in productive firms producing below their optimal level, while allowing unproductive ones to continue operating and consuming resources. This could also happen if the unproductive firms are subsidized.

**Figure 16:** Physical Productivity and Capital **Figure 17:** Physical Productivity and Output Wedge                                                                 Wedge

<!-- Start of picture text --> 4<br>log_tfpq = -1.370 + 0.13693 l_tau_k_wedge<br>n = 7412 RMSE = 1.117<br>2<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>R = 3.8%<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>log TFPQ -2<br><!-- End of picture text --> <!-- Start of picture text -->
-4<br><!-- End of picture text -->

<!-- Start of picture text -->
-5<br><!-- End of picture text -->

<!-- Start of picture text --> 0 5<br>log capital wedge (log_tau_k_wedge)<br><!-- End of picture text --> <!-- Start of picture text --> 10<br><!-- End of picture text --> <!-- Start of picture text --> 4<br><!-- End of picture text --> <!-- Start of picture text --> 2<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>-2<br>log TFPQ<br><!-- End of picture text --> <!-- Start of picture text -->
-4<br><!-- End of picture text -->

<!-- Start of picture text --> 2 = 27.3%<br><!-- End of picture text --> <!-- Start of picture text --> log_tfpq = -1.212 + 0.99083 l_tau_y_wedge<br>n = 7412 RMSE = 0.971<br><!-- End of picture text --> <!-- Start of picture text --> R<br><!-- End of picture text --> <!-- Start of picture text -->
-2<br><!-- End of picture text -->

<!-- Start of picture text --> 0 2<br>log output wedge (l_tau_y_wedge)<br><!-- End of picture text --> <!-- Start of picture text --> 4<br><!-- End of picture text --> Source: Authors’ calculations Source: Authors’ calculations
<!-- Start of picture text --> 8)<br>— Fal<br>FDI<br>6<br>o<br>=<br>w<br>a<br>><br>=<br>Ss<br>oO<br>4<br>°<br>ea<br>2<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 90) ew<br>FDI<br>40<br>=<br>[ak<br>E<br>wn<br>oO<br>é<br>= 30<br>&<br>2<br>—5<br>&<br>20<br>10<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br><!-- End of picture text -->
# **Table** **<u>3. Determinants of FDI at the District Level</u>**

||(1)|marginal effects|(2)|
|---|---|---|---|
|ln(L)|0.030***|0.015|0.031***|
||(2.607)||(2.628)|
|ln(firm age)|-0.089***|-0.043|-0.088***|
||(-4.638)||(-4.507)|
|ln(GDP PC)|0.042|0.02|0.041|
||(1.453)||(1.480)|
|ln(city distance to port +1)|-0.007|-0.003|-0.007|
||(-1.531)||(-1.611)|
|city phone density|0.032*|0.016|0.035*|
||(1.830)||(1.959)|
|ln(city avg wage)|0.006|0.003|-0.003|
||(0.136)||(-0.062)|
|dist.avg share of coll grad in L|0.243***|0.118|0.261***|
||(3.171)||(3.433)|
|city share of good air days|0.129*|0.062|0.123*|
||(1.911)||(1.828)|
|dummy: open city|0.061*|0.032|0.056*|
||(1.828)||(1.654)|
|ln(dist.avg of days passing customs)|-0.148***|-0.072|-0.153***|
||(-5.365)||(-5.377)|
|dist avg of priv firm access to loans|-0.100***|-0.048|-0.110***|
||(-2.726)||(-2.889)|
|dist avg of priv firm ETC|0.452<br>|0.219|0.331<br>|
||(0.372)<br>||(0.279)<br>|
|dist avg of foreign advantage in ETC|0.022***|0.011|0.022***|
||(3.281)||(3.328)|
|dist avg of priv firm contract enforcement|0.037|0.018|0.038|
||(0.688)||(0.716)|
|dist avg of foreign advantage in contract enforcement|-0.006|-0.003|-0.006|
||(-0.259)||(-0.272)|
|dist avg of priv firm tax burdens|-0.647|-0.313|-0.677|
||(-1.270)||(-1.367)|
|dist avg of foreign advantage in tax burden|0.021|0.01|0.021|
||(1.097)||(1.116)|
|party secretary young|0.150**|0.082|0.155***|
||(2.513)||(2.607)|
|party secretary old|0.174**|0.099|0.175**|
||(2.066)||(2.099)|
|PS young*ln(avg PS tenure)|-0.124***|-0.06|-0.124***|
||(-2.706)||(-2.657)|
|PS midde*ln(avg PS tenure)|0.027|0.013|0.030|
||(0.922)||(1.013)|
|PS old*ln(avg PS tenure)|-0.095|-0.046|-0.091|
||(-1.489)||(-1.434)|
|GDP growth rate, city|||-0.000|
||||(-0.371)|
|dist avg of share of loss of sales due to elec. outage|||0.272|
||||(1.259)|
|Observations|909||909|

Note. The standard errors are clustered at the city level. *, **, and *** represent statistical significance at the 10, 5 and 1 percent levels. For marginal effects, the magnitude reported concerns about a change from 0 to 1 for a dummy explanatory variable, and dY/dx conditional on Y not being censored.

28
# **Table 3. R-Squared Shapley-Owen Decomposition** **_Estimations using Program-Level Data_**

|**Outcome:**|**(1)**<br>**Dropout**<br>**Rate**|**(2)**<br>**Extra**<br>**Time to**<br>**Graduate**|**(3)**<br>**Formal**<br>**Employment**|**(4)**<br>**Wages**|
|---|---|---|---|---|
|_Percent of explained variation attributable to:_|||||
|All quality determinants|60.22|51.95|50.51|15.48|
|Infrastructure|-|-|6.54|0.25|
|Costs|12.85|12.92|-|-|
|Training and curriculum|11.45|24.59|22.36|7.68|
|Faculty|17.22|12.47|12.41|4.03|
|Link with productive sector|18.46|1.97|6.99|0.84|
|Other practices|0.23|-|2.21|2.68|
|Student characteristics (PCA Score)|0.24|0.25|2.15|0.04|
|Program characteristics (PCA Score)|0.60|0.31|0.23|1.84|
|HEI characteristics (PCA Score)|10.48|4.93|2.30|3.32|
|Country fixed effects|7.37|31.57|38.58|71.19|
|Field of study fixed effects|21.08|10.99|6.21|8.13|
|R-squared|0.080|0.087|0.134|0.178|
|Obs.<br>_Source_: Own estimations using WBSCPS data. For varia|1,526<br>ble definitions, see|1,693<br>Section 3 and A|1,270<br>ppendix 1.|1,752|

_Source_ : Own estimations using WBSCPS data. For variable definitions, see Section 3 and Appendix 1. _Notes_ : This table present results from the R-squared Shapley-Owen decomposition for the regressions reported in Tables A11 (dropout rate and extra time-to-graduate) and A12 (formal employment and wages), estimated with WBSCPS program-level data. For each regression, the table shows R-squared (net of the variation explained by survey noise variables) and the percent of the (net) explained variation attributable to each set of variables. “Obs.” indicates number of programs (equal to the number of observations in the underlying regression).

44
is a salient concern, with 41% of firm managers considering it a major or severe constraint – the most common of the potential constraints to be ranked as such alongside electricity access.<sup>7</sup>

# <FIGURE 1 HERE>

The data also contain information on how long it took firms to obtain construction permits, operating licenses, electrical connections, and to clear customs for importing and exporting. These self-reported policy implementation times will be our key dependent variables. The surveys furthermore asked entrepreneurs whether _“In relation to that application for a construction permit_ [operating license/electrical connection/clearing customs] _was an informal payment or gift expected or requested_ ?” The answers to these questions will be our key explanatory variables. The descriptive statistics presented in Table 1 suggest that demands for such side payments are prevalent; 19%, 17% and 14% of firms reported having been either expected to, or explicitly asked to, pay a bribe when applying for a construction permit, operating license, electrical connection, respectively. In a subset of Middle Eastern and Northern African countries, firms were also asked about whether they were asked for bribes when dealing with customs, where corruption appears even more rife. As many as 38% and 44% of firms in our data reported having been asked to pay a bribe when dealing with customs to export and import respectively.

We complement the data on de facto policy implementation times with information on the _de jure_ time it should take to get things done from the Doing Business Indicators. Figure 2 plots the 10<sup>th</sup> percentile, median, and 90<sup>th</sup> percentile _de facto_ log policy implementation times in a given country-year reported by entrepreneurs surveyed by the World Bank Enterprise Surveys against the

> 7 The figure may suggest that corruption is the most important constraint facing firms. Note, however, that when managers asked which constraint they considered to be the most important, corruption features less prominently, with 6% of managers considering it their most pressing problem, making it the fourth most burning issue, after finance (16%), electricity (14%), competition from the informal sector (14%), and tax rates (11%).

14
# **6. Conclusions and implications**

The paper explores the determinants of labor force participation (i.e., employed, unemployed, not in the labor force) and employment type choice (wage vs. self), with a focus on the role played by gender role attitudes and personality traits using multi-topic household survey data collected in two regions – Ashanti and Northern – of Ghana. The study uses both descriptive and econometric analytical approaches. The econometric approach employs multinomial logit and linear probability models to examine the drivers of labor market participation and employment choice, respectively.

The descriptive results indicate that employment is higher among educated individuals, and those that live in urban areas and non-poor households. Self-employment is more prevalent than wage employment in the study area, and more common among women than men. The gender disaggregated descriptive comparison shows that men are more traditional, compared to their female counterparts. While some personality traits are more prevalent among men (work centrality, tenacity, achievement and power motivation), others are more dominant among women (i.e., polychronicity, locus of control and organization skills).

The empirical results in general show that the main drivers of labor market participation and employment choice include the age and education level of an individual, geographic location of the household, gender of the head, and poverty status of the household. In terms of the choice between wage and self-employment, the results show that individuals who have at least secondary education who live in male-headed households, and reside in urban areas are more likely to be employed in a wage job. On the contrary, Muslim individuals who live in large-sized households are more likely to be self-employed. In the gender disaggregated analysis, the results show that the education level of an individual is vital in shaping their decisions to be employed in wage jobs, while Muslim men and women are more likely to be self-employed. In addition, women who live in urban areas are more likely to land a wage job than those that live in rural areas. We also find that while the presence of a man’s spouse in the household motivates him to embark on self-employment work, this does not matter in the female sample.

Turning to personality traits and gender role attitudes, the empirical results show that both personality traits and gender role attitudes are vital in explaining labor market participation and the type of employment in the study area. Among the male sample, the results show that men with high locus of control have lower odds of being employed, while for women, 18
data for all individuals, while at midline, we only have it for these two groups.) At midline (1.5 years after treatment began), treatment led to 2.3 more visits (preventive or curative) per year for children aged 0–5 (column 2) and 1.1 more visits for those aged 60 and over (column 3). Relative to the baseline mean number of visits for each age group, these represent increases of 28 and 39 percent, respectively, which are comparable to findings in the literature. For example, Levy and Ohls (2007) find that a CCT program in Jamaica increased preventive health center visits by children aged 0–6 by 0.28 visits every six months (a 38 percent increase relative to the baseline mean), while Akresh et al. (2014) find that a CCT program in Burkina Faso increased annual preventive care visits by children aged 0–5 by 0.43 visits (a 49 percent increase relative to the control group mean). These statistically significant effects, however, disappear at endline (2.5 years after treatment began) for both age groups.<sup>16</sup> The results are robust to instead estimating a Poisson model that accounts for health clinic visits being a count data outcome (Appendix Table A9)<sup>17</sup> and to instead estimating the impact of treatment on the treated (Appendix Table A3).

In Table 3, we examine the impact of the CCT program on the rate of compliance with annual clinic visits conditions. While we lack administrative data on clinic visits, comparing self-reported visits over the last 12 months with program conditions for total annual visits is instructive. The conditions required six visits for those under age 2 and one visit for those over age 60; 2–5 year olds needed six visits at midline but two visits at endline. We see that treatment increased compliance with program conditions among both 0–5 year olds and those over aged 60 at midline. By endline, however, treatment increase compliance with program conditions only among those over aged 60, and not among children aged 0–5.

Several caveats and observations are warranted. First, households in control villages that by endline anticipated receiving the program within a few months<sup>18</sup> may have increased

> 16At endline, those in treatment villages were still receiving the CCT program and expected it to continue. Those in control villages anticipated being enrolled within the next few months (by late 2012).

> 17Results hold whether we use heteroskedasticity robust or bootstrapped standard errors.

> 18The endline survey was carried out during August–October 2012, and control villages were told at baseline to anticipate receiving the program in late 2012.

13
banks owned by emerging markets and developing countries grew by 84% and 102% respectively over this period.

Despite the increasing presence of foreign banks, the role played by foreign banks in improving access especially to the SME sector has been up to debate. On the one hand, foreign banks are thought to introduce new more diverse products, use more advanced technologies and are considered more efficient and profitable than incumbent banks, which could have positive spillover effects on SME lending (e.g. Levine 1996; Cull and Martinez Peria 2011). On the other hand, foreign banks, due to their large size and lack of familiarity with local culture, lose out to smaller local banks in relationship lending to small or otherwise opaque firms (e.g. Berger et al. 2005). Ultimately it is an empirical question on whether the increase in foreign ownership leads to limited credit access to the SME sector or if the increased competition for large customers due to foreign bank entry leads to other banks serving the SME sector better and expanding access.

Using data from the World Business Environment Surveys, Clarke, Cull, and Martinez Peria (2006) found that in countries with sizeable foreign bank shares, small firms are less likely to rate high interest rates and access to long-term loans as major obstacles for growth and operations. Giannetti and Ongena (2012) trace bank relationships of unlisted firms in Eastern Europe to show that foreign bank presence enhances credit access.

There have however been studies finding more negative results on the role of foreign banks. Detragiache, Gupta, and Tressel (2008) show that the presence of foreign banks in the poorest low-income countries is associated with less credit 19
For example, the 2004 World Bank Global Monitoring Report suggested that $30 billion in extra aid could be effectively used today by developing countries rising to $50 billion in they introduced policy and institutional reform. If these numbers are taken at face value, an effort to increase aid flows to developing countries by $50 billion next year to speed growth would likely lead to wasted aid.<sup>79</sup> In a narrow growth-promoting sense this might suggest caution in moving towards an International Finance Facility model which would increase aid over the short term at the expense of significantly lower resources over the longer term, with a net value of interest payments of over $200 billion in interest charges.<sup>80</sup> It may even be that donors should examine the potential for direct cash payments to poor people in developing countries if there is significant pressure to increase resource transfers tomorrow. Such payments may have a more direct impact on poverty than greater flows through weak institutional structures.<sup>81</sup> At the same time, it is important to remember that we are setting the wrong standard when we say aid must lead to income growth to be considered a success. Aid should lead to adequate consumption, to longer life, better education and a range of other outcomes to be valued in their own good. Viewed through that lens, it may well be that aid has had many more successes than we give it credit for, that different allocations of aid would lead to dramatically improved outcomes and that front-loading may be more justified.

Our focus for aid, perhaps, should be in maximizing outcomes we value in their own right (longer life, literacy) through proven techniques subject to (preferably repeated) rigorous, credible, independent evaluation.<sup>82</sup> It may be that such interventions will lead to economic growth –but even if they do not, it would be hard to argue that the money has been wasted. There are a number of such interventions that remain underfunded by donors, which is a strange outcome given their comparative chance of success.

**<u>Table Two: Aid by Income and Institutional Strength</u>**

|LI<br>|Cs<br>|M<br>|ICs<br>|
|---|---|---|---|
|Weaker Institutions|Stronger Institutions|Weaker Institutions|Stronger Institutions|
|Significant<br>analytic/consensus<br>building/TA<br>Local/regional<br>work?<br>NGOs<br>Internatiuonal<br>support<br>“Insulated”<br>investments with<br>cofinancing.<br>Direct payments to<br>the poor?|Budget support<br>Analytic/TA<br>Large-scale pilots<br>(with cofinancing)<br>Regional/global<br>public good<br>financing|Analytic/consensus<br>building<br>Local/regional TA|Large scale pilots<br>(with cofinancing)<br>Crisis budget<br>support<br>Analytic work<br>TA|

17
**Table A5** Impact of Treatments on Males: IPV Attitudes and Perpetration <u>(Restricted</u> to Ever had Sex at Baseline, Male Data)

||(1)|(4)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|Violence attitudes index|||||
|treatment x post|-0.413***|-0.100|-0.000|1,100|
||(0.138)|(0.143)|||
|treatment x post x goal|0.205||||
||(0.206)||||
|Tolerate violence from|||||
|husband/partner|||||
|treatment x post|-0.204***|-0.057|0.107|1,100|
||(0.062)|(0.061)|||
|treatment x post x goal|0.069<br>(0.086)||||
|Men can beat women under<br>certain circumstances|||||
|treatment x post|-0.069|-0.008|0.214|1,100|
||(0.088)|(0.079)|||
|treatment x post x goal|0.072<br>(0.115)||||
|IPV often index|||||
|treatment x post|0.015|-0.060|0.000|1,100|
||(0.133)|(0.122)|||
|treatment x post x goal|-0.169||||
||(0.171)||||
|IPV in last year index|||||
|treatment x post|0.109|-0.010|-0.000|1,100|
||(0.201)|(0.204)|||
|treatment x post x goal|-0.001<br>(0.270)||||
|Girls have a right to ask to use a condom|||||
|treatment x post|0.106|-0.002|0.881|1,100|
||(0.091)|(0.081)|||
|treatment x post x goal|-0.026<br>(0.098)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. See notes from Table 3 for detail on table structure, outcome definitions, and control variables. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Male baseline and endline data, balanced panel, but restricted to males who had ever had sex at baseline.

64
Policy Research Working Paper 8750

# **Abstract**

This paper proposes a framework that examines three levels of access to infrastructure—nominal, effective, and quality-adjusted access. Most conventional indicators measure nominal access—whether a household has physical access to a service in or near the house. By contrast, effective access incorporates functionality and use of service, and quality-adjusted access raises the bar by incorporating quality metrics. The paper illustrates the analytical utility of this conceptual framework by deploying data from a survey of 14,200 households in 15 Kenyan cities in 2012–13.

First, the analysis finds that these cities fall far short of delivering universal access to basic infrastructure. Second, for most services there a large gap—3 to 41 percentage points—between nominal and effective access. When the bar is raised to include quality of service, the drop-off in the proportion of those with access is even more dramatic. These findings suggest that conventional nominal measures overreport the level of service in urban communities, and that current approaches to infrastructure delivery might be enhancing availability of a service without ensuring that the service is usable—that is, functional, reliable and affordable. Third, there is an infrastructure access gap between nonpoor and poor households, as well as formal and informal settlements. Fourth, hedonic regression analysis reveals that four services—electricity, water, toilets, and garbage collection— are associated with higher rents. The analysis has broader implications for understanding and measuring service access. It raises important questions as global discussions turn to indicators for the Sustainable Development Goals.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice. 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 at sgulyani@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