rafmacalaba/datause-encoder-data huggingface dataset:
# **5.1 Replacing Rule of Law with More Exogenous Proxies**

The first concern is that institutional quality might be endogenous to unobservable political factors that affect credit growth; e.g., governments that are captured by incumbent business interests might deliberately block institutional reform to keep the financial system from developing. To address this concern, we replace our measure of institutional quality – the rule of law – with variables that capture fundamental exogenous factors that drive institutional outcomes. Work by Easterly, Ritzen, and Woolcock (2006) shows that ethnic fractionalization undermines social cohesion, which undermines the rule of law, which in turn affects economic growth.<sup>17</sup> Obviously, any institutional measure is going to contain some component that is endogenous to politics and political institutions. One would be hard pressed, however, to argue that ethnic fractionalization is caused by political factors.

We therefore substitute ethnic fractionalization for the rule of law variable in Table 5, models 1 and 2. Related lending is positive and significant in both specifications, while related lending interacted with ethnic fractionalization is negative and significant. The regressions indicate that at low levels of social cohesion, the impact of related lending on credit growth is negative, but that at high levels of social cohesion related lending has a positive impact on credit growth.<sup>18</sup> These findings are therefore consistent with those we obtained from the rule of law regressions. They also provide additional information about the type of society in which related lending is less likely to offer benefits.

> 17 They also show that these measures of social cohesion pass over-identification tests for excludability in growth regressions.

> 18 The measure of ethnic fractionalization used by Easterly _et al_ . ranges from 0 to 1 with higher values indicating more fractionalization. In our sample, the mean value is 0.44 and the highest value is 0.93 for Uganda. The lowest values are for Japan (.01) and Korea (.002). One might interpret the positive coefficient for ethnic fractionalization as indicating that at low levels of related lending fractionalization has a positive effect on private credit growth. However, that coefficient can be explained in the context of the values for related lending in our sample and its size relative to the coefficient on the interaction term. At the lowest levels of related lending in our sample, fractionalization is positively associated with private credit growth, but the relationship is never significantly different from zero, and thus we cannot reject the hypothesis that there is no relationship between fractionalization and private credit growth for those countries. At higher levels of related lending, the relationship is negative and becomes significant for related lending values near 50. So, for about a quarter of the sample we find no significant relationship for fractionalization. For the remaining three-quarters we find a negative, significant relationship, as one would expect.

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since women participate in far lower numbers than men to the labor market and, among those who do participate in the labor force, unemployment levels among women tend to be higher. However, focus group participants held the view that for men employment was an economic and social imperative, while for women it was more of a choice. Hence, the self-esteem of males was tied more closely to their employment status than that of females. The World Bank (2011) study summarizes focus group participants’ viewpoints as follows: “ _Young men are thus seen as suffering most from adverse labor market conditions, not because the objective situation is more adverse for them, but rather, because it is much more socially detrimental for men to be excluded from the labor market. Although it may be highly desirable for women to work, it was not a social requirement: a woman without work would still be respectable and able to find satisfaction in life primarily in marriage and children._ ” (p.41) Quantitative data are also indicative of strong gender norms. For example, the time use module in the MHYS 2009-10 suggests that young women on average do far more of the housework than do men. In fact, employed women spend much more time in household chores than non-employed men as shown in Table 1. Akerlof and Kranton (2000) argue that such patterns, seen in the wealthier developed countries as well, results not from women’s comparative advantage in home production but rather from the importance of gender identity.

**Table 1: Share of Time (%) spent on household chores and child care for 15-29 year olds (as a fraction on non-sleep / non-personal time)**

|**Characteristics**|**Female**|**Male**|**Characteristics**|**Female**|**Male**|
|---|---|---|---|---|---|
|**Education**|||**Household Wealth**|||
|No education|34|1|Bottom Quintile|31|1|
|Primary|27|1|2nd Quintile|24|1|
|Secondary|17|1|3rd Quintile|23|1|
|Post-Secondary|11|1|4th Quintile|21|1|
|**Marital Status**|||Top Quintile|19|1|
|Not married|19|1|**Location**|||
|Married|36|1|Rural|28|1|
|**Employment Status**|||Urban|20|1|
|Not employed|25|1|All|24|1|
|Employed|13|1||||

Quantitative evidence from the MHYS further indicates that most young men and women hold the belief (‘prescription’) that women should perform more household tasks than men, as documented in Table 2. Women presently do more work inside the home and less work outside as compared to men. Interestingly, the "how would you like it to be" scenario is not much different across men and women. Further, while men are open to women working more outside of the house, they also want them to continue their housework routines. Women also reveal that they would like to engage in more of home production than men do, while simultaneously aspiring to work outside of the home. These responses reveal strong gender norms related to the labor market participation on the part of both men and 6
Table A2.1: Proportion of Firms Reporting Business Damage by Distance from the Coastline

|DistancefromCoast(metres)|Proportionreportingdamage<br># Obs|
|---|---|
|<br><=150m|<br> <br>0.649<br>37|
|151to250m|0.779<br>95|
|<br>251to500m|0.604.<br>149|
|<br>501 to 750m|0.367<br>60|
|751to1000m|0.040<br>25|
|<br>1001to1500m|0.000<br>70|
|<br>1501to2000m|0.000<br>18|
|<br>2001fo2500m|0.042<br>24|
|<br>2501ormoremetres|0.028<br>106|

Nofe: proportions are of firms reporting profits in three or more waves of the survey
**Table 11: Potential experience**

|**Variable**||**1993**|||**1**|**999**||
|---|---|---|---|---|---|---|---|
|**Pexp**|**Wage**|P>Izl<br>**Productivity**|**P>jzl**|**Wage**|**P>Izl**<br>|**Productivity**|**P>Izl**|
|ooled|0.04|0.00<br>0.00|0.72|0.04|0.00|0.01|0.01|
|**\4icro**|0.04|0.00<br>0.02|0.18|0.04|0.00|0.02|0.16|
|**Small**|0.05|0.00<br>-0.01|0.30|0.03|0.00|0.00|0.76|
|**LediUM**|**0.04**|**0.00**<br>**-0.01**|0.04|0.04|0.00|0.00|0.56|
|Large|0.05|0.00<br>0.00|**1.00**|0.04|0.00|0.02|0.02|

_Source._ Author's calculations based on ENTRAM-ENESTYC. **iv. Gender** This paper also examines the effect that gender has on both wages and productivity for various firm sizes. Not surprisingly, female employees were paid less than their male co-workers, but they were also less productive than men in both 1993 and 1999 (see Table 12). This finding indicates that the wage gap according to gender is due to productivity differentials. For 1993, the hypothesis test indicates that the productivity gap is much larger than the wage gap. However, in 1999, we cannot reject the hypothesis of the equality of coefficients of gender on the wage and productivity equations. This finding implies that there is no gender discrimination since wage differentials are explained by equal or larger productivity differentials.

**Table 12: Gender**

|**Variable**||**1**|**993**|||**1**|**999**||
|---|---|---|---|---|---|---|---|---|
|**Gen**|**Wage**|**P>jzl**<br>|**Productivity**|**P>Izl**|**Wage**|**P>zl**<br>|**Productivity**|**P>zl**|
|ooled|0.21|0.00|<br>0.40|0.00|0.25|0.00|<br>0.29|0.01|
|Micro|0.27|0.11|0.69|0.03|0.32|0.03|0.64|0.08|
|Small|0.31|0.00|<br>0.18|0.11|0.02|0.79|<br>0.69|0.00|
|Medium|0.13|0.02|0.11|0.25|0.22|0.00|<br>0.16|0.31|
|Large|0.18|0.00|0.56|0.00|0.17|0.03|0.02|0.92|

_Source:_ Author's calculations based on ENTRAM-ENESTYC.

By using interaction variables, we found some results that were surprising and some that were inline with our hypotheses. The results of the interaction variables show that investment in education of men does not significantly increase men's wages or productivity more than that of women (see Table 13).

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incorporate European Union (EU) legislation forces changes for the EU Accession countries.<sup>17</sup> This issue has a wider importance in that Stern (1997) conjectured that different economic, cultural and political systems might produce different forms of regulator, including types not yet observed. In this case, we might observe a set of good alternatives to the standard model derived from US, UK and similar. If so, we should expect considerable variation in the institutional forms in which basic governance attributes (accountability, transparency, etc) were met – and successfully met. This is what one would also expect from higher level reasoning on the relationship between economic functions and institutional forms (eg North (1994), Rodrik (2003), etc). However, as yet, no alternative institutional or governance forms have emerged for utility regulation. Even for concession contracts in infrastructure, standard regulatory institutions with an external regulator and standard governance features appear significantly to improve performance. (See Guasch, Laffont and Straub (2002), Stern (2003), etc.) A second interesting point is that both Stern (1997) and Smith (1997) concentrate on discussing regulatory independence as _independence from government_ . They both implicitly assume independence from regulated companies and discuss this much less. However, the 1996 EU Electricity and Gas Directives concentrated on establishing independence from regulated companies (typically the state-owned monopoly incumbent). Only in the 2002 Directives has the EU defined independence to include independence from government. Given the history of regulation over the last 5 years, particularly in Latin America, maybe more attention should be given to the issue of the independence of the regulatory agency from the dominant utility companies.

The distinction between formal and informal accountability in Stern (1997) was further elaborated in Stern and Holder (1999). However, the empirical results reported in the latter are a summary of the main results of a 1998 NERA study of 13 Asian developing country infrastructure regulatory regimes for the Asian Development Bank. (The Stern-Holder paper also includes some additional analysis of general issues). We will discuss the results of the study for the ADB in a later section. The point to note here is that the governance criteria set out in Stern and Holder were specifically developed to provide a _framework for systematic and comparative data collection by case study_ and this they broadly achieved<sup>18</sup> .

The six governance criteria used in Stern and Holder and the study for the ADB were:

- 1) Clarity of Roles and Objectives

- 2) Autonomy

- 3) Participation

- 4) Accountability

- 5) Transparency

- 6) Predictability

> 17 See inter alia Stern (1999)

> 18 The Stern-Holder criteria were also used as a major starting point for the electronic/postal questionnaire developed by Preetum Domah that provided the data for the empirical work in Domah, Pollitt and Stern (2002).

9
**Figure 26. Application timing and results**

# _Source_ : Authors.

Note: The data used are a subsample of the transactions associated with jobs that were posted in or before 2017Q1. The red line (the probability of an application being viewed by employers) is based on an estimate of a seven-order polynomial function of elapsed days.

Employers’ decisions on short listing are examined by regressions that control for job posting fixed effects (Figure 27). Since the regressions control for job posting fixed effects, the analysis compares the probability of being shortlisted between applications submitted to the same jobs.<sup>24</sup> The key findings are as follows:

- _Finding 1: In the case of job postings that do not indicate gender preference, female applicants are 3.3 ppt more likely to be shortlisted than males._ This difference is economically significant given that the mean probability of being shortlisted is 9.5%.

- _Finding 2: In the case of job postings that indicate gender preference, matched genders lead to a higher probability of being shortlisted._ In the case of male-preferred jobs, male applicants are 1.4 ppt more likely to be shortlisted than female applicants. In the case of female-preferred jobs, female applicants are 11 ppt more likely to be shortlisted than male applicants. It may be surprising that gender-unmatched applicants are shortlisted in some cases.

- _Finding 3: Later applications are less likely to be shortlisted._ If elapsed days from a job posting to the timing of application is twice as long, the probability decreases by 1.1 ppt.

24 Information as to whether an application was shortlisted was voluntarily updated by employers. In fact, many applications seem to be not updated. Thus, we apply the following data restrictions. First, we use only the job postings that were posted in or before 2018Q1. Those job postings must have completed short listing by April 2019. Then, we restrict the sample to the job postings for which at least one application was shortlisted. In other words, if none of the applications to a certain job posting are recorded as having been shortlisted, we exclude the job posting and all the applications to the job posting. The above two steps make the subsample for the analysis here. Regarding application status, we interpret that an application was not shortlisted if the status of the application was not updated, and another application was shortlisted.

30
16

**0**

Figure 3: Percent Distribution of Workers Aged 65-71: Males, 1974 ,, <!-- Start of picture text --> 10<br>7..<br>5<br>2.5<br>0<br>I<br>_~~~~<br>0 1000 2000 3000 4000<br>Earnings Interval (Beginning Value]<br><!-- End of picture text --> Source: Kotlikoff (1978). Earnings intervals are $200; 32 percent earned more<sup>than $4000. The Social</sup> Security earnings limit is $2400.
||F|ull Sampl|e|Trimming Tail 5%|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Power Structure_|||||
|YRD|-0.034|-0.072|-0.053|-0.094|
||(0.111)|(0.104)|(0.109)|(0.109)|
|_Initial Conditions_|||||
|Ln_GVOPC_78||-0.154|-0.165|-0.277***|
|||(0.208)|(0.199)|(0.092)|
|Ln_pop_78||0.023|0.046|-0.008|
|||(0.099)|(0.114)|(0.090)|
|_Geography_|||||
|Share of Plain (%)|||-0.004|0.002|
||||(0.009)|(0.006)|
|Distance to Xiamen|||-0.000|0.000|
|(Km)|||(0.001)|(0.001)|
|Obs.|57|56|56|51|
|_R_<sup>2</sup>|0.001|0.046|0.054|0.144|

Table 19: The Effect of Factions on the Population Growth Rates in the Counties in Fujian, 1984-1998.

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|**Table A4: Balance Checks a**|**mong Busi**<br>(1)<br>Mean [SD]<br>|**nesses Su**<br>(2)<br>Differe<br>and|**rveyed a**<br>(3)<br>nce betw<br>Controlg|**t second**<br>(4)<br>een [...]<br>roup|**follo**<br>(5)|**w‐up su**<br>(6)<br> <br>P‐valu|**rvey**<br>(7)<br> <br> <br>e for diffe|(8)<br> <br> <br>rence...|(9)<br> <br>P‐values<br>joint tests<br>|
|---|---|---|---|---|---|---|---|---|---|
||in Control|||||Group 1|<br>Group 1|<br>Group 2|<br>G1=G2=G3|
||Group|Group1|Group2|Group3|N|<br>and 2|<br> <br>and 3|<br> <br>and 3|<br>=0|
|_Firm owner characteristics_||||||||||
|Female owner|0.627<br>[0.484]|0.002<br>(0.003)|‐0.001<br>(0.002)|‐0.001<br>(0.002)|3,064|0.55|0.403|0.787|0.836|
|Age of the owner|39.37|0.93|0.55|‐0.01|3,035|0.671|0.246|0.276|0.407|
||[10.58]|(0.81)|(0.51)|(0.46)||||||
|Firm owner has at least|0.724|‐0.049|‐0.009|0.015|3,061|0.309|0.069*|0.28|0.256|
|some formal education|[0.447]|(0.035)|(0.023)|(0.02)||||||
|_Firm characteristics_||||||||||
|Trade|0.536<br>[0.499]|‐0.006<br>(0.004)|0.001<br>(0.003)|0.002<br>(0.002)|3,064|0.085*|0.036**|0.779|0.206|
|Services|0.262|0.017|0.006|0.005|3,064|0.712|0.655|0.956|0.915|
||[0.44]|(0.026)|(0.017)|(0.015)||||||
|Craft|0.176|‐0.015|‐0.023|‐0.005|3,064|0.781|0.707|0.278|0.523|
||[0.381]|(0.026)|(0.017)|(0.015)||||||
|Firm area in m2|19.4|6.39*|0.5|‐2.56|3,059|0.16|0.017**|0.199|0.084*|
||[43.52]|(3.77)|(2.39)|(2.14)||||||
|Business connected to|0.617|‐0.016|0.006|0.003|3,063|0.602|0.616|0.9|0.961|
|electricity network|[0.486]|(0.038)|(0.024)|(0.021)||||||
|Number of employee|1.22|‐0.07|‐0.05|0.07|3,064|0.849|0.177|0.072*|0.21|
||[1.7]|(0.11)|(0.07)|(0.06)||||||
|The firm does any form of|0.174|‐0.007|0.002|0.005|3,062|0.804|0.688|0.843|0.976|
|accounting|[0.379]|(0.03)|(0.019)|(0.017)||||||
|Amount of sales in an|59,792|174|968|‐1,237|3,064|0.776|0.572|0.163|0.543|
|average week|[56,781]|(2,500)|(1,586)|(1,414)||||||
|Amount of profit in the last|46,563|648|‐47|‐979|3,064|0.79|0.487|0.529|0.821|
|month|[45,839]|(2,345)|(1,488)|(1,327)||||||
|Firm owner owns a bank|0.227|0.005|0.004*|0.005**|2,998|0.805|0.915|0.545|0.066*|
|account|[0.419]|(0.003)|(0.002)|(0.002)||||||
|_Taxes_||||||||||
|Firm pays taxes|0.555<br>[0.497]|‐0.015<br>(0.038)|‐0.027<br>(0.024)|‐0.033<br>(0.021)|3,037|0.776|0.642|0.816|0.454|
|Amount of taxes paid in the|19,779|‐4,285**|‐657|‐1,413|2,976|0.131|0.184|0.575|0.227|
|Notes: Baseline survey data (<br>Standard deviations presente<br>OLS regression of the firm <br>(dummies for each triplet). ***<br>previous year|[28,779]<br>March 2014)<br>d in bracket<br>owner/firm <br>, **, * indic|(2,160)<br>. Only bus<br>s. Column<br> characteri<br>ate statisti|(1,369)<br>inesses s<br>s 2‐4: coef<br>stic on t<br>cal signif|(1,214)<br>urveyed a<br>ficients a<br>reatment <br>icance at|t seco<br>nd sta<br> dumm<br>1, 5 and|nd follo<br>ndard er<br>ies, con<br>10%.|w‐up are <br>rors (in p<br>trolling f|included<br>arenthes<br>or strat|. Column 1:<br>es) from an<br>a dummies|

45
**Figure 1.8: Industrialization as an Engine of Growth: Manufacturing and Income Growth, 1993–2007**

<!-- Start of picture text --> GDP and Manufacturing Value Added Growth Rates GDP and Manufacturing Value Added Growth Rates<br>World, 1993-2007 Africa, 1993-2007<br>ARM CHN MMR LSO<br>AZE LSO AGO<br>VNM<br>LAO BLR IND BTN KHM MOZ<br>CUB MOZ<br>VCT LKA UGA BWA UGA<br>MUS ETH<br>JOR NAM TZA<br>MLI NAM<br>JAM MDG GIN<br>CIV GAB KEN<br>SLB PNG ZMB<br>ERI CIV<br>ZWE ZAR GAB<br>COM<br>ZAR<br>ZWE<br>-.05 0 .05 .1 .15 -.05 0 .05 .1 .15<br>MVA growth rate, 1993-2007 MVA growth rate, 1993-2007<br>Source: WDI, 2011<br>.08<br>.06<br>.04<br>.02<br>0<br>-.02<br>.1<br>.05<br>0<br>-.05<br><!-- End of picture text --> Except for a few oil exporting countries, no country has achieved high-income status without industrializing and dynamic industrial upgrading. In general, a change in GDP per capita is strongly and positively correlated with growth in value added in the manufacturing sector (Figure 1.8). If natural resources- or land-rich countries have achieved a middle income status without a large manufacturing sector, they have rarely succeeded in sustaining growth. More importantly, the growth in resources sector will not create much employment.

# **_Sub-Saharan Africa – Failure to industrialize explains failure to catch up_**

The failure to catch up in Sub-Saharan African countries is largely a failure to industrialize and is illustrated by two statistics: a relatively small share of manufacturing value added (MVA) in GDP (Table 1.4), and a GDP growth rate that averaged only 3 percent per annum during 1970 and 2000 (Figure 1.9). At least two factors explain these disappointing outcomes.

**<u>Table 1.4: Relative to other developing countries, Africa’s manufacturing sector is small</u>**

|Share of manufac<br>|turing in<br>|GDP (%)<br>||||
|---|---|---|---|---|---|
|**1970**|**1980**|**1990**|**2000**|**2005**|**2008**|
|World<br>26.7|24.4|21.7|19.2|17.8|18.1|
|Developing countries<br>17.6|20.2|22.4|22.6|23.3|23.7|
|African countries<br>6.3<br>|11.9<br>|15.3<br>|12.8<br>|11.6|10.5|
|_Source: Economic Development in Africa Report, UN_|_IDO, 2011_|_.Chp 2, Ta_|_ble 1._|||

13