rafmacalaba/datause-encoder-data huggingface dataset:
31 average income elasticities, as Langhammer claims, but the relatively simple developing country manufacturing exports (textiles, clothing, food products, consumer products, chemicals, steel etc) also have higher price elasticities. Moreover, there is likely to be increased competition from aggressive NIC-type industries in Southern Europe. So a significant expansion of the trade in developing country manufactures is not likely, and trade diversion will tend to offset any trade creation. Conversely, the income elasticities for primary commodity exports may be smaller than those for manufactures. But the price elasticities are in most cases smaller still. Consequently, trade creation is more likely to dominate any diversion here. Hence, existing trade patterns will be reinforced; and the effect will be to obstruct those developing countries which are making a serious attempt to diversify away from a dependence on one or two commodity markets, especially if their strategy is to diversify into manufacturing industries. It will also make things more difficult for those Latin American and Asian countries which are trying to diversify away from too great a dependence on the US (or Japanese) markets. And the EC's preference system will also deter any diversification of the poorest African countries away from their heavy dependence on particular EC markets.

# _J.1_ The Inmact of EC-92 on DeveloRine Countries Manufacturing Output by Country Groups

If EC-92 reinforces existing trading patterns, how are the developing economies likely to fare? Table 3, which is taken from Page (1991), gives some typical estimates by country grouping. The main feature is that, just as in the aggregate results quoted above, the total effects of EC-92 on any developing country grouping is extremely small. Positive trade creation effects are offset by negative trade diversion figures, so that not one of the group's total exports is changed by more than 1X. Of course the impact on exports to the EC itself is rather larger; 6X down for the NICs, 41 up for OPEC, and rather less for the others. But those figures are also remarkable for being so small against a background where year-on-year changes in exports of 10-201 are commonplace.
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Dargay, Joyce M., D. Gately, and M. Sommer (2007), Vehicle Ownership and Income Growth, Worldwide, 1960-2030. _The Energy Journal_ , 28, no 4.

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Deacon, Robert T. and S. Saha (2006), Public Good Provision by Dictatorships: A Survey. In A. F. Ott and R. J. Cebula (eds.): _The Companion in Public Economics: Empirical Public Economics._ Edward Elgar Publishers _._ Dee, Thomas S. (2004), Are There Civic Returns to Education? _Journal of Public Economics_ , 88, 1697-1720.

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Hammar, Henrik, Åsa Löfgren and Thomas Sterner (2004), Political Economy Obstacles to Fuel Taxation. _The Energy Journal_ , 25, 1-17.

Huber, J. D. and P. Stanig (2009), Individual Income and Voting for Redistribution Across Democracies. Working paper, Columbia University.

23
<u>Table 6: Openness, Technological Innovation and the Demand for Skills: Robustness</u>

||<br>(1)|<br>(2)|(3)|
|---|---|---|---|
|Exporter|-0.0923**|-0.120***|0.0773*|
||[0.0395]|[0.0369]|[0.0408]|
|Foreign Ownership|0.259***|0.276***|0.187***|
||[0.0423]|[0.0413]|[0.0432]|
|Technological Innnovation|0.264***|0.285***|0.220***|
||[0.0350]|[0.0344]|[0.0368]|
|City>1mln individuals|0.165***<br>[0.0621]|-|-|
|City 250k-1mln individuals|-0.127**<br>[0.0626]|-|-|
|City 50k-250k individuals|-0.127**<br>[0.0567]|-|-|
|City > 50k individuals|-0.253***<br>[0.0618]|-|-|
|Access to External Finance|-|-|-0.050<br>[0.0403]|
|Baseline Firm Characteristics?|Yes|Yes|Yes|
|Country-Industry-Size Fixed Effects?|Yes|No|Yes|
|Country-Industry-City Fixed Effects?|No|Yes|No|
|Observations|5,592|5,592|4,937|
|Rsquared|0.26|0.23|0.30|

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) assumes the baseline specification (column 5 of table 5) controlling in addition to the location of the firm (city level). Column (2) controls for the firm characteristics in the baseline specification controlling for country-industry-city fixed effects (instead of the country-industry-size fixed effects in the baseline). Column (3) reports the baseline specification controlling in addition to the firm's access to external finance. All the variables are defined in table A1 in the appendix.
## **Figure 3: Percentage of GWh consumed by system size**

|< 150 MW|150–1000 MW|> 1000 MW|
|---|---|---|
|Burundi|Benin|Angola<sup>a</sup>|
|Cape Verde|Botswana|Cameroon|
|Central African Republic|Burkina Faso|Congo,Dem. Rep.<sup>a</sup>|
|Chad<sup>a</sup>|Congo,Rep.|Côte d’Ivoire|
|Comoros|Gabon|Ethiopia|
|Equatorial Guinea<sup>a</sup>|Guinea|Ghana|
|Eritrea<sup>a</sup>|Madagascar|Kenya|
|Gambia,The|Malawi|Mozambique|
|Guinea-Bissau<sup>a</sup>|Mali|Nigeria|
|Lesotho|Mauritania|South Africa|
|Liberia|Mauritius|Sudan|
|Niger|Namibia<sup>a</sup>|Tanzania|
|Rwanda|Senegal|Zambia|
|São Tomé and Príncipe|Togo|Zimbabwe|
|Seychelles|Uganda||
|Sierra Leone|||
|Somalia<sup>a</sup>|||
|South Sudan<sup>a</sup>|||
|Swaziland|||

<!-- Start of picture text --> 1%<br>7%<br>>1000 MW<br>250 - 1000 MW<br>92%<br><250 MW<br><!-- End of picture text --> _Sources_ : Utility annual reports and other documents.

a. Country not included in this study.

For the 34 countries for which data on the breakdown of consumption by consumer category were available, residential consumers were the largest group, accounting for 41 percent of sales. The second largest consumer category was industry, including mining companies (excluding captive power generation not connected to the grid), accounting for 30 percent of sales and in some countries more than half of sales, such as 57 percent in Kenya in 2015. This was followed by commercial users (21 percent), exported electricity (4 percent), government and agriculture (1 percent each), and the rest of consumers making up the remaining 1 percent.

# 3.6 Quality of service

In addition to the low levels of access discussed in section 1, quality of service for those connected is a critical factor. In the context of financially strained utilities, spending on operations and maintenance is often one of the first line items to be cut, reducing the quality of service.

Quality-of-service data are available for 16 countries. However, data were reported in an inconsistent manner. Only four utilities reported data according to the international standards of system average interruption duration index (SAIDI), the annual average outage duration for each customer served, and system average interruption frequency index (SAIFI), the average number of interruptions that a customer would experience in a year. These four are shown in Table 4. These statistics are not necessarily comparable across countries, because it is not clear if the measurements and methodologies are the same.

**Table 4: Available SAIDI and SAIFI data**

||**SAIDI**|**SAIFI**|
|---|---|---|
|**Cameroon**|105|25|
|**Liberia**|35|212|
|**Mozambique**|Transmission 59, distribution 1.25|Transmission 52, distribution 1.25|
|**South Africa**|36|20|

26
Policy Research Working Paper 10864

# **Abstract**

Laws and regulations represent a central tool for governments to achieve policy objectives, and they also represent a fundamental condition for making desirable individual and business decisions. While laws and regulations regularly have to be adapted to changing circumstances, frequent and sudden modifications indicate legislative unpredictability and are expected to impose considerable costs on citizens and businesses. Legislative predictability is assumed to be the consequence of high-quality laws, and existing evidence shows that regulatory management systems indeed impact legislative predictability. This paper proposes and implements an innovative legislative big data analytics approach to measuring legislative predictability in the Kingdom of Jordan and selected global comparator countries. It also maps out the feasibility of such an approach for the wider Middle East and North Africa region. Legislative data gathered from official government sources point to the high frequency of modifications in Jordan compared to a wide range of countries where data are available Around 10 to 15 percent of the original laws have been modified within 24 months of enactment over the past 20 years. In addition to prevalent modifications of new laws, even older laws face a comparatively high risk of modification in Jordan. Additional data collection following the template outlined in this paper could deliver a comparative data set, enabling a better understanding of the drivers and trends of legislative predictability and hence better evidence-based policies.

This paper is a product of the Governance 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/prwp. The authors may be contacted at FazekasM@ceu.edu, BrennerD@ceu.edu and pladegaard@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
9 In practice, we will measure the ratio λ at<sup>/λ</sup> bt using exports of countries to the United States. While it would be preferable to use their worldwide exports, our data for the U.S. are more disaggregate, and allows for a finer measurement of “unique” products sold by one country and not another. Specifically, for 1972 – 1988 we will use the 7-digit Tariff Schedule of the U.S. Annotated (TSUSA) classification of U.S. imports, and for 1989 – 1997 we shall use the 10-digit Harmonized System (HS) classification of imports.

To measure the ratio λ at<sup>/λ</sup> bt , we need to decide on a consistent “comparison country.” For this purpose, we shall use the _worldwide exports_ from all countries to the U.S. as the

* C c comparison.  Denote this comparison country by *, so that the set I t = Uc = 1 I t is the complete

* * set of varieties imported by the United States in year t, and pit<sup>q</sup> it is the total value of imports for good i.  Then comparing country c to country * in year t, it is immediate that the common set of c * c goods exported is I t ∩ I t = I t , or simply the set of goods exported by country c.  Therefore, from c (7) we have that λ t I(<sup>c</sup> t ) = 1 , and:

Noting from (8) that product variety in country c relative to the comparison is measured c c * c as λ t I( t /) λ t I( t ) , but this has a negative coefficient when σ < 0, let us instead invert it and

* c c c * c measure product variety of country c relative to the world by λ t I( t /) λ t I( t ) = λ t I( t ) , which

c* enters (8) with a positive coefficient 1/(1– σ ). For brevity we denote this by λ t in (9). It is interpreted as the _share of total U.S. imports from products that are exported by country c._
<!-- Start of picture text --> 1000000<br>900000<br>800000<br>700000<br>Ethiopia<br>600000<br>Gambella<br>500000 Somali<br>400000 Tigray<br>Benishangul G.<br>300000<br>Afar<br>200000<br>100000<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Number of refugees<br><!-- End of picture text --> _Notes:_ This figure presents the total number of refugees hosted in Ethiopia and its five largest refuge-hosting regions in the period 2008-2018. The horizontal axis is years while the vertical axis is number of refugees, as recorded in December of the corresponding year except for 2018 it reflects the refugee population in August.

Figure 2: Ratio of refugees to host population in Ethiopia <!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>Ethiopia<br>0.5 Gambella<br>Somali<br>0.4<br>Tigray<br>0.3 Benishangul G.<br>Afar<br>0.2<br>0.1<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Ratio of refugees to hosts<br><!-- End of picture text --> _Notes:_ This figure presents the ratio of refugees to host population in Ethiopia and its five largest refuge-hosting regions in the period 2008-2018. The horizontal axis is years while the vertical axis is ratio of refugees to host population. The number of refugees is as recorded in December of the corresponding year except for 2018 it is in August.

Figure 3: Survey villages and refugee settlement sites 36
notes that capital flows are concentrated in specific countries and sectors, such as natural resource extraction, thus benefiting only narrow parts of the economy and society.

Standard indicators of financial intermediary development, such as liquid liabilities to GDP, bank deposits to GDP, and private credit to GDP have demonstrated financial deepening in most African countries over the past years (Figure 10). As credit has been growing faster than deposits in most countries, financial intermediation—that is, the extent to which banks intermediate society’s savings into private credit—has also increased, although from very low levels. Efficiency and profitability indicators, on the other hand, have shown no clear trend over the past years, with the median fluctuating, pointing to continuous problems of scale and competitiveness.

The improvement in banking system indicators and continued weakness in financial markets is consistent with the status of African financial sectors as bank- rather than marketbased systems. The focus of financial policymakers on improving the infrastructure necessary for sound and efficient banking—such as establishing credit registries, upgrading collateral registries, and improving creditor rights—is thus consistent with level of economic and financialsector development in Africa. An overemphasis on capital market development, on the other hand, would be premature, given that the necessary legal and regulatory conditions are not in place and—most importantly—the necessary demand from corporations is lacking. Furthermore, international experience suggests that small economies have an increasingly difficult time supporting liquid stock exchanges in a global economy, independent of their income level, as stock exchanges are subject to scale and network economies.<sup>15</sup> The more successful large enterprises across the developing world increasingly list abroad at a few exchanges, be it through

> 15 Domowitz et al. 2001.

8
effects without instruments (table 2).

# **8.2 Time Trends in Student Characteristics**

The research design assumes that dormitory and non-dormitory students’ GPAs do not have different time trends for reasons unrelated to the change in assignment policy. I present three arguments against this concern. First, I extend the analysis to include data from the 2001–2002 academic years (“early tracking”), in addition to 2004–2005 (“late tracking”) and 2007–2008 (random assignment). I do not observe dormitory assignments in 2001–2002 so I report only intention-to-treat effects.<sup>38</sup> The raw data are shown in the first panel of figure 4. I estimate the effect of tracking under several possible violations of the parallel trends assumption. The average effect of tracking comparing 2001-2005 to 2007-2008 is -0.09 with standard error 0.04 (table 7, column 4). This estimate is appropriate if one group of students experiences a transitory shock in 2004/2005. A placebo test comparing the difference between Cape Town and non-Cape Town students’ GPAs in 2001-2002 and 2004-2005 yields a small positive but insignificant effect of 0.06 (standard error 0.05). I subtract the placebo test result from the original treatment effect estimate to obtain a “trend-adjusted” treatment effect of -0.18 with standard error 0.10 (table 7, column 6). This estimate is appropriate if the two groups of students have linear but non-parallel time trends and are subject to common transitory shocks (Heckman and Hotz, 1989). Finally, I estimate a linear time trend in the GPA gap between Cape Town and non-Cape Town students from 2001 to 2005. I then project that trend into 2007–2008 and estimate the deviation of the GPA gap from its predicted level. This method yields a treatment effect of random assignment relative to tracking of 0.14 with standard error 0.09 (table 7, column 5). This estimate is appropriate if the two groups of students have non-parallel time trends whose difference is linear. The effect of tracking is relatively robust across the standard difference-in-differences model and all three models estimated under weaker assumptions. However, there is some within-policy

> 38The cluster bootstrap standard errors do not take into account potential clustering within (unobserved) dormitories in 2001–2002 and so may be downward-biased. I omit the 2003 academic year because the data extract I received from the university had missing identifiers for approximately 80% of students in that year. I omit 2006 because first year students were randomly assigned to dormitories that still contained tracked second year students. The results are robust to including 2006.

29
Table 4: Impact on mechanism outcomes

||(1)<br>SRH<br>knowledge<br>(15 yo+)|(2)<br>Gender<br>attitudes|(3)<br>Aspirations|(4)<br>SES|
|---|---|---|---|---|
|T1 (safe spaces)|0.307**|0.208**|-0.183|-0.022|
||(0.121)|(0.092)|(0.134)|(0.113)|
|T1 + Leaders|0.207*|0.082|-0.161|0.247**|
||(0.115)|(0.105)|(0.120)|(0.113)|
|T2 (safe spaces + IGA)|0.312***<br>(0.119)|0.160**<br>(0.076)|0.127<br>(0.108)|0.248***<br>(0.090)|
|T2 + Leaders|0.183|0.071|-0.036|0.301***|
||(0.117)|(0.102)|(0.098)|(0.099)|
|T3 (safe spaces + husbands’ clubs)|0.348***<br>(0.120)|0.220***<br>(0.076)|0.012<br>(0.098)|0.191**<br>(0.092)|
|T3 + Leaders|0.226|0.204***|-0.005|0.459***|
||(0.147)|(0.069)|(0.158)|(0.105)|
|T4 (control spillover)|0.008|0.042|-0.029|0.120|
||(0.110)|(0.071)|(0.089)|(0.094)|
|T1 = T1 + L|.408|.273|.888|.0465|
|T2 = T2 + L|.3|.397|.189|.62|
|T3 = T3 + L|.402|.838|.922|.0199|
|T1 = T2|.97|.627|.0503|.0273|
|T2 = T3|.77|.464|.358|.569|
|T1 = T3|.735|.906|.195|.0838|
|T1 + L = T2 + L|.842|.933|.366|.667|
|T2 + L = T3 + L|.77|.192|.852|.188|
|T1 + L = T3 + L|.898|.254|.375|.0998|
|N|2,953|3,003|2,991|3,003|
|Control mean|0|0|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; SRH= sexual and reproductive health; yo = years old; SES = socio-emotional skills. The table presents estimated impacts of our 6 interventions’ combinations and the control spillove relative to the pure control group. The p-values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

21
Table 2. Demand Side: Determinants of Income Per Worker<sup>a,b,c</sup> (robust standard errors in parentheses)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||GMM-IV|OLS|GMM-IV|The effect of<br>increase<br>in covariate<br>based on(1)|
|Average Schooling|0.298***|0.280***|0.271***|0.375|
||(0.032)|(0.026)|(0.033)||
|Ln(marketpotential)|0.363***|0.048**|0.333***|0.365|
||(0.080)|(0.018)|(0.070)||
|Ln(no. workers)|-0.304***|0.005|-0.290***|-0.344|
|[ln(population)for(3)]|(0.095)|(0.016)|(0.079)||
|Ln(inter-citytransport costs)|-0.216*|0.016|-0.178*|-0.074|
||(0.112)|(0.032)|(0.092)||
|state capital dummy|0.019|-0.090|0.075||
||(0.146)|(0.062)|(0.144)||
|time dummies|Yes|Yes|Yes||
|Observations|369|369|369||
|R<sup>2</sup>||0.807|||
|Hansen J statistic<br>(overidentificationtest)|1.593||1.439||
|(p-value)|(0.661)||(0.696)||
|Average of Partial R<sup>2</sup>|0.435||0.425||
|Average of Partial F's|52.67||51.58||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, ln(distance to state capital), ln(distance to São Paulo), manufacturing/service employment ratio (1970),  infant mortality (1970), ln(humidity), average years of schooling (1970), state capital and time dummies.

- b. GMM estimates are from the two-step efficient GMM in the presence of arbitrary heteroskedasticity and arbitrary intra-group (within-state) correlation.

- c. OLS regressions are with robust cluster standard errors. We assume the observations may be correlated within states, but would be independent between states.

31