rafmacalaba/data-use-mentions-extended huggingface dataset:
# <u>Introduction</u>

In a recent issue of the _Quaderni di Scienza Politica,_ Roberta Maffio published a comparative analysis of parliamentary oversight<sup>3</sup> in which she elaborates a conceptual mapping of the oversight tools, discusses the characteristics of oversight tools, and investigates whether the adoption of oversight tools by parliaments is related to the _model_ of democracy (i.e. majoritarian, consensual, or mixed)<sup>4</sup> .

The analysis between type of democracy and oversight potential was conducted in a sample of 24 countries. Twenty-one of these countries are the democracies investigated by Lijphart (1984),<sup>5</sup> while the other three are Spain, Portugal, and Greece. Maffio divided the 24 countries into three groups depending on whether their government was majoritarian, consensual, or mixed. She then estimated the oversight potential for each country, using the 1986 data of the Inter-Parliamentary Union (IPU)<sup>6</sup> .

Maffio concluded that there is no correlation between model of democracy and oversight potential. “There are some majoritarian democracies with strong oversight potential (such as Greece) and others with weak oversight potential (such as Ireland, New Zealand, United Kingdom).” Similarly, among consensual democracies, she found cases

> 3 Roberta Maffio, “ _Quis custodiet ipsos custodes?_ Il controllo parlamentare dell’ attivitá di governo in prospettiva comparata”, _Quaderni di Scienza Politica_ , vol. IX, n. 2, (Agosto) 2002, pp. 333-383.

> 4 Roberta Maffio, “ _Quis custodiet ipsos custodes?_ Il controllo parlamentare dell’ attivitá di governo in prospettiva comparata”, _op. cit._ , p. 359.

> 5 Arend Lijphart, _Democracies. Patterns of Majoritarian and Consensus Government in Twenty-One Countries_ , New Haven, Yale University Press, 1984.

> 6 Inter-Parliamentary Union, _Parliaments in the World: a Comparative Reference Compendium_ , Aldeshot, Gower, 1986
From an analytical point of view, an important difference between this round and earlier negotiating rounds has been the availability of much more detailed data on trade and trade barriers, and much greater capability for quantitative analysis, which allowed researchers to keep up with the details of agreements as they evolved and to provide assessments at critical times. By contrast with the Uruguay Round, when data on actual tariffs and trade policies became available only for a limited number of countries, and only far after the event, analysts had access to much more detailed information and analytical tools during the Doha negotiations. Important contributing factors to this difference included detailed tariff from the CEPII-ITC MAcMAPs project (Boumellassa, Laborde and Mitaritonna 2009); data on all agricultural protection from the OECD (2011) and the Anderson (2009) study; and analytical databases and models building on the Global Trade Analysis Project (GTAP) (see Hertel 1997 and www.gtap.org).

Unfortunately, the complexity of the negotiating proposals grew even more quickly than the ability of researchers to analyze them, making it a serious challenge to keep up with the details and to form an assessment of their implications (for some attempts, see Hufbauer, Schott and Wang 2010; Decreux and Fontagne 2009; Laborde, Martin and van der Mensbrugghe 2012; and Martin and Mattoo 2011). Further, communicating the results of research is not necessarily easy. When the estimated economic value of a Round was revised downwards in line with changes in trade and protection data--and with evident reduction in the likely achievement of the round in terms of trade barrier reduction—the reasons for the changes in estimates needed to be very carefully explained (van der Mensbrugghe 2006).

Our focus is primarily on the exchange of concessions on protection and subsidies that is central to the negotiations. As a Development Round, the Doha Agenda also includes elements such as Aid for Trade (Taylor and Wilson 2011); Trade Facilitation (Hoekman 2011) and the 3
using any functional forms on the weather indicator (Dell et al., 2014). Some studies use measures of variability changes or anomalies in weather realizations from the geographyspecific long-run distributions, where the latter help detect irregular patterns in weather conditions (Hoffmann et al., 2021).<sup>16</sup> We create weather anomalies based on the 15-year national distributions of the growing season weather, characterized by their mean and variance, for our cross-sectional analysis. We difference the 2018 growing season realization of the weather indicator at the cluster level from the national 15-year growing season mean and scale it by the country’s 15-year growing season standard deviation: where weather value is observed for a cluster, _c_ in district _d_ of province _p_ in 2018 growing months ( _g_ ). The 15-year national mean and standard deviation is constructed for the growing months _g_ .<sup>17</sup> Note that such construction allows a measure of irregularities from the long-run weather conditions in the country: level changes in a cluster matter in proportion to the national variation in weather. In so far as 2018 was dubbed one of the worst in two decades, this measure should also capture the drought-like conditions in 2018. Moreover, by eliminating scale effects, this measure allows us to rank clusters on a common scale and retain the spatial variation in the intensity of deviations from the ‘usual’ weather, which is the source of variation we exploit in our analysis.

# **3.6 Weather Anomalies in 2018**

In Figures 2, 3 and 4, we map the rainfall, NDVI and temperature anomalies from 2018, respectively, at the district-level. The maps highlight the spatial differences across districts, with some districts drier, hotter, or with less vegetation than others.

Figures 5, we plot the distributions of growing season weather anomalies in 2018 (a declared drought year) and 2019 (a typical weather year). In Figure 6, we also plot the

> 16In this approach, the weather
ion Project (LAPOP) in 2004–2016 for outcomes related to trust and engagement.<sup>26</sup> We used the data to construct four indicators of political attitudes and behaviors including: political participation, engagement with politicians, nondemocratic engagement, and trust in institutions (see more details in Appendix A).

Although individuals living in former FMLN areas are not less likely to participate in politics or to engage violently with politics (Columns 1 and 3), they exhibit less engagement with politicians and less trust in institutions (Columns 2 and 4). We also find evidence of more trust towards members of the community in these areas, providing further evidence on how former guerrilla governance may have reinforced social capital within the community and distrust in the state.<sup>27</sup> Consistent with the lack of trust in politicians and the state, Table D.12 shows that residents of former FMLN areas were more likely to cast blank votes in the 2014 presidential elections and the 2015 municipal elections. This effect is small given that on average only a low percentage of people turn in blank votes (one percent at each polling station). We find that, if anything, residents of former guerrilla areas were less likely to vote for the left. However, these votes did not seem to benefit the right. For example, in the 2015 municipal elections, the share of blank votes increased at the expense of both the left and the right.<sup>28</sup>

> 26LAPOP conducts surveys of public opinion throughout the Western Hemisphere, including North, Central, and South America and the Caribbean. LAPOP’s core project is the AmericasBarometer, a rigorous comparative survey of political and social attitudes and demographic and economic characteristics.

> 27As we show in Table D.13, results are robust when we use the simple sum of questions related to each outcome instead of the inverse covariance index as in Table 4.

28During the (post-conflict) 1989–2009 period,
for job search increased the likelihood that a previously unemployed found a job by 10 percentage points—a particularly large increase, given that the mean of the dependent variable is 0.038.

The results presented in Table 4 may be concealing heterogenous effects. In Table 5, I therefore investigate heterogeneous effects by gender, area of residence, age, and education. I estimate separate regressions for men and women in columns (1) and (2), respectively. Indeed, the results in Table 5 suggest that Internet job search is effective only for the subsample of unemployed men, as I do not find any significant effect for the subsample of unemployed women in 2012. It is worth mentioning that the magnitude of the estimated coefficient for men is much larger, as I find that using the Internet for job search is associated with a 15 percentage points increase in the probability of employment within 5 years among unemployed men in 2012. The differential impact of Internet job search by gender might be due to several potential reasons. There are structural differences between men’s and women’s unemployment and employment in Egypt and these differences viewed together help rationalize the differential results obtained by gender. First, marriage is concieved to be one of the main barriers to women’s employment in Egypt, as women find it difficult to find jobs that help them combine work and family life. As shown, in the descriptive statistics, the incidence of marriage among unemployed women is found to be higher than among unemployed men. This reasoning brings in the second argument linked to the importance of public sector employment for women. Egyptian women have a strong preference for the public sector, which offers flexible working hours and allows them to more easily balance work and family. This is reflected in the descriptive statistics on the search methods employed by women, which show 11
alternative clusterings of the standard errors, including robust standard errors, allowing them to be clustered by project type within organization (so at the _jn_ level), by project type within sector, and by sector. In all cases, the coefficients of interest, _γ_ 1 and _γ_ 2, remain precisely estimated and statistically di↵erent from zero at conventional significance levels ( _p < ._ 05 throughout).

# **3.3 IV**

Before digging deeper into what drives the _negative_ partial correlation between project completion rates and incentives/monitoring of bureaucrats, we first present additional evidence that this might represent a causal relationship. A chief concern is that management practices are endogenous to organizational performance, so the negative result could reflect that if an organization has low project completion rates, it increases the provision of incentives/monitoring to its bureaucrats.

We address such reverse causality using an IV strategy that exploits the fact that similar interviews on management practices had previously been undertaken for a subset of the same organizations in 2013, by one of us in a separate and independent study [Williams 2015]. While these interviews were primarily qualitative and on a much smaller scale (based on one or two senior bureaucrats in each organization), they also took a similar semi-structured format and adapted the approach of BSVR, coding the quality of each management practice on a 1-5 scale, with overall organization z-scores compiled in the same way.

Table A4 details the questions on management practices administered in the 2013 interviews. While the same six topic areas were covered as in our 2015 survey, there is a far more limited set of questions on the topic of bureaucrat roles, which meant it was not possible to reconstruct a measure of CS-autonomy from the 2013 data. We therefore focus on exploiting the earlier measure of management practices related to the provision of incentives/monitoring, as measured two years prior to the main management and project completion measures used for the core analysis. We instrument current practices related to incentives/monitoring with the historic practices
percent have only one or two workers, and only 6.2 percent have five to nine workers.<sup>1</sup> Likewise, among manufacturing firms in Tanzania, only 8.9 percent of firms with less than 10 workers have 5 to 9 workers, whereas 60.8 percent have 1 or 2 workers. Based on Table 1, these numbers imply populations of around 770 to 2150 manufacturing firms with 5 to 9 workers. This is about the same size as the number of manufacturing firms with 10 or more workers, which is in the 1000 to 2000 firm range for most countries in the table. Once one starts getting towards large firms, with 100 or more workers, the population sizes in most African countries get quite small – there are 80 of such firms in Tanzania, 114 in Uganda, and 251 in Ghana according to this data. Some policies are targeted just at exporting firms. Mauritius is the only one of the countries in Table 1 for which data on the number of export-oriented firms is available, reporting 386 such firms in 2010.<sup>2</sup> Second, note that there is considerable heterogeneity in what firms produce within the broad category of manufacturing. For example, of the 2,203 Ethiopian manufacturing firms with 10 or more employees, 562 were involved in the manufacture of food products (itself a heterogeneous category), 127 in paper and paper products, 363 in furniture, 47 in textiles, 87 in rubber and rubber products, 75 in chemical and chemical products, and the remainder in a variety of other products. As a consequence, the number of medium and large firms in most African countries in any given manufacturing sector is likely to be relatively small.

# **2.2 Fact 2: There is considerable heterogeneity in firm performance among these firms.**

There is also tremendous heterogeneity in firm outcomes. Table 2 uses the World Bank Enterprise Surveys to illustrate the variation among firms in three key outcomes that policy
they could have been relatively more impacted by this economic disruption because womens’ labor force participation rates are higher than for men up until age 30 (this is confirmed in our pre-intervention census). Young women are especially reliant on self-employment, and were more exposed to agriculture (where 54% of working women are employed).

The schooling policy response had particularly acute impacts on young women. The loss of one year of human capital accumulation is non-trivial given their lower levels of human capital to begin with. For example, women aged over 24 have 1 _._ 8 years of completed schooling in our baseline census, while men have completed 4 years on average. As schools reopened, given the formal ban on pregnant girls, potentially large numbers of adolescent girls might have been unable to re-enrol, irrespective of their actual pregnancy status. This lower level of human capital accumulation worsens lifetime labor market earnings, and such schooling policies might also increase social

> 13In May 2015, it was announced that an alternative ‘bridging’ education system would be established to allow pregnant girls to continue schooling, but in different premises or times to their peers. Other temporary measures, such as community learning centres and home-based approaches, were also implemented. At best, this bridging system varied in effectiveness, and did nothing to help pregnant girls find an alternative way to take national exams.

> 14In absolute terms, this corresponded to 79 doctor, nurse, and midwife deaths. By the end of November there had been a further 179 confirmed Ebola cases among health workers.

9
confirms the deteriorating economic conditions associated with structural weaknesses and the crisis itself.

<!-- Start of picture text --> Chart Kl Chart K2<br>INDEX OF INDUSTRIAL PRODUCTION SHARE PRICES<br>1<br>99 I3 ,.932 0030 00S7 90;9 90 ' 97i. ' 9 7b3 90709 ,97;7 9700 *i*i g 90 6 90 3 ,- IL '0~ 9&09 9..O* 9&00 00I tObi 97 00 97~S3. 97S7 *i3. o "so i<br><!-- End of picture text --> In order to both combat the inflationary pressures arising from won depreciation and restore credibility of its foreign exchange and financial markets, the Central Bank has liberalized and raised interest rates in January 1998. The three-year benchmark bond yield peaked to 22% in the middle of January from **12%** on the year-earlier period. In the meantime, banks reportedly became reluctant to lend and started calling in loans that would have been rolled over in different circumstances.

The evidence from monetary and credit aggregates are not clear-cut on whether a credit crunch occurred in Korea. On the one hand, growth rate of real loans remained positive (Chart K3), though there has been a downward trend since March 1997 and there was a moderate downward trend in banks' reserves in 1997, after the major reduction in 1996 induced by the lowering of the reserve ratios (Chart K4). On the other hand, real money market interest rates -- as long as we consider CPI actual inflation -- rose substantially by the end of 1997 (Chart K5).

20
8 increase efficiency given the expected contemporaneous correlation in the residuals of each equation .8 Lastly, by pooling the data we minimize sample size problems.

The specification is as follows<sup>9</sup> :

**_In_** _n p_ **_p q_** A4Y, _= Z/, 1_<sup>_A_</sup> _4_<sup>_Y,,_</sup> _4_<sup>_+Za_</sup> _1_<sup>_A_</sup> _4_<sup>_TO1I,,+÷ JVUS1i,_</sup> _+±S CA1, G j + L AjA4SPD, j_ /=1 _,j=0 j=0_ 1=0 _j=O +± )jISO, 1 +C,_ .J=0

Where, Y},: Output _TOTit:_ Terms of Trade _USIi,:_ US Federal Funds real interest rate. CAIt: Current Account Industrial Countries (millions of US dollars at 1996 prices). _SPDit:_ Non Latin lrady Bond Sovereign spreads. _ISOit:_ Southern Oscillation Index. Cj: Constant term. And, A4: year-over-year variation _Subscript i:_ ith country Estimation results (Appendix 1) show that coefficients are significant and with the expected sign: terms of trade improvements and better climatic condition (as measured by the ISO index) have a positive effect on growth whereas higher external interest rates and spreads have a negative effect, as does a lower availability of funds.

The estimated coefficients are then used to calculate 1998 and 1999 growth rates under two scenarios: _Actual_ (external variables take actual values) and _Constant_ (assuming external variables remain at their 1997 average level). That is, we estimate the N-step forecast under these two alternative external conditions, and the difference between the two is the effect of the external factors on growth. For the region as a whole, we calculate that external factors account for 4.6 percentage
br>4.1|30.26|
|6 20460 75|Corn gluten feed|||4,199.8|<br>226.6|53.95|
|12 20460 77|Corn gluten meal|||1,000.7|<br>122.2|122.11|
|13 20460 79|Other wet process corn byproducts|mill.|lb.|1,564.0|<br>117.9|75.38|
|20460 00|Other wet corn mill products. nsk, >-15 empl|.|||7.8||
|20460 02|Other wet corn mill products, nsk. <15 empl.||||6.4||
||TOTAL SPECIFIED 2041.2046||||5.624.7||

Source: US 1977 Census of Manufactures; Note: (a) Figure included under the figure for the total category 20411
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- Lamanna, C., Hachhethu, K., Chesterman, S., Singhal, G., Mwongela, B., Ng’endo, M., Passeri, S., Farhikhtah, A., Kadiyala, S., Bauer, J.-M., et al. (2019). Strengths and limitations of computer assisted telephone interviews (cati) for nutrition
Adamawa (these statistics are not displayed in any table).

We use these data to build variables that count the number of Boko Haram attacks

> attacks nor distant Fulani attacks. Results are available upon request.

> 8In order to ensure that respondent confidentiality is maintained, the GPS latitude/longitude points are randomly displaced. Urban clusters are displaced from 0 to 2 km while rural clusters are displaced from 0 to 5 km, with an additional 1 percent displaced from 0 to 10 km (https://dhsprogram.com/Methodology/GPS-Data.cfm). Given that the displacement of household clusters is orthogonal the number of distant attacks, it does not cause inflated results. It may, however, introduce measurement error in our key explanatory variables which can yield to attenuation bias—i.e., conservative point estimates.

> 9There are other data sources for violent events—for example, the Uppsala Conflict Data Project. For the purposes of this study, however, ACLED has the advantage of collecting data on violent events perpetrated by communal militias (specifically, Fulani) that other sources do not. Further, in our study interval, the ACLED data set has better coverage than that of the Uppsala Conflict Data Project, in terms of both number of events recorded and geographic coverage (Clionadh and Roudabeh, 2019).

15
Policy Research Working Paper 10065

# **Abstract**

The information contained in the Enterprise Survey— administered by the World Bank (WB), the European Bank for Reconstruction and Development (EBRD), and the European Investment Bank (EIB)—is used to build a firm-level “Corporate Environmental, Social, and Governance Responsibility” composite indicator. The novelty of the indicator, compared with the corporate social responsibility scores and environmental, social, and governance (ESG) scores already available, is due to its unique coverage, namely, a large number of private sector small and medium-size enterprises in selected emerging economies (more than 40 economies covered by the Enterprise Survey). The composite indicator summarizes information on private sector actions on environmental, social, and governance factors. The analysis shows that the actions of private sector small and medium-size enterprises in emerging economies to foster sustainability and green growth significantly lag in the transition to a more sustainable business environment, and large gaps persist. Among emerging economies, those in the Middle East and North Africa—which deserve special attention due to the urgent need to green their growth model—are among the worst performers. Larger companies in the Middle East and North Africa show better environmental, social, and governance performance than small and medium-size enterprises in other areas; but smaller firms in the Middle East and North Africa show extremely weak performance in many aspects, even if controlling for the relative level of economic development. The weakness of environmental, social, and governance practices among firms in the Middle East and North Africa is due to the social (with large gaps in female participation in the workforce and management) and environmental topics. This calls for urgent policy action to address such weaknesses and exploit the full potential of the region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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