rafmacalaba/datause-encoder-data huggingface dataset:
Finally, the surveys in the GMD also contain a substantial amount of missing data for particular characteristics, either because of non-response by respondents, or because questions were not asked in particular surveys. If the variable is missing for a fraction of the national sample, we assume this results from non-response and/or errors during the data collection and cleaning process, and include that country when generating the global profile, in order to maximize the geographical coverage of our sample. In the latter case, when a question is entirely missing in a survey, the country is excluded when generating poverty statistics for that characteristic. Therefore, although the analysis draws on data from as many as 89 countries, the number of countries in the sample varies when considering different characteristics.

Missing data, however, is less prevalent in more populous countries. Therefore, both missing and unreported data generally account for a small share of the weighted global sample, as shown in Table 2. The main exception is sector of work, which is only collected in surveys from 64 of the 89 countries. Consequently, data exist on only about 82 percent of the employed adult population, with 8.7 percent missing due to non-response and 9.6 percent missing due to the complete absence of information in the survey. The poverty profile by employment sector reported in this paper, thus, should be interpreted with appropriate caution.

**Table 2: Missing data in key profiling variables**

||**Nu**<br>**cou**|**mber of**<br>**ntries**|**Share of**<br>**extreme**<br>**poor**|**Share of**<br>**moderate**<br>**poor**|**Share of**<br>**non‐poor**|**Share of**<br>**population**|
|---|---|---|---|---|---|---|
|f|Reported|87|100.0|100.0|99.9|99.9|
|Sector o<br>id|Non‐response|.|0.0|0.0|0.0|0.0|
|resence|Not in survey|2|0.0|0.0|0.1|0.1|
|Employment|Reported|64|68.5|78.2|84.6|81.7|
|sector|Non‐response|.|18.2|15.4|5.6|8.7|
|of working<br>adults aged 15+|Not in survey|25|13.2|6.4|9.8|9.6|
||Reported|89|100.0|100.0|100.0|100.0|
|Age|Non‐response|.|0.0|0.0|0.0|0.0|
||Not in survey|0|0.0|0.0|0.0|0.0|
||Reported|89|100.0|100.0|100.0|100.0|
|Gender|Non‐response|.|0.0|0.0|0.0|0.0|
||Not in survey|0|0.0|0.0|0.0|0.0|
|Educational|Reported|83|94.3|94.1|95.3|95.0|
|Attainment|Non‐response|.|4.3|5.2|2.3|3.0|
|of adults aged<br>15+|Not in survey|6|1.4|0.7|2.3|1.9|
|Educational|Reported|70|92.3|93.8|93.2|93.2|
|attainment of|Non‐response|.|6.3|5.3|1.9|3.5|
|children aged<br>12‐14|Not in survey|19|1.4|0.9|4.9|3.3|

8
**Figure 1. Trends of Income per capita and Gini Coefficients, RLMS 1994-2015**

<!-- Start of picture text --> 9.5 .5<br>Total income per capita<br>Ginni for income per capita<br>9 .45<br>8.5 .4<br>8 .35<br>7.5 .3<br>Log of income Gini coefficient<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. All numbers are deflated with December to December regional CPIs and weighted with population weights. The repeated cross sections are used for each year.

42
# **1 Introduction**

Firm capabilities are key drivers of productivity. Firm capabilities include both managerial and organizational practices, as well as innovation competencies. They include the skills that workers and managers bring to the firm. They are elements of the production process that cannot be bought “off the shelf” (Sutton 2012).

There is widespread evidence from across low, medium and high-income countries that firm capabilities are associated with higher productivity. Better worker skills increase productivity as well as generate productivity spillovers (Syverson 2011, Moretti 2004); firms that are better managed can leverage their technology, capital and workforce more effectively and achieve higher levels of production (Bloom & Van Reenen 2007, Bloom et al. 2014). Bloom, Sadun & Van Reenen (2016) argue that about a third of the observed differences in GDP across countries are related to differences in management practices.

Mozambique has seen two decades of continued positive GDP growth, mostly driven by gains in productivity. However, little of this growth in productivity was due to firms becoming more productive. Decompositions of labor productivity show that most of the gain in aggregate productivity in the last decade was due to on-going structural transformation, the movement of workers and factors of production from agriculture to higher value-added manufacturing and especially to services activities (World Bank 2018). Productivity improvements (also called “within” productivity growth, see Olley & Pakes 1996, Melitz & Polanec 2015) contributed to only a tenth of aggregate productivity growth in the manufacturing sector in Mozambique. In the services sector, the average firm productivity even declined in the period, contributing negatively to overall productivity growth (World Bank 2018).

Mozambique, just like many other developing countries, experiences a high degree of heterogeneity in productivity. The 2018 Enterprise Survey data shows that a formal firm (with five or more employees) in the 75<sup>th</sup> percentile of productivity is between 6 to 12 times more productive – as measured by sales per employee – than a formal firm in the 25<sup>th</sup> percentile. The corresponding figure in higher income economies is much lower. In the United States, a firm in the 75<sup>th</sup> percentile is less than twice as productive than a firm in the 25<sup>th</sup> percentile (Syverson 2004). The figure in Mozambique is also higher than the dispersion found in India or China (Hsieh & Klenow 2009). Taking into account that many enterprises in Mozambique operate in the informal sector and are 2
projected changes in sectoral composition, scale and ownership are sufficient to maintain or reduce total COD loads. For two cities - Beijing and Chongqing - COD discharges increase, but at a far slower rate than industrial output.

<!-- Start of picture text --> COM Loads Under oonordic Refonm COD Loads: 5% Annual Levy Increase<br>(Constant Levy, Loads In TonslYear)<br>200,000 2DO,ODO<br>1-- - - D----------. ........... l<br>ts400o 150,000<br>-_Bn _ Beijing<br>Q s \ Shanghai<br>100,o00 ' , < A9< Sh-,_> ................................. WV......... ¢ =y 100,00D0 t Shenyan<br>--- e--. .- qmg -~ Guangzhou<br>0 0<br>1sts 2co 2D0to 202D 1993 2000 2010 2020<br><!-- End of picture text -->

**Figure 24: COD Loads in Three Scenarios (cont'd)**

Our first (5%) levy scenario reflects a continuation of recent trends in water pollution regulation. The results for China are very encouraging: a projected reduction from 8.3 million tons to 3.3 million tons. In the five cities, a fourfold increase in the effective levy (coupled with the reforms) induces a COD load reduction of 66%. This amounts to over 120,000 tons annually in Shanghai alone. The imposition of a 10% annual increase in the levy leads to a fall in emissions from 8.3 million tons to 1.1 million tons nationally and 88% reductions for the five cities: 160,000 tons annually in Shanghai, 145,000 in Beijing; 60,000 in Chongqing, 55,000 in Guangzhou and 21,000 in Shenyang.

All three scenarios lead us to an optimistic view of the potential for solving China's industrial water pollution problem, at least in the case of COD. The best available evidence suggests that total COD loads will increase only modestly as economic reform proceeds, even if regulations are not tightened. Moreover, the projected results of higher pollution levies are quite striking. We project that a 10% annual increase in the levy through 2020 would eliminate most of the COD emissions from regulated factories in China.

<!-- Start of picture text --> Oao /lfloLebn *<br>; . _<br>SU ...<br>man A. awva<br>X G*h.<br>Chm-g*0<br>0o<br><!-- End of picture text -->

**Figure 24: COD Loads in Three Scenarios**

# **Air Pollution**

For TSP and S02, we project national and city emissions using the same approach:

- We use projected pollution levies and shares of large plants, SOE's, 'dirty' sectors and 'clean' sectors to project national and provincial pollution intensities. We assume that

26
type of occupation in Morocco. Its share has not dramatically decreased over the 1990s. The lowest value is 59.3 percent of the total workforce in 1993 (see Panel 5, Figure 3).

# **2.2.2 Public sector employment**

The public sector (central government, municipalities, and public enterprises) plays an important role in Morocco. It represented around 18 percent of Morocco's GDP in 2000. With about 635,000 employees (central government excluding the military, and employees of municipalities), equivalent to 15 percent of total urban employment-or 19.5 percent if we add public enterprises. As a result, wage policy in the public sector has important consequences on the functioning of the labor market. A recent study by the World Bank (2002) found that monetary compensation are 8 percent higher in the public sector than in the private sector. If we were to include non-pecuniary compensation, such as job security, a generous pensions fund, and a higher shirking rate, then the divergence between private and public sector wages would be even larger. Using the living standard measurement survey for 1998-99 to assess the value of such non-pecuniary compensation suggests that these features are on average equivalent to 60-100 percent of the monetary remuneration. This implies that for a corresponding nominal remuneration, a civil servant will get a global wage between 1.5 and two times higher than what he would obtain in the private sector. This outcome may explain the significant queues for employment in the public sector, particularly by skilled workers.

## **2.2.3 Unemployment**

As in many developing countries, in Morocco open unemployment is essentially an urban phenomenon (see Panels 4 and 5, Figure 2). In 2000, the urban unemployment rate stood at 21.4 percent, against 15.8 percent in 1990. This is equivalent, in 2000, to 1.1 million unemployed out of an active population of 5.4 million. Several facts reveal important tensions on the domestic labor market. The youth (15-24 years old) are particularly vulnerable to unemployment in urban areas, with a rate often well above 30 percent since the'early 1990s (see Panel 4, Figure 3). Whereas the unemployment rate for individuals with no instruction is somewhat low (12.2 percent in 2000) and fluctuates significantly in conjunction with economic activity, the unemployment rate is persistently high for individuals with a higher level of 9
country loses some of its most able individuals.<sup>9</sup> Alternatively, if the low-ability group acquires education, we obtain the same result as for individual heterogeneity (Section 2.2.1).

# 2.3. Unskilled Migration

Most analyses in the new brain drain literature examine the incentives to acquire education in the absence of migration and compare them to the incentives prevailing in the case of skilled worker migration. However, the reality is that out-migration of unskilled workers is substantial in most source countries, and their expected wage is higher under migration, just as is true for skilled workers.<sup>10</sup> Denote the migration probability of skilled (unskilled) labor by _p_ ( _q_ ), skilled (unskilled) variables by subscript _S_ ( _U_ ), and destination country variables by *. In the absence of migration ( _p = q_ = 0), the education benefit or skill premium is With a brain drain ( _p_ > 0, _q_ = 0), the expected benefit of education is

> 9 Stark (...) includes two groups in his model where, as assumed here, low-ability individuals do not acquire education when migration takes place, though high-ability individuals invest more in education when incentives improve. The model presented here assumes, as in most papers dealing with the brain gain, that individuals can only acquire a fixed amount of education.

> 10 The new data set on migration by educational attainment put together by Docquier and Marfouk (Chapter 5 in this volume), and which covers 174 countries for 1990 and 195 countries for 2000, indicates that, for 1990, the average migration share—i.e., the migration probability--of the middle and high-education groups put together is about _twice_ as large than in the low-education group. The recent immigration policy change favoring skilled migrants is reflected in the 2000 figures, with the share of the middle and higheducation groups about _2.5_ times larger than in the low-education group. Though the share of the middle and high-education groups is larger than that of the low-education one, _2_ to _2.5_ times larger is less than _infinitely_ larger, which is the assumption in the new brain drain literature where the share of migrants in the low-education group is set equal to zero.

11
We also document additional test score gains of 0.10 standard deviations for children in public schools in response to the intervention. Although public schools face few market or administrative disciplining mechanisms, social (nonprice) disciplining actions that the community can take may alter teacher effort and other inputs in these schools. Importantly, these test score gains did not differ by the baseline test scores of the public school (if anything, initially low quality public schools have smaller gains compared to initially high quality public schools), suggesting that the private school impacts were driven by the structure of the market equilibrium rather than a mechanical relationship between baseline test scores and subsequent improvements following the provision of information or simply due to changed parental investments.<sup>7</sup> Our study contributes to the literature on information and the quality of services (see Dranove & Jin, 2010 for a recent review in education and health sectors) by providing, to our knowledge, the first _market_ -level experimental evidence on the impact of information on market outcomes.<sup>8</sup> Banerjee et al. (2010) and Bjorkman and Svensson (2009) assess whether information leads to consumers demanding better services from public providers. Similarly, Hastings and Weinstein (2008) demonstrate parental responsiveness to information (when provided with school rankings, parents with high scoring alternatives nearby 7 The combination of test score and price changes suggests that schools altered their investment as a consequence of the report cards. However, test scores are jointly produced by school and household/child-level investments. Using household survey data, we can also examine parental involvement in education, time use and expenditure. We find no evidence of parental input changes (see Online Appendix Table VI). We also study changes in school inputs (also see Online Appendix Table VI). While we find no evidence of changes in teacher quality or indices of infrastructure in private schools, we do find evidence of reduction in break time in low-achieving private schools. This is plausible given the context of constraints in teacher supply and limited financial resources, which imply that changing teachers or improving infrastructure may be less feasible; however, low-achieving schools may still be able to increase quality relatively cheaply by reducing break/play time in order to increase overall instruction time.

8 A second class of papers looks at the impact of information in markets where there is uncertainty over supply and demand. Jensen (2007), for instance, shows that the introduction of cell phones leads to price convergence across different markets for fish, because fishermen are better able to choose the market that they take their catch to.

5
<!-- Start of picture text --> Southafrica: Value Added per Worker (log)<br>Manufacturing<br>0.20<br>0.01<br>0.01 0.10<br>0.01 8<br>000 5=oc<br>0.01 =é<br>3<br>0.01 wl<br>0.01 0.20<br>iS = a) 2 St<br>8 & 8 8 8<br>—— Aggregate Productivity [NNN Covariance<br>employment weights<br><!-- End of picture text --> <!-- Start of picture text --> ;<br>Southafrica: Value Added per Worker (log)<br>Construction<br>0.00 0.20<br>000<br>0.10<br>0.00 000 =8oc<br>Sg<br>0.00 3}<br>3<br>-0.10<br>0.00<br>0.00 -0.20<br>° = a to) x<br>R R R R R<br>—— Aggregate Productivity [EEN Covariance<br>employment weights<br><!-- End of picture text --> <!-- Start of picture text --> Southafrica: Value Added per Worker (log)<br>BusinessFinance<br>0.00 0.00<br>0.00<br>0.10<br>0.00 o2<br>120 =<br>0.00 3<br>(6)<br>0.00 0.30<br>0.00<br>0.40<br>e = Ss 2 &<br>& & & & &<br>——— Aggregate Productivity [ENN Covariance<br>Employment weights<br><!-- End of picture text --> <!-- Start of picture text --> Southafrica: Value Added per Worker (log)<br>Commerce<br>oot 020<br>0.01<br>010<br>0.00 8<br>000 ©<br>0.00 g<br>. 8<br>0.10<br>0.00<br>0.00 0.20<br>S = sy @ sf<br>& & & 8 &<br>—— Aggregate Productivity [NMI Covariance<br>employment weights<br><!-- End of picture text --> <!-- Start of picture text --> Southafrica: Value Added per Worker (log)<br>0.00 OtherServices 0.20<br>0.00 0.05<br>0.00 -0.10 3<br>8<br>0.00 0.25<br>0.00 -0.40<br>= = ~ 2 =<br>& & & Rg &<br>—— Aggregate Productivity [EMM Covariance<br>employment weights<br><!-- End of picture text --> <!-- Start of picture text --> Southafrica: Value Added per Worker (log)<br>TransportStorageComm 0.20<br>0.00<br>0.00 010<br>0.00 ooo E<br>0.00 8g<br>0.10<br>0.00<br>0.00 -0.20<br>2 a aS 2 =<br>& & 8 & &<br>—— Aggregate Productivity [NM Covariance<br>employment weights<br><!-- End of picture text -->
the scores are based on citations to a document by other documents in the same database. SSCI covers primarily journal articles and is limited to about 2,500 social science journals. SCOPUS covers over 18,000 titles covering all disciplines including 16,500 peer-reviewed journals, as well as some book chapters, including the North-Holland handbook series. The RePEc database currently covers 280,000 working papers, 900 journals, 2,800 book chapters, 2,400 books and 1,700 software components; however, the citation data are drawn from a subset of these.

By contrast, Google Scholar includes journal papers, conference papers, technical reports, pre-prints, post-prints, and abstracts, as well as books and dissertations lodged in the Google Books database.<sup>2</sup> This means we are able to get from Google Scholar citation data not just for journal articles (and some book chapters) by Bank authors, but also for books, book chapters and working papers. It also means we are able to get citation data for a broader set of journals including open-access journals, and new and less well-established journals. The comprehensiveness of Google Scholar not only allows us to get citation data for a larger subset of Bank publications, but also to get citations to these publications in a broader subset of other <u>people’s</u> publications. Through Google Scholar we are able to probe for influence beyond a narrow set of established journals. Google Scholar also has the merit of being “global” in its reach, including research outputs from everywhere in the world and in multiple languages. Google Scholar is also timelier than SSCI, as it does not wait for formal publication processes to be completed; this is important given how long it typically takes for an economics research paper to appear in print (Ellison, 2002). These differences can matter greatly to citation counts. For example, Adler and Harzing (2009) give the following (possibly rather extreme) example: “The world’s most cited computer scientist, Hector Garcia-Molina, has gathered nearly 30,000 citations in Google Scholar, with most of his papers having been published and cited in conference proceedings. In the Science Citation Index, however, Garcia-Molina only receives slightly more than 250 citations, as Thomson Reuters ISI fails to appreciate the importance of timeliness, and chooses not to recognize citations in conference proceedings.” Against these advantages of Google Scholar there is one obvious drawback: data quality. The typos and other mistakes in the internet sources complicate the task of tracking down citation numbers and are visible in the (otherwise neat) tabulations and summary statistics produced by POP. For example, according to POP, one of the authors of this paper, Ravallion,

> 2  Google Scholar searches “journal papers, conference papers, technical reports, or their drafts, dissertations, preprints, post-prints, or abstracts” and does not cover “news or magazine articles, book reviews, and editorials”. See <u>http://scholar.google.com/intl/en/scholar/inclusion.html.</u>

3
# _5.2 Recurrence_

In empty multilevel models of instability, 41% of the natural variability of recurrence is due to variation between states. Basic logit models for this sample disqualified a number of independent variables found significant in models for all civil wars. These effects are confirmed by Multilevel models; domestically, only instability, population, ethnic fractionalization, and anocracy are positively and significantly related to conflict recurrence (see Table 5). Neighboring anocracies and income interacted with autocratic neighborhoods are weakly related to higher risks of civil conflict at particular income points. Overall, these models are not particularly good predictors of civil war recurrence. Since recurrence is approximately 40% of all civil wars in the sample, models 1-4 are perhaps only explaining correlates to a portion of all wars.

There is a remaining 13%-17% of unexplained country level heterogeneity across models 5- 7. States differ significantly in their recurrence risk determinants. Models 5-7 are also run controlling for select regions (Africa, South Asia, etc); these controls are highly insignificant. Because the model is a poor fit, and considerable heterogeneity, figures such as those for the models 1-4 do not display significant differences across regime neighborhoods.

**Table 5: Recurrence Models**

|_Variables_|_Model 5_<br>_Level 1 N:6820_<br>_Level 2 N: 161_<br>_-2ll: -465_<br>_Dep Var: Recur_|_Model 6_<br>_Level 1 N:6820_<br>_Level 2 N: 161_<br>_-2ll: -461_<br>_Dep Var: Recur_|_Model 7_<br>_Level 1 N:6820_<br>_Level 2 N: 161_<br>_-2ll: -459_<br>_Dep Var: Recur_|
|---|---|---|---|
|All Neigh.Wars|.345(.238)|.309(.235)|.292(.233)|
|Instability|1.00(.234)***|.961(.236)***|.852(.239)***|
|LGDP|-.117(.192)|.141(.238)|.068(.22)|
|Lpop|.273(.083)***|.257(.081)***|.267(.081)***|
|Terrain|.077(.096)|.060(.093)|.052(.092)|
|Oil|.362(.318)|.526(.321)|.495(.318)|
|Eth. Frac|1.55(.507)**|1.43(.501)**|1.34(.494)**|
|Polity|-.0013(.026)|-.0125(.025)||
|Polity(2)|-.0044(.0055)|-.004(.005)||
|Autocracy|||.080(.333)|

25