rafmacalaba/datause-encoder-data huggingface dataset:
studying the task content of jobs. For example, Autor and Dorn (2013) and Goos, Manning, and Salomons (2009) document the process of employment and wage polarization affecting labor markets in the US and Europe since the 1980s and 1990s. This process is characterized by job and wage growth being higher at the tails of the skill and wage distribution than at the middle. They argue that new technologies that allowed the automation of routine jobs (which tend to be in the middle of the wage distribution) and increased the demand for non-routine tasks (which tend to be at the top and bottom of the wage distribution) fostered this process.

There is also a growing and large body of research on the task content of jobs in developing countries. Even for developing countries, these studies use US-based skill measures such as the O*NET or other broader occupational categories. Using a broad occupational classification, World Bank (2016) shows that labor market de-routinization is pervasive in the developing world. In comparison, studies that used more detailed data on skills show a more nuanced picture. Hardy, Keister, and Lewandowski (2016) find that in contrast to the US, jobs that are intensive in middleskill, routine, cognitive tasks increased in most Central and Eastern European countries. They also find that improvements in educational attainment and a decline in the share of agricultural jobs, rather than technology, were the main drivers of these changes. Accordingly, Apella and Zunino (2017) find that the evolution of the skill content of jobs in Argentina and Uruguay was more similar to that of Central and Eastern European countries than to that of rich countries. Maloney and Molina (2016) use the same aggregate classification of World Bank (2016) and find that only in two, out of twenty-one, developing countries there is evidence of labor market de-routinization. Aedo et al. (2013) estimate trends for 30 countries at different stages of development and find that the share of jobs intensive in non-routine, cognitive tasks is higher in richer countries.

To our knowledge, there are only three studies that use data on the task content of occupations from developing countries instead of relying on data from the US. Dicarlo et al. (2016) use data from STEP surveys to determine if the skill content of jobs is different from that suggested by USbased skill surveys. Messina, Pica, and Oviedo (2014) analyze trends in the task content of jobs in four Latin American countries but do not investigate the drivers of such trends. Finally, Hardy et al. (2018) investigate the task content of jobs using country-specific skills surveys for 46 economies, mostly in the developed world. They analyze if the findings are different from those obtained when using US data from O*NET and investigate the drivers of the heterogeneity in the 4
gravity models, trade volumes are negatively related with the distance between trading partners (Anderson, 1979; Evenett and Keller, 2002; Anderson and van Wincoop, 2003). Interestingly, GVC activities are highly clustered around three major manufacturing hubs worldwide: Germany, China and the United States (Baldwin and Lopez-Gonzalez, 2013). Kowalski et al. (2015) provide evidence that the farther a country is from these hubs, the lower its GVCs engagement. In contrast to Kowalski et al. (2015) who used the shortest distance from a given country to the three major manufacturing hubs, this study uses the average distance between a given SSA country and the three major hubs above. This is because African countries are increasingly diversifying their trading partners toward emerging countries such as China, Brazil, and the Russian Federation. This is demonstrated by the increasing trade volumes between SSA and China over the past two decades even though China is relatively remote from SSA. Because of the strong presence of China in Africa, using the closest manufacturing hub (which de facto is Germany) will exclude China, potentially making our results biased. We therefore use average distance between SSA countries and the three major hubs. Finally, as widely used in gravity models, we also include GDP per capita as a proxy of countries’ economic mass.

The GVC participation equation defined in equation (1) is the sum of backward and forward participations. Consequently, we can write GVC participation as a function of X GVC Participation��� � f�X���; X��; ε���� �6� where is an error term. Based on the discussions above, a fixed-effect estimation technique is used to estimate equations (4) to (6) accounting for sector, year, and country group fixed effects. The fixed-effects estimation technique is widely use because it yields consistent estimates without strong structural assumptions (Head and Mayer, 2013). Equations (4) to (6) are therefore estimated focusing on the effects of skills, migration, human capital endowment on GVC participation. As discussed previously, skilled labor data are derived from the GTAP IO tables and skill mobility is proxied by net migration-to-population ratio to analyze the impact of foreign labor contribution on GVC activities. For comparative purpose, we independently estimate equations (4) to (6) for SSA countries and for the rest of the world. This is done to assess if the effects of skills and migration on GVC participation in SSA are any different from what is observed in other parts of the world. To the best of our knowledge, this study is the first to assess the impacts of skills, migration, human capital endowment on the integration of SSA countries in backward and forward GVCs.

16
**_Energy demand models for policy_** **_<u>formulation</u>_**

availability specified by the users. The model identifies the optimal feasible configuration that would ensure least-cost supply of energy to satisfy the demand.

The model covers the entire energy system – from energy resources to end-uses through energy conversion processes. Like other bottom-up models, the model provides a detailed technological representation of the energy system and can be used to analyse the environmental effects as well. The building blocks of the standard model are indicated in Fig. A1.1 below.

# Fig. A1.1: MARKAL building blocks

<!-- Start of picture text --> Resources Processes Generation Energy<br>Services<br>Import Refineries<br>Electricity End Use D<br>Fuel e<br>Mining processing Heat m<br>a<br>n<br>Emissions d<br>Control s<br>Export Stocks<br>Devices<br><!-- End of picture text --> Source: Seebregts et al (2001) The original model has been extended in various ways and now a family of MARKAL models exists. Table A1.2 indicates some such MARKAL extension efforts. The model is the outcome of an international collaborative effort under the International Energy Agency’s Energy Technology Systems Analysis Programme. Although the model was designed initially for the mainframe computers, the PC version of the model is now available. A database of technological choices is now available with the model.

131
# **“Create” or “Buy”: Internal vs. External Sources of Innovation and Firm Productivity**

By Jieun Choi<sup>1</sup> _Key words_ : innovation, R&D, royalties, productivity, knowledge transfer, firms, Tobit model _JEL codes_ : D24, L16, L24, M11, M16, O14, O25, O32, O38 1 The World Bank. The authors may be contacted at jieunchoi@worldbank.org. Special thanks to Xiaolan Fu and Christopher Adam for their valuable comments and advice. Thanks to the participants of CASE conference, WBG Trade and Competitiveness Group seminar, Marie Francoise Marie-Nelly, Paulo Correa, Denis Medvedev, Xavier Cirera and Afef Haddad for their thoughtful comments. I gratefully acknowledge the Tunisian Institut National de la Statistique for allowing me to access to L'Enquête Nationale sur les Activités Économiques dataset which this paper was based.

Disclaimer: Policy Research Working Papers are posted on the Web at <u>http://econ.worldbank.org. The</u> 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 author and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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.
O.A.M.D.G.

Households in violent regions are more affected than those in peaceful municipalities. About 10% of households in peaceful areas have lost at least one member, compared to 14% in those with a public order problem. Neither education nor asset wealth appears to be correlated with migration. However, we do observe a positive relationship between migration and violence at the municipality level.

The measurement of savings with the Familias survey is not straightforward. Of the income measures available, only the salary is reliable, as most enterprises in SISBEN 1 do not have any form of accounting. This obviously does not capture the full household income: 20% of the households do not report a salary, but conduct a revenue generating enterprise. And of the 80% waged, 38% run a business on the side. However, we have comprehensive data on the asset stocks held by households, including real estate, vehicles and durables, and their respective prices from 2001. Additionally, we have enquired the productive lives of the different assets, with people from SISBEN 1 and manufacturers/breeders. They are reported in Data Table 2.

price of the asset in the current period, n is the productive lifetime in years, _Di_ is the net _r_ benefit proceeding from the asset, and _i_ the cost of capital in year i from now, all real terms. We assume a constant real payoff from the asset, as well as a constant real cost of capital, and approximate the latter with the Colombian average real credit interest in 2001. This formula is used for assets with an estimable productive life. Bank deposits are assumed to earn the average real 2001 deposit interest. The estimated yearly payoffs are

> 10 See e.g. Ross, Westerfield and Jaffe (1996)
(57,003 square kilometers), where there is a potential for mangroves to migrate inland with a 1‐ meter SLR (Table 3). Another 28 percent of mangroves fall into categories WMP3 and WMP4 (22,753 square kilometers), in which climate change will seriously compromise the existence of mangroves. Category 4 mangroves account for the 18 percent of mangrove area where survival of mangroves is possible, but at risk depending on local conditions. Category 3 mangroves account for the remaining 9 percent; these mangroves are the most vulnerable to SLR and are likely to be lost.<sup>15</sup> Table 3. Mangrove area and wetland migratory potential by region

||Total<br>mangrove area<br>(sqkm)|Mangrove area<br>(Percen<br>WMP2|by Wetland Migra<br>t of total mangrov<br>WMP3|tory Potential<br>e area)<br>WMP4|
|---|---|---|---|---|
|East Asia & Pacific|45,119(57%)|34,770(77%)|8,795(19%)|1,554(3%)|
|Latin America &<br>Caribbean|20,636 (26%)|8,830 (43%)|6,053 (29%)|5,703 (28%)|
|South Asia|8,803(11%)|8,181(93%)|333(4%)|290(3%)|
|Sub‐Saharan<br>Africa|5,197 (7%)|5,172 (~100%)|20 (<1%)|5 (<1%)|
|**Total**|**79,756(100%)**|**57,003** (**71%)**|**15,201(19%)**|**7,552(9%)**|

Notes: Mangroves in WMP 2 are potentially capable of migration; those in WMP 3 & 4 are not able to migrate. Totals may not sum to 100% due to rounding Source: Authors’ estimates as described in the text.

Once again, the vulnerability of mangroves varies a great deal by region and by country. In South Asia and Sub‐Saharan Africa, more than 90 percent of mangroves respectively are in

> 14 For this computation, the following adjustments were made: (i) The coastline in DIVA is not the same as the SRTM coastline. Therefore, the DIVA database was spatially joined to watersheds delineated from HydroSHEDS (Lehner et al 2008) to allow the connection between the DIVA coastline and SRTM coastline; (ii) For more complete coverage, the HydroSHEDS adapted DIVA data are extended via the closest HydroSHEDS grid cell (via the ESRI ArcGIS Expand command) in areas with mangroves and areas where the 450 sq. m grid of HydroSHEDS and the 90m coastline do not overlap; (iii) In some areas due to a data constraint, the mangroves are well outside the HydroSHEDS coastline and given a WMP value of NA; (iv) Elevation of mangroves is considered 0m above sea level.

> 15 Due to the spatial differences in the datasets, approximately 4% could not be reliably mapped into WMP categories (2, 3 or 4) directly, so the remaining mangroves are allocated to the country level by proportions)

15
where _dit_ is the Polity2 democracy index of country _i_ in period _t_ . The lagged value of this variable on the right-hand side is included to capture the persistence of democracy. The parameter _NRAit−1_ is the lagged value of the protection level in agriculture. Other covariates are included in the vector _Xit−1_ . The parameters _μt_ and _δi_ denote full sets of year and country fixed effects, respectively, and ε _it_ is an error term capturing all other omitted factors.

For the same reason given above, the model in equation 3 is estimated using D-inD<sup>27</sup> and first-difference GMM estimators. Moreover, because democracy is a persistent variable, we run a system GMM regression (see Arellano and Bover 1995). We find that the lagged protection coefficient is always insignificant in these additional regressions (see the results in table S.4 in the supplemental appendix). Thus, these results suggest that there is no feedback effect of agricultural protection on the transition to democracy.

# **<<B>>** _Definition of regime change_

The evidence presented thus far has been obtained from approximately 40 political reform episodes based on the Polity2 index. We have checked whether our results are driven by the specific definition of our political reform variable. We have employed three alternative approaches: (a) defining a democracy variable using _all_ of the 67 reform episodes from Polity2; (b) using the data from Papaioannou and Siourounis (2008) and including only 23 (permanent) democratization episodes; and (c) only considering permanent transitions from Polity2, namely, those that lasted at least eight years. These democratization dummies differ in terms of not only the number of regime transitions considered but also the timing of the reform episodes (see table S.1).

The results are presented in table S.5 in the supplemental appendix. The results remain robust using these different measures of democratic transitions. Moreover, the additional regression results suggest that permanent transitions are most important. In line 27
**Table 4. Factor scores and other summary statistics for first principal** component **from analysis of** pooled Western and Central Africa and Eastern and Southern Africa data

||_WesternandCentral_<br>_Africa_|_EasternandSouthern_<br>_Africa_|
|---|---|---|
|**Factorscores**|||
|Ownaradio|0.346|0.351|
|Ownarefrigerator|0.431|0.461|
|Ownatelevision|0.479||
|Ownabicycle|-0.047|0.072|
|Ownamotorcycle|0.169|0.185|
|Ownacar|0.292|0.396|
|Househaselectricity|0.453|0.507|
|Numberofroomsperperson|0.046||
|Floorismadeof"finished"surface|0.376|0.459|
|**Summary statistics**|||
|Proportionofvariation explainedbyfirstprincipalcomponent|33.1|34.7|
|Valueoffirsteigenvalue|2.98|2.43|
|Differencebetween first andsecondeigenvalue|1.75|1.28|
|Numberofhouseholds|71,673|65,309|

_Source: Author's calculation from pooled and weighted DHS data._ In order to define quintiles, individuals are sorted by the wealth index within each region, and cutoff values for the quintiles of the population are derived. Households are then assigned to each of these groups on the basis of their value of the asset index.'<sup>4</sup> The interpretation is therefore that the poorest quintile is the group in which the poorest 20 percent of the population live. Note that the use of the term "poor" here differs from the across the different types of variables, and the pairwise correlation (across replications) between the loading from a continuous variable and weight on the corresponding dichotomous variable are very high as well, on the order of .9.

11. Principal components in typically derived for continuous variables. Monte Carlo simulations comparing the first principal component derived from (1) continuous variables to (2) dummy variables with the same mean (derived from the ranking of the continuous variables) yields very similar factor loadings across the different types of variables, and the pairwise correlation (across replications) between the loading from a continuous variable and weight on the corresponding dichotomous variable are very high as well, on the order of .9.

12. This method of ranking households is analogous to fairly standard approaches which use consumption expenditure quintiles.

13. Principal components in typically derived for continuous variables. Monte Carlo simulations cornparing the first principal component derived from (1) continuous variables to (2) dummy variables with the same mean (derived from the ranking of the continuous variables) yields very similar factor loadings across the different types of variables, and the pairwise correlation (across replications) between the loading from a continuous variable and weight on the corresponding dichotomous variable are very high as well, on the order of .9.

14. This method of ranking households is analogous to fairly standard approaches which use consumption expenditure quintiles.

9
NBIM achieved an excess return that averaged 48 basis points over the period 19982006. Over the 3-year period 2004-2006, internal management made a higher contribution to excess returns, after deducting operating costs, than external management (Table 2.4).

Table 2.4: Annualized <u>Contribution to Net Excess Returns, 2004-2006</u>

|in basis points|External|Internal|Total|
|---|---|---|---|
|Equities|13|18|31|
|Fixed Income|3|14|17|
|Total|16|32|48|

Source: NBIM The inferior contribution of external equity management was caused by a poor performance of external managers in the Japanese equity market in 2006. This is reflected in the negative contribution of external equity management in 2006. In earlier years, external equity management achieved a superior performance (Table 2.5).

Table 2.5: Annualized <u>Contribution to Excess Returns, 2002-2006</u>

|in basis points|2002|2003|2004|2005|2006|
|---|---|---|---|---|---|
|Internal Equities|1|6|11|20|31|
|Internal Bonds|9|18|17|20|13|
|Internal Total|10|24|28|40|44|
|External Equities|10|20|21|65|-32|
|External Bonds|5|15|4|5|3|
|External Total|15|35|25|70|-29|
|Total Gross Excess Return|25|59|53|110|15|
|Management Costs|4|5|4|5|3|
|TotalNetExcessReturn|21|54|49|105|12|

Source: NBIM The operating costs of NBIM are low and compare favorably with those of other large international pension funds. Internal management costs declined over time and reached slightly less than 5 basis points of average internally-managed assets in 2006 (Table 2.6). In contrast, external management experienced a rising trend and its costs amounted to close to 30 basis points of average externally-managed assets in 2006.

Overall operating costs increased slightly to 10 basis points. However, after deducting performance-based fees, the other costs fell from 7.9 basis points in 2002 to 7.3 in 2006. This is well within the target agreed by negotiation between the Ministry and the Bank. Norges Bank charges directly the Ministry for these costs. Performance-based fees are paid separately by the Ministry of Finance, which examines and approves each individual contract.

32
# **Table 8 - Quid Pro Quo: Rewards to the Bank**

The table investigates whether banks who extend more favorable loans to politically connected firms also extend more loans to public firms as a reward. The dependent variables in Panels A and B are loan terms at origination and ex post performance for public loans respectively. _LargePubLoan (SmallPubLoan)_ is a dummy variable that equals one if the bank receives high (low) level of public loans relative to the median. In Panel A, we examine political lending made by banks that extend a relatively high level of public loans. In Panel B, we focus on the public loans lent by type of bank, where a unit of observation is a loan at the state-banktype(LargePubLoan/SmallPubLoan)-quarter level. Please refer to Appendix A for variable definitions. State-level GDP growth and unemployment rate are included as controls in all specifications. All regressions include _state*banktype_ fixed-effects. Standard errors are reported in parentheses and are clustered at the state level. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.

||Loan V|olume|Loan S|pread|Mat|urity|D(Coll|ateral)|D(D|efault)|
|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A Lending by Banks**|**to Private**|**Firms**|||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Chairman|0.762**|0.719**|-0.019*|-0.020*|0.106***|0.091***|-0.021***|-0.018***|0.007***|0.007***|
||(0.290)|(0.290)|(0.011)|(0.012)|(0.036)|(0.033)|(0.007)|(0.006)|(0.002)|(0.002)|
|Chairman*LargePubLoan|0.658*|0.639*|0.052|0.053|-0.005|-0.010|-0.036***|-0.036***|0.009***|0.008**|
||(0.398)|(0.379)|(0.032)|(0.033)|(0.067)|(0.066)|(0.011)|(0.010)|(0.003)|(0.003)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry*Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.021|0.021|0.414|0.415|0.201|0.202|0.368|0.372|0.021|0.022|
|N|594,534|594,534|594,534|594,534|594,534|594,534|594,534|594,534|480,354|480,354|
|**Panel B Public Loans**|||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Chairman*SmallPubLoan|-0.065|-0.030|-0.107|-0.062|0.284|0.481*|0.004|0.015*|-0.008|-0.007|
||(0.109)|(0.100)|(0.139)|(0.083)|(0.426)|(0.286)|(0.017)|(0.008)|(0.005)|(0.005)|
|Chairman*LargePubLoan|0.149*|0.132*|-0.101|-0.060|0.242|0.487*|0.004|0.008|-0.009*|-0.008*|
||(0.091)|(0.079)|(0.135)|(0.081)|(0.423)|(0.290)|(0.017)|(0.009)|(0.005)|(0.005)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*BankType FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.768|0.815|0.122|0.694|0.305|0.668|0.304|0.845|0.255|0.422|
|N|2,181|2,181|2,181|2,181|2,181|2,181|2,181|2,181|2,181|2,181|

42
Table A1: Test for Misreporting Weeks Pregnant at 1<sup>st</sup> Prenatal Visit

|Dependent Variable:<br>Weeks Pregnant at 1<sup>st</sup>Prenatal Visit, by Gestational Age|at Birth|
|---|---|
|Weeks Pregnant by EDD|0.90***<br>(0.02)|
|1(Weeks Pregnant by EDD<13)|‐0.13<br>(0.31)|
|1(Weeks Pregnant by EDD<13 ) x 1(Treated=1)|‐0.03<br>(0.44)|
|Constant|1.33***<br>(0.39)|
|Observations|1730|
|Adjusted_R_<sup>2</sup>|0.82|

The dependent variable is weeks pregnant at the first prenatal visit constructed using gestational age at birth. The independent variable is weeks pregnant at the first visit constructed by using the last day of menstruation or estimated delivery date (EDD). The interaction term interacts a dichotomous indicator for whether the visit was before week 13 and a dichotomous indicator for whether the clinic was actually treated. The regression controls for clinic fixed effects by adding a binary indicator for each clinic in the sample. Standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

45