rafmacalaba/datause-encoder-data huggingface dataset:
definitions of skill increased more than any other country in the group, tripling over the last decade.

With respect to the question of whether industry premiums or skill premiums changed more over time, the evidence from Table 12 is mixed: in the Philippines and Cambodia industry premiums changed more than skill premiums –suggesting that they are the main driver of wage differentials; in Thailand, Mongolia and China we observe significant changes in both industry and skill premiums which go in opposite directions – skill premiums are confirmed to be the main wage driver in China and Mongolia, while industry premiums are the main driver in Thailand; in Vietnam we see a spectacular increase in skill premiums and hardly any increase in industry premiums; finally in Indonesia the answer depends on whether we compare over the 1994-2007 or the 19962007 period and on the skill definition used.

Table 13 reports estimates of the variance of industry and skill premiums over time. Overall, dispersion of industry premiums for Indonesia, Vietnam, China and Mongolia remained low with little tendency for change, while it increased sharply over time for Cambodia and moderately for the Philippines and Thailand. The variance levels as well as their evolution would seem to indicate that labor markets are more segmented in Cambodia, Thailand and Philippines than elsewhere (noting the evidence above on countries with faster rising industry premiums), pointing to the need for focusing on policies that make labor markets less segmented. An open question for Thailand and the Philippines – which show both evidence of raising industry premiums and increasingly industry-specific skill premiums over the long-run – is to what extent industry-specific skill premiums are associated with higher overall industry premiums in the most skilled intensive sectors or correspond to “real” variation in skill premiums across sectors. The variance and sector analysis of skill premiums provided below will help clarify this issue.

With respect to the dispersion of skill premiums, the dispersion for Cambodia, China and Mongolia remained very low with no significant change over time for both definitions of skill. The dispersion of skill premiums increased for the first definition of skill in the case 37
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Now, as the OLS error terms are of mean zero by construction, (2) can be represented by In the twofold version of OB that we use in this paper, (3) can be represented either as or as depending on which country is used as a reference country. We focus on this paper on the approach of (4), using Vietnam as the reference, and leave (5) and additional OB variations to subsequent research.

We base the choice of regression specification on the findings so far. In line with per capita GDP of Vietnam compared with the Dev7 variables, there are a series of income level or wealth related variables for which Vietnam does poorly in comparison with Dev7. We term these variables WEALTH related variables. We include in it all variables for which Vietnam has poorer endowments, and the term WEALTH denotes that they have higher mean values in Dev7 countries, which are wealthier than Vietnam. These variables have typically not been included in the Fryer-Levitt regressions as they would have exacerbated rather than reduced the Vietnam gap. For example, the set includes mother’s highest level of education, or MISCED, which is one ISCED level higher for Dev 7 as compared to Vietnam. A second set of variables, which we term as ED WEALTH are typically variables for which Vietnam does better and are good for education results. For instance, Vietnam has a higher level for PRESCHOOL and for time spent by students in mathematics lessons after school (OUTMATH). These two sets of variables together constitute the specification of the **_X_** _V_ and **_X_** _O_ vectors.

Now it is an established result that the matrix decomposition reported in equation 8 29
||**Table A4: Reaso**<br>**Mal**|**ns for pendi**<br>**awi**|**ng vaccinatio**<br>**Nig **|**n by residenc**<br>**eria**|**e**<br>**Tan**|**zania**|
|---|---|---|---|---|---|---|
|**Reasons**|||||||
||**Rural **|**Urban **|**Rural **|**Urban **|**Rural **|**Urban **|
|Currently ineligible|1.9|1.7|3.4|3.6|2.0|1.2|
||(-0.2 to 4.1)|(-1.7 to 5.0)|(1.8 to 5.1)|(-0.1 to 7.2)|(0.3 to 3.7)|(0.2 to 2.3)|
|Don't know how to get|3.8|0.3|41.9|34.8|8.6|6.1|
||(0.4 to 7.2)|(-0.3 to 1.0)|(35.8 to 48.0)|(26.6 to 43.0)|(5.3 to 11.8)|(1.3 to 10.9)|
|Too crowded at vax site|3.6|5.1|5.7|9.0|0.8|0.8|
||(0.5 to 6.6)|(0.8 to 9.4)|(3.1 to 8.2)|(5.1 to 12.9)|(-0.0 to 1.7)|(-0.3 to 1.8)|
|Facility not disability friendly|||0.7|0.8|1.1|0.6|
||(0.5 to 6.6)|(0.8 to 9.4)|(0.0 to 1.4)|(-0.2 to 1.9)|(0.1 to 2.0)|(-0.5 to 1.7)|
|Too far away|15.1|6.4|13.6|8.4|14.4|7.1|
||(9.6 to 20.6)|(-1.8 to 14.6)|(10.0 to 17.2)|(4.9 to 11.9)|(10.6 to 18.2)|(3.2 to 10.9)|
|Not enough vaccines|20.7|8.4|9.3|10.4|11.6|3.4|
||(13.4 to 28.1)|(3.5 to 13.3)|(6.0 to 12.5)|(5.7 to 15.1)|(8.2 to 15.0)|(1.5 to 5.4)|
|Unable to register|0.2|0.0|2.1|1.8|1.6|0.9|
||(-0.1 to 0.5)||(0.4 to 3.9)|(0.4 to 3.2)|(0.2 to 3.0)|(0.0 to 1.8)|
|Work commitments|3.5|13.5|9.5|16.4|24.0|37.2|
||(1.1 to 5.9)|(2.7 to 24.3)|(5.8 to 13.2)|(9.8 to 22.9)|(19.3 to 28.7)|(30.1 to 44.3)|
|Domestic commitments|4.9|5.9|0.7|3.2|7.1|7.3|
||(1.4 to 8.3)|(0.7 to 11.0)|(-0.1 to 1.5)|(0.3 to 6.2)|(4.4 to 9.9)|(3.7 to 10.8)|
|Religious reasons|0.6|0.0|0.0|0.5|0.3|0.4|
||(-0.3 to 1.4)||(-0.0 to 0.1)|(-0.1 to 1.1)|(-0.0 to 0.7)|(-0.3 to 1.2)|
|Medical reasons|7.4|10.5|2.8|5.3|5.8|9.4|
||(2.5 to 12.3)|(-0.7 to 21.6)|(0.8 to 4.7)|(1.9 to 8.8)|(3.4 to 8.1)|(5.2 to 13.6)|
|Waiting for appointment|2.0|2.0|10.6|5.6|3.2|3.4|
||(0.4 to 3.7)|(-1.1 to 5.1)|(7.2 to 13.9)|(2.1 to 9.1)|(1.3 to 5.1)|(1.1 to 5.6)|
|Afraid of side effects|32.8|46.3|4.4|8.6|0.0|0.0|
||(24.9 to 40.8)|(31.2 to 61.4)|(2.1 to 6.8)|(4.3 to 13.0)|||
|Other reason|8.2|9.6|8.1|7.2|25.5|27.4|
||(3.5 to 12.8)|(-2.3 to 21.5)|(5.2 to 11.0)|(3.6 to 10.8)|(20.8 to 30.2)|(21.0 to 33.9)|
|_N_<br>Note: Reasons for pendin<br>percent. 95% confidence|333<br>g vaccination des<br>intervals in paren|160<br>pite being wil<br>theses.|653<br>ling to get va|447<br>ccinated, by u|660<br>rban/rural. All|332<br>values in|

17
<u>Table 1: Baseline characteristics</u>

||Treat|ment|Con|trol||
|---|---|---|---|---|---|
||Mean|SD|Mean|SD|p-value|
|_Household characteristics_||||||
|Male household head|0.96|0.20|0.97|0.18|0.41|
|Age of household head|41.93|12.13|41.02|11.85|0.25|
|Household head is literate|0.32|0.47|0.24|0.43|0.01|
|Household size|6.10|2.62|5.61|2.08|0.00|
|Wealth index|0.39|0.13|0.39|0.20|0.79|
|Annual income, of-farm activities|24.70|24.82|19.82|26.17|0.00|
|Hh benefts from credit/training program|0.62|0.49|0.58|0.49|0.17|
|Any serious debts|0.63|0.48|0.47|0.50|0.00|
|Able to raise 1000 rupees in one week|0.61|0.49|0.33|0.47|0.00|
|_Farm characteristics_||||||
|Value of agr. production|28.49|45.76|24.43|125.14|0.56|
|Value of variable inputs|14.51|21.34|14.46|69.62|0.99|
|Area cultivated (acres)|4.15|4.57|2.74|5.47|0.00|
|Time in crop production (hours per year)|2085|2280|1369|1310|0.00|
|Irrigated area (% of total)|0.18|0.32|0.14|0.31|0.07|
|Fertilizer (dummy)|0.98|0.15|0.87|0.33|0.00|
|HYV seeds (dummy)|0.77|0.42|0.63|0.48|0.00|
|Participated in labor sharing (dummy)|0.75|0.43|0.78|0.41|0.23|
|Risk index of crop portfolio|0.36|0.12|0.26|0.08|0.00|
|SD of crop returns|2472|1110|3111|2129|0.00|
|Risk index (CV of trend corrected yield)|0.22|0.10|0.16|0.07|0.00|
|Risk index (alt. aggregation)|0.37|0.12|0.27|0.08|0.00|
|Price risk of crop portfolio|0.15|0.04|0.18|0.08|0.00|
|SD of portfolio return|2239|1093|2889|2125|0.00|
|Labor intensity of crop portfolio|0.27|0.07|0.28|0.07|0.00|
|Cost intensity of crop portfolio|21166|7374|26207|10097|0.00|
|Herfndahl index of crop portfolio|0.76|0.25|0.80|0.23|0.00|
|Number of crops<br>_Shocks_|2.04|1.03|2.08|1.33|0.65|
|Rainfall (deviation)|0.33|0.28|-0.06|0.16|0.00|
|Rainfall (deviation, lag)|-0.39|0.10|-0.12|0.10|0.00|
|Self-reported shock<br>_NREGS participation_|0.81|0.39|0.52|0.50|0.00|
|Household registered with NREGS|0.66|0.47|0.00|0.00|0.00|
|Household generated income from NREGS|0.54|0.50|0.00|0.00|0.00|
|Income, NREGS|1.24|2.39|0.00|0.00|0.00|
|Observations|750||337|||

Notes: All values in constant INR 1,000 (July 2006). One US$ is equivalent to 46.38 INR (July 2006). Variable definitions and sources are described in appendix B. Source: Author’s estimation based on the Young Lives data.

36
2009 shown in the middle panel of table 15A. Per UBI 2002, average labor productivity in comparative advantage industries was 44 percent higher than average labor productivity in the traded sector as a whole. The labor productivity premiums of comparative advantage industries over the rest of the traded sector per UBI was significantly lower than in that UBI 2009 but still quite high at about 30 percent. Table 15A also shows that labor productivity increased between the two waves in both components of the traded sector growing faster in comparative disadvantage industries than in comparative advantage industries.

Because labor nonetheless remained to be more productive in comparative advantage industries between the two waves, the shift in employment from comparative advantage industries to the rest of the traded sector captured between UBR 2001 and UBR 2010 must in and of itself has held back the pace of productivity growth within that sector just as the shift from the production of tradables to that of non‐tradables must have reduced the pace productivity growth in the economy as a whole. We can see from table 15 that the scale of reallocation of labor observed between UBI 2002 and UBI 2009 was consistent with and comparable to that captured between UBI 2001 and UBR 2010.

===Table 16 and table 16A here====

# **_3.3. Labor productivity gaps across the size and age distribution of firms_**

3.3.1. Across the size distribution of firms: Micro enterprises vs. SMEs and larger firms Part of the reason that the traded sector was more productive than the non‐traded sector was because employment was more concentrated in microenterprises in the non‐traded sector than it was in the traded sector, which can be seen from the UBI (table 16) as well as we saw it earlier from the UBR. Tables 16 also shows that microenterprises contributed far less to value added than they did to employment as they lagged relative to larger employers in labor productivity (table 16A). The greater informalization of employment that accompanied the reallocation of labor from the traded sector to the non‐traded sector must therefore have been part of the reason why the structural change observed in the 2000s was productivity reducing.

===Tables 17, 18 and 19 here==== Tables 17, 18 and 19 illustrate that the patterns seen in tables 16 and 16A in labor productivity gaps across employment size groups of businesses economy‐wide also holds across all single digit ISIC 29
go beyond the year 2000, from which the markup level started to decrease again with a sharp decline between 2001 and 2002.

Figures 2 and 3 showed that while average markup computed on materials declined over the decade, markup computed on labor increased except for small firms. This seems to suggest that firms hold different amounts of market power in the product and labor market. A trade-off hypothesis can therefore be formulated: firms that face higher competition compress wages to make up for lost margins on the product market. This hypothesis is the starting point for analyzing resource misallocation, which is commonly found in Africa and other developing regions (Restuccia and Rogerson, 2013).

# **5 Trade Openness and Market Power**

Three main research methodologies–structural models, randomized control trials, and quasi-experimental–methods have been used to study the welfare effects of trade policy (see Goldberg and Pavcnik (2016) for an overview and description of the methods). The article builds upon a quasi-experimental research design to detect the effect of trade policy on firms’ market power. The key challenges to address in the setup of the quasiexperimental design concern potential endogeneity, anticipation, and uncertainty of trade policy. The process trade liberalization in Ghana discussed in section 2.1 is referred to in seeking ways to overcome these difficulties in the identification strategy of the estimation methodology

## **5.1 Identification Strategy**

Using a difference-in-difference estimator in a quasi-experimental framework, it might seem natural to use the tariff rates in 1995 (the year of Ghana’s WTO accession) to estimate the _ex ante_ and _ex post_ impact of WTO accession on the outcome variable. However, such an approach does not adequately take into account the full trade liberalization process that took place in Ghana. First, tariff reductions started with conditionalities imposed by the SAP in 1991. Second, and perhaps most importantly, the breakthrough in the Uruguay round of negotiations was common knowledge; therefore, as Ghana had just begun to embark on trade liberalization under the IMF and World Bank conditions, further tariff reforms were expected to be implemented eventually. Given such expectation, powerful firms, for instance, may have lobbied the government to raise tariffs during the pre-WTO period in order to protect them against the coming liberalization. This could have led to a correlation between firms’ political “power” and market power. For this reason, the 1995 tariffs should not be used because of trade policy endogeneity concerns.

To resolve the trade policy endogeneity threat, this article borrows insights from the Bartik instrument approach in setting up the identification strategy. Therefore, the initial tariff rates in 1991 are used as an exogenous proxy for the endogenous 1995 tariffs. The motivation for this choice is that tariff levels in 1991 were imposed as part of the SAP 16
- 40 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

- Ciccone A; Hall RE (1996). Productivity and the density of economic activity. _American Economic Review_ , 86: 54–70.

- de Roo, G. and Miller, D. (2000) Compact Cities and Sustainable Urban Development: a critical assessment of policies and plans from an international perspective, Aldershot: Ashgate.

- Deng, F.F., Huang, Y., 2004. Uneven land reform and urban sprawl in China: the case of Beijing. Prog. Plan. 61, 211–236.

- Duque, J. C., Lozano-Gracia, Patino, J. E., and Restrepo, P. 2017a. Urban form and productivity: in what shape are Latin American cities? LCR Flagship Report on Cities and Productivity. World Bank ( _forthcoming_ ).

- Duque, J. C., Lozano-Gracia, Patino, J. E., and Restrepo, P. 2017b. Institutional fragmentation and metropolitan coordination in Latin-American cities: What consequences for productivity and growth? LCR Flagship Report on Cities and Productivity. World Bank ( _forthcoming_ ).

- Ewing, R., Pendall, R., Chen, D., 2002. Measuring Sprawl and its Impact. Smart Growth America, Washington, DC.

- Fallah, B; Partridge, M; Olfert, M (2011). Urban sprawl and productivity: Evidence from US metropolitan areas, Papers in Regional Science, 90(3): 451-473.

- Filion, P., 2001. Suburban mixed-use centres and urban dispersion: what difference do they make? Environ. Plan. A 33, 141–160.

Forbes, D. J. (2013). Multi-scale analysis of the relationship between economic statistics and DMSP- OLS night light images. _GIScience & Remote Sensing_ , _50_ (5), 483–499. https://doi.org/10.1080/15481603.2013.823732 Freire, S; Pesaresi, M (2015): GHS population grid, derived from GPW4, multitemporal (1975,
-31 -

# Avallabillty and Access to Electricity

Thirty years ago, the supply of electric power In most developing countrles was confined to owie or two large citles and the major source of power was small thermal plants. During the past thirty years, however, growth of the sector has been very high and now most major towns and many smaller towns have been supplied with electricity from an Integrated network using various kinds of power plants operated and developed as a system. Some countries now serve much of the urban population and have embarked on ambitious rural electrificatlon programs.

Installed capacity Increased by 9.7% p.a. between 1968 and 1982 compared to 10.6% p.a. In the 1950s and 19609. Power generation showed similar growth of 9.6% p.a. between 1968 and 1982, and 11.3% p.a. In the 1950s and 19609. Growth In the power sector has easily exceeded that of real GDP In most countries and also has been far higher than population growth rates, Indicating that overall access to electricity service has been Increaslnu.

Access to service can be measured by several Indicators, Including (I) generation per capita, which Indicates the amount of electricity per capita produced; (II) Insta:led capacity per capita; and (III) percent of the population actually connected to the public grid. Data for 51 countries In 1968 and 1982 showed that, on average, generation per capita Increased by 7% p.a. and Installed capacity by nearly 8% p.a. These growth rates compare very favorably with an average real growth rate of nearly 2% p.a. for per capita GDP over the same time period. In over 90% of countries, per capita generation and Installed capacity growth rates were more than double the real growth rate for GDP and In 57% of countrles more than three times as high.

Eariler data on the percentage of the populatlon having electricity connectlons were not readlly available, but data for just a sample of projects showed an average growth rate of 9% a year In connectlons - about two and a half times the population growth rate. For most countrles,
the total population in the economy. We further weighted the data by the square root of the cellsize to correct for differences in measurement error variance between thin and thick cell samples.

# **5. Data**

This study uses the World Bank’s _International Income Distribution Database,_ a harmonized collection of 919 household surveys from 147 economies. A list of the economies and total number of surveys from each economy is presented in table A1 in the Appendix. The surveys were conducted between 1960 and 2012 with 78% of the surveys collected on or after 2000. The database includes economies from all regions and income groups. Of the 147 economies, 32 are from industrialized nations, 16 from Asia and the Pacific, 20 from Central Asia and Eastern Europe, 23 from Latin America, 10 from the Middle East and North Africa, 8 from South Asia, and 38 from Sub-Saharan Africa. From each survey, we include individuals in the age range 25 to 65 so that they are likely to have completed their schooling. The upper bound of age 65 is chosen to avoid the selection issues related to mortality.

Our observations are aggregated to birth-year cohorts from each survey in each economy. This approach allows us to access the full set of data, as many of the data sets are privileged and not open to use by non-Bank researchers. The 919 surveys totaling 44.6 million individuals in the age range 25-65 are placed in one of 3,583 economy survey-birth year cohorts. There are up to 87 birth-year groups per economy with birth years ranging from 1901 to 1987. We further subdivide the birth cohorts by urban versus rural residence and by gender. Our analysis requires information on each birth-cohort’s average completed years of schooling, proportion living in urban or rural residence, and gender.<sup>9</sup> All of these variables are harmonized to be measured consistently across surveys, economies, cohorts, genders, and regions of the economy. We also

> 9 We will not know where an individual was at the time of birth and so we will have some mismatch between urban and rural residence during the survey versus birth-place.

13
# I. INTRODUCTION

Reliable data on real welfare over time and space in poor countries are rare. Statistical agencies mostly focus on the temporal Consumer Price Index (CPI), which lets one compare changes in, but not levels of, prices over space. Few poor countries have a spatial price index, despite their weak infrastructure and limited market integration permitting large spatial price differences.<sup>1</sup> Without consistent time-space comparisons of living standards, it is unclear if reports of rising inequality in some developing countries (e.g., China) reflect spatially diverging prices more than growing disparities in real welfare levels. Debates about where and by how much poverty has fallen also depend critically on accurate cost-of-living comparisons over time and space.

Amongst ways to spatially deflate in countries without spatial prices, the most startling results use a food Engel curve to calculate the deflator that lets different nominal incomes have the same real standard of living (based on the same food share). This adapts a method developed for temporal comparisons by Hamilton (2001), who estimated Engel curves to back out the implied true price index and real income growth over time.<sup>2</sup> Almås (2012) uses Hamilton’s method for spatial comparisons; assuming a unique food Engel curve for the world, gaps between the food share for a particular country and the base country imply bias in the Purchasing Power Parity (PPP)

> 1. Gibson (2013) provides examples of the priority that statistical agencies in poor countries give to collecting nominal living standards data over price data, despite both types of data being needed for measuring real welfare.

> 2. Applications of the temporal method include studies of the historical United States (Costa 2001; and Logan 2009) and contemporary Australia (Barrett and Brzozowski 2010), Brazil (Filho and Chamon 2012), Canada (Beatty and Larsen 2005), China (Chamon and Filho 2014), Indonesia (Olivia and Gibson 2013), Japan (Higa 2013), Korea (Chung, Kim and Gibson 2010), Mexico (Filho and Chamon 2012), New Zealand (Gibson and Scobie 2011), Norway (Larsen 2007), and Russia (Gibson, Stillman and Le 2008).

2