rafmacalaba/datause-encoder-data huggingface dataset:
Table 8. Indonesian Exports, ICT use, and Log of Skills Measures. High-educated workers. 2005-2010

||||||No|n-routine<br>|skill measur<br>|es|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||M|anual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000557||0.000526|-0.000125|0.000627||0.000596|-0.0000211|-0.000649||-0.000615|-8.96E-06|
||(0.000318)*||(0.000321)|(0.000507)|(0.000301)**||(0.000305)*|(0.000492)|(0.000303)**||(0.000308)*|* (0.000501)|
|Share of hhlds with computer||0.205|0.227|0.234||0.178|0.201|0.211||-0.156|-0.182|-0.194|
|||(0.145)|(0.140)|(0.140)*||(0.145)|(0.139)|(0.138)||(0.164)|(0.157)|(0.155)|
|IDN exports per worker *||||0.00389||||0.00366||||-0.00357|
|Share of hhlds with computer||||(0.00175)**||||(0.00171)**||||(0.00173)**|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.424|0.425|0.425|0.424|0.281|0.281|0.281|0.280|0.758|0.758|0.758|0.758|
|||Ma|nual|Routine ski|-0.545<br>ll measures|Co|-0.52<br>gnitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.0004||-0.0003|-0.0000763|-0.000545||-0.00052|0.0000156|||||
||(0.000402)||(0.000403)|(0.000663)|(0.000329)*||(0.000335)|(0.000531)|||||
|Share of hhlds with computer||-0.00113|-0.0282|-0.0461||-0.0796|-0.0989|-0.106|||||
|||(0.249)|(0.244)|(0.242)||(0.138)|(0.131)|(0.129)|||||
|IDN exports per worker *||||-0.00142||||-0.00319|||||
|Share of hhlds with computer||||(0.00219)||||(0.00175)*|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.931|0.931|0.931|0.931|0.756|0.756|0.756|0.755|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. High-educated workers: workers with at least complete secondary education. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects and linear province time trend. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

29
These results measure the extent of learning after spending four years in the education system in Pakistan and five years in India. Although the test went on to ask about division and simple fractions, these questions were simply out of the grasp of the vast majority of children. There were five students who scored more than 90 percent; but that only 19 percent knew how to divide 384 by 6 (Pakistan) suggests that these children were truly exceptional. In terms of the curriculum standards, most children were just at the benchmarks proposed by the curriculum wing for children in first grade in the case of Pakistan and second grade for India.

The results in English (Pakistan only) were equally poor (Table 5). Most children (86 percent) could recognize and write an alphabet when spoken and 70 percent could fill in a blank alphabet in a sequence (D _ F) but less than 50 percent could complete basic words like BALL or FLAG when given a picture and asked to fill in 2 blanks (BA_ _) or a single blank (FLA_). Indeed, for the latter only 29 percent were able to correctly fill in the blank. Again, the test went on to check children’s ability to construct more complicated words and sentences as well as paragraph comprehension, but as in Mathematics, these concepts were too advanced for 90 percent of the children tested.

English is conceivably a language for the elite and to enjoy the benefits of reading and writing a basic knowledge of the vernacular may be sufficient. Unfortunately, the scores from the Urdu test (Pakistan) suggest that children are equally disadvantaged in the vernacular as they are in English. A minority of children can form basic sentences in Urdu given a word; only 31 percent can form a sentence using “school.” Levels are similar in India—only 30 percent of the tested children could construct a sentence in Hindi using a commonly used word like “message” and a minority (46 percent) was able to use the word “house” in a sentence.

These numbers suggest that the average citizen in Pakistan (or India) will be in no position to compete in a rapidly globalizing world. Although international comparisons are inexact, in the case of Mathematics the recently completed Trends in International Mathematics and Science Study (TIMSS) gives a sense of where the children we tested fall in the global distribution. For instance, 72 percent of fourth-graders correctly answered the question 15 x 9 internationally compared to the 50 percent who could answer 32 x 4 in Pakistan (the former is a tougher question since it requires a “carry-over”); 73 percent could graphically represent simple information (bar charts) internationally compared to less than 10 percent in Pakistan. Both in India and Pakistan, average levels of learning are at the bottom of the international chart.

- 12 -
# **2. Background**

# A. _Urbanization_

While Bangladesh’s population is predominantly rural, it has a sizeable urban population. According to the 2011 Bangladesh population and housing census (GOB 2014), 23% of the country’s population is urban. Based on official measures, Bangladesh’s urban population share ranks it in the middle among South Asian countries. In turn, South Asia, with an urban population share of 28%, ranks lowest among all regions in the world (Ellis and Roberts 2016).

Bangladesh’s urban population is spatially concentrated. The country is organized into eight administrative divisions and, under them, 64 administrative districts.<sup>10</sup> According to the 2011 census (GOB 2014), at the division level (in 2011, there were seven divisions), Dhaka division had an urban population of 16 million (accounting for 46% of the country’s urban population); Chittagong division, seven million (21%); and the remaining divisions, one to three million each. Based on the same data, at the district level, Dhaka district had an urban population of 9.3 million (accounting for 28% of the country’s urban population); Chittagong district, 3.2 million (9%); and the other districts, 100,000 to one million each.

Establishing the rate at which Bangladesh is urbanizing is complicated by a recent adjustment in the definition of an urban area. The 2011 population census (GOB 2014) introduced a stricter administrative-based definition of an urban area compared to preceding censuses.<sup>11</sup> If the older census definition of an urban area was retained, the country’s urban population would have increased from 13.5 million (15.5% of the total population) in 1981 to 42 million (28% of the total population) in 2011, an annualized urban population growth rate of 3.8%.<sup>12</sup> Over the 2001–11 period, the annualized urban population growth rate was lower, at 3%.

> 10 Mymensingh division was formed in 2015, divided from Dhaka division; Ranjpur division was formed in 2010, divided from Rajshahi division.

> 11 In the 1981, 1991, and 2001 censuses, the definition of an urban area included urban developments next to large cities. The 2011 redefinition reduced the total area classified as urban from 10,711 square kilometers in 2001 to 8,867 square kilometers in 2011 (a 17% reduction). The reductions affected some districts more than others. In particular, Dhaka, Chittagong, Gazipur, and Khulna districts experienced reductions in their urban areas of more than 50%. With the adjustment in urban areas, Bangladesh’s urban population in 2011 totaled 35.1 million, or 23.4% of the total population.

> 12 The 2010–11 Bangladesh Household Income and Expenditure Survey (HIES) data was drawn using the 2001 census as the sampling frame, and used the pre-2011 definition of an urban area. An estimated 27% of the country’s population was urban based on these data.

8
associated with the use of computers at the workplace between 2000 and 2006 (Benavente et al., 2011).

For emerging economies, there is still a dearth of studies on the labor market impacts of ICT adoption at the firm level. Among the few, Dutz et al. (2012) show that ICT-intensive firms in Brazil exhibit higher wage growth across all skill groups, but not faster employment growth. Wage growth is especially high for workers changing firms but not so high for workers remaining within the same firm. Their evidence suggests that, during a time of technological change, firms in Brazil were able to absorb jobs and generate wage increases across all skills. Brambilla and Tortarolo (2017) study the impact of ICT investment on productivity, employment and wages for Argentinean firms in the manufacturing sector using survey-based retrospective information for 2010-2012. They find that in the short run ICT adoption leads to increases in firm productivity and wages with the effects being larger for initially high-productivity and high-skill firms, and to decreases in the share of unskilled labor, supporting the view that ICT is complementary with skilled labor.

Second, this paper relates to the emerging literature stressing the fact that technological innovation is an ongoing process, with more sophisticated technologies being developed constantly and producing substantive impacts in the labor market. Examples include advances in automation, robotics, and artificial intelligence, with potentially worrying consequences for labor markets (Frey and Osborne, 2013; Brynjolfsson and McAfee, 2011; Brynjolfsson and McAfee, 2014; Graetz and Michaels, 2015; Acemoglu and Restrepo; 2016). In particular, Brynjolfsson and McAfee (2014) argue that more sophisticated technological innovations are no longer confined simply to routine tasks but rather they are spreading to domains usually defined as non-routine, and they are performing tasks that are typically led by more high-skilled workers. An example of this trend is the number of tasks usually performed by lawyers and accountants that are being undertaken by sophisticated algorithms. Machine learning techniques are advancing in the direction of being able to program a machine to master a non-routine task autonomously (Autor, 2015). The ensuing concern is therefore that such technological innovations may in the future replace many types of jobs previously insulated from more routine-biased technological developments, such as those imbedded in the use of computers. World Bank (2016) follows Frey and Osbourne (2013) in investigating the feasibility of automating existing jobs given current and potential technological advances, based on the occupations of workers and show for OECD 7
# _Complementarities between digital and non-digital (cognitive, and socioemotional) skills_

Next, we investigate complementarities between digital, cognitive, and socioemotional skills in Malaysia’s jobs.<sup>30</sup> We first examine pairwise correlations. The pairwise correlations show the magnitude of relationships between two subsets of skills. We use Pearson correlations and test the significance of the correlation through t-test.

We then carry out a factor analysis using the skills count of the nine digital, cognitive, and socioemotional skills subsets (three each) detailed in section 2. The exploratory factor analysis allows us to model the relationships among skills subsets by reducing them to fewer variables thus highlighting stronger links between several skills subsets. We use a standard exploratory factor analysis that finds a number q of factors that linearly reconstruct our nine original variables according to the following relationship: where is the value of the _i_ th observation on the _j_ th variable, is the _i_ th observation on the _k_ th common factor, yyooii i is the set of linear coefficients called the factor loadings, and zzooii is similar to a residual but is known as the _j_ th variable’s unique factor (StataCorps 2019). We use varimax rotation ooii bb ee to produce orthogonal factors,<sup>31</sup> to get maximized factor loadings. Again, we use the SEAD data for Malaysia, weighted by the distribution of employment across occupations, to study those relationships within the existing job structure. Occupation-level results (unweighted by the distribution of occupations in a particular country) and replications for other countries are also estimated and presented in Annex 4.

# _Occupation digitalization and skills requirements in Cambodia, Malaysia, Thailand, and Vietnam_

Finally, we use descriptive statistics to calculate the levels of occupation digitalization and skills requirements across the four countries we study. We use the SEAD data set to understand the distribution of skills across the four countries of interest. We also generate occupation-weighted data sets over several years for Malaysia, Thailand, and Vietnam to understand how the distribution of skills across each country evolves over a 4-6 year period.<sup>32</sup>

> 30 We repeat this analysis for unweighted occupations, and weighting for the Cambodian, Thai, and Vietnamese occupational distributions. Again, results are similar to those when using the Malaysia-weighted data.

> 31 By definition, orthogonal factors are not correlated with each other.

> 32 We were unable to access multiple years of the Cambodian SES.

22
For most of the provinces, as was expected, both geographic and concentration urban welfare ratios are lower than the unconditional ones (Table 6). However, the reasons are not the same. In the geographic profile, this is because urban households tend to have better characteristics than households at national level. In the concentration profile, this is because the returns to better characteristics tend to be larger in urban areas than nationally. By analogous reasoning, average consumption for geographic and concentration simulations in rural areas is larger compared to an unconditional profile.

**Table 6.  Welfare Ratios by Province and by Urban/Rural Area (logarithms)**

|**Province**|**Geogr**<br>**Pro**|**aphic**<br>**file**|**Concen**<br>**Pro**|**tration**<br>**file**|**Uncon**<br>**Pro**|**ditional**<br>**file**|
|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|
|Pichincha|0.69|0.45|0.68|0.26|0.91|0.25|
|Azuay|0.83|0.45|0.70|0.17|1.06|0.16|
|Bolívar|0.42|0.21|0.70|0.04|0.66|-0.23|
|Cañar|0.66|0.52|0.43|0.12|0.63|0.16|
|Carchi|0.36|0.01|0.59|0.18|0.49|-0.26|
|Cotopaxi|0.52|0.39|0.68|0.09|0.74|0.00|
|Chimborazo|0.49|0.25|0.77|-0.03|0.81|-0.26|
|El Oro|0.50|0.31|0.59|0.34|0.63|0.16|
|Esmeraldas|0.50|0.28|0.51|0.05|0.53|-0.07|
|Guayas|0.44|0.39|0.59|0.11|0.58|0.06|
|Imbabura|0.49|0.16|0.55|0.03|0.59|-0.26|
|Loja|0.61|0.18|0.75|0.15|0.90|-0.15|
|Los Ríos|0.31|0.34|0.49|0.22|0.33|0.11|
|Manabí|0.33|0.26|0.55|0.09|0.42|-0.08|
|Morona Santiago|0.71|-0.15|0.60|-0.12|0.84|-0.74|
|Napo|0.47|0.10|0.84|-0.13|0.86|-0.50|
|Pastaza|0.57|0.08|0.63|-0.24|0.72|-0.61|
|Tungurahua|0.65|0.36|0.68|0.18|0.88|0.07|
|Zamora|0.49|0.15|0.57|0.05|0.59|-0.25|
|Sucumbíos|0.69|0.24|0.64|0.18|0.86|-0.02|
|Orellana|0.65|0.12|0.26|-0.11|0.42|-0.43|

_Source:_ Own calculations based on ECV Survey.

There are important and positive correlations between geographic and unconditional profiles (0.73 for urban, 0.91 for rural and 0.91 for national), and between concentration and unconditional profiles (0.72 for urban, 0.87 for rural and 0.96 for national), meaning that both structural differentials (returns) and disparities in characteristics (endowments) are important to explain urban-rural differences in welfare ratios within provinces.

Mean consumption—conditional on non-geographic variables set at national level—is higher in urban than in rural areas, meaning that the returns are larger for the urban sector than for rural areas. When the welfare ratio is simulated setting all parameters at the national level and explanatory variables at the mean of the province, urban measures are also higher compared to the rural ones. This is due to household characteristics that tend to increase living standards, such as education, which is higher in urban than in rural sectors.

11
_<u>Table 7: Inter-sectoral links between Agriculture and Food processing</u>_

||**Agriculture**|**Food**<br>**Processing**|
|---|---|---|
|**_Initial tariffs, %_**|12|5|
|**_Intermediates as % of output_**|35|72|
|**_Share (%) of tot inputs from sector:_**|||
|**Agriculture**|22|63|
|**Food Processing**|13|14|
|**Mining and Energy**|1|3|
|**Other Manufacturing**|52|14|
|**Services**|12|6|

Factor price changes as well as the inter-sectoral intermediates cost savings explain why certain sectors reduce or marginally increase imports following tariff abatement. For instance, the absence of import surges for _Livestock_ , after the market opening, is due to increased domestic sales of local producers who can produce at lower costs and are able to gain market share. A partial equilibrium framework where tariff reduction can only lead to increased imports and lower prices would not account for these types of inter-sectoral linkages.

# **_<u>CAFTA bilateral trade liberalization</u>_**

The full unilateral trade liberalization serves as a benchmark against which the CAFTA regional agreement can be compared. Table 8 reports sectoral results for the simulation of this regional free trade agreement. This policy discriminates between import origins and has trade diverting effects that may not be compensated by trade creation. However, as shown below, this geographic discrimination is not the most relevant aspect to be considered in an evaluation of this policy option.

27
the distributive effect, excluding the significant distributive effects due to different average changes for rural and urban incomes.

For the trade reform scenarios analyzed here, the aggregate income change rates – consistently estimated by aggregation from the micro data shocked with the factor and goods prices changes – are shown in Table 14.

_<u>Table 14: Real income rates of change (percent) estimated by aggregation of the household surveys</u>_

||**All**<br>**Urban**|**Rural**|
|---|---|---|
|<br>**_Equation (1) use_**|**_d for all households_**||
|**Full unilateral liberalization**|**1.43**<br>**1.66**|**0.82**|
|**CAFTA bilateral liberalization**|||
|**Nicaragua and US liberalize**|**0.84**<br>**1.01**|**0.38**|
|**Nicaragua liberalizes only**|0.59<br>0.82|-0.01|
|**US liberalizes only**|0.24<br>0.19|0.38|
|<br>**_Equations (1) and (2) used fo_**|**_r different groups of households_**||
|**Full unilateral liberalization**|**1.48**<br>**1.73**|**0.82**|
|**CAFTA bilateral liberalization**|||
|**Nicaragua and US liberalize**|**0.91**<br>**1.05**|**0.55**|
|**Nicaragua liberalizes only**|0.69<br>0.88|0.18|
|**US liberalizes only**|0.22<br>0.16|0.37|

Table 14 highlights that trade liberalization is positive overall, however it tends to favor urban households more than rural ones. It also shows that calculating income changes for households headed by self-employed farmers with equation (1) creates a downward bias. Given the uncertainty linked to the imputation method implied by equation (1), we report poverty results when the income changes are estimated using the combined method of equations (1) and (2).<sup>24</sup> These results are also clearly summarized in Figure 3: a gain of around 2% is the most likely change in income for a random household in the full liberalization scenario, and a gain close to 1% has the highest probability of occurring in the CAFTA case, which also shows less dispersion around this average gain.

> 24 The figures in Table 14 are not fully consistent with those obtained from the CGE model. In fact even by modifying the SAM in accordance with the household survey, some discrepancies between the macro and micro databases remain. Besides, as already outlined, despite the segmentation in the factor markets of the CGE model, intra segment factors’ mobility is allowed, and substitution across goods in consumption is permitted, whereas this is not case for the micro data. At the micro level, individuals never switch jobs and households’ consumption patterns do not change: the parameters θ ’s are fixed.

36
- 2 -

# **I. INTRODUCTION**

Infrastructure is one of the most important driving forces for economic development. A strong correlation between growth and infrastructure stocks is usually expected (e.g., World Bank, 1994; Canning, 1998; Fay and Yepes, 2003). The reason is that accumulated public infrastructure is conducive to increasing productivity of individual firms, whence enhancing overall efficiency of the economy.

Importantly, however, not only the quantity but also the quality of infrastructure is essential for increasing firm competitiveness. High coverage of infrastructure is no doubt necessary, but it is not sufficient. For instance, many transition countries have well-established infrastructure networks; therefore, the access to services is granted. However, it does not necessarily guarantee the quality or reliability of service provision. Without proper maintenance, infrastructure services remain unreliable in some transition countries. A traditional example is this; there is no clear correlation between quantity and quality of roads at least on a cross-country basis (Figure 1). High road density does not always mean low transportation costs unless the quality of road surfaces is maintained. In this regard, the share of paved roads may be a better proxy for the quality of infrastructure.

Figure 1. Quantity and Quality of Roads, 2003 <!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>0 20 40 60 80 100 120<br>Share of paved roads (%)<br>Source: World Development Indicators .<br>Road density (km/100 sq. km)<br><!-- End of picture text --> However, the quality of public infrastructure services is not always easy to measure in an objective manner. In general, quality data are thin, as often pointed out (e.g., Fay and Morrison, 2005). Among the existing aggregate data, several measurements of infrastructure
# **May Growth Lead to Higher Deprivation Despite Higher Satisfaction?**

Quentin Wodon and Shlomo Yitzhaki<sup>1</sup> World Bank and Central Bureau of Statistics of Israel Key words: growth, inequality, satisfaction, deprivation JEL categories: D63, O15

> 1 The views expressed here are those of the authors and need not reflect those of The World Bank, its Executive Directors or the countries they represent.

1
**Figure 3:** _Yearly Infant and Neonatal Mortality in percent._ The dashed line mark the introduction of the HIPC Initiative in 1999. The year 1985 includes all babies born in the 1960’s, 1970’s and 1980’s up until 1985 and 2012 includes the roughly 1,400 babies born in 2013.

<!-- Start of picture text --> 1985 1990 1995 2000 2005 2010<br>Year<br>HIPCs Non-HIPCs<br>(a) Infant Mortality<br>1985 1990 1995 2000 2005 2010<br>Year<br>HIPCs Non-HIPCs<br>.3<br>.25<br>.2<br>.15<br>Infant Mortality<br>.1<br>.05<br>0<br>.15<br>.125<br>.1<br>.075<br>Neonatal Mortality .05<br>.025<br>0<br><!-- End of picture text --> (b) _Neonatal Mortality_ 23