rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

The explosive growth of telecommunication networks in developing countries is yielding an unprecedented wealth of highly granular real-time data, and governments have only begun to leverage the enormous potential of this new information source. This paper explores analytic methodologies for using aggregated and encrypted cell phone call data to map the distribution of poverty in Guatemala. To estimate poverty rates, Guatemala, like many other developing countries, relies on conducting and analyzing household surveys and population censuses that are both expensive and administratively demanding. By contrast, analyses based on cell phone data and machine-learning technology have the potential to generate reliable and timely information on the spatial distribution of household poverty at a far lower cost than traditional household surveys or population censuses.

## **_Poverty in Guatemala_**

Poverty rates in Guatemala are higher than in other comparable middle-income countries, and the distribution of poverty reflects a set of overlapping regional, rural/urban and ethnic dimensions. Contrary to the trend observed in other Latin American countries, poverty rates in Guatemala have increased in recent years. The poverty rate<sup>2</sup> rose from 55 percent in 2001 to 60 percent in 2014, as the number of people living below the poverty line increased by 2.8 million. Poverty rates vary dramatically by administrative department. In 2014, Guatemala’s poorest department, Alta Verapaz, had a poverty rate of 83 percent and an extreme poverty rate<sup>3</sup> of 54 percent. Meanwhile, the poverty and extreme poverty rates in the country’s wealthiest department, where the capital of Guatemala City is located, were far lower at 33 percent and 5 percent, respectively. Moreover, while urban areas are now home to a majority of the country’s poor, poverty rates remain substantially higher in rural areas. In 2014, 35 percent of the rural population was living in extreme poverty, compared with 11 percent of the urban population. Poverty rates are also significantly higher among Guatemala’s indigenous population. Indigenous peoples represent 42 percent of the country’s total population, but in 2014 they accounted for 52 percent of the poor and 66 percent of the extremely poor in the country.<sup>4</sup> Guatemala’s high poverty rates and persistent income inequality are reflected in the country’s weak human development indicators. Limited and unequal access to public services such as education and health care have constrained human capital formation, while a lack of basic

> 2 The moderate poverty rate reflects per capita household consumption equivalent to US$4.00 per day in purchasingpower parity terms.

> 3 The extreme poverty rate reflects per capita household consumption equivalent to US$2.50 per day in purchasingpower parity terms.

> 4 Sanchez, Scott, and Lopez (2016).

**2**
The GNI per capita estimates and the updated operational thresholds are circulated to the Board of Executive Directors prior to the end of the financial year, so that these operational guidelines may be used in operational decision-making in the following financial year. The income classification is published at the beginning of the financial year, as close to July 1 as practical.

There is a broad misconception among both Bank staff and external users about the relationship between the income classification and the Bank’s operational guidelines. In the minds of many, low income countries equate to countries eligible to borrow from the International Development Association (IDA), and middle income countries equate to countries eligible to borrow from the International Bank for Reconstruction and Development (IBRD). This is not the case, because the threshold levels are different. Furthermore, GNI per capita is only one of several factors taken into account when determining access to World Bank lending windows and terms. In the case of IDA, an eligibility threshold guides access to concessional resources (for FY16, the threshold is GNI per capita US$1,215), but the major determinant is country creditworthiness. Similarly, while surpassing the IBRD threshold (which is not the same as the high income threshold) informs decisions about a borrower’s graduation from IBRD, factors such as the institutional capacity of the country and its credit rating are also considered.

The difference between the two systems is illustrated in Table 1. For example, for FY16, just 30 of the 77 countries eligible for IDA financing were low income, with the remaining 47 countries classified as middle income. 13 countries classified as high income were still eligible for IBRD lending.

**Table 1: Number of countries eligible for World Bank lending and their income classification, for FY16**

||IDA|Blend|IBRD|Not eligible|**Total**|
|---|---|---|---|---|---|
|Low income|29|1|0|1|**31**|
|Lower middle income|26|12|12|1|**51**|
|Upper middle income|4|5|42|2|**53**|
|High income|‐|‐|13|67|**80**|
|**Total**|**59**|**18**|**67**|**71**|**215**|

_Source_ : http://siteresources.worldbank.org/DATASTATISTICS/Resources/CLASS.XLS, accessed November 20, 2015.

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that is more based on knowledge and technology intensity and that can also be more inclusive (World Bank, 2017).

Our empirical methodology is straightforward. We consider a reduced-form specification relating the adoption of complex software with different occupation shares and measures of the task content of occupations at the firm-level, between 2007 and 2013.<sup>1</sup> In this setting, there are two concerns. First, the firm’s decision to adopt complex software is likely to be made jointly with employment and skills choices and be based on firm characteristics, such as managerial quality, that are to a large extent unobservable in our data set. Second, the decision to adopt complex software may depend on the firm’s actual mix of occupations and skills used. We mitigate these concerns in two ways. First, the panel nature of our firm-level data set allows us to account for all time-invariant firm unobservable characteristics through the inclusion of firm fixed effects in our reduced form. Hence, for each firm, we relate _changes_ in the adoption of complex software with _changes_ in shares of different occupations in total employment and changes in the task content of occupations. Second, we instrument the adoption of complex software at the firm level with a proxy for the degree of technological progress at the sub-national level, which is the regional share of households with access to a computer; furthermore, we allow this technological rollout to impact differentially firms depending on their sector’s ICT intensity. ICT intensity is measured as of 2003, prior to our sample period, for exogeneity reasons.

Our paper makes several contributions to the literature. First, to our knowledge, this paper is the first assessment of the impact of advanced technology adoption on the task content of occupations at the firm-level. In order to assess that impact, we construct several firm-level task indexes, weighting the task content of each occupation category by its share in the firm’s total employment. <mark>We divide the task content of occupations into four categories: abstract, routinecognitive, routine-manual, and non-routine-manual following the task-based literature (see Autor and Handel (2013) for the United States (U.S.) and Messina et al. (2016) for Latin America). Second, we p</mark> rovide evidence on the labor market impacts of ICT adoption at the firm level for a high-income Latin American country with very high inequality levels where there are important policy concerns on how to make the economy more productive and diversified while inclusive.

> 1 The sample period - 2007-2013 - encompasses the global financial crisis of 2007-2008, when aggregate gross domestic product (GDP) fell and labor market outcomes worsened in Chile. However, the impacts of the crisis were short-lived (Cruces et al., 2017; SEDLAC, 2017).

4
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# V. The Potential for African Structural Adiustment Policies

Considerable evidence shows that trade policy reforms in developing countries can make an important contribution to industrialization and growth (see Nash and Thomas 1991 for a discussion and empirical evidence). Trade restrictions and domestic policy interventions frequently create a bias against exports that prevents the achievement of otherwise attainable rates of growth.<sup>2</sup> ' Given that the previous analysis showed external barriers do not account for Africa's diminished role in world trade, this raises the question of whether the regions own trade policies were a factor. Although it previously would have been difficult to analyze this question empirically (due to a lack of detailed statistics on African and other developing countries' trade barriers) several initiatives by UNCTAD (1987) and UNCTAD and the World Bank (1995) provide data that allow one to address the issue.' Table 7 utilizes these data sources for cross-country comparisons of trade barriers. The table shows: (i) the average tariff rate; (ii) the average incidence of tariffs and all other import charges; and (iii) the nontariff barrier coverage ratio on imports into Africa and several other country groups or countries. Specifically, the table provides this information for those developing countries that achieved 1962-64 to 1992-94 compound annual growth rates for non-oil exports that were at least one percentage point greater than the corresponding rate of growth in world trade. These "fast growing exporters" trade 2'For example, Sachs and Warner (1995) found that countries with open trade policy regimes over 1971-89 had average per-capita GDP growth rates 2.5 percent a year higher than countries with closed ones, and also had a much higher degree of success in shifting their exports from primary commodities to manufactures. The World Bank (1996, Chapter 2) provides extensive empirical information showing that countries with liberal trade regimes experience superior export and economic growth rates.

22The UNCTAD (1987) report provides detailed statistics for the mid- to late 1980s (generally down to the fivedigit SITC level) on 89 developing countries trade and trade barriers, 24 of which were in sub-Saharan Africa. In recognition of the value of such information for research and policy purposes, -the World Bank commissioned UNCTAD (UNCTAD sand the World Bank 1995) to compile similar up-to-date information on trade barriers in 19 sub-Saharan countries. This information is maintained in a computerized format in both organizations. The reader should note that the discussion in this section is based on data for African countries for which trade barrier information was available (see Table 8 for a listing). However, there is no reason to believe that the missing countries had protectionist profiles that differed substantially from those that were included.
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Figure 3 compares FDI and FAI from different types of parent country into the host countries in our sample, expressing each series in shares relative to the total. The key differences highlighted above—that FAI is reported by ultimate parent and reflects real investment, whereas FDI is reported by immediate parent and reflects financial flows— appear evident in the data. Over the sample period, low tax countries, defined as those with a statutory tax rate in the first quartile of the distribution (which means a statutory tax rate of less than about 20 percent), account for a greater share of FDI than of FAI (Panel (a)). The same is true, and even more marked, in relation to investment hubs, defined as countries with an average FDI/GDP ratio of above 150 percent (Panel (b)).

Importantly, many of the observations in the FAI dataset—around 34 percent—are zero.<sup>31</sup>

# **4 Empirical Strategy**

This section describes the approach taken to estimation.

## **4.1 Specification and Other Data**

The focus of interest to which the analysis in Section 2 points is the impact of the _I − METR_ and, more generally, of its key components: the rate differential ∆ _h_ , the host country _KF_ - _METR_ and—operating through some channel not captured in the modeling above— the parent country _KF_ - _METR_ . Taking 1 _/_ (1 + ∆ _h_ ) _≈_ 1 _−_ ∆ _h_ , and now with many parent countries and time periods, we take as our most general estimating equation: where _FAIhpt_ denotes bilateral investment _Ihpt_ in tangible capital goods in host country _h_ (of which there are 32) from ultimate parent country _p_ (of which there are 187) in year _t_ (running from 1998 to 2016), scaled by the lagged capital stock in _h_ attributable to investment from

> 31This is also the case for FDI, with around 43 percent of zeroes.

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Among non-contributory transfers, family benefits and social assistance programs are the most progressive, but social assistance has a relatively small redistributive effect given its small size (Table 6). Social pensions have the largest redistributive effect, and together with family benefits have the largest impact on extreme poverty. With the exception of the child birth grant, all of the noncontributory programs are targeted to the bottom 20 percent of the distribution (Figure 6).

In assessing how education and health spending benefit the poor, we have to caution that our analysis does not address the quality of such spending. We use government expenditure data on the various forms of education and health services to estimate unit costs of these programs. The analysis thus assumes that the actual benefit received by individuals is equal to the amount spent per capita. As the quality of school infrastructure, teachers, and health clinics and hospitals vary across the country then this is a clear limitation of the analysis.

The results show that both education and health are progressive and equalizing.<sup>16</sup> For education, 29 percent of total education spending goes to the bottom 20 percent of the distribution, while only 15 percent was concentrated at the top 20 percent. This is mostly on account of primary school spending which has the largest redistributive effect (Table 6). Indeed, primary, gymnasium and vocational school spending benefit families at the bottom of the distribution slightly more than those at the top, in part because lower income households tend to have more children (Figure 7). Interestingly, tertiary education spending is much less concentrated at the top of the distribution than what is typically observed in middle-income countries. It is also interesting to note that 26 percent of spending on kindergarten is benefiting the top 20 percent of the distribution, mostly on account of greater access to these services in urban areas. However, this could also reflect the fact that the subsidy for private kindergarten is as much as 75 percent of the standard subsidy for public services.

Health spending is equally distributed across the population, with slightly more (20.3 percent) spending concentrated at the bottom 20 percent of the distribution given that some wealthier households opt-out of public care (so that 19.7 percent of health spending is concentrated at the top 20 percent). Note that in relative terms, health spending is strongly progressive, as it makes up a larger share of the incomes of the bottom of the distribution.

> 16 Following CEQ conventions, we do not assess the poverty reducing impact of health and education spending, since households do not necessarily feel wealthier on account of this spending. Moreover, since these are investments in human capital, the full benefit of this spending will not be clear in a static setting such as this one.

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