rafmacalaba/datause-encoder-data huggingface dataset:
better measure and understand the causal impacts of policies, policy externalities, and access to services. In addition, using GPS can improve the quality of the household survey data collected.

Moreover, one of the greatest arguments for collecting GPS information now is the option value it gives for unforeseen future applications.<sup>22</sup> As the stock of geo-referenced data increases within developing countries, we are likely to see a number of innovative applications which combine household survey data with other geographical information. There are also a number of interesting new research questions in econometrics and sampling methodology which arise out of the use of GPS. For example, typical household surveys often involve populationbased clusters, which are not randomly spread across geographic areas (Montana and Spencer, 2004). As a consequence, more research is needed to determine how to best estimate spatial models using a sample which is not spatially representative at the local level, and to determine how to best sample within communities in order to best allow both spatial and non-spatial uses of the data.

# **References**

Andrabi, Tahir, Jishnu Das, Asim Khwaja, Tara Vishwanath, and Tristan Zajonc (2007) “The Learning and Educational Achievement in Punjab Schools (LEAPS) Report” _Mimeo_ The World Bank.

Anselin, Luc (2002) “Under the hood: Issues in the specification and interpretation of spatial regression models”, _Agricultural Economics_ 27: 247-67.

Bateman, I. J., J. S. Brainard, G. D. Garrod and A. A. Lovett (1999a), “The impact of journey origin specification and other measurement assumptions upon individual travel cost estimates of consumer surplus: A Geographical Information Systems analysis”, _Regional Environmental Change_ 1(1), 24–30.

Bateman, I, A. Jones, A. Lovett, I. Lake and B. Day (2002) “Applying Geographical Information Systems (GIS) to Environmental and Resource Economics”, _Environmental and Resource Economics_ 22:219-69 Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon (2006) “Kagera Health and Development Survey 2004 Basic Information Document” The World Bank <u>www.worldbank.com/lsms/country/kagera2/docs/KHDS2004%20BID%20feb06.pdf</u> [accessed March 13, 2007].

Bithell, John (1990) “An application of density estimation to geographical epidemiology”, _Statistics in Medicine_ 9(5): 691-701.

Borjas, George (2004) “Economics of migration” _International Encyclopedia of the Social and Behavioral Sciences_ pp. 9803-9809.

Brady, Henry and Iris Hui (2006) “Is it worth going the extra mile to improve causal inference? Understanding Voting in Los Angeles County”, Mimeo. Department of Political Science, UC Berkeley.

> 22 See Turner (2006) for some intriguing ideas with regard to visual representation of geo-coordinates.

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# **-** **<u>Table 7: Development of a multi dimensional poverty proxy</u>**

The creation of the poverty proxy is based on the search for objective and readily verifiable indicators that show a strong statistical correlation with household income. In order to minimize administrative costs, the search was limited to variables that are already collected is the Argentine SISFAM (Unique Identification and Registration System for Beneficiaries of Social Programs), many of which also appear in the commonly used index of Unsatisfied Basic Needs.

The multi-dimensional poverty proxy aims to capture per capita family income, based on a weighted index of the following five variables, selected on the basis of being simple, objective and easily verifiable.

Overcrowding ( _O_ ) – More than three persons per room in the family dwelling. Inappropriate housing ( _H_ ) – Housing in workplace, guesthouse, or shantytown. Unsanitary conditions ( _S_ ) – Housing without bathroom, latrine, or sewerage. Presence of infants ( _I_ ) – Presence of children under the age of five in the household. Head of household education ( _E_ ) – Head did not complete primary education.

## _Proxy_ =β0+β1 _O_ +β2 _H_ +β3 _S_ +β4 _I_ +β5 _E_

The weighting factors used to calculate the proxy are determined by a statistical estimation that maximizes the correlation between per capita household income and the five variables indicated. Different models are estimated for each geographic region, with results reported in Table A5 of the Annex. In order to determine whether a household qualifies for subsidy based on this indicator, the household’s score on the multidimensional poverty proxy is compared to the official poverty (or extreme poverty) line. Those below the line are eligible, while those above the line are ineligible.

5.8 However, a recent international study on the issue of targeting social programs concludes that programs that make use of targeting devices are able to transfer, on average, 25% more resources to low income households than those that do not<sup>14</sup> . Moreover, in the case of better-designed programs the difference increases to 200% or even 400%. Nevertheless, the efficacy of targeting mechanisms differs substantially according to the details of implementation in each case.

5.9 One possible means of reducing the problems associated with individual targeting is to target geographically, based on zones or neighborhoods that present high aggregate poverty indices. This approach substantially reduces the administrative costs associated with targeting, however its efficacy is contingent on the extent to which poor households are indeed clustered in particular geographical areas, which varies considerably according to the urbanization patterns of different cities. Without considerable spatial clustering of poor households, a geographic approach is likely to lead to very higher errors of inclusion and exclusion.

> 14 Coady, D., Grosh, M., and Hoddinot, J., 2003. _Targeting of Transfers in Developing Countries: Review of Experiences and Lessons_ , Social Protection Discussion Paper Series forthcoming, World Bank, Washington DC.

34
in Section 4.2 and Appendix 2) provides information on household expenditures on various types of school fees faced by migrants and local residents in 15 cities across China. Figure 1 graphs the distribution of mean (total) school fees paid by migrant households, which ranges from 1,100 yuan in Bengbu (a city with a dominant food industry in the Northern Anhui province) to more than 4,500 yuan in Shenzhen (the fastest growing migrant receiving city in the South).<sup>7</sup> School fees as a share of migrant households’ consumption range from 4 percent in Hangzhou to 25 percent in Shenzhen, and represent, on average, 10 percent of migrant household consumption. For local households (i.e., urban residents with a _hukou_ ), school fees represent on average 11 percent of their consumption. School fees can be further broken down into different components, with tuition fees representing 40 percent and 31 percent of total school fees for migrants and urban residents respectively.

Figure 2 plots the shares of migrant households that bring school-age children with them against the median school fees at the city level. These two variables show a negative relationship, with a correlation coefficient of -0.28.<sup>8</sup> Tuition fees in rural areas were formally abolished by the central government in 2006, thus the urban school fees paid by migrant households represented additional education expenses to their budget (which they would not have to pay in rural areas). The central government announced the abolition of these fees in urban areas as well after 2008 (State Council, 2008), but in practice, migrant households still often have to pay various “hidden” school fees (Li, 2013; Lu and Zhou, 2013). Using the RUMiC survey, we estimate that migrant households received a school fee reduction of approximately 20 percent, but not 100 percent, in 2008 (Appendix 3, Table 3.4).<sup>9</sup>

> 7 Unless stated otherwise, school fees are calculated from the sample of migrants with at least one child enrolled in a school in the city. One yuan was approximately equal to 0.14 US dollars in 2008 (World Bank, 2016). 8 This correlation is somewhat stronger at -0.44 for the mean school fees. We describe how we construct different measures of school fees in section 4.2.

> 9  Our estimates using the most recent household survey from China in 2012 (i.e., the China Family Panel Studies implemented by Peking University) also indicate that, four (six) years after the official abolition of school fees in urban (rural) areas, both urban and rural households still paid various school-related fees. See Figures 3.1a and 3.1b in Appendix 3 for more details.

6
The first order conditions are given by: Where �� is the multiplier of the budget constraint.

Thus the competitive equilibrium consists of quantities and prices ����, ����, ��������. In general, it is possible to show that the competitive equilibrium allocations are Pareto optimal, since the household’s problem is the same as the planner’s problem. Later we will see that in the presence of distorting taxes this may not be the case.

# _External shocks_

There are multiple external risks, including macroeconomic crisis, natural disasters, weather-related events, health-related shocks, crime and violence, that individuals and societies as a whole face over time which can have pernicious consequences for the income generation capacity of households (World Bank, 2013b). Risks turned into shocks could potentially lead to asset loss, disinvestment, unemployment, malnutrition, and child labor, when people lack means to effectively manage and cope with risks. The realization of a household’s market income may then vary due to such external shocks.

Uncertainty can be added to the basic model in several ways. For instance, by directly affecting the stock of productive asset at, or the technology f�at�. We follow the second specification. Building upon the definition of market income given in (1), the first constraint can be modified to include the presence of a random i.i.d. shock ��. As mentioned above, shocks could be aggregate such as weather shocks, financial crisis, or idiosyncratic; through machinery destruction, health shocks, etc.

16
scarce and highly irregular, yielding crops of low quality and low income generating capacity. These small farmers lack modern production technology, basic infrastructure to store harvests to take advantage of cyclical price fluctuations, technical assistance to improve productivity, and organized marketing facilities. Family income is therefore highly variable and there is little opportunity for saving. They have very few assets, including education, and are very vulnerable. Despite the limitations, poverty in rural areas has been reduced during 1995-2001 by 3 percentage points to 52 percent. In the same period, urban poverty remained unchanged at 37 percent.

The differing characteristics of the smallholders, sharecroppers, and wageworkers suggest that a poverty reduction strategy needs to provide multiple paths out of poverty tailored to the heterogeneous cross-section of poor rural households. A national study for Brazil<sup>11</sup> suggests that this will involve at least a five-pronged approach aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural non-farm sector, (iv) migration of the young, and (v) provision of safety nets for those “trapped” in poverty. The recommended measures include improving human capital endowments, reforming the land, labor and financial markets, enhancing research and extension, improving the supply of public goods and services, pricing and trade policies, and transfer programs.

In order to increase land productivity and labor-intensive farming, it is necessary to facilitate the movement toward farming medium-sized land holdings, in part via facilitating land rentals and sharecropping arrangements.<sup>12</sup> This can be done by providing more secure titles to land and by the revision of the land legislation so as to secure longer-term tenancy arrangements, resolution of disputes regarding interpretation, and enforcement of land rental arrangements. The impact of such a program would be greatly enhanced by simultaneous adjustments of the labor code and of the land tax system. Labor laws have had an anti-sharecropping bias. In this context, the experience with the Rural Leasing Exchange in the Triângulo Mineiro contains useful lessons that are worth considering for Bahia.

A more direct way of improving farm productivity and revenues is via the community-based approach to land reform. Under this approach, beneficiary groups negotiate directly with potential sellers of suitable properties, and then obtain financing for the purchase of the land and complementary sub-projects and receive technical assistance. Two successful pilot projects—the Ceará Rural Poverty Alleviation Project and the Cédula da Terra—redistributed about 640,000 hectares to benefit about 23,700 households using this approach (about 1 percent of the rural poor).

> 11 See World Bank (2001c).

> 12 See World Bank (2001c).

26
about half of that of Bangladesh (7 percent versus 15 percent). While this highlights the growing attractiveness of Ethiopia as an investment destination, it may also highlight that Ethiopian employers in this sector have higher monopsony power compared to their counterparts in Bangladesh.

# **3.1 Robustness to use of alternative data sources**

The productivity figure used for Ethiopia are consistent with those obtained by an independent survey run in Addis Ababa at a similar point in time for the study of Abebe et al. (2017). I show this by comparing the distribution of sales for worker in the two A.7 A.7. surveys in Figure and by showing means and medians in Table Average sale for workers in Addis Ababa is remarkably similar across the two surveys (22,708 USD and 22,462 USD). Median sales are somewhat higher in the survey of Abebe et al. (2017), but the difference is not excessively large.

Finally, the figures reported in the previous section align well with the perception of foreign firm managers. I have carried out qualitative field in the Hawassa Industrial Park at several points during 2018. During these visits I had several personal conversations with firm managers about productivity and labor management. On average, firm managers believed that productivity in Ethiopia’s garment factories is between a third and a half of that of comparable factories producing similar products in India; and that wage costs are about one third of those in India. These figures confirm the aggregate A.1. statistics reported in Figure

# **4 Where does Ethiopia’s low productivity come from?**

**Fact 5.** _Capital stock, firm size and sectoral composition do not explain the low productivity of Ethiopian firms._

To study the factors that determine the low productivity of manufacturing firms in Ethiopia we run a regression model where we regress sales per worker on firm size, sector, and value of the capital stock. Specifically, we create dummies for quartiles of firm size and capital stock. We then create dummies for each sector and introduce them in the model on their own, and interacted with the dummies for firm size and capital stock. We compute the residual productivity for this regression model, and standardise this value using the average and standard deviation of the residual productivity for firms in Vietnam. We present the result of this exercise in Figures 3 and A.8.

The main conclusion from this analysis, is that, after accounting for size, sector, and capital, Ethiopian firms are still less productive than firms in competitor countries. In 8
# **Measuring the Effect of Internet Adoption on Paper Consumption**

**Luis Andrés, Alejandro Zentner, and Joaquín Zentner**<sup>**1**</sup>

**Key words:** Internet adoption, paper consumption, contamination, fixed effect model

**JEL:** L86, O13, O33

> 1 Luis Andrés is Lead Economist in the sustainable Development Department for the South Asia Region at World Bank, Alejandro Zentner is Associate Professor of Managerial Economics of the Naveen Jindal School of Management at University of Texas at Dallas, and Joaquín Zentner is a PhD student at the Universidad Catolica Argentina. They are grateful to Marianne Fay for her comments and suggestions. Financial support from the World Bank Research Support Budget (RSB) is gratefully acknowledged. Authors’ email addresses are: landres@worldbank.org, <u>azentner@utdallas.edu, and</u> joaquinzentner@hotmail.com .
with innovation and adoption of other non-automation technologies in industries with high exposure to robots. In that case, our estimates would be biased.<sup>12</sup> To mitigate such concerns, we exploit 2006 census year information on R&D units, product and process innovation, use of computers and the Internet to construct an index of “innovation intensity”. Then we interact the index with year effects. To the extent that innovation activity and IT use are correlated to plants’ propensity to adopt non-automation technologies, this approach should allow us to purge the estimates from their impact.

# **2.5 Is the Plant-Level Approach Solid?**

This section presents several pieces of evidence on the robustness of the key assumption underlying equation (3), i.e. that secondary education is a good proxy for routine taskintensive employment. If that is the case, plants with a larger share of workers with secondary education should be more likely to adopt robots.

First, we check whether the industries with an initial large share of secondary education workers have adopted relatively more robots in subsequent years. Figure 3 shows that this is indeed the case, thus providing some initial support to our approach. In particular, the two industries with the highest penetration of robots, Motor vehicles and Rubber and plastics, are among the industries with the largest share of secondary education workers.

Next, we exploit the fact that in our manufacturing data plant level investment in machinery and equipment should also include investment in robots. Consistently with this assumption, Figure 4 shows that imports of industrial robots shipped to each manufacturing industry in a year are positively related to total investment in machinery and equipment in the same industry-year pair.<sup>13</sup> Having established a positive link between imports of robots and aggregate investment in the data, an important check of the validity of our plant level proxy for exposure to automation is whether _ETRf,t_ in (3) is positively correlated with machinery and equipment investments at the plant level. This is done in column (1) of Table 3, which reports

> 12For instance, logistics management software might be particularly valuable in the motor vehicle industry, which is also a strong adopter of robots. If large plants are more likely to have a large share of secondary education labor, the estimates of _γ_ 1 in (3) might capture the impact of software.

> 13The figure excludes industry-year cells with zero imports.

13
An estimated 90% of the population of the population of northern Uganda was uprooted throughout the conflict, with the majority of IDPs living in squalid displacement camps, sometimes for as long as a decade (Brookings 2010). Our Uganda surveys thus capture perceptions at two key junctures in the displacement crisis in northern Uganda: during ongoing violence (2007) and as resettlement efforts began after the LRA fled to neighboring countries (2010).

Together, these three contexts are united in that they experienced large scale forced displacement due to ongoing on recently ended episodes of wide-scale political violence. The displacement in all contexts analyzed in this paper was overwhelmingly internal and driven by violence. As such, the theoretical and empirical scope of the paper does not include cross-border refugee flows or economic migration, which likely have distinct causes and consequences. We are instead able to examine the consequences in behaviors and perceptions of experiencing internal displacement across the cases.

Differences in the character and stage of violence that caused the displacement waves across contexts that may limit direct comparisons while also providing analytically useful variation to draw on. Each survey focuses on subnational geographic units: Mosul city in Iraq, Central Mindanao region in the Philippines, and the northern Acholi districts in Uganda. When we conducted our surveys, ISIS has recently been defeated and fled Mosul, so our Iraq sample is recently postviolence. Similarly, our 2007 sample for Uganda took place recently post-violence and during on-going peace talks. In contrast, the 2010 Uganda sample took place after the peace negotiations collapsed, but violence had not resumed after the Lord’s Resistance Army fled to neighboring South Sudan and Democratic Republic of the Congo. Our survey in the Philippines occurred as violence was ongoing in Central Mindanao.

The perpetrators of the violence also varied across the contexts in ways that may have implications for how willing or able an IDP to participate in their local community. In Iraq, the population we surveyed had recently lived under ISIS rule for 3 years and recently experienced the battle where government and coalition forces retook Mosul. In northern Uganda, fighting between the government and the Lord’s Resistance Army (LRA) caused massive displacement. By one count, some 90% of the population in northern Uganda was been uprooted due to this fighting (Miller 2007). In the Philippines, cyclical local-level clan conflicts (known as _rido_ ) and civil war co-existed and produced large levels of displacement within Central Mindanao. The ethnic diversity in the regions we sample from also varies: Acholi district in Uganda is homogenous and our sample Philippines is mostly homogenous because of the relatively restricted geographic scope of our enumeration area. Mosul is a diverse city, with a number of ethnic and religious groups represented.

5