rafmacalaba/datause-encoder-data huggingface dataset:
|Other manufacturing||||||||
|---|---|---|---|---|---|---|---|
|industries|0<br>0|1|0|3|2|6|8|
|**Total**|1<br>11|89|237|391|635|508|669|

_Source_ : Linked ENESTYC-EIA panel data. _Note_ : Firm size is defined as follows: micro with 1-15 workers, small with 16-100 workers, medium with 101-250 workers and large with over 250 workers.

Considering the distributions presented in Table 17, selecting a control group based on observable attributes such as sector and size is likely to be inadequate. Even with similar sector-size distributions, the treatment and control groups can have very different preprogram values of sales, productivity or wages.

The EIA panel contains annual data on measures of firm performance such as sales, gross value of production, employment, total compensation, and income from exports, as well as some intermediate outputs that the programs may affect, such as technology transfers. Comparing the means for the key outcome measures of the treatment and control groups, several points emerge (Table 18).<sup>4</sup> First, there are statistically significant difference in means between the treatment and the control group for wages, employment, and worked hours, suggesting that the treatment group was doing fairly well. Second, these differences are similar before and after the treatment, meaning that the programs were not having additional effect on the performance of firms. This conjecture is analyzed further in the next section, which estimates the impact of programs controlling for selection biases.

**Table 18. Differences in Means Between the Treatment and the Control Group, Any Program**

||**Year When P**<br>**Starts (****_t_**<br>**Difference in**|**rog**<br>**_-_0)**<br>|**ram**|**Two Years Afte**<br>**Started (**<br>**Difference in**|**r Program**<br>**_t_+2)**<br>|
|---|---|---|---|---|---|
|**Outcome Measures**|**Means**||**t-test**|**Means**|**t-test**|
|Employment|95|**|3.84|100|**<br>3.89|
|Worked hours|217|**|3.65|240|**<br>3.79|
|Wages|16,389|**|3.11|18,388|**<br>3.37|
|Gross production|78,708||0.72|99,553|0.81|
|Inputs|73,491||1.01|78,533|0.99|
|Value added|5,216||0.12|21,020|0.41|
|Fixed assets|-167,693|**|-5.56|-144,686|**<br>-3.89|
|Domestic sales|64,334|*|1.66|52,902|1.16|
|Foreign sales|34,052||0.44|56,737|0.72|
|Total sales|98,386||0.99|109,639|1.01|
|Technology transfers payments|-712||-0.35|107|0.07|
|Technology transfer income|-30||-0.18|-46|-0.19|
|Maquila services expenditures|-372||-0.54|-844|-1.17|
|Maquila services incomes|4,215|*|1.77|4,638|1.46|

_Source_ : Linked ENESTYC-EIA panel data. _Notes_ : (1) Monetary variables are in real 2005 pesos.

(2) * and ** denote significant differences at 10% and 5% level, respectively.

> 4 Annex 1 presents the means for the other programs considered in the analysis.

21
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**TABLE 2.6 Reconciliation of US Census of Manufactures and National Accounts Rstimates for 1975**

||**ASM**|**BEA**|**BEA**|**Taxes**|**Col.(2)**|**Ratio of**|
|---|---|---|---|---|---|---|
|<br>V<br>a<br>(m|Census<br>Concept<br>of Gross<br>alue Adde<br>t Factor<br>Cost<br>ill. USS)|National<br>Accounts<br>Concept<br>d of Gross<br>Value Adde<br>at Market<br>Prices<br>i|Inventory<br>Valuation<br>Adjustmen<br>(mill.USS<br>d<br>|<br>Minus<br>Subsidies<br>t (mill.US<br>)|Minus<br>Col.(3)&(4)<br>S)<br>at Factor<br>Cost<br>(mill.USS|Adjusted<br> National<br>Accounts to<br>Census Values<br>) (per cent)|
|||(mll. USS|||||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Food & Kindred|||||||
|Products|39,985<br>|}38,886<br>|}1,193<br>|}6,219<br>|}31,474<br>|165.4<br>|
|Beverages|8,110|}|}|}|}|}|
|Tobacco Products<br>|3.722<br>|<br>5,071<br>|-<br>87<br>|2,432<br>|2,726|**73.2**|
|Textile Mill Product|s12,044|10,088|-<br>62|408|9,742|80.9|
|Apparel, Other|||||||
|<br>Textile Products<br>|14,749|11,204|-<br>47|168|11,083|**75.1**|
|Lumber and Wood|||||||
|Products|10,356<br>|10,406<br>|-<br>166<br>|371|10,201|98.1<br>|
|Furniture & Fixtures<br>|<br>6,290|4,933|-<br>63|124|4,872|77.5|
|Paper and Allied|||||||
|Products<br>Printing and|17,944|14,286|-<br>214|708|13,792|76.9|
|<br>Publishing<br>|24,641|18,237|-<br>101|406|17,932|72.8|
|Chemical, Allied|||||||
|<br>Products<br>|44,976|28,982|-<br>473|1.376|28,079|62.4|
|Petroleum and Coal|||||||
|Products<br>Rubber, Miscellaneou|10.500<br>s|10,882|-<br>42|4,700|6,224|59.3|
|Plastic Products<br>Leather, Leather|13,599|10,507|-<br>75|1,011|9.571|70.4|
|Products|3,187|2,421|-<br>33|41|2,413|75.7|
|Stone Clay Glass|||||||
|, ,<br>Products|14,849|11,506|-<br>165|507|11,164|75.2|
|Primary Metals<br> <br>|30,367|28.439|-<br>178|1,424|27.193|89.5|
|Fabricated<br>Metal|||||||
|Products<br>Machinery,<br>except|34.203|27,152|-<br>415|744|26,823|78.4|
|<br><br>Electric|51,044|40,997|-1,422|948|41,471|81.2|
|Electric, Elec-|||||||
|tronic Equipment|34,845|28,480|-<br>508|657|28,331|81.3|
|Motor Vehicles &|||||||
|Equipment|21,466|21,654|-<br>277|1,058|20.873|97.2|
|Other Transportation|||||||
|<br>Equipment<br> <br>|<br>23,871|18,415|-1,401|118|19,698|82.5|
|Instrument,<br>Related|||||||
|<br>Goods<br> <br>|14,158|9,548|-<br>177|214|9,511|67.2|
|Miscellaneous<br>Manu-|||||||
|facturing<br>Goods|7,580|6,058|-<br>67|144|5,981|78.9|
|**Totala**|442,485|358,152|-4,780|23,778|339,154|76.6|

a) excludes USS 19,014.9 million payrolls of head office and auxiliary personnel.

Source: Annual Survey of Manufactures 1975-1976. Bureau of Census, US Dept. of Commerce, May 1979, and information supplied by Robert Parker, Associate Director of National Economic Accounts in July 1985.
Policy Research Working Paper 10000

# **Abstract**

This paper studies the impacts of the large-scale Road Sector Development Program in Ethiopia between 1997 and 2016 on local economic activity and land cover (urbanization and cropland). It exploits spatial and temporal variation in road upgrades across Ethiopia, together with high-resolution panel data derived from satellite imagery. The findings show that road upgrades contributed to increases in local economic activity, as proxied by nighttime lights and urban land area. However, there is significant heterogeneity in the results across baseline levels of economic activity. Specifically, gains from road upgrades are concentrated in areas with moderate-to-high initial levels of economic activity. By contrast, there was little, or even negative, growth in areas with low levels of initial economic activity. Finally, the findings show that road upgrades contributed to a reduction in cropland in areas with medium-to-high baseline nighttime lights. The results suggest that Ethiopia’s ambitious road infrastructure development program overall increased local economic activity and urbanization, but that it also had important distributional implications that need to be taken into account when planning such infrastructure programs.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aduhaut@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The 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 authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. 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._ Produced by the Research Support Team
**1. Introduction**

Establishing an investment<sup>promotionagency hasbecome acentralpart of most</sup> countries' development<sup>strategies.Today,there are worldwidemore than160national</sup> investment promotion agencies<sup>(IPAs)and over 250sub-national ones(UNCTAD,2001).</sup> This trend is relatively<sup>newsinceonlya handfulof theseagenciesexisted20 yearsago.</sup> Yet, the reality is that very<sup>little isknown aboutwhat theseagencies are actuallydoing in</sup> most countries, especially<sup>in the developing world.</sup> The objective of this paper<sup>is toassess to what extent doesinvestmentpromotionhelp</sup> explain cross-country variations<sup>in foreigndirectinvestment(FDI) flows.We proposea</sup> model<sup>in whichthe promotionagency'sbehavior is assumed to reflect</sup> simple analytical the interest of the government<sup>(or a supervisoryboard) under the constraint of limited</sup> budgetary resources. It<sup>followsa certain traditionin public finance,see Heller,1975.</sup> Our model remains simple,<sup>as</sup><sup>**it**is principallyuse to derive an equationthat can be easily</sup> estimated for our sample<sup>of countries. It shouldalso be seenasa complementto the</sup> seminal work of Wells and<sup>Wint, 2001,whohaveprovidedthe theoreticalunderpinnings</sup> of adopting investment promotion<sup>policies toattractFDI.2</sup> Due in part to a lack of reliable<sup>data,no broad empiricalstudy of investment promotion</sup> agencies and their effectiveness<sup>in attractingFDI hasbeen doneto date3.This empirical</sup> gap means that the debate<sup>onthe effectivenessof IPAsis still very open.Weuse data</sup>

**2** Wells and Wint (2001)<sup>defineinvestment promotionas</sup><sup>_"activities that disseminate information about, or_</sup> _attempt to create an image of_<sup>_the investment site and provide investment services for the prospective_</sup> _investors"._ This definition<sup>encapsulatesthetwo mostimportant analytical justificationsfor IPAs.Thefirst</sup> consists of its role **in** communicating<sup>and disseminatinginformation.Since thislatter can be consideredas</sup> a public good, **it is** possible<sup>that the privatesector behaviorwillnot leadtothe optimalsocialwelfare. Asa</sup> matter of fact, local firms<sup>may voluntarilyrestrict information flowstoprevent the entryof new potential</sup> competitors. The second<sup>justificationis that theIPA canplay a roleof coordinatingmost activitiesaimed</sup> at improving the business<sup>environmentin the host country.This role can rangefrom providingassistance</sup> to potential and existing investors<sup>**in**their daily problems to lobbying forkey policyand legal reforms.</sup>

3 The only empirical examination<sup>of theimpactof FDI promotionon FDI flowswasconductedby Wells</sup> and Wint, 2001, based on data<sup>gatheredin1988.Thisstudyhad seriousshortcomingsinterms of the</sup> concept used to measure the<sup>promotion</sup> 3
frequently considered policy change; second, it is a very well-defined and discrete treatment that is amenable to various analytical approaches (as contrasted with vaguer concepts such as teacher quality).

The extensive focused analysis on the causal effects of class size reduction unfortunately does not provide any clear indication that such policies consistently improve student outcomes. This work, again well-summarized elsewhere (Hanushek (2003), Wößmann (forthcoming-a)), sometimes indicates benefits of reductions and equally as frequently does not.<sup>70</sup> From these analyses, general class size reductions do not appear to be worth their expense in terms of student outcomes.

The interpretation of this work is important, because it also carries over to discussions of developing country policies. A simplistic view of this argument – convenient as a straw man in public debates – is that ‘money never matters.’<sup>71</sup> The research of course does not say that. Nor does it say that ‘money cannot matter.’ It simply underscores the fact that there has historically been a set of decisions and incentives in schools that have blunted any impacts of added funds, leading to inconsistent outcomes. That is, more spending on schools has not led reliably to substantially better results. We return to these issues below.

The most current research on school inputs and achievement has also led to another set of conclusions – that teacher quality is enormously important in determining student achievement. This work has concentrated on whether some teachers consistently produce more gains in student achievement than other teachers.<sup>72</sup> Working with extensive panel data on individual students, these studies have confirmed large differences among teachers in terms of outcomes in the classroom.

But, they have also shown that the observed differences are not closely related to common characteristics of teachers (such as amount of teacher education). Some attributes of teachers – such as having one or two years of experience – have explained part of the differences in teacher quality, but these factors are a small part of the overall variance in teacher results. This inability to identify specific teacher qualities makes it difficult to regulate or legislate having high quality teachers in classrooms. It 70 More details on the class size analyses and debates can be found in Angrist and Lavy (1999), Hoxby (2000b), Krueger (2000), Hanushek (2002a), and Wößmann and West (2006).

71 For the historical framing of the question, see the exchange between Greenwald, Hedges, and Laine (1996) and Hanushek (1996).

72 See, for example, Hanushek (1971, 1992), Murnane (1975), Aaronson, Barrow, and Sander (2003), Rockoff (2004), Rivkin, Hanushek, and Kain (2005), Hanushek, Kain, O'Brien, and Rivkin (2005), Boyd et al. (2005), and Kane, Rockoff, and Staiger (2006).

65
<mark>on social media or crowd sourcing. For example, it has been shown that in Indonesia, food price related tweets correlated with real events, and that the number of tweets on food prices correlated with food price inflation (Global Pulse 2014). Twitter, blogs and similar social networks may also provide early signs of emerging disasters. Extracting these early signs requires processing of huge amounts of data, a process where analytic tools developed for Big Data analysis will be useful. The potential of Big Data in agricultural research is in combining large amounts of data collected from various sources for making predictions over time, e.g., pest outbreaks, livestock behavior, or soils and weather.</mark> <mark>Sophisticated precision farming systems are more commonly applied at technologically advanced farms and plantations, with the underlying logic to combine various remote sensing data and satellite imagery for a given farm parcel to provide precise information for growth (e.g., sensors for soil conditions, ground water level, and rain water precipitation detectors combined with irrigation optimization systems). As this can be done remotely, it saves significant time and labor when compared to manual sampling, and the use of calibrated technology makes the system less prone to error when assessing appropriate growth conditions (Hamrita and Hoffacker 2005). The benefit of the system is the resource optimization that can be done with the help of the information acquired. For example, the system can detect where there are nutrient deficiencies in the soil and thus additional fertilizers can be distributed to the areas where there is the most acute need for fertilizers instead of fertilizing the entire farm land as has traditionally been done. Irrigation or pesticide needs can similarly be detected and precisely applied. Digital tools can be applied to irrigation systems such as pumps that can be automated and controlled via mobile phones, such as the Nano Ganesh system in Pune, India where the farmers are able to save water, energy, and time by remotely controlling their irrigation pumps (Tulsian and Saini 2014). Precision farming requires investments in these systems, but once the systems are installed, it contributes directly to time, resource and cost-efficiency and thus to growth by releasing resources. Precision farming systems have also been shown to support environmental sustainability as the natural resources are being continuously monitored, and actions are taken accordingly, before nutrition depletion or drought takes place.</mark> <mark>Although the demand for accurate production advice and early warning systems is high, there are significant transaction costs associated with providing these services such as the cost of</mark> 17
mind and deemed the HDI a success after noting the attention the index attracted and the subsequent debates it fostered.<sup>9</sup> The European Skills Index (ESI) was first released in 2016 under the title “Making Skills Work Index” and recently updated in 2020. The ESI developed by CEDEFOP measures the performance of EU skills systems. The ESI consists of three pillars - _skills development; skills activation; skills matching_ - each of which measures a different aspect of a skills system. The ESI is based on 15 individual indicators and then averaged to arrive at a composite score. The scores range from 0 (lowest) to 100 (highest). A score of 75, for instance, indicates that a country has reached 75% of the ideal performance leaving a 25% margin for improvement.

Our objective in this paper is to develop a skills index for developing countries in Asia. Consequently, our focus is on developing an “Asian Skills Index” (ASI) roughly modeled on the ESI. An Asian Skills Index is, however, substantially more complicated to develop. Most obviously, consistent, comparable data series for many Asian countries is not as widely available as OECD data. In addition, several structural and institutional features of labor markets in Asian countries such as part-time employment, vulnerable employment, and unemployment among the highly educated means that the ASI cannot simply replicate the ESI. Recent work by Angrist et al (2021, p.403) point out that _“For decades, studies used schooling as a proxy for human capital. This applies even to the most prominent index of human capital to date, the United Nation’s human development index (HDI). However, using schooling as a proxy for human capital assumes that being in school translates to learning. Evidence suggests that this is often not the case”._ In

> 9 Most recently, the Economist (June 3-9, 2021, page. 84-85) developed a “Normalcy Index” based on indicators like sports and cinema attendance, air and road travel, office attendance, retail store visits, etc. to track lifestyle changes since COVID-19.

12
possibilities frontier is the demand problem of self-exclusion, the appropriate policies would emphasize raising financial literacy, both for households as for enterprises. If—as is more likely—the main problems reside with sub-optimization in credit supply, a wider range of policy options can be considered, starting with competition policy, allowing entry from reputable market players and securing access to the payment system for all financial institutions. This also includes reviewing barriers stemming from bank regulation and AML/CFT regulation that might reduce banks’ appetite for expanding lending and their customer bases.

A third category of government policies, defined as _market-harnessing_ , tries to prevent the financial system from moving to an unsustainable equilibrium beyond the frontier because of imprudent lending. Market-harnessing policies therefore aim at keeping banks’ incentives to take aggressive risks in check through a mix of measures aimed at strengthening market and supervisory discipline. Marketharnessing policies are also important on the demand side to avoid predatory lending, which results in unsustainable overborrowing by individual borrowers.

i) Beck and de la Torre, 2007.

The institution-building agenda in the contractual system is substantial and reflects the deficiencies in the respective laws and their enforcing institutions. It ranges from modernizing bankruptcy legislation to improving the court system or building alternative dispute resolution mechanisms and establishing or improving asset and collateral registries. Few countries in Africa currently have credit registries or credit reference bureaus, an indispensable part of an effective and competitive system of financial intermediation. Many central banks and regulatory authorities still do not have political and operational independence; they have limited supervisory skills and lack the required enforcement powers.

Recent cross-country research has helped identify some priorities within the institutionbuilding agenda. Specifically, improvements in the contractual framework, such as creditor 17
periment, guidelines included washing hands, wearing a mask, maintaining a distance from others when outside, and praying at home instead of at a mosque. In the second half, the government removed the recommendation to avoid congregational prayers, emphasizing social distancing in general instead. The overview reflected whatever guidelines were in place at the time of the survey. The script is shown in section A.1 in the Appendix.

Respondents were likely aware of at least some of these guidelines through other channels prior to the survey experiment. We collected data on all Twitter posts made by two major media groups, ARY and GEO, in the 5 weeks prior to and throughout the duration of our experiment.<sup>8</sup> This data shows that 31% of tweeted articles focused on the coronavirus pandemic. Of these articles, 23% mentioned at least one of the guidelines. The overview therefore reinforced already available information.

Respondents assigned to a treatment group received an _additional_ message designed to increase support for these guidelines and improve assessments of the state’s capacity to manage the pandemic. Each treatment is described in detail below.<sup>9</sup>

# **2.2.1 Past state success**

Reminding respondents of the state’s success in managing a crisis in the past may change their views about the state’s ability to manage a crisis today. Respondents may not otherwise remember past successes or even if they do remember, may not link them to similar events in the present (Acemoglu et al., 2019). In this treatment, we remind respondents of the Punjab government’s response to a dengue outbreak in 2011. Post hoc, this response was considered by several experts to have been instrumental in containing the outbreak (Bhatti et al., 2015). A World Bank case study, for example, argued government measures such as closing public places for fumigation and testing slowed the spread of dengue and prevented a similar epidemic from happening the following year (World Bank, 2018). These reports draw on analysis generated by the Punjab Information Technology Board, an autonomous body set by the Punjab government. We shared an overview of this report with respondents in this treatment group. The script for this and all other treatments are shown in Appendix A.

> 8Both media groups jointly published 8766 tweets over this time period. The tweets always link to an article published on the media groups’ main website. We used 3 independent human coders to identify mentions of government directives in a random sample of 55% of the articles linked in a tweet that included the following terms: covid, coronavirus, virus, influenza, flu.

> 9We elicited up to three questions about the content of the information treatments at the end of the survey. In Appendix Figure A1, we document that a large share of the respondents assigned to a treatment condition retained the information provided as part of the treatment.

8