rafmacalaba/data-use-mentions-extended huggingface dataset:
clude an aggregate housing quality index, total household consumption, respondent mental health, and child socio-emotional well-being, measured using the standardized Strengths and Difficulties Questionnaire (SDQ). In fact, the analysis unexpectedly shows that the socio-emotional well-being of children in treated households decreases substantially, by 0.34 standard deviations on average; we return to interpretation of these patterns below. Due to the non-compliance noted above, estimated instrumental variable (IV) treatment on the treated (TOT) effects are relatively imprecise. But the primary analysis, which pools across multiple rounds of follow-up survey data, does allow us to reject the existence of some moderate positive treatment effects.

A second main finding of the study is that the refugee-targeted housing subsidy program led to a meaningful and statistically significant deterioration in community perceptions towards Syrian refugees among the Jordanian neighbors of treatment households. We estimate a 0.33 standard deviation unit decrease in an index of their social attitudes and perceptions of refugees. The analysis also shows that neighbors become better informed about how much assistance Syrian refugees receive. There is no evidence of effects on neighbors’ own economic outcomes. We speculate that the substantial transfers received by treatment refugee households were observed by Jordanian neighbors and led to tensions in some cases, perhaps especially given that these neighbors also live in the same relatively poor neighborhoods as the refugees and are typically not well-off themselves. The easy visibility of assistance in this case – including construction activity in the treated housing units – may have raised the salience of the intervention relative to some other forms of aid (i.e., mobile money transfers or health care subsidies) that are easier to keep private.

The estimated null and even negative results for most pre-specified primary outcomes are in contrast to the predictions of experts. Building on an increasingly common practice in economics and other social sciences (DellaVigna and Pope 2017; DellaVigna et al. 2020), the research team gathered forecasts from both researchers and policy experts regarding the primary impacts of the program. Forecasters generally predicted positive but modest
|0.991|0.796|0.796|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. “Log (light per capita)” is the natural log of night light per capita intensity for each ethnic group. Variable “excluded” is a binary flag indicating whether ethnic group is assigned a status of “Self-excluded”, “Powerless” or “Discriminated”. Variable “powerful” is a binary flag indicating whether an ethnic group is assigned status of “Monopoly” or “Dominant”. Variable “high executive” constraint is a dummy that takes a value of one if “exconst” (executive constraint) variable in Polity IV dataset equals 7. Each observation is weighted by group’s population size as a fraction of the country’s total population.
of transportation in international trade, and air freight services. With canceled sailings and flights, port delays, and container shortages, COVID-19 measures may have differences in trade transported by sea or air to an archipelago country like Indonesia (see Majune and Addisu, 2021), with implications for trade facilitation measures.

The reminder of this paper is organized as follows. The data is described in section 2. The methodology is presented in section 3 with the results discussed in section 4. Finally, section 5 concludes our study.

# **2 Data**

## **2.1 Lockdown data**

Lockdown is a binary variable, a composite indicator where one indicates the month any of the following measures were in place: workplace closure, the closure of public transport, stay at home requirements, restrictions on internal movement, and international travel controls. Lockdown is twofold: external and domestic. External lockdown refers to when the indicator refers to lockdown policies by Indonesia’s trading partners and domestic lockdown is a dummy where any of the above-mentioned indicators are applied by Indonesia in a particular month. This definition of lockdowns has been adopted from Majune and Addisu (2021) and differs from country to country for the external lockdown since lockdowns were in place at different times. Data for these indicators is from the Oxford COVID-19 Government Response Tracker (Hale et al., 2021).

To explain the pattern of lockdown measures, we display the trend of Indonesia’s lockdown Stringency Index and that of selected countries (Papua New Guinea, India, Thailand, Malaysia, Australia, Singapore, the Philippines and Vietnam) between January 1, 2020, and August 31 2021 in Figure 1. The index is constructed by re-scaling the maximum ordinal values of nine indicators (one health measure and eight closures and containment policies) to range between 0 and 100. The scores are then averaged to get a composite index for each 5
2011/2012 (MOEVT, 2013). While most programs offered by NACTE institutions are at the diploma and certificate level, about 22 percent of enrollments are in degree programs, up from 9 percent in 2006/2007. This rapid growth in tertiary-level enrollments over this period was made possible in large part by the expansion, registration and accreditation of private universities, colleges, and technical education institutions.<sup>16</sup>

**<u>Figure 2:  Education Distribution of the Population</u>**

<!-- Start of picture text --> South Africa (2007) Malaysia (2010)<br>Above secondary (14%) Above secondary (20%)<br>Secondary (24%) Secondary (33%)<br>Primary (50%) Primary (41%)<br>Less than primary (12%) Less than primary (5%)<br>Vietnam (2010) Tanzania (2012 Census)<br>Above secondary (8%) Above secondary (5%)<br>Secondary (16%) Secondary (6%)<br>Primary (69%) Primary (62%)<br>Less than primary (7%) Less than primary (27%)<br>Note: Author’s calculations using South Africa and Vietnam harmonized microdata from the International<br>Income Distribution Database-I2D2 version 6 (World Bank 2013). Estimates for Tanzania are from the<br>2012 Tanzania National Census. Malaysia levels of attainment are estimated using microdata from the<br>2010 Malaysia Labour Force Survey.<br><!-- End of picture text --> The actual availability of skills for enterprises in Tanzania is undoubtedly much lower than is suggested by the average years of educational attainment because of the low quality of education, 16 The number of private universities increased from 20 to 34 while public universities/colleges intuitions increased from 11 to 13
inflammations) and better nutritional outcomes as indicated by anthropometric measurements, particularly during years with poor harvests.

However, the gender of the transfer’s recipient as well as the conditionality leads to differentiated impacts. For school enrollment and most child health outcomes, conditional cash transfers outperform unconditional cash transfers. The gender of the transfer’s recipient does not lead to such strong contrasts, with cash given to mothers and fathers having similar impacts on child health and education. While giving cash to mothers seems slightly, but not significantly, better for education outcomes, giving cash to fathers leads to significantly better nutritional outcomes during years with low rainfall. Cash transfers given to fathers also lead to increased livestock ownership, higher agricultural production in cash crops, and relatively more investment in the household’s equipment (electricity and metal roof).

The remainder of the paper is organized as follows. Section 2 describes the context of our experiment and the design of the cash transfer pilot program. In Section 3, we discuss our empirical identification strategy. Section 4 presents the main results, the robustness checks, and a discussion of the cost-effectiveness of the different cash transfers. Section 5 concludes.

# **2. Context and Experimental Design**

# _2.1 Context_

Burkina Faso offers an important setting for exploring the effects of cash transfers on rural households, focusing in particular on children’s education and health outcomes. Even by African standards, child education and health in Burkina Faso are poor. The United Nations Human Development Index ranks Burkina Faso 181 out of 187 countries. In 2010, the net attendance ratio for primary school in rural Burkina Faso was 44.4 (45.5 for boys and 43.1 for girls) and the gross attendance ratio was 64.9 (66.2 for boys and 63.5 for girls) (Institut National de la Statistique et de la Démographie and ICF International, 2012).<sup>4</sup> Approximately 33 percent of children in Burkina Faso
2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ Mbps = megabits per second; — = data not available.

# **Challenges**

Completing the expansion of GSM network coverage is particularly challenging because of the spatial characteristics of the country. Analysis done around 2006 suggested that up to 80 percent of the population could be reached on a commercially viable basis, but the remaining 20 percent are dispersed across remote areas that cannot be covered without some degree of public subsidy. This “coverage gap” is among the largest found for any country in Africa (figure 5). As of 2010, a number of further developments are jeopardizing the further expansion of mobile coverage. A vigorous price war between mobile operators had pushed prices below economic levels to under US$0.04 per minute. In order to stem losses, operators have stopped coverage rollout and indeed reduced are coverage in some cases.

17
# **Executive Summary**

Services account for over 60 percent of GDP in Turkey and are critical drivers of employment and exports. Yet, there are signs that services may not be living up to their potential in Turkey, for instance when looking at productivity and the growth of services value‐added.

Services are also an increasingly important component of manufacturing and global value chains. Services _inputs_ as well as services activities _within_ manufacturing firms account for over half of the value of manufacturing exports. This global trend toward more use of services within manufacturing firms is known as servicification. For countries to be competitive, appropriately regulated and productive services are necessary, both for the services sector itself and for manufacturing.

Thanks to a unique firm‐level data set from the Turkish Ministry of Science, Industry, and Technology, this paper looks at a previously unexplored area—the production of services by manufacturing firms and links to productivity. While servicification has the potential to increase firm performance, this does not appear to be happening in Turkey: manufacturing firms with services tend to be less productive. For instance, non‐ exporter manufacturing firms with a services affiliate are 18 percent less productive than firms without a services affiliate. Exporters with a services affiliate are 9 percent less productive than those without one.

To better understand the lower productivity, the paper breaks down services by stages of the production process. Productivity differences between manufacturing firms that provide services in each stage are then highlighted. For instance, firms that have post‐manufacturing (e.g. transport and distribution) service affiliates are found to be particularly less productive in Turkey.

Regulatory restrictions are explored as a potential explanatory factor, given that removing regulatory restrictions on services has increased their contribution to productivity growth in other countries. Three regulatory areas are briefly examined: i) FDI restrictions in services; ii) domestic regulatory barriers in services that affect both Turkish and foreign firms; and iii) discriminatory services trade barriers that prevent foreign service providers from entering and operating in the
- SULTAN, B., M. BELLA-MEDJO, A. BERG, P. QUIRION and S. JANICOT (2010). “Multi-scales and multi-sites analyses of the role of rainfall in cotton yields in West Africa.” _International Journal of Climatology, 30_ , pp. 58–71.

- TOVAR, P. 2009. “The effects of loss aversion on trade policy: Theory and evidence.” _Journal of International Economics, 78_ (1), pp. 154-167.

- TOWNSEND, R.F. 1999. “Agricultural Incentives in Sub-Saharan Africa. Policy Challenges.” World Bank Technical Paper No. 444.

- TSCHIRLEY, D, C POULTON and P LABASTE (Eds) (2009). <u>Organization and Performance of Cotton Sectors in Africa:  Learning from Reform Experience. Agriculture and Rural Development</u> series. Washington DC, World Bank.

- UCDP/PRIO Armed Conflict Dataset v4-2009. <u>http://www.prio.no/CSCW/Datasets/ArmedConflict/UCDP-PRIO/ (accessed May, 2011)</u>

- WORLD METEOROLOGICAL ORGANIZATION (2010). “Guide to Agricultural Meteorological Practices 2010 Edition”, WMO-No.134.

24
decision maker’s choices (or expectations over them). Obviously, if information is made available outside of a controlled experimental setting, more than one person per village can obtain it and demand complementarities or substitutabilities kick in. This makes the decision over the value of others’ learning much more complex. While my experiment cannot speak to this by design, it also provides a scenario in which adoption is bounded from below and the valuation decision has high stakes. If none of the participants buys the training, no one in the village will adopt the techniques.

# **5.2 Belief Update Intervention**

My willingness-to-pay elicitation survey included a belief update experiment to understand how concerns for _i)_ knowledge externalities and _ii)_ contagion externalities affect how much a farmer values training one other farmer.

Knowledge externalities between two farmers depend on the probability that the two farmers share agricultural information with each other _p_<sup>_L_</sup> (I restrict my analysis to direct communication between farmers and exclude indirect communication that involves other members of the community). The enumerator induces the participant to update her belief over _p_<sup>_L_</sup> by informing her that the research team would organize a meeting between the participant and that specific farmer after training had taken place, if the participant is the decision maker and their valuation for that specific farmer is selected by the choice lottery. I call this the “meeting treatment”.

Contagion externalities between two farmers depend on the physical distance between their plots, because the pest spreads by proximity. The enumerator induces the participant to update her belief over _p_<sup>_T_</sup> _ij_<sup>followingatwo-stepprocedure.Firsttheenumeratorasks</sup> the participant how long it would take to walk from her largest maize plot to the closest plot of a specific farmer _j_ , _d_<sup>_P_</sup> _ij_<sup>.Thentheenumeratorinformstheparticipant</sup><sup>_i_ofthe</sup> true as-the-crow-flies walking distance
sources not previously covered. The combination of all these sources makes this review among the largest and most thematically comprehensive undertaken. In contrast to earlier work, the review will focus mainly on specific types of infrastructure – namely energy, digital, and transport – and specific dimensions of development impact – such as output and productivity, poverty and inequality, labor market outcomes, human capital formation, and trade. In this way, the paper seeks to develop a more nuanced understanding of the pathways through which infrastructure contributes to the development process. To avoid publication bias, the corpus of papers contains a significant number of unpublished working papers and reports. Nevertheless, about 70 percent of the papers are published in peer reviewed journals, and 80 percent include at least one academic co-author.

<mark>Following the selection of the papers, their most salient characteristics were recorded in a database, from which it is possible to obtain a descriptive overview of the literature under review. What emerges very clearly is the large and sustained increase in the volume of research addressing infrastructure and development linkages since 2005, with over 60 percent of the articles considered being published in the last decade, 2011-2021 (Figure 1). However, in terms of the historical period analyzed within these papers, two-thirds relate to data representing the period 1980-2000 (e.g., Bogart, 2009; Burgess et al, 2010; Haines and Margo, 2006), while only around one-third is based on post-2000 data (e.g., Aggarwal et al, 2018; Rodríguez-Castelán et al, 2022). About 70 percent of the studies are based on longitudinal data exceeding one decade in length.</mark> Over time, there has also been a noticeable shift from articles focusing on cross-sectoral infrastructure broadly defined, to research that examines specific types of energy, digital and transportation literature, again primarily during the last decade (Figure 1). From 2010 the literature became more oriented towards micro-economic approaches, exploiting increasingly granular data that is often spatially explicit.
Scheve and Slaughter (2001) and Mayda and Rodrik (2003), which use survey evidence to evaluate determinants of individual preferences. They find that factor endowments guide trade preferences. But their focus is on the relationship between individual characteristics and protectionism and not on the conditions for trade barriers to be considered acceptable by a majority. Loss aversion may play a very important role in people’s perceptions, as the argument for free trade relies on the same weight being given to gains and losses.

In order to test to what extent loss aversion carries over to peoples perception of trade policy, we conducted a simple survey. The following three questions were asked with each person answering only one of the questions:<sup>4</sup>

1. A manufacturing industry faces competition from imports.  Its losses are expected to be $20 million this year and firms are expected to reduce salaries by 10%.  If a tariff is placed on imports of competing goods then losses will be avoided and salaries will be maintained.  The tariff will cause the price of the good, which everyone consumes, to rise from $30 to $40. Do you support the tariff?

N= 102 Yes: 60 percent No: 40 percent 2. A manufacturing industry faces competition from imports. If a tariff is placed on imports of competing goods then profits will increase by $20 million and firms in the industry will increase salaries by 10%. The tariff will cause the price of the good, which everyone consumes, to rise from $30 to $40. Do you support the tariff?

N= 100 Yes: 37 percent No: 63 percent 3. A manufacturing industry faces competition from imports, which are subject to a tariff. Removal of the tariff will cause profits to decline by $20 million and

> 3 We would like to thank Sendhil Mullainathan for suggesting us to conduct a survey to motivate the paper.

> 4 The survey was given to 303 people visiting the Washington Mall in June 2004.
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>CES SCS health SCS education IHDS<br>2011 USD PPP per month<br><!-- End of picture text --> _Note_ : CES refers to Consumption Expenditure Surveys, SCS Health and Education to the Surveys on Social Consumption and IHDS to India Human Development Survey. Consumption expenditures are in 2011 USD PPP using price deflators as in Atamanov et al. (2020).

24
pointing to a positive effect of incidents on the poverty gap and a deterioration of perception of police competence.

Table 5: DiD and IV estimates for the effect on employment and earnings

||Proportion of household members<br>(aged 15-50) employed in<br>the previous week|Proportion of household members<br>(aged 15-50) earning income<br>in the previous week|
|---|---|---|
||DiD<br>(1)<br>IV<br>(2)|DiD<br>(3)<br>IV<br>(4)|
|DiD or IV|-0.147**<br>-1.952***|-0.150*<br>-1.682***|
|coefficient|(0.066)<br>(0.326)|(0.086)<br>(0.285)|
|Fixed effects|District<br>Region|District<br>Region|
|Full set of controls|Yes<br>Yes|Yes<br>Yes|
|Observations|1,525<br>2,221|1,525<br>2,221|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an OLS regression for Mogadishu and IV regression for urban areas with exposed and control households in Wave 1 and 2. Estimates obtained from our most complete specification including household size, receiving remittances, age, sex and literacy of the household head, tenure, roof and floor material of the dwelling, water source and sanitation type. Drought affected status and humanitarian assistance are not included in the OLS regression due to the lack of variation in the data within Mogadishu. Humanitarian assistance is not included in the IV regression due to collinearity with the region fixed effects. Standard errors considering sampling weights in
# **Appendix C. Statistical framework of Henderson et al. (2012)**

We introduce the statistical framework to relate lights to GDP growth in Henderson, Storeygard, and Weil (2012) (HSW hereafter). We specify how we apply the modified version to our analysis.

Let _y*_ be the growth (or log difference) in true real GDP, _y_ the officially reported version, and _l_ the growth of observed light. HSW assumes the following relationships between reported GDP growth, light growth, and true GDP growth: GDP growth: ∗ ; (A.1) yy ii,tt ii,tt∗ ii,tt yy = yy + εε , (A.2) ll where _i_ and _t_ index region and year, respectively. ii,tt _ε_<sup>_y_</sup> in Eq. (A.1) is the classical measurement error in the official ii,tt ii,tt ll = ββyy + εε 2 2 2 GDP growth data. _β_ is the elasticity of lights to true GDP. The variance of _y_<sup>∗</sup> , _ε_<sup>_y_</sup> and _ε_<sup>_l_</sup> are denoted ∗ , ∗ and , respectively. The error terms of the above two equations are assumed to be uncorrelated, i.e., ) = 0. yy yy ll σσ σσ σσ Then we use lights growth to predict GDP growth with the following regression: YYc (εε<sup>yy</sup> ,εε<sup>ll</sup> , (A.3) where _ψ_ is the elasticity of light growth to the true real GDP growth. In practice, Eq. (A.3) is estimated by using ii,tt ii,tt ii,tt yy = ψψll + μμ levels of lights and GDP, augmented with region and year fixed effects. Here the unit of analysis is the city. We further