rafmacalaba/data-use-mentions-extended huggingface dataset:
impact of land property rights interventions on investment and agricultural productivity in developing countries: a systematic review.</mark> _<mark>Journal of Development Effectiveness</mark>_ <mark>,</mark> _<mark>9</mark>_ <mark>(1), 61-81.</mark> Lerman, Z., & Mirzakhanian, A. (2001). Private agriculture in Armenia. Lexington Books.

- Moore, J. C. (1988). Self/proxy response status and survey response quality. _Journal of Official Statistics_ , 4(2), 155-172.

<mark>Namubiru-Mwaura, E. (2014). Land Tenure and Gender : Approaches and Challenges for Strengthening Rural Women's Land Rights. World Bank, Washington, DC. © World Bank.</mark> <mark>Proto, E., & Sgroi, D. (2017). Biased beliefs and imperfect information.</mark> _<mark>Journal of Economic Behavior & Organization</mark>_ <mark>,</mark> _<mark>136</mark>_ <mark>, 186-202.</mark> Statistical Committee of the Republic of Armenia. (2016). Main Findings of Agricultural Census 2014 of the Republic of Armenia. <u>http://armstat.am/en/?nid=82&id=1860</u> Statistical Committee of the Republic of Armenia. (2021). Statistical Yearbook of Armenia. <u>https://armstat.am/file/doc/99526883.pdf</u> Todorov, A., & Kirchner, C. (2000). Bias in proxies' reports of disability: data from the National Health Interview Survey on disability. _American Journal of Public Health_ , 90(8), 1248.

- UNECE. (2005). Inventory of Land Administration Systems in Europe and North
# **_Financial literacy affects insurance take-up in lower-income countries_**

Several studies on microinsurance suggest that knowledge of and attitudes towards insurance products play an important role in take-up. Giné et al. (2008) find that after insufficient funds, lack of understanding of the product is the second most commonly cited reason for not purchasing rainfall insurance policies in rural India. However, they find that many fewer households cite lack of understanding in a survey two years later after becoming more familiar with the product.

Similarly, Cohen and Young (2007) note that the main reason for non-renewal of insurance policies issued by the company CALUMNA in Guatemala was lack of understanding. They also observe that Ugandans who understand health insurance are much more willing to buy it, while negative attitudes, such as thinking insurance will be bring ―bad luck‖ or that it’s ―only for the rich‖, tend to reduce take-up. Tran and Yun (2004), an evaluation of a microinsurance project in Vietnam, find that most clients had previously been unexposed to insurance products, and generally lacked a thorough understanding of the concept of insurance and its benefits once they had enrolled in microinsurance. Some clients were subsequently unaware that they were able to make claims upon events such as death or illness.

Indeed, the FinScope surveys indicate that basic awareness of insurance is low in many low-income countries in Africa. Only four percent of Ghanaians have formal insurance, and an even smaller percentage has informal insurance. While two-thirds of those surveyed cited affordability as the main reason for not purchasing insurance, more than a quarter of individuals also reported reasons such as not knowing what insurance is, how it works, or how to buy it. In Malawi, almost 50 percent of adults ―did not know that insurance products can protect you when you have a problem.‖ Many people rely instead on support from their family and community or loans to cover costly medical and burial expenses. Half of those surveyed in both Nigeria and Mozambique had never heard of insurance
, where in the past at least national governments have tended to neglect the urban agenda; and (d) donor agencies’ risk aversion in the face of difficult governance and social safeguard challenges in urban areas, which is particularly the case for projects requiring land acquisition and resettlement of people.

Urban advocates will have to find ways to overcome these obstacles to urban aid and research may be helpful in doing so, by focusing on the development of better urban aid data, development of clearer urban development results metrics, demonstration of the importance of urban poverty, articulation of a national‐level rationale for effective urban development and investigation of better means for urban land management.

2. <u>Design of partnerships in support of urban development</u> One area in which the urban sector appears to have an advantage over other areas of donor engagement is in its track record of development of innovative and effective coalitions of diverse partners. “Partnerships intuitively offer a way around some of the problems associated with today’s aid architecture, such as fragmentation and coordination. New urban partnership models have, at different times, combined the efforts of a range of different stakeholders: urban residents including slum dwellers, often arranged in community‐based organizations (CBOs) 20
participation of group members in the management of the system; and vii) low frequency of disputes among group members (Mukherji et al., 2009).

We assessed JCLIS projects when they were still quite new. 80 percent of the systems in our sample had been operating for less than two years, and one in four were less than a year old. Therefore, it is difficult for us to assess their performance on some of the indicators listed above or to predict their future success. In this section, we present our conjectures based on the insights from the large existing body of work on irrigation management transfer and from the data we gleaned from our survey of farmers and system operators and managers during semi-structured discussions with them.

Since solar pumps have very low operating costs, many groups in our sample were being lax in setting the irrigation fees and collecting them regularly from farmers. Three of the 50 groups had yet to set an hourly irrigation charge, and another nine had yet to start collecting fees from the farmers. Altogether, collection of irrigation fees had not started for 12 of the 44 solar powered systems in our sample. None of the water user groups or their operators maintained any written records of hours of irrigation service provided to different farmers and the fees collected from them.

Fifteen of the systems in our sample had experienced a breakdown, and in each of these cases water group members promptly arranged the necessary repairs. In 6 of the 15 cases, the machine was still under warranty, and therefore no payments were needed. In other cases, farmers pooled money to pay for the repair or used the irrigation fee they had collected. Members of water user groups that were not collecting any fees told us that they were not keen to start doing so and suggested that they will pool funds when there is a need for repair and maintenance. This _ad hoc_ approach to mobilize resources for repair and maintenance of systems may become hard to sustain with time as systems get older and need more frequent repairs. This
pattern (Acemoglu and Autor, 2011; Autor, 2014; Autor, Levy, and Murnane, 2003; MacCrory et al., 2014). These findings have generated further interest in examining the role of technology (or computer use) at an individual level to explain changes in hourly earnings.

Most of the evidence on this matter has been documented for high-income countries. Krueger (1993) was among the first to attempt to establish a link between computer use at work and a wage premium. He used the supplemental questions containing information about computer use from the Current Population Survey (CPS) in 1984 and 1989. His findings indicated that the computer use payoff over the same time period ranged from 10 to 15 percent. He also found that about 40 percent of the increase in earnings during the second half of the 1980s was attributable to computer use. DiNardo and Pischke’s (1997) study and, later, Handel’s (2007) study noted that the premium on computer use might not be reflective of changes in the wage structure but instead reflect unobserved heterogeneity within a job or occupation.

Sakellariou and Patrinos (2003) put forth another interpretation for the wage premium on computer use. They suggested that the observed premium is a reflection of the ease in recovering the costs that high-wage workers incur when they gain these skills. Their correlational research with higher education graduates in Vietnam showed wage premiums close to 26 percentage points. In addition, Borghans and ter Weel (2003) aimed to disentangle computer _use_ from computer _skills_ . Their study used the Skills Survey of the Employed British Workforce from 1997 to estimate the returns to computer, writing, and math skills. The study, which sampled workers ages 18 to 60, found positive and significant returns for writing (25 percentage points), math (17 percentage points) and computers (33 percentage points), but found no significant relationship between wages and computer _skills_ at work. They inferred that higher wage premiums are associated with computers _if_ computers are used in
complexity are also needed. All these recommendations, which are reasonably within reach, are needed before more conclusive findings can be drawn as to the relationship between specific forms of citizen participation and specific categories of political trust.

# References

<mark>ACLED (2022). “Armed Conflict Location & Event Data Project (ACLED) Codebook”</mark> Adler, R. P., & Goggin, J. (2005). What do we mean by “civic engagement”? _Journal of Transformative Education_ , 3(3), 236–253.

Ahrens, A., Hansen, C. & Schaffer, M. (2020). Lassopack: Model selection and prediction with regularized regression in Stata. _The Stata Journal_ , 20(1), 176-235.

Alvaredo, F. & L. Gasparini (2015). Recent Trends in Inequality and Poverty in Developing Countries. In Atkinson, A. and F. Bourguignon (eds), Handbook of Income Distribution (pp. 697– 805). London: Elsevier.

Arizti, P., Brumby, J., Manning, N., Senderowitsch, R. & Thomas T. (2010). Results, Performance Budgeting, and Trust in Government. Washington, DC: World Bank Bächtiger, A., Dryzek, J., Mansbridge, J., & Warren, M. (2018). _The Oxford Handbook of Deliberative Democracy_ . Oxford University Press.

Banerjee, A. & E. Duflo (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty, New York: PublicAffairs Barron, P., Cord. L, Cuesta, J., Espinoza, S., Larson G. & Woolcock, M. (2023). Social Sustainability in Development: Addressing the 21<sup>st</sup> Century Challenges. Washington, DC: World Bank.

Belloni, A., Chernozhukov, V., & Hansen, C. (2014).
|8208|7872|7872|7776|7776|7968|7968||

Note: the data used covers January-April of 2018-2020. For every country where google trends data is available, the search index reflects searches in English and the most dominant local language. Each regression includes weekly dummies to capture aggregate temporal trends. Standard errors, clustered at country level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

24
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Niger’s road indicators**

|**Indicator**|**Unit**|**Low-**<br>**income**<br>**countries**|**Niger**|**Middle-**<br>**income**<br>**countries**|
|---|---|---|---|---|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|132|13|318|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|88|11|278|
|GIS rural accessibility|% of rural pop. within 2 km of all-season road|25|15|31|
|Household survey rural accessibility|% of rural pop. within 2 km of all-season road|34|37|63|
|Classified paved network condition|% in good or fair condition|72|67|86|
|Classified unpaved network condition|% in good or fair condition|53|58|65|
|Classified paved road traffic|AADT|1,131|387|2,451|
|Classified unpaved road traffic|AADT|57|31|107|
|Primary network overengineering|% of primary network paved with 300 AADT or less|30|53|18|
|Primary network underengineering|% of primary network unpaved with 300 AADT or more|13|23|20|

_Source_ : AICD Road Sector Database. _Note_ : [1] Includes the classified and unclassified network. GIS = geographic information system; AADT = average annual daily traffic.

Traffic levels are extremely low, making it difficult to justify heavy road engineering. Traffic in the classified paved network,
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**Figure 6:  Rate of Increase per Year of Age in COVID-19 Mortality Rate** p g y

<!-- Start of picture text --> Afghanistan<br>Pakistan<br>Philippines<br>South Africa<br>Colombia<br>Peru<br>Brazil<br>Mexico<br>Malaysia<br>Argentina<br>Chile<br>Hungary<br>Portugal<br>Spain<br>Korea, Rep.<br>Italy<br>Japan<br>France<br>Canada<br>Germany<br>Netherlands<br>Sweden<br>Australia<br>Denmark<br>Norway<br>Switzerland<br>0 .05 .1 .15<br><!-- End of picture text --> Source: Author’s analysis. Countries are ranked by GDP per capita LMICs are shown in red and HMICs in blue.

**Figure 7: Rates of Increase per Year of Age in COVID-19 Mortality vs. GDP per Capita**

<!-- Start of picture text --> CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>SWE<br>ESP<br>KORITA DEU<br>HUN PRT NOR<br>FRA<br>JPN<br>CHL<br>ARG<br>BRA<br>COL MYS<br>ZAF<br>PER<br>MEX<br>5000 10000 50000 100000<br>GDP per capita, 2018<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Source: Author’s analysis, World Development Indicators for GDP per capita (current US$). GDP per capita is on a log scale. LMICs are shown in red and HMICs in blue.
<br>2010 2011 2012 2013 2014 2015<br>50<br>2010 2011 2012 2013 2014 2015<br>2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2012 2014 2010 2012 2014 2010 2012 2014<br><!-- End of picture text --> Sources: Haver Analytics, PRS Group, Ministry of Finance of Sri Lanka, Reserve Bank of India, World Bank. A. Weighted averages. Includes annual data for Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka.

B. Share of SAR economies with investment growth below its long-term average or with negative investment growth. C. Weighted average for Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka.

D. GDP-weighted averages.

E. Investment-weighted averages. An increase denotes terms of trade improvements.

F. Investment-weighted average of ICRG index of Political Risk. An increase denotes greater political stability.

39
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such that φ= 1 indicates the central tendency of TFP in the cross section. A value of φ above 1 indicates high TFP relative to the firms in the cross section, while a value below 1 indicates low TFP. Rearranging (3): If we assume Cobb-Douglass production technology and that the TFP index can be written φ _i_ = _e_<sup>ν</sup> _i_ , (4) is specified as, which can be transformed into a linear expression amenable to regression methods: Here, the natural logarithm of the TFP index is equal to the residual term in the econometric production function. Interpretation of the residual term in this way should be done with caution, however. Measurement error is also likely to have an effect on the size and distribution of the residuals. A more conservative conclusion is that firm level variations in TFP account for a substantial component, but not all, of the residual values.<sup>4</sup> The TFP analysis in this paper is based on cross-sectional data at the firm level. It is important to bear in mind that in a cross section collected in one year or over a relatively short interval, all firms have access to the same level of technology. Thus

> 4 Stochastic frontier analysis (SFA) attempts to isolate that component of the residual that may be attributed to efficiency variations from a “white noise” component that depends principally on measurement error. Since this method has complex statistical requirements that are often not satisfied in the data, we do not employ it in this paper. More limited results of SFA application in China data will be provided in a companion paper. For a review, see Kumbhakar and Lovell (2000).

> The authors also applied Escribano-Guasch (2005) one-step methodology by combining the two equations estimated, namely (1) production function and (2) TFP function, into a single equation, and estimating the models for both the Brazil and China data.  The results were very similar, with no instances
_Figure 1: Trends Growth and Inflation Rates in CAMU Countries, 1981-2018_ _Figure 2: Trends Growth and Inflation Rates in WAMU Countries, 1971-2018_ <!-- Start of picture text --> 25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>1981 1986 1991 1996 2001 2006 2011 2016<br>-5.0<br>-10.0<br>CAMU-GDP Growth CAMU-Inflation Rate<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br>-5<br>-10<br>WAMU-GDP Growth WAMU-Inflation Rate<br><!-- End of picture text --> _Figure 3: Comparison of Median Growth and Inflation Rates of CAMU Countries, 1981-2018_ _Figure 4: Comparison of Median Growth and Inflation Rates of WAMU Countries, 1981-2018_ <!-- Start of picture text --> 7.0 8.0<br>6.0 7.0<br>6.0<br>5.0<br>5.0<br>4.0 4.0<br>3.0 3.0<br>2.0 2.0<br>1.0<br>1.0<br>0.0<br>0.0<br>Central Congo, Gabon Cameroon Chad Equatorial<br>African Rep. Guinea<br>Republic<br>Growth Inflation Inflation Threshold Growth<br>Source: UNSD, WDI and ECOWAS database.<br><!-- End of picture text --> <!-- Start of picture text
The country-specific IO tables that we construct partition all sub-Saharan countries exhaustively in three groups, and these three countries/regions are added to the above list:

1. <u>OWN: the country being considered itself</u>

2. <u>CLO (Close sub-Saharan African countries): countries neighboring the country</u> considered 3. <u>FAR (Faraway sub-Saharan African countries): the remaining countries from the</u> region.

Hence, in total there will be 9 industries with 1 final demand vector and 8 countries/regions, which makes for an IO table with approximately 5000 coefficients. While the total amount of information in the 45 country-specific tables with these dimensions is comparable to the single, regional alternative, most of the information in the different tables is identical and will not be used.<sup>13</sup> In our analysis, we will primarily concern ourselves with the interactions between the country of interest and the different regions. It means that we will use the first nine rows and the first nine columns of each country-specific IO table to construct the GVC indicators of interest.

# **3.2 GVC indicators**

From the 45 country-specific IO tables for each of the three selected years, we now proceed to calculate a number of ratios that are of particular interest to illustrate the GVC participation of the different countries. There is still too much information to be able to show results in any exhaustive way. Each metric of interest that we can calculate, we observe in 9,720 cases: (45 sub-Saharan countries) x (8 origin or destination regions) x (9 sectors) x (3 years). In addition to the level of each indicator, we are also interested in another 6,480 changes for the same indicators over the 1995-2005 and 2005-2015 time periods.

To convey as much of this rich information as possible, we report information in three different ways. First, we make available a data file that
# **1 Introduction**

Existing evidence suggests that air pollution has negative effects on human health, especially for children and elderly adults.<sup>1</sup> A recent literature has also started to document negative effects among healthy adults in the form of reduction of labor productivity (Graff-Zivin and Neidell, 2012; Adhvaryu et al., 2014; Chang et al., 2014; Li et al., 2015) and hours worked (Hanna and Oliva, 2015).<sup>2</sup> These findings, from the U.S., India, and Mexico, point to changes in labor outcomes as relevant pollution externalities.

In this paper we focus on the causal relationship between air pollution and hours worked. The empirical analysis of this relationship is complicated by two empirical challenges. First, there could be omitted variables that affect both air pollution exposure and the outcome of interest; which may bias the estimated response to air pollution. Second, households may move in response to air pollution according to their specific vulnerability introducing sorting bias.

These challenges have been addressed in a previous study with the help of an exogenous and sudden change in air pollution in Mexico City in the early nineties (Hanna and Oliva, 2015). This research design, however, limits the extent to which one can explore non-linearities in relationship between air pollution and hours worked as well as the mechanisms at play. This paper takes advantage of panel data on hours worked from Lima, Peru, to address the empirical challenges described above while preserving enough exogenous variation in pollution exposure to uncover the non-linear relationship between pollution and hours worked.

By comparing the shape of the dose-response function across different household compositions, we shed light on how age-specific vulnerabilities to air pollution can result in different labor supply responses to air pollution across households. We also explore whether intra-household substitution in labor supply can dampen the effects at the household level. Finally, the research design also allows us to focus on a somewhat newly monitored pollutant