rafmacalaba/data-use-mentions-extended huggingface dataset:
assessment which values of WTP are plausible. For example, in the South Africa data, a WTP below -100 would indicate that a subject would rather pay cash than receive the R100 electricity voucher.

Table 3 columns (1) to (4) implement these two variants of interval regression (unrestricted max./min. WTP or restriction to _±_ 100). For all regressions in the table, odd columns use observations with descending value difference, and even columns with ascending value difference, mimicking again typical MPL data. They can therefore be compared with columns (1) and (2) in Table 2, respectively. As in the scaled probit, the WTP estimates are sensitive to choice order, but they are also attenuated toward zero; slightly more so when a maximum and minimum WTP are imposed in columns (3) and (4).<sup>26</sup> Some MPL implementations also suppress MSB observations, either ex ante or ex

> 25This follows the approach in Andersen et al. (2007).

> 26Note that the number of subjects is lower in (1) and (2) because of subjects whose upper and lower bounds are both undefined. This happens if a subject chooses option B (the lower-value option) in _t_ = 1 but option A in _t_ = 10 (again the lower-value option).

29
Kondylis (2008) evaluated the policy of Imidugudu in Rwanda by which returnee households where housed in new villages where they received land and inputs. Her results show that for the group of returnees that moved into imidugudu, who received more land than they would have otherwise, the program had a positive asset effect on their agricultural production. Nevertheless, this group experienced lower returns to inputs than the group of returnees who were not in the imidugudu program. She suggests that the episode of displacement may have lowered the returnees' level of know‐ how, but also that the imidugudu policy did not allow for transfer of skills between stayers and returnees, probably due to remaining ethnic and political tensions.

This resonates with the Burundi case where the land constraint is also causing a lot of tensions between returnees and stayers. The question whether or not the welfare of formerly displaced will improve after their return to the home country is entirely empirical and can only be answered on a case‐by‐case basis. It is worth nothing that panel data on (former) refugees before, during and after displacement is lacking, due to the difficulty of tracking displaced and formerly displaced households. What is clear from the Verwimp & Munoz‐Mora study is that the plight of (former) refugees does not end upon return. Once returned they face the political and economic legacy of the home country, including (in the case of Burundi) land scarcity and land disputes.

Liz Alden Wily (2009) in that respect argues that post‐conflict economies should seize the opportunity to reform the land tenure system, rather than attempting to restitute the land to previous owners. Not only were land disputes often one of the underlying issues causing the broader conflict, but mismanagement of land in the post‐conflict setting may sow the seed for renewed conflict, at least at the local level, if not nationwide. This is of course highly correlated with the agrarian nature of countries involved in conflict in Sub‐Saharan Africa. This again calls for a
income and an assumed or observed rate of return are used to estimate the value of the capital generating the observed capital income (Roine and Waldenstrom, 2015, Saez and Zucman, 2016).

In recent times other less traditional forms of data have been used to measure the top of the wealth distribution. One source is rich lists compiled by Forbes or other organizations (Piketty et al. 2022, Bach et al. (2019), Xie and Jin (2015)). These sources provide wealth estimates for the extreme top of the wealth distribution and can be used as a check on the household survey or administrative data wealth estimates.

A second set of non-traditional data sources was used by Alstadsæter et al. (2019). These sources were an HSBC Switzerland leak of customer data, voluntary declarations of hidden assets from tax amnesties in Norway, Sweden and Denmark and leaked information of shell company owners in Panama, the Panama Papers.

23
# **1. Introduction**

In December 2023, representatives of 199 countries confirmed their commitments to addressing the global climate emergency and accelerating climate actions (UN 2024). The success of these global efforts rests on the willingness of individual countries to act upon their voluntarily-set mitigation targets, known as nationally determined contributions (NDCs). The ambitions to implement such policies vary widely across countries (Albalate et al. 2023).

Among factors that influence cross−country variations in climate change actions, the “climate change age gap” − the notion that younger people care more about climate change than older people − causes concerns about the implementation of the climate change agenda.<sup>2</sup> Young climate activists capturing the media and public attention reinforce the perception of a widening climate change generational gap.

The age structure of populations plays a significant role in climate policies. Population aging results in a growing share of older people who establish themselves as a powerful political force in decisions about the provision of public goods. The influence of older generations on public policy, including climate change, is reinforced by their greater participation in the electoral process compared with younger generations (Goerres 2007).

Differences in attitudes toward climate change among generations are often attributed to the fact that younger people will live longer under deteriorating climate conditions and, thus, be more affected by its consequences. Inversely, the shorter time horizons of the aging population and the fact that the main climate change costs accrue in the future suggest that older people should be less supportive of climate change policies. In fact, the empirical evidence of the presence and the size of the climate change age gap is mixed and context-dependent (e.g., Gray et al. 2019).

In this paper, we develop a theoretical model that explains why and how people of different ages perceive the increasing threat of global warming. The model guides our empirical analysis, which relies on data from 38 countries of Europe, Central Asia, the Middle East,
in the hands of the private sector. There is a distinct pattern of trade over the season that is partly influenced by the timing of the harvest, liquidity constraints of farmers, and ADMARC’s participation in the market (Jayne et al. 2008).

By far the largest component of the cost of domestic maize trade is transport costs, with an estimated share of 48 to 57 percent of total transaction costs (Fafchamps et al. 2005). Transport costs in Malawi are high because the secondary road infrastructure (the so-called feeder roads) is not well developed (Lall et al. 2009). The quality of the major trunk road network (international routes) is good and is not a constraint, but for rural areas, the conditions are fundamentally different. Average unit transport costs per ton per kilometer from rural to urban areas are approximately 20 times higher than from urban to urban areas, which is caused by differences in the average distance (85 km in rural transport, more than 2000 km in international transport), the average truckload (2.5 tons in rural transport, more than 20 tons in international transport), the demand for transport services and the availability of backhaul cargo (Lall et al. 2009). The type of trucks used on the international routes is also not appropriate for transport in rural areas (Lall et al. 2009). Transport costs are also high because fuel for transport is expensive. The import and distribution of fuel for transport are managed by a parastatal of the Government of Malawi, which also sets pan-territorial pump prices. Domestic pump prices are high relative to import unit values and thus relative to world market prices, and they have increased stepwise over the years. The gap between import unit values and fuel prices at the pump is 80 to 220 percent. Annual increases in pump prices average approximately 26 percent and reached a peak between June 2008 and January 2009. High transport costs clearly restrict the maize trade. Survey data indicate that the average distance between the
8 World Index. To control for the commodity channel, we use the Bloomberg’s commodity price index.

Last, we also use data on fiscal expenditure and tax event in the United States and natural disasters as source of arguably exogenous variation to identify growth shocks in both AEs and EMs. US spending news data are the narrative military expenditure shock obtained from Ramey (2011). US tax news are the narrative tax shock obtained from Romer and Romer (2012). AE and EM natural disaster shocks are the damage stemming from large natural disasters (larger than one billion US dollars) in the relevant country group over the group GDP. The data are originally from the online version of the International Disaster Database (EM-DAT).<sup>14</sup>

# **B.   Empirical Approach**

We exploit VAR techniques to capture the interrelationships between AEs and EMs growth and quantify the dynamic spillovers between these blocks. Our VAR model specification is as follows: follows: = Φ<sup>′</sup> tt tt tt tt where ] yy xx + uu , uu ~NN(0, Σ) tt tt−1 tt−pp tt xx = [yy , ... , yy , zz We first use a parsimonious model that simply employ bivariate model including GDP growth of EMs and AEs.<sup>15</sup> Second, we augment the model with various other control (endogenous) variables such as the main financial and trade variables, commodity prices. Last, we augment our bivariate model with exogenous variables to explore a different identification strategy.

> 14 See URL link to the natural disasters database: http://www.emdat.be/database

> 15 A dummy is also included to control for the period covering the global recession that is from 2007Q3 to 2009Q3.
have 10 or more surveys. If there is only one survey for a country then we estimate measures for each reference year by applying the growth rate in real private consumption per person from the NAS to the survey mean,<sup>22</sup> assuming that the Lorenz curve for that country does not change. We also use the annual NAS data for interpolation purposes, given the irregular spacing of the surveys, following the method outlined in Chen and Ravallion (2010).

In the aggregate, 90% of the population of the developing world is represented by surveys within two years of 2008.<sup>23</sup> Survey coverage varies by region and over time. Table 1

> 21 The population weights (for urban and rural poverty measures, as well as across countries) are also from the World Bank’s Development Data Platform.

> 22 For a few countries for which private consumption per capita is missing from the DDP we use GDP.

> 23 Some countries have graduated from the set of developing countries; we apply the same definition over time to avoid selection bias. In this paper our definition is anchored to 2005.

12
~ optot_ _~~2~~ 014_ . GHS-BUILT at 300m spatial resolution is the source of built-up area within each administrative unit from which we derive the variables: _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~1~~ 975_ , _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~1~~ 990_ , _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~2~~ 000_ , and _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~2~~ 014_ .

The share of urban population within an administrative unit is constructed as follows. We first define urban population as the number of people within a given threshold of density from the 2014 GHS population grid ( _ghspop14_ ) and the Landscan 2012 population grid ( _lpop12_ ). Next we use the World Urban Prospects estimates of the urban share at the country level provided by the World Bank World Development Indicators to inform a population density threshold that sums to the estimated urban share at the country level<sup>22</sup> . Finally, we construct the share of urban population that meet these density criteria within the administrative unit as the urban share of total population ( _urban_ _~~l~~ pop12_ ).

# **Socio-economic: Travel time and accessibility**

Transport infrastructure enables improved economic growth by lowering costs of moving goods and people. Nelson (2008) derives the travel time from all land area to

> 21For a recent review of gridded population datasets see Leyk et al. (2019).

> 22Another method of country consistent urban rates from World Urbanization Prospects is the iUrban method by Aubrecht et al. (2016).

48
movement between rural and urban and between agriculture and non-agriculture sectors, and changes in wages of various types of labor. These simulated economic effects are used in the GIDD model in Box 5 together with the new set of recalibrated weights in Box 3. The GIDD model uses all this information to calculate the income distribution and poverty effects of largescale expansion in biofuels in some year in the future.

In projecting the data in the household survey into the future using the simulated results of key economic variables from the CGE and the recalibrated new sampling weights, two other processes are undertaken in the GIDD model. Detailed discussion of the processes is also given in Bussolo, de Hoyos and Medvedev (2008).

where _Xj_ , and _Zj_ are vectors of personal and household characteristics of individual j, respectively. The vector of coefficients in equation 11 is **βp** . Given this set of coefficients and the personal and households characteristics, workers are then ordered based on probability score calculated using equation 10. Workers with higher probability to be in non-agriculture are moved out of agriculture up to a point where the predicted share of workers by sector (a macro constraint) is satisfied.

Once the labor movement takes place, the second process involves adjusting income of those who have moved. This income assignment to the “new entrants” in the expanding sector is done through a Mincer equation in agriculture ( _A_ ) and non-agriculture ( _NA_ ).

where s = ( _A,NA_ ). The “new entrants” will carry their personal endowments **X** j and their residual _εj_ to sector where they move. However, those who have moved from agriculture to nonagriculture will be paid with prices **β** _NA_ . In order to incorporate the variances in the distribution 10
income individuals and microenterprises also need access to low cost and safe payment mechanisms, whether for salaries, benefits, remittances, or for business transactions.

**MENA has the lowest score on the Doing Business Legal Rights index of any region, however** .  As a result banks face higher costs of overcoming information asymmetries and higher levels of credit and default risk for micro lending, and have not yet directly entered the low income consumer or microenterprise market at any great scale. The Legal Rights index assesses the use of movable assets as collateral, the strength of creditor rights, and the availability of out of court enforcement mechanisms – all key areas for improvement for expanding financial inclusion. No MENA country scores above 4 out of 10 on this index.  Banks therefore do not extend lending further into the micro and small enterprise sector. Microfinance providers are forced to rely on solidarity group lending methodologies or on collateral that is

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adaptation benefits in Mumbai: an initial assessment of socio-economic consequences of present and climate change induced flood risks and of possible adaptation options. OECD Publishing.

- Hallegatte, S., Hourcade, J.C., Dumas, P., 2007. Why economic dynamics matter in assessing climate change damages: illustration on extreme events. Ecological Economics 62, 330–340.

- Hallegatte, S., Przyluski, V., 2010. The economics of natural disasters: concepts and methods. World Bank Policy Research Working Paper No. 5507.

- Hanaoka, C., Shigeoka, H., Watanabe, Y., 2015. Do Risk Preferences Change? Evidence from Panel Data Before and After the Great East Japan Earthquake. NBER Working Paper No. 21400.

- Hasan, I., Tucci, C.L., 2010. The innovation–economic growth nexus: Global evidence. Research Policy 39, 1264–1276. doi:10.1016/j.respol.2010.07.005

- Heaton, J., Lucas, D., 2000. Portfolio Choice in the Presence of Background Risk. The Economic Journal 110, 1–26.

- Helgeson, J., Dietz, S., Hochrainer, S., 2013. Vulnerability to weather disasters: the choice of coping strategies in rural Uganda. Ecology and Society 18.

- Henriet, F., Hallegatte, S., Tabourier, L., 2011. Firm-network characteristics and economic robustness to natural disasters. Journal of Economic Dynamics and Control.

- Hochrainer, S., 2009. Assessing the Macroeconomic Impacts of Natural Disasters: Are there any? World Bank Policy Research Working Paper Series, Vol.

- Hoddinott, J., 2006. Shocks and their consequences across and within households in Rural Zimbabwe. The Journal of Development Studies 42, 301–321. doi:10.1080/00220380500405501

- Hsiang, S.M., Jina, A.S., 2014. The Causal
# IV.   Earnings and decision-making roles

## Complementing missing data on earnings

In surveys, earnings are measured through (a) individual wage/salary earnings; (b) profit from the farm (measured at the household or plot level); and (c) profit from the nonfarm enterprise (measured at the enterprise level, which if own-account could be assigned to an individual household member). The quality of rural earnings data can vary substantially across country surveys, often resulting in missing and outlier values for occupations where rural women also tend to be overrepresented. In wage employment, for example, this includes informal and seasonal jobs that can be paid out irregularly and/or in-kind; and in self-employment, in family farms/businesses where the value of household members’ labor is not recorded (if, as is common in rural areas, multiple family members are involved).

For individual wage/salary earnings, carefully collected community-level data on average wages or earnings for men and women, for formal/casual work across different sectors, could be useful as complementary data to understand expected labor market returns — particularly in contexts when many women are out of the labor force. For self-employment, within farming, several recent studies have discussed the use of individual-level data on productivity that is associated with farm income (see, for example, studies from Sub-Saharan Africa including Doss, 2018; Kilic et al, 2015). And as discussed in more detail below, for cases where income is pooled (across individual earnings, and farm/non-farm enterprises) the LSMS and other household surveys are exploring alternative questions about decision making over earnings to better understand individuals’ control over income. A joint pilot study in Sri Lanka by the World Bank and ILO under the WWEP, to coordinate survey questions across the LFS and LSMS, will also test questions on decision making in agriculture (Gaddis and Palacios-Lopez, 2018, Walsh, 2018).

## A closer look at decision making over earnings

Questions on decision-making over wage earnings,
g., users insert square feet even though the platform requires square meters). The matter is even more complicated in economies where local measurement units for properties are used alongside more “standardized” measurements. South Asian websites (Nepal; Pakistan) allow for the insertion of different size units including Biga, Kattha, Dhur, Ropani, Aana, Paise, and Daam, alongside square feet and square meters. However, not all users consistently specify the measurement unit making data cleaning cumbersome. In addition, particularly for houses, size data can be somewhat muddled as it is unclear whether the plot size or the usable property size is indicated. To account for this difference, we distinguish apartments and houses (cf. Section 4.1) and, where available, use plot size for houses to also account for the value of the land, which – in some EMDEs – can be a significant portion of the property price (cf. Section 2.2).

Finally, in some EMDEs, listing websites do not specify whether a property is for rent or for sale. Usually, rental properties can easily be distinguished by relatively low prices. However, in some EMDEs, it is common to pay one year’s rent upfront. In these instances, it is challenging to discern low sales prices from annual rents in cases where listings do not distinctly indicate sale versus rent. In addition, price data do not always include a currency marker, which is mainly problematic in countries where both euros and US dollars are used to transact properties in addition to local currencies.

When the aim is to estimate representative property prices of the housing stock in a country or region, scraping at least 0.5-1 percent of the number of HHs in that area is considered to be a large enough sample (cf. Bricongne et al. 2019). The same principle applies when the statistical population being analyzed is the universe of transacted properties in the market over a given period: in developed economies where residential property markets are
investments are complementary and that they increased economic development in Ethiopia. He finds starkly different patterns of bundled infrastructure investments on sectoral employment compared with only road investments. Roads alone cause service sector employment to increase at the expense of agriculture and, especially, manufacturing employment. In contrast, the interaction of roads and electrification causes a strong reversal in manufacturing employment. This paper adds to the strand of literature initiated by Moneke (2020) by studying the impact of internet, electricity and transport, and their complementarities, for other African countries.

There is a growing body of literature using quantitative spatial general equilibrium models to assess the impacts of infrastructure. Allen and Arkolakis (2014) develop a general equilibrium framework to determine the spatial distribution of economic activity and uses to assess the impact of the US interstate highway system. Michaels et al. (2011) look at changes in sectoral employment as an outcome that captures the underlying infrastructure-induced effects.<sup>3</sup> Bustos et al. (2016) and Fried and Lagakos (2020) study the general equilibrium implications of electrification via its effect on productivity. Several papers provide policy counterfactuals for future road and border infrastructure improvements for the Belt and Road Initiative (Lall and Lebrand, 2020; Bird et al., 2020); in Bangladesh (Herrera Dappe and Lebrand, 2019); and between Bangladesh and India (Herrera Dappe et al., 2021). The paper contributes to this strand of the literature by assessing the spatial general equilibrium impacts of several planned transport investments and trade facilitation measures in neighboring countries that are at different stages of development.

The paper is structured as follows. Section 2 presents the data and descriptive statistics. Section 3 presents the empirical strategy and results. Section 4 develops a spatial generalequilibrium model to produce counterfactuals for more domestic and regional integration through future infrastructure investments. Section 5 concludes.

# **2 Data and descriptive statistics**

## **2.1 Data**

This paper uses household survey data that have