rafmacalaba/datause-encoder-data huggingface dataset:
link project staff to key characteristic data. This required significant time to work through the cases where names scraped from PADs were not immediately located in HR reports, or in some cases more than one UPI was associated with a name, and therefore it was necessary to investigate further through the Bank’s internal staff directory “PeopleFinder” to match the person with their project. Moving forward, more reliable population of these internal staff directory pages and access to the back-end data is one possible circumventing solution to the issue of the Bank’s project-level systems not storing historical data on a project’s TTL.

Staffing characteristics such as education and age are sensitive and confidential. These were provided by HR reports with the corresponding restrictions and permissions. TTL location at preparation and supervision had to be sourced from historical end-of year snapshots of the active staff directory provided by HR. Table A2.2 provides breakdowns for where each measure was sourced, and for how many projects in the full data set the information was available. While most variables have good coverage across the full data set, certain staffing information does not. For example, in addition to the TTL, it would be of interest to assess other team members’ contributions, but that information is not available for the preparation phase. More sensitive data for senior staff members was also not secured. Notwithstanding legitimate data privacy and confidentiality concerns, there are ways in which this data could be stored and shared for projects in an anonymous fashion, and this should be considered by the institution for future purposes.

**_Table A2.1 Coverage of analytical data set_**

|**Approval Fiscal Year**|World Bank IPF Lending<br>(N = 3,151 Projects)|Analytical Data Set<br>(N = 2,845 Projects)|
|---|---|---|
|FY95|221|213|
|FY96|228|221|
|FY97|213|204|
|FY98|252|243|
|FY99|244|226|
|FY00|205|194|
|FY01|205|181|
|FY02|192|181|
|FY03|208|186|
|FY04|210|192|
|FY05|228|198|
|FY06|220|200|
|FY07|192|164|
|FY08|186|145|
|FY09|147|97|
||**Analytical Data Set as a**<br>**share of WB IPF Lending**|**90%**|

41
migration is cost-prohibitive and that adverse weather shocks amplify liquidity constraints. Similarly, the study by Schutte et al. (2021) on the association between climatic conditions and asylum migration reveals that temperature anomalies are weak predictors of bilateral asylum migration to the European Union, and concludes that future asylum migration will mainly be driven by political changes rather than by climate change. This is in line with the findings of <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) for West and Central Africa, who estimate that a standard deviation decrease in soil moisture leads to a 25-percent reduction in the number of international migrants, likely due to liquidity constraints. Bekaert, Ruyssen and Salomone (2021) leverage individual-level data from Gallup World Polls conducted in 90 countries to show that exposure to environmental stressors increases the probability of intending to migrate both domestically and internationally (but more so intra-regionally), especially in rural and less developed regions.

In short, there seems to be wide heterogeneity in the type and destination of migratory flows triggered by climatic shocks, depending on a variety of factors including, among others, the severity and frequency of the shock, the gender and resources of the affected individuals, and the context of the case study. Despite such heterogeneity, however, meta-analyses of the existing studies have found evidence that the case for weather-related migration is much more compelling for internal, within-country movements than for international cross-border flows (Hoffmann et al., 2020), a finding which contradicts the ‘conventional wisdom’ about large-scale international migration triggered by climate change. Lastly, an important missing piece, indeed also due to data gaps, is the paucity of studies reconstructing a potential ‘climate migration chain’ that might be triggered, directly or indirectly, by climatic changes in the original affected areas. It is not implausible to imagine a scenario in which a future increase in the frequency or intensity of weather shocks in a rural developing context will determine rural-urban internal migration which, in turn, gives rise to international, cross-border movements of urban workers due to changes in local labor markets. Marchiori, Maystadt and Schumacher (2012) show that a similar ‘economic geography’ mechanism had already taken place in Sub-Saharan African countries in past decades. If and how much this kind of climate migration chain will become more relevant as climate change intensifies, for the moment remains speculation.

# _D. Heterogeneous strategies and the role of liquidity constraints_

A crucial insight from the most recent literature is that climate-related hazards can cause or worsen liquidity constraints (Bryan, Chowdhury, and Mobarak, 2014; Cattaneo and Peri, 2016; Cottier and 9
destination of transactions. Lischke and Fabian (2016) leverage publicly available Bitcoin transaction data and the IP addresses of transactions to gauge geographical location. However, this approach assumes that the Bitcoin node that broadcasts the transaction to the network is also its source.

### _Crypto-assets prices and inflation_

There is a small literature that studies the link between crypto-asset prices and inflation. Using a vector-autoregressive framework, Blau et al (2021) find an empirical relationship between bitcoin prices and inflation expectations suggesting that bitcoin acts as an inflation hedge (i.e., changes in bitcoin Granger-cause changes in inflation expectations). Conlon et al (2021) confirm a link between forward inflation expectations and bitcoin and ether prices, but this relationship is only limited to the onset of COVID-19 casting doubt over the ability of these crypto-assets to hedge expected inflation going forward.

# 3 Data

## 3.1 Description of the on-chain crypto-activity data set

We use a large global monthly country-panel of on-chain crypto-asset transaction volumes of value sent in US dollars provided by Chainalysis, a global blockchain analysis company. “On-chain” transactions are directly recorded on the distributed ledger that underpins a crypto-asset. The sample spans the period April 2019 – June 2021 and covers 174 countries, 114 different cryptoassets, and five transaction size categories. We have mapped crypto-assets into four groups: 1) Bitcoin; 2) Ethereum; 3) Stablecoins<sup>11</sup> ; and 4) DeFi and Others.<sup>12</sup> We also group transaction sizes into two categories: a) less than or equal to $10,000, which is more reflective of retail use and b) greater than $10,000.

While on-chain crypto-assets transaction data are fully transparent for most crypto-assets, in light of their pseudonymous nature, the destination country of a particular transaction may not be known

> 11 These include some of the major US$-linked stablecoins: USD Coin (USDC), Tether (USDT), DAI (crypto-assets backed), TrueUSD (TUSD), Paxos USD (Dollar (PAX), Binance USD (BUSD), and Gemini Dollar (GUSD). In November 2021, the top 3 stablecoins account for about 85 percent of the total stablecoin market capitalization. 12 This category includes 103 different crypto-assets. The top 10 with the largest volume are: Wrapped Ether (WETH), XRP, Litecoin (LTC), Wrapped Bitcoin (WBTC), Chainlink (LINK), Bitcoin Cash (BCH), EOS, Uniswap (UNI), Yearn Finance (YFI), and Sushiswap (SUSHI).

13
The large welfare gains we estimate in our data stand in sharp contrast to Gibson et al.’s (2010) estimates of the adverse effects of winning a New Zealand migration lottery program on the extended family members of Tongan emigrants who remained behind. That study is the closest to ours in terms of research design and rigor, but their setting is very different from ours in several important aspects. Most importantly, their lottery program was for permanent migration where the winner could migrate with their spouse and children to New Zealand. Hence, any effects measured in Tonga are for distant household members, who naturally would not share in as much of the gains from migration. In contrast, Bangladeshi migrants leave their families behind and remit a large share of their income back to their families in Bangladesh, which results in large gains for their families. Likewise, permanent migration programs like that between Tonga and New Zealand represent a relatively small share of the global flow of migrants.<sup>2</sup> The migration episodes we study in Bangladesh typify the most common form of global flows, with Bangladesh being one of the largest suppliers of such migrants.

Another closely related study, Clemens and Tiongson (2017), estimates the impacts of a G2G program for temporary migration from the Philippines to South Korea. They employ a regression discontinuity (RD) design around the potential migrants’ performance on a mandated Korean language test and find large consumption increases with migration. Unlike this paper, however, they do not measure household income increases that can rationalize the large cross-country gap in wages observed in macroeconomic data (Clemens et al. 2018). Our paper also differs in the sample and representativeness. Applying to the Korean employment scheme required a high school degree, work experience, and Korean language ability. In contrast, applicants to the Bangladesh-Malaysia lottery program were lower skilled and more representative of the high volume of international migrants from developing countries. Their RD generates internally valid estimates for the set of applicants who invested in learning Korean but performed marginally on the language test. Marginal performance may be correlated with cognitive ability or other unobserved dimension of skill. In contrast, the impact of migration that we estimate generalizes more broadly to a large and policy relevant migrant population that is willing and able to migrate internationally for low-skilled work.

Our research is related to the very large literature that estimates the returns to migration but has had to grapple with difficult selection issues (Akee 2010, Grogger and Hanson 2011, Gibson et al 2013). Review papers by McKenzie and Yang (2010) and McKenzie (2012) cite studies that exploit exogenous variation in immigration policies to study the effects of international migration (Clemens 2010,

> 2 For instance, the migration program only takes 250 Tongan migrants each year.

5
**Romania**

<!-- Start of picture text --> 35.0<br>25.0<br>15.0<br>5.0<br>-5.0<br>-15.0<br>-25.0<br>-35.0<br>-45.0<br>location vulnerable households household structurehousinghealth head of the household individual characteristics<br>rural areas towns and suburbs hh with pensioner hh with disability recipient hh with +5 members hh with children single-elderly hh hh with unemployed female headed hh number of >64 number of 18-64 number of <18 crowded house unmet medical needs student inactive retired unemployed tertiary educ. secondary educ. home ownership age male<br><!-- End of picture text --> Note: Average marginal effects from the Probit regressions expressed as percentage points. See Annex A4 for further details on the regression analysis. See Table A4.1 to Table A4.4 for regressions results. Households with social aid cannot be identified as a vulnerable group in the Romania EU-SILC. Source: Own estimates based on 2022 EU-SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

# **The unconditional analysis (descriptive results reported in Table 6) gives us a considerably**

**good approximation of the profile of the AROPE.** The profile of the AROPE population in terms of their individual and socio-economic characteristics (gender, education, employment status, home property, and location) is the same under the unconditional analysis (Table 6) and under the conditional analysis (regression results reported in Table A4.1 to Table A4.4 and Figure 3). Further, the conclusions about which type of households are more vulnerable to being multidimensionally poor across all countries are the same, except for female-headed households, which  were identified as more likely to be AROPE in the unconditional analysis in all countries, but only in Bulgaria and Poland after controlling for other relevant characteristics.

**<mark>The analysis of the factors related to multidimensional poverty can point out key policy areas.</mark>**

<mark>The fact that being more educated and being employed is systematically associated with a lower probability of being multidimensional poor highlights the important role of implementing and enhancing policies that promote labor force participation, access to economic opportunities, and the accumulation of human capital. Policies in these areas may contribute to lessening the incidence of multidimensional poverty through multiple mechanisms – either by reducing monetary poverty via rising labor income, by improving work intensity within the family, or by diminishing some of the multiple material and social deprivations. Further, since homeowners are less prone to endure material and social deprivations, but individuals living in overcrowded houses are more likely to, tackling multidimensional poverty may need policies that also address access to housing, especially in Bulgaria and Romania. Additionally, public health policies may have positive externalities on the reduction of multidimensional poverty since individuals living in families with unmet needs for medical examination or treatment experience a higher</mark> 25
15 A more specific model can now be developed by defining the firm's technology and setting an appropriate time horizon. Usually, economists think of the firm as having two time horizons, long run and short run. In the long run all primary inputs, capital, labor and raw materials would be variable, together with each type of electricity. In the short run, some inputs would be fixed. For our purposes, it is useful to think of two different short run cases; one in which all capital, including generators is fixed, and another in which primary production capital is fixed while generator capital is variable. Nigeria better fits the former sort of short run. Most Nigerian firms have considerable excess capacity, and operate with quantities of primary capital and generating capital that were chosen long ago. Indonesia better fits the latter sort of short run. Primary capital is not varied often, so the Indonesian firms we observe, few of which are new firms, did not settle upon their primary capital requirements recently; but generators are more easily added than some other capital equipment, like structures, and there appears to be an active market for generator sales to new and old firms alike.

We first specify the firms' embedded cost function in the fixed generators case, which we will apply to the Nigerian data. The embedded cost function takes the form: (7) c(w,e;k) = c (w,,w,,,e;k), where w, and **wm** are the embedded input prices of labor and material, and k is the stock of generators. Variable embedded cost in this instance is labor costs plus materials costs. Because of expected scale economies in embedded production we expect ac(w,e)/ae < c(w,e)/e (marginal cost is below average cost). The firm values embedded power at its marginal cost. Let **Pe** denote the price of embedded power, it is calculated from (7) as follows: (8) **Pe** = MC. = ac(w1,w.,e;k)/ae
# **4.2 The Micro evidence**

# **4.2.1 Introduction**

The most popular micro-level approach uses observations at the individual level - typically at the level of the worker, the student or the household - to run regressions of individual wages or earnings on individual levels of human capital plus some additional controls. The human capital returns obtained in this way are returns for a given individual as a result of human capital investment on herself. The impact on overall growth is therefore generated through the impact on wages and labor market performance. The standard specification is log linear: where a measure of labor income ( _wi_ ) is regressed on the human capital variable of interest ( _hci_ ) and a set of controls ( _xi_ ). The relative return to the human capital investment, the coefficient of interest in the regression, is ��.

Empirically, there are advantages of using this micro-level approach. First, micro-level regressions use variation at the level of the individual workers instead of aggregating at the country level. Second, they can estimate more flexible specifications because each country (sometime different groups of workers within a country, such as men and women) is allowed to have a different return to the specific human capital component under study. Third, the range of additional controls that can be added to the specification is larger thanks to the richer amount of information included in the survey data used in this context. Fourth, they may take into account the individual heterogeneity which is an integral part of the original human capital model. The original human capital model posits an investment decision in human capital made _by the individual agent_ . If individuals are different, they may make different decisions. Estimates based on micro-evidence allows to take into account at least some of these individual-agent level differences while the macro evidence has to aggregate over them.

The main disadvantage of using the micro-level approach is that the link to the variable of interest, growth, is less direct. The issue is sometime referred to as the difference between _private returns_ and _social returns_ . The macro-evidence presented in Section 4.1 uses as dependent variable a measure of GDP either in levels or growth rates. This measure is a direct proxy for the total output variable considered in all growth models. Assessing impacts of human capital investments on GDP is assessing the overall aggregate impact of the investment: the _social return_ . The micro-evidence presented in this Section uses as dependent variable a measure of individual labor income which is the payoff based on which the individual agent is making the choice: the _private return_ .<sup>8</sup>

> 8 In both cases, there can be non-pecuniary returns that are not captured either by GDP or by labor income measures. Some of these returns may be captured by taking into account the externalities we discuss in the rest of the paper. Other returns go beyond growth rates and while very important will not be discussed in the paper.

12
The results are presented in Table 5 and indicate that increases in all income sources are positively correlated with increases in consumption, as expected, with the exception of agricultural wage income. Agricultural income growth is more strongly correlated with consumption growth than the other sources of income growth, and the correlation is larger for the bottom 40 percent (column 2). These results suggest that agricultural income growth has been more important for poverty reduction during this period than other types of income growth.

This confirms the finding of other work undertaken on this period in Uganda. Christiaensen and Kaminski (2014) undertake decomposition analysis using the same panel data set and find that agricultural income growth contributed to 18 percent of consumption growth from 2006 to 2010. They also estimate that agricultural growth contributed to 70 percent of the poverty reduction observed from 2006 to 2010, confirming the greater importance of agricultural income growth among the poorest households. Decomposition analysis undertaken using the Uganda National Household Survey<sup>10</sup> for the Uganda Poverty Assessment also indicates that it was poverty reduction among those households that cited agriculture as their main source of income that accounted for most of the poverty reduction in Uganda throughout the period 2006 to 2013 (World Bank 2016). These findings are also consistent with a literature that points to agricultural income growth as a major source of poverty reduction in the country (Kassie et al. 2011, Dorosh and Thurlow 2012, Government of Uganda 2014).

**Table 5. Relationship between income and consumption, 2006-2010**

||(1)|(2)|
|---|---|---|
|Dependent variable: log of per capita consumption|All households|Bottom 40 percent|
|Log of per capita real crop gross income|0.0324***<br>(0.00805)|0.0416***<br>(0.0103)|
|Log of per capita real livestock gross income|0.00573**<br>(0.00283)|0.00479<br>(0.00347)|
|Log of per capita real agricultural wage|0.00127<br>(0.00239)|0.00186<br>(0.00278)|
|Log of per capita real non- agricultural wage|0.00553**<br>(0.00271)|0.00505<br>(0.00359)|
|Log of per capita real self-employment income|0.00934***<br>(0.00246)|0.0106***<br>(0.00302)|
|Constant|10.28***<br>(0.140)|9.942***<br>(0.189)|
|R-squared|0.086|0.095|
|Number of HHID|2,644|1,853|

Source: author calculations using the UNPS 2006 and 2010. Notes: The dependent variable is log of real per capita consumption. Household, year and month of interview fixed effects are included but not shown. Robust standard errors in parentheses. Coefficient statistically significant at: ***1%, ** 5%, *10%

> and this may have resulted in a reduction in reported consumption. CAPI was not introduced in the nationally representative cross-sectional survey, the UNHS.

> 10 A nationally representative cross-section household survey conducted every three to four years to provide (among other things) data on poverty trends in the country.

14
Table 2: Dierence in survival association matrices (in percentage points)

|||(a) 1|985 compar|ed with 196<br>Capital|6|||
|---|---|---|---|---|---|---|---|
|Labor|Top 50%|Top 40%|Top 20%|Top 10%|Top 5%|Top 1%|Top 0.5%|
|Top 50%|0.68|-1.59|-1.05|-0.95|-1.08|-0.23|-0.09|
|Top 40%|1.11|-1.15|-0.96|-0.87|-1.06|-0.25|-0.09|
|Top 20%|0.67|-0.78|-0.94|-0.75|-0.77|-0.27|-0.10|
|Top 10%|0.33|-0.42|-0.82|-0.71|-0.65|-0.26|-0.10|
|Top 5%|0.10|-0.30|-0.66|-0.59|-0.53|-0.25|-0.10|
|Top 1%|-0.01|-0.06|-0.25|-0.26|-0.24|-0.16|-0.08|
|Top 0.5%|0.00|-0.02|-0.11|-0.13|-0.13|-0.08|-0.06|

(b) 2006 compared with 1985

|Labor|Top 50%|Top 40%|Top 20%|Capital<br>Top 10%|Top 5%|Top 1%|Top 0.5%|
|---|---|---|---|---|---|---|---|
|Top 50%|1.78|2.05|2.53|2.08|1.43|0.35|0.18|
|Top 40%|1.87|2.11|2.55|2.08|1.41|0.37|0.18|
|Top 20%|1.35|1.63|2.03|1.57|1.07|0.36|0.18|
|Top 10%|0.58|0.92|1.41|1.17|0.80|0.31|0.16|
|Top 5%|0.22|0.50|0.96|0.81|0.59|0.26|0.15|
|Top 1%|0.03|0.07|0.27|0.26|0.21|0.12|0.09|
|Top 0.5%|0.01|0.03|0.12|0.13|0.11|0.05|0.05|

Description: Share of tax units who were in the top 0.5% of both labor and capital increased by 0.05pp between 1985 and 2006.

|||(c) 2<br>|006 compar<br>|ed with 196<br>Capital<br>|6<br>|||
|---|---|---|---|---|---|---|---|
|Labor|Top 50%|Top 40%|Top 20%|Top 10%|Top 5%|Top 1%|Top 0.5%|
|Top 50%|2.46|0.47|1.49|1.13|0.35|0.13|0.09|
|Top 40%|2.98|0.96|1.59|1.20|0.35|0.12|0.09|
|Top 20%|2.02|0.86|1.09|0.82|0.30|0.09|0.08|
|Top 10%|0.91|0.50|0.60|0.46|0.14|0.05|0.06|
|Top 5%|0.32|0.20|0.30|0.22|0.06|0.01|0.05|
|Top 1%|0.02|0.01|0.02|0.00|-0.03|-0.05|0.01|
|Top 0.5%|0.01|0.01|0.01|0.00|-0.02|-0.03|-0.01|

Note: Bold cells: Value of survival association matrix is lower in nal year (e.g. 2006) than initial year (e.g. 1985).

17