rafmacalaba/datause-encoder-data huggingface dataset:
individuals form lifetime plans for consumption. In period 0, they also chose climate change action, which is then applicable for the duration of their life. The individual’s optimal climate change action should be interpreted as making the desired regular contributions, such as taxes, to combat climate change.

We show that the optimal climate change action of an older individual can exceed that of a younger individual if the following conditions are met. First, the impact of climate change on wellbeing intensifies significantly with age (i.e., is sufficiently high). Second, individuals discount the future sufficiently strongly – that is, is sufficiently high − so that young people are not too θθ concerned about the higher intensity of climate change impacts in their old age. Third, the pollution rr stock grows sufficiently slowly − that is, is sufficiently small − so that in the future, young individuals do not expect to live in a significantly worse environment than they currently do. γγ

# _Linking the theoretical and empirical models_

The theoretical model identifies three parameters governing the impact of age on willingness to pay for climate change policies. These are the perceived effectiveness of government in mitigating the impact of climate change , the perceived intensity of climate change effect , and the individual’s discount rate . Because these parameters are not directly observable, we use proxy tt ηη κκ variables to identify their impacts on respondents’ attitudes to climate change and their willingness rr to pay higher taxes to support climate change actions. In the theoretical model, the effect of on concerns about climate change and willingness to finance climate change policies is ambiguous, as the analysis in the appendix shows. As ηη increases, the climate change action becomes more effective, prompting the individual to substitute ηη away from consumption to climate change action. At the same time, higher means that the individual can achieve higher utility with the same income, relaxing the budget constraint. This ηη income effect pushes people to consume more and spend less on climate change action. As substitution and income effects work in opposite directions, the overall effect of is ambiguous.<sup>7</sup> In reality, the size of may differ depending on the specific nature of the climate change measure ηη in question. Furthermore, it will also depend on the individuals’ trust in the government and beliefs ηη in its ability to deploy these resources toward effective mitigation or adaptation measures (OECD

> 7 Similar intuition explains the backward-bending labor supply curve (e.g., Dickinson 1999).

8
<!-- Start of picture text --> Figure 1<br>World Bank Commodities Price Forecast - Nickel, Copper, &<br>Zinc<br>(Constant US dollars; Unit: $/mt)<br> 18,000<br> 16,000<br> 14,000<br> 12,000<br> 10,000<br> 8,000<br> 6,000<br> 4,000<br> 2,000<br> -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030<br>Nickel Copper Zinc<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2<br>World Bank Commodities Price Forecast - Gold & Platinum<br>(Constant US dollars; Unit: $/toz)<br> 1,600<br> 1,400<br> 1,200<br> 1,000<br> 800<br> 600<br> 400<br> 200<br> -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030<br>Gold Platinum<br><!-- End of picture text --> 12
concerns thus generate a trade-off with regard to _b_ , the amount of the bribe.<sup>19</sup> In particular, the expected income maximization problem is We can, therefore, view the local irrigation official as trading-off lower bribe income against greater net surplus to head outlets and, consequently, a higher _equilibrium_ probability of retaining his position.

Given equation (8), it is straightforward to derive an explicit expression for the optimal bribe amount from the first-order conditions to problem (9):

# ˆ ˆ ˆ where _r_ = ( _N − n_ ) _s_ 0 _/nθ_ and ∆ _s_ = _s − s_ 0.<sup>20</sup> Directly, we obtain (see Appendix for all proofs) **Lemma 1** _b_<sup>_∗_</sup> _θ_<sup>_>_0</sup><sup>_,_</sup>

which says that the greater the relative influence of the head coalition, the greater the bribe that head outlets have to pay. Intuitively, a more influential head coalition can be left with less net surplus (through a higher bribe) and still exert the same amount of effective lobbying power in favor of the status quo.

Although we do not observe bribes, lemma 1 helps deliver testable implications concerning water theft. Expected water availability at the head and tail are, respectively, _wH_ ( _θ_ ) _≡ P_<sup> ̃</sup> _w_ ˆ+ 1 _− P_<sup> ̃</sup> _w_ 0 and _wT_ ( _θ_ ) _≡_ 1 _− P_<sup> ̃</sup> _w_ 0. We have for the theft percentage _τ_ ( _θ_ ) _≡_ log _wH/wT ,_ � � � � **Proposition 1** _τ_<sup>_′_</sup> ( _θ_ ) _>_ 0 _._

> 19See Iyer and Mani (2012) for evidence on the role of career concerns in India’s civil service. Banerjee (1997) provides a more general theory of corruption in which there is an exogenous probability that the bureaucrat is punished for malfeasance.

> 20This is the smaller root of a quadratic equation. The larger root _r_ + ∆ _s_ + � _r_ ( _r_ + ∆ _s_ ) is precluded by the requirement that _b ≤_ ∆ _s_ ; i.e., the bribe cannot exceed the net gain from theft.

12
whose earnings were deposited into savings accounts, for example, spent 13 percent more on farming equipment and increased their crop value by 15 percent (Brune, et al., 2016). In another example, a coffee buyer in Uganda digitalized payments to 5,500 smallholder coffee farmers, leading to saved time and money (Ogwal and Mugabi, 2015).

However, unlike with government and private sector wage payments, the majority of which are already digital, a large majority of payments for agricultural goods continue to be made mostly in cash. Establishing a relationship with a formal financial service provider could enable farmers to access additional financial services, such as crop and fertilizer financing and insurance products. In developing economies, 11 percent of adults received payment for the sale of agricultural goods but only one-fourth of them received their money directly into an account. The exceptions are in Sub-Saharan Africa, where the share of adults receiving agricultural payments is more than twice the developing economy average (26 percent). Some economies have a much higher share of recipients getting their money directly into accounts, driven by mobile money proliferation and the availability of rural agent networks for withdrawals.

# **4. The benefits of savings and credit**

Beyond payments, using financial accounts can help people build savings and access credit, which can lead to higher income-generating investments, such as in education or in a business.

# **_4.1 The anti-poverty role played by savings_**

“Saving” refers to the act of putting aside money for a specific goal or for the purpose of having a nest egg in the case of an emergency. This is distinct from “storing money” for shorter term, cash management purposes, such as paying an upcoming bill.

Research finds that access to savings can help adults better manage risk—including women. In Chile, a study of low-income members of microfinance institutions found that women who received free savings accounts reduced their reliance on debt and improved their ability to make ends meet during an economic emergency (Kast and Pomeranz, 2022). Access to mobile moneybased savings tools has been shown to also reduce women’s reliance on high-risk sources of income (such as transactional sex), according to a study of low-income women in urban and rural western Kenya (Jones and Gong, 2021).

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simple statistics should by no means be considered as reflecting a causal effect,<sup>**†††**</sup> they do not go against a correlation between the inclusion on the FATF list and weaker tax revenues.

**Figure 1. Tax revenue and IFFs before** **_vs_ after inclusion in FATF list of Non-Cooperative countries (a) Average IFFs (b) Average Non-resources Tax revenue**

<!-- Start of picture text --> Average Illicit Financial Flows (%<br>GDP) Before and After the Treatment<br>3 2.66<br>2.38<br>2.5<br>1.88<br>2<br>1.5<br>1<br>0.41<br>0.5<br>0<br>Pre- Post- Pre- Post-<br>Period Period Period Period<br>Non-Cooperatives countries Cooperatives<br>countries<br>Illicit Financial Flows (% GDP) from DCs<br><!-- End of picture text --> <!-- Start of picture text --> Average Non-resources Tax revenue<br>Before and After the Treatment<br>20 18.23<br>18 16.08 16.16<br>16<br>13.94<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Pre- Post- Pre- Post-<br>Period Period Period Period<br>Non-Cooperatives countries Cooperatives countries<br>Non-resource Tax revenue(%GDP)<br><!-- End of picture text -->

# **IV. Methodology**

To evaluate the impact of being a country on the FATF list (i.e. the treatment) on tax revenues (i.e. the outcome), we compute the Average Treatment on the Treated (ATT) _ATT_ = _E_ [ _Yi_ 1 − _Yi_ 0 _NonCoop_ = 1 ] = _E_ [ _Yi_ 1 _NonCoop_ = 1 ]− _E_ [ _Yi_ 0 _NonCoop_ = 1 ] , (1) with<sup>_Y_</sup> _i_ 1<sup>the value of tax revenues for a country in the FATF list, and</sup><sup>_Y_</sup> _i_ 0<sup>the value of tax</sup> revenues in the same country had it not been on the FATF list. Unfortunately, the latter variable is not observable, and one convenient solution would be to replace it by the mean value of tax revenues in the countries that are not on the FATF list. However, the treatment—being included in the FATF list—is probably not random but dictated by some observable factors (political institutions, macroeconomic conditions, and so forth) that also determine tax revenues. Therefore, as previously pointed out, comparing the mean value of tax revenues between NonCooperative and Cooperative countries can generate a “selection on observables” problem that would lead to biased results.

To address this problem, we compute the ATT by replacing the last term of (1) by the average of tax revenues in countries that are not on the FATF list but present a set _X i_ of characteristics comparable with those of countries on the FATF list, namely _ATT_ = _E_ [ _Yi_ 1 _NonCoop_ = ,1 _X i_ ]− _E_ [ _Yi_ 0 _NonCoop_ = ,0 _X i_ ] . (2)

> **†††** The simple comparison of mean values between Cooperative and Non-Cooperative countries may be biased by a “selection on observables” issue—the fact that the treatment (i.e. being a Cooperative country or Not) may not be random but depending on some variables.

7
is relatively weak, which raises questions about the real magnitude of the economic slowdown. Second, in the same year, Afghanistan experienced one of the most violent conflict outbreaks since the fall of the Taliban - the Battle of Kunduz. But this was by no means the only episode of conflict experienced in Afghanistan. It is reasonable to assume that conflict affects economic activity, but there is not much data to determine by how much. Third, in 2016, India went through demonetization, a policy intervention that withdrew large amounts of currency from the economy. While this intervention has potential benefits in the medium term, in the short-term it might have affected economic activity negatively; however, it is difficult to tell how strong the short-term impact was, or how it was distributed across population groups.

# a. EARTHQUAKES IN NEPAL

With two major earthquakes in April and May, and a severe disruption of trade with India from August onwards, the year 2015 was no doubt Nepal’s most turbulent year since the end of its civil war. The two earthquakes killed about 9,000 people, injured at least twice as many, and destroyed uncountable houses and buildings. Later in the year, dissatisfaction among the Madhesi minority about their representation under the new federal arrangements triggered protests that culminated in the complete shutdown of international trade with India. Official statistics put GDP growth for FY2015 (which starts in October 2014) at 1.6 percent, and for FY2016 at 0.8 percent. This represents a drop of roughly 4 percentage points relative to previous years.

Based on monthly nightlight data, the economic impact of the 2015 earthquakes was smaller than official statistics suggest (Figure 5). The earthquakes affected most severely rural areas that were characterized by low nightlight intensity even in good times. The fact that these areas were already mostly dark suggests that even if local impacts were large in relative terms, they may not have made a major difference at the aggregate level. The impact of the trade disruption, on the 16
For LICs, the longer‐run effects of the decline in capital flows are serious, particularly because deficiencies in domestic intermediation systems are likely to prevent them from compensating for a reduced foreign presence. However, LICs are not without a potential remedy— particularly regarding the improvement in their policies, institutions, and the overall regulatory environment. Recent empirical work shows that the inefficiencies in domestic financial sectors greatly influence borrowing costs in developing countries. Thus improvements in policies and institutions governing the financial sector can have a significant impact in boosting domestic financial intermediation to an extent

# **Total Lending by Foreign Banks**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20 Emerging Asia<br>Emerging Europe<br>10<br>Latin America<br>0<br>Percent<br><!-- End of picture text --> Source: IMF WEO that could outweigh the potential negative impact of higher global risk premiums, offsetting some of the long‐term effects of the financial crisis.<sup>14</sup> Faced with a less active external financing system, it is all the more imperative that authorities in developing countries take steps to improve public DeM practices, including efforts to develop the domestic debt markets, and to build the institutional capacity necessary to adapt to changes in the international financial environment to cushion the effects of exogenous shocks. Such improvements will decrease market perceptions of risk as well as increase cost efficiency, mitigating the effects of less favorable external financial conditions. Importantly, such improvements will also help to reduce vulnerabilities to future crises.

<!-- Start of picture text --> Figure 7: Growth in Remittances (Percent) Figure 8: FDI Flows to Developing Countries<br>40 Developing 140 123 121<br>120 106<br>30 ECA<br>86<br>100<br>73<br>20 LAC 77<br>80 69<br>10<br>60<br>0 40<br>20<br>‐10<br>0<br>‐20<br>2006 2007 2008 2009a 2010b 2011b<br>Source: Mohapatra and Ratha (2010). Source: World Bank GEP<br>US$ in billions<br><!-- End of picture text --> Source: Mohapatra and Ratha (2010).

It is in this context that this paper assesses DeM capacity in developing countries. However, first it is important to identify how DeM is directly related to effective crisis response and how developing countries face unique challenges in this area.

14 World Bank (2010) shows that a decline in interest spreads of 25 bps a year on average could produce a 13 percent increase in long‐term potential output, increasing potential average annual output growth by 0.3 percent.

6
think of negative externalities of agricultural employment, such as the exposure to weather shocks or hard physical work. These externalities are difficult if not impossible to quantify. If we accept the existence of an income differential between agriculture and non-agricultural sectors, the question then becomes why individuals do not respond to this differential by moving to the nonagricultural sector until incomes in both sectors equalize. A likely answer is that there must be barriers to mobility between agricultural and non-agricultural employment and that these barriers are relevant to the period under our analysis. A potentially important factor that may act as a barrier to mobility, although we do not test for this hypothesis, is represented by the specificity of human capital acquired in the agricultural sector.

_Table 8: Labor market segmentation estimation results_

|Dep. Var: Labor income|Coefficient|Robust Std. Err.|P>t|
|---|---|---|---|
|_Individual characteristics_||||
|Years of schooling|0.196***|0.015|0.00|
|Experience|0.06***|0.006|0.00|
|Experience squared|-0.001***|0.000|0.00|
|Male|0.4***|0.052|0.00|
|Self-employed|0.122|0.126|0.33|
|_Geographic dummy variabl_|_es_|||
|(Kingston)||||
|St. Andrew|1.014***|0.146|0.00|
|St. Thomas|0.4**|0.171|0.02|
|Portland|0.454***|0.145|0.00|
|St. Mary|0.177|0.129|0.17|
|St. Ann|0.248*|0.131|0.06|
|Trelawny|1.151***|0.229|0.00|
|St. James|1.609***|0.244|0.00|
|Hanover|0.366**|0.150|0.02|
|Westmoreland|0.856***|0.135|0.00|
|St. Elizabeth|0.525***|0.127|0.00|
|Manchester|0.238*|0.128|0.06|
|Clarendon|0.704***|0.155|0.00|
|St. Catherine|0.873***|0.122|0.00|
|_Sectoral dummy variables_||||
|(Services)||||
|Agriculture Sectors|-0.339***|0.094|0.00|
|Manufacturing|-0.134|0.082|0.10|
|_significance levels:*** 1%_|_;** 5%;* 10_|_%: no stars insignificant_||
|Number of obs||2461||
|F( 20, 2440)||33.31||
|Prob > F||0.00||
|R-squared||0.21||
|Root MSE||1.25||

To accommodate the above evidence of a) a bias in the distribution of remittances receipts towards unskilled headed households and b) the labor market segmentation, we 21
Another avenue for further work would be an analysis of the effects of differing levels of public funding for education on the above results. This would enable an analysis more focused on the welfare implications of labor market liberalization, and as such may identify more policy prescriptions. Clearly a positive net effect on human capital stocks is not enough to conclude that the economic impact of labor market liberalization is positive if the gains from increased participation in tertiary education are funded entirely from the public purse. The positive implications of the brain gain rely on an increase in the private investment in human capital.

Empirical analyses that attempt to quantify the net brain effect continue to be severely hampered by data deficiencies. Nevertheless incremental steps such as those presented in this paper, can help to clarify what has for a long time been, and will likely continue to be, a highly contentious area.

# **Conclusion**

The brain gain literature relies for its positive prognosis on the assumption that human capital is endogenous. This paper has shown that this assumption is well founded. It has examined the relationship between migration opportunities and the private decision to invest in higher education, looking first at the impact of the announcement to open negotiations, through a difference-in-difference comparison between the Czech Republic and Slovakia, and secondly at the impact of accession on a cross-country panel of 13 current EU members. Taken together these results represent evidence that an increase in the opportunity to migrate, heralded by accession to a free-trade area such as the EU, has a significant effect, both economically and statistically, on the proportion of the population that choose to invest in higher education, and hence the skill level of the country. While it is important to remember that this result cannot be entirely attributed to the increased migration opportunities, as increased FDI and trade flows will also raise the domestic return to education, micro-level survey data regarding 24
on the one originally proposed by Binswanger, Deininger and Feder (1995), and not dissimilar from the approach used by several others including, most recently, Barrett et al. (2010). We estimate the following function at the household level: _A_ where<sup>_Y_</sup> _i_ represents net agricultural revenues for each household _i_ , _i_ the total area operated, _X i_ denotes a vector of households‟ characteristics that influence production such as the availability of family labor, the gender, age and education of the household head, value of inputs used, and a set of land quality variables, R is the rounding effect when net revenues per acre of area cultivated are measured with land size as reported by farmers, and<sup>_u_</sup> _i_ is the error term. We estimate two versions of this relationship, one using GPS and the other one with the self-reported land measure, and compare the two to gauge the impact of differences in measurement on the IR, which is captured by the coefficient on the land size variable. A negative coefficient on the land area variable indicates the presence of the IR. The further below 0 the coefficient, the stronger the IR. As in the previous model, we also include PSU fixed effects to control for survey team and local environmental effects.

The left hand side term - net agriculture revenues per acre - is computed as the value of total crop production net of value of variable inputs (hired labor, seeds, organic and inorganic fertilizers, chemicals, and animals rented for plowing), divided by the amount of land cultivated. While we maintain this is the preferred way to calculate net revenues, some may argue that subtracting hired but not family labor may give an unfair advantage to small farmers who rely mainly on the latter. We therefore also tried running our estimates without subtracting hired labor costs from gross revenues and the results (not reported) did not change to any significant extent.

Regarding the terms on the right hand side, we expect productivity to be positively related to the use of variable farm inputs, including hired labor. Given the supportive evidence provided by several of the contributions to the inverse farm size productivity debate (Lau and Yotopoulos 1971; Berry and Cline 1979; Kutcher and Scandizzo 1981; Carter 1984; Eswaran and Kotwal 1985; 1986; Barrett 1996; Udry 1996; Benjamin and Brandt 2002), we expect labor market imperfections to give an advantage to farms able to rely on family labor, and hence a positive sign on the coefficient on this variable.

To control for various aspects of land quality, variables on farmer-assessed soil quality, steepness and irrigation of the plots are included as regressors. The soil quality variable is computed as the share of total cultivated land the farmer reports being in plots with good soil quality. The share of steep land is similarly computed using plot level data, and taking the sum of plot size with steep slope as a share of the total landholding. In the same fashion the share of land irrigated is constructed 9