rafmacalaba/datause-encoder-data huggingface dataset:
in the original recipient-donor-year format: This procedure yields sectoral residual variables ( _RES_<sup>�</sup> _sRDY_<sup>) that are added to CRS sectoral disbursements to</sup> create more complete measures of sectoral aid (labeled _CRS_<sup>�</sup> _sRDY_<sup>).Summing across donors arranges the sec-</sup> toral disbursements in the required recipient-year format. For some donors, insufficient information is available in DAC5 to allocate the total residual across sectors; therefore, for some observations, the constructed sectoral aid variables still do not reflect the total amount of aid received. Therefore, as a final step, I scale the sectoral disbursements to ensure that their sum matches the aggregate disbursements ( _DISBRY_ ): Aid disbursements are constructed for the following sectors: education (DAC5 sector code 110), health (120), commodity aid/general program assistance (500), action relating to debt (600), donor administrative costs (910), support to NGOs (920) and other sectors (the sum of all remaining sector codes). In addition, data that partition education and health disbursements into four prefix codes or aid types are constructed: investment projects (IP), sector program (SP) aid, technical cooperation (TC), and other (no mark) (ONM). As I explain below, the prefix codes are useful because, to some extent, they allow for the separation of on- and off-budget aid flows and thus enable a test of fungibility that is consistent with the framework that is discussed in section I.

This data construction method takes into account that donors that report only a small portion of their aid to CRS might allocate aid across sectors differently than donors that report a larger portion of their aid. Similarly, this method considers that, for a given donor, the sectoral allocation of unreported aid may differ from that of the reported portion. The method ensures that the distribution of aggregate aid across sectors for each donor-year closely follows the sectoral allocation in DAC5, which contains complete disaggregated aid data. Subsequently, the main assumption is that the donor-year-specific sectoral allocation of the total residual applies equally to each recipient that receives aid from the donor in that year that is not accounted for in CRS.

In the final step of the data construction, I scale the sectoral aid variables such that their sum matches the aggregate aid received, similar to the scaling performed in previous studies (recall equation (5)). However, because the sectoral disbursements prior to scaling are based on more extensive information than in previous studies, these disbursements are more likely to provide a useful guide to the true sectoral allocation of total disbursements. Therefore, the scaling should be less problematic. On average, the constructed disbursements before scaling constitute more than 76% of the complete aggregate disbursements, whereas this value for CRS 8
excluding natural resources. High-income OECD countries registered the lowest TFP growth by either measure over 1996-2014, but recall that these figures are not estimated based on the superior data available in these countries (see Annex I). Turning to developing regions, East Asia and the Pacific clearly dominates TFP growth (thanks to China), while Sub-Saharan Africa is the second lowest. The striking result is for Latin America and the Caribbean, where TFP growth was extremely low over this time period.

**Figure 5. TFP growth by income class**

<!-- Start of picture text --> Average TFP growth 1996‐2014<br>3.00%<br>2.50%<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>Low income Lower middle Upper middle High income: non‐ High income: OECD<br>income income OECD<br>With natural resources Excluding natural resources<br><!-- End of picture text --> Note that the averages reported for OECD countries may vary from OECD (2016) and Brandt et al. (2017). High income non-OECD countries are largely petroleum exporters. China dominates the figures for upper middle income countries, while India dominates lower middle income.

**Figure 6. TFP growth by developing region**

<!-- Start of picture text --> Average TFP growth 1996‐2014<br><!-- End of picture text --> <!-- Start of picture text --> 4.50%<br>4.00%<br>3.50%<br>3.00%<br>2.50%<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>East Asia & Pacific Europe & Central Latin America & Middle East & South Asia (low Sub‐Saharan<br>(low and middle Asia (low and Caribbean (low North Africa (low and middle Africa (no NGA,<br>income) middle income) and middle and middle income) ZAF)<br>income) income)<br>With natural resources Excluding natural resources<br><!-- End of picture text --> Note that the Sub-Saharan Africa figures exclude Nigeria and South Africa owing to their combined 70% 16
also shows a causal link between malnutrition, as proxied by height, and success in school (see Glewwe and Miguel, 2008, for a review) and in the labor market (Strauss and Thomas, 1998). In short, child height-for-age is a good indicator of adult human capital.<sup>3</sup> But what sets our approach apart is that we analyze a long-term measure of nutritional status (height-for-age) in combination with a short-term measure (weight-for-age) for the same sample of children.

Like school attendance in Jacoby and Skoufias (1997), a child’s weight-for-age should be sensitive to the _current_ economic conditions of the household (see also Foster, 1995). This insight along with a key observation about the timing of rainfall shocks allows us to extract compelling evidence of an income effect from anthropometric data. Given its hilly terrain that makes large-scale irrigation facilities infeasible, Nepali agriculture is highly dependent on rainfall, roughly 80% of which falls in the summer monsoon. All the major staples (paddy, maize, and millet) are cultivated during this season. This stark seasonality of agricultural production means that the realization of a shock to income in our setting takes place with a lag relative to the realization of rainfall; that is, income is only obtained once crops have been harvested and, possibly, sold. The influence of rainfall on _disease environment_ , by contrast, is largely contemporaneous. Excessive rainfall in poor countries is linked to higher incidence of diarrheal disease and, indirectly, to an increase in vector-borne diseases. Episodes of acute illness induced by high rainfall can lower a child’s capacity to take in and retain essential nutrients from food. Thus, weight changes observed around the time of a monsoon rainfall shock but before the associated harvest should mainly reflect the change in disease environment whereas weight changes observed postharvest should reflect the combined effects of income and the _lagged_ effects of the disease environment that prevailed during the most recent monsoon.<sup>4</sup>

> 3 Among others, Rose (1999), Kudamatsu et al (2011) and Baird et al. (2011) look at the impact of shocks on child _mortality_ in developing countries.  Given that vastly more children survive into adulthood than die, however, a greater focus on survivors’ human capital seems warranted.

> 4 Our analysis also addresses the question of whether households prioritize boys’ welfare over that of girls during times of economic hardship (see Dreze and Sen, 1989, for an early review of this literature in the South Asian context).  Among many studies, Behrman (1988) and Behrman and Deolalikar (1990) show biases in favor of boys in nutrient allocation among Indian households during lean times. In India again, Rose (1999) shows that the gender bias in infant mortality, which normally favors boys, narrows when districts experience higher rainfall. In rural Pakistan, Alderman and Gertler (1997) find that the demand for girls’ medical care is more income- and price-elastic than the demand for boys’ medical care.

3
Wara M.,W. and Victor, D. G. (2008). “A Realistic Policy on International Carbon Offsets”. Program on Energy and Sustainable Development - Working Paper 74 -Stanford University Williamson, O. E. (1971). “The Vertical Integration of Production: Market Failure Considerations”. The American Economic Review, Vol. 61, No. 2, Papers and Proceedings of the Eighty-Third Annual Meeting of the American Economic Association (May, 1971). Pages 112-123.

Williamson, O. E. (1979). “Transaction-Cost Economics: The Governance of Contractual Relations “. Journal of Law and Economics, Vol. 22, No. 2 (Oct., 1979), pp. 233-261. The University of Chicago Press.

Williamson, O. E. (1981). “The Modern Corporation: Origins, Evolution, Attributes”. Journal of Economic Literature Vol.19, No.4 (December 1981), pp. 1537-1568.

Williamson, O. E. (1993). “Transaction Cost Economics and Organization Theory”. Industrial and Corporate Change, 2 (2), 107-56. In Transaction Costs and Property Rights, edited by Claude Ménard. The International Library of the New Institutional Economics. Edward Elgar Publishing. 150-199.

Williamson, O. E. (1998). “Transaction Cost Economics: How it Works; Where it Headed”. De Economist 146, No.1.

Williamson, O. E. (2007). “Transaction Costs Economics: An Introduction”. University of California, Berkeley. Economics Discussions Paper.

Williamson, O. E. (2010). “Transaction Cost Economics: The Natural Progression”. American Economic Review 100: 673-690.

Woldie, G.A. and · Nuppenau, E.A. (2011), “A Contribution to Transaction Costs: Evidence from Banana Markets in Ethiopia, Agribusiness 27 (4): 493-508.

World Bank (2009). “Low-Carbon Development for Mexico”. The World Bank, Washington, DC.

World Bank (2010). “Brazil Low-carbon Country Case Study”. The World Bank, Washington, DC.

Worrell, E., Ramesohl, S. and Boyd, G. (2004). “Advances in Energy Forecasting Models Based on Engineering Economics”. Annual Review of Environment and Resources 29 (1): 345-381.

- 40 -
_<mark>Table 8: Falsification Test based on Pre-determined Covariates</mark>_

||(1)<br>**Control**<br>**Mean**|(2)<br>**Treatment**<br>**Mean**|(3)<br>**Diff in**<br>**means (T-**<br>**C)**|(4)<br>**P-value**|(5)<br>**Observations**|
|---|---|---|---|---|---|
|Age|38.776|40.162|1.386|0.084|208|
|Catholic|0.813|0.811|-0.002|1.000|208|
|Other Christian|0.104|0.149|0.045|0.372|208|
|Ilonggo ethnicity|0.104|0.122|0.018|0.800|208|
|Bisaya ethnicity|0.209|0.230|0.021|0.863|208|
|Bikolano ethnicity|0.149|0.122|-0.027|0.678|208|
|Waray ethnicity|0.112|0.068|-0.044|0.326|208|
|Tagalog ethnicity|0.112|0.162|0.050|0.380|208|
|Cebuano ethnicity|0.142|0.108|-0.034|0.515|208|
|Grantee’s years of education|9.722|9.946|0.224|0.724|207|
|Partner’s years of education|13.196|14.048|0.851|0.524|175|
|Years of marriage|13.328|13.297|-0.031|0.983|208|
|<br>Recommended window is [-202|1.9; 2021.9]|with 208 observ|ations (134 bel|ow, 74 abov|e).|

_Note:_ Recommended window for local randomization tests based on Cattaneo et al. (2018)

# <mark>6.2.3 Effect on Placebo Outcomes</mark>

Finally, we conduct a falsification test to investigate whether the CCT program had any effect on placebo outcomes. Considering that CCT programs can have an overarching effect on a multitude of outcomes, it is rather difficult to identify suitable placebo outcomes. Nonetheless, after careful consideration we identified a few placebo outcomes via utilizing a list randomization measurement experiment that was embedded as a part of the IE study to measure GBV.

Under the list randomization (LR) method, we divided the study sample into two groups, i.e., treatment and control. From the control group, we asked questions on innocuous topics (i.e., on topics that are unrelated to the CCT program and exposure to GBV), whereas from the treatment group, we asked the same innocuous questions and a few questions on GBV. Notably, both the treatment and control group included equal number of respondents that were assigned beneficiary and non-beneficiary status. Each respondent, in both LR treatment and control group, was asked 8 set of questions - one corresponding to the 8 different types of GBV, namely physical IPV, emotional IPV, economic IPV, sexual IPV, physical NPV, emotional NPV, sexual NPV and economic NPV.<sup>23</sup> Each set included 4 statements (one on GBV and three on innocuous topics) for the treatment group and 3 statements (all three on innocuous topics) for the control group. The respondents were then asked to report the total number of statements they agree or disagree with and not whether they individually agree or disagree with each statement. Notably, the list

> 23 NPV refers to non-partner violence. This could take place either within the household (i.e., NPDV) or outside the household (i.e., NDV). Due to budget and sample size restrictions, we asked questions on 8 types of GBV (instead of 12) under the LR method.

20
**Table A.5: RIF decomposition of the Gender Differential in Agricultural Productivity- Export crops only (2016)**

||**Y= RIF estim**|**ate of Log [**|**Self-reporte**|**d Productivit**|**y (XOF/ha)]**|**-Export crop**|**s**|||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|**overall**||||||||||
|Male headed households|10.447***|11.015***|11.398***|11.620***|11.874***|12.089***|12.347***|12.635***|12.983***|
||(0.069)|(0.052)|(0.039)|(0.037)|(0.037)|(0.034)|(0.035)|(0.038)|(0.036)|
|Female Headed households|10.246***|10.611***|11.113***|11.577***|11.884***|12.182***|12.448***|12.659***|12.878***|
||(0.116)|(0.137)|(0.151)|(0.135)|(0.112)|(0.096)|(0.102)|(0.094)|(0.084)|
|difference|0.200|0.405***|0.286*|0.043|-0.010|-0.094|-0.101|-0.024|0.104|
||(0.133)|(0.142)|(0.153)|(0.137)|(0.115)|(0.098)|(0.106)|(0.099)|(0.092)|
|**% gap**|**18%**|**33%**|**25%**|**4%**|**-1%**|**-10%**|**-11%**|**-2%**|**10%**|
|<br>explained|0.323|0.069|0.078|-0.001|-0.045|-0.019|-0.006|0.032|-0.061|
||(0.240)|(0.172)|(0.162)|(0.145)|(0.128)|(0.114)|(0.107)|(0.107)|(0.104)|
|unexplained|-0.123|0.336|0.208|0.044|0.035|-0.075|-0.096|-0.056|0.165|
||(0.263)|(0.221)|(0.210)|(0.186)|(0.152)|(0.136)|(0.130)|(0.134)|(0.125)|
|**explained**||||||||||
|<br>Log [Total land area cultivated in ha]|<br>-0.274***|-0.211***|-0.198***|-0.155***|-0.164***|-0.167***|-0.169***|-0.182***|-0.209***|
||<br>(0.071)|(0.054)|(0.041)|(0.035)|(0.036)|(0.036)|(0.036)|(0.040)|(0.043)|
|Household head age (years)|-0.033|-0.021|-0.017|-0.012|-0.008|-0.007|-0.020|-0.033|-0.026|
||(0.054)|(0.036)|(0.025)|(0.023)|(0.023)|(0.023)|(0.023)|(0.026)|(0.030)|
|Married|0.169|0.091|-0.014|0.070|0.012|0.031|0.055|0.055|0.147**|
||(0.161)|(0.118)|(0.080)|(0.076)|(0.076)|(0.078)|(0.078)|(0.079)|(0.066)|
|Divorced/Separated|-0.010|-0.013|-0.001|-0.011|-0.003|-0.004|0.002|-0.000|-0.002|
||(0.016)|(0.012)|(0.009)|(0.010)|(0.009)|(0.009)|(0.008)|(0.009)|(0.007)|
|Widowed|0.236|0.001|0.079|-0.094|-0.063|-0.081|-0.067|0.012|-0.082|
||(0.263)|(0.179)|(0.151)|(0.132)|(0.113)|(0.107)|(0.107)|(0.104)|(0.095)|
|Household head has Primary|-0.019|-0.011|-0.007|-0.002|0.001|0.000|-0.002|0.001|0.002|
|<br>education|(0.015)|(0.010)|(0.007)|(0.006)|(0.006)|(0.006)|(0.006)|(0.007)|(0.007)|
|Household head has Secondary|-0.038*|-0.017|-0.011|-0.013|-0.017|-0.005|-0.004|-0.000|-0.009|
|<br>education|(0.021)|(0.015)|(0.011)|(0.011)|(0.012)|(0.012)|(0.012)|(0.012)|(0.011)|
|Household head has Tertiary|-0.014*|-0.007*|-0.005*|-0.006**|-0.005**|-0.003*|-0.004**|-0.004***|-0.003***|
|<br>education|(0.008)|(0.004)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.001)|(0.001)|
|Household head disability|-0.020|-0.032*|-0.003|-0.002|-0.004|-0.007|0.012|0.011|0.002|
||(0.022)|(0.018)|(0.013)|(0.012)|(0.012)|(0.012)|(0.013)|(0.013)|(0.016)|
|Dependency ratio|-0.001|-0.007|-0.005|-0.004|-0.003|0.006|0.004|0.009|0.006|

44
(2002) find that female college students have been shown to have less knowledge and willingness to learn about personal finance topics than do male college students.

Although FinScope only surveys adults, financial literacy among youth in developing countries may be a potential area of concern, based on InterMedia (2010) and similar studies in other regions. InterMedia (2010) reports the results of nationally representative surveys of youths aged 15-24 in Ghana and Kenya on their access to financial information. It predictably finds that older youths (aged 20-24) have more access to financial information than younger youths (aged 15-19), make greater use of financial services, and are more likely to be the sole financial decision makers in their household. Female and low income youth are also less likely to have access to banking services.

In Kenya, they find a correlation between use of financial services (such as having a bank account) and exposure to financial information. Radio, followed by word of mouth from friends and family members, is the most common source of financial information, although urban youth also have greater access to television and other media. A higher proportion receives information on money transfers and mobile financial transactions than on borrowing, saving, or bank accounts. In Ghana, youth apparently have greater difficulty accessing financial information. Awareness of mobile financial transactions is particularly low compared to Kenya, though this is unsurprising as mobile banking was only introduced in Ghana after the survey period. The collection of similar information in other countries could help to improve the design of financial education programs targeted towards youth, some of which are discussed in the Section III.

# **_Financial literacy is associated with higher educational attainment and income_**

Lusardi and Mitchell (2011b) find that those with lower educational attainment (e.g. lacking a high school degree) in the U.S. are less likely to answer questions correctly, and also more likely to say they don’t know the answer. This pattern is found in all countries. At the same time, Lusardi and Mitchell (2011a) also note that education may be a weak proxy for financial literacy, as they are both statistically significant when included in regressions on retirement planning, for example.

Surveys have also found a relationship between financial literacy and income, and there are a number of studies focusing on whether financial literacy has a causal effect on wealth 12
# **Table 12 Sias Momentum Regressions**

This table presents the results of the regression of the fraction of funds buying a given asset at a moment in time on the contemporaneous rate of return and the lagged rate of return, alternatively and combined. The first specification (columns 1 to 4) takes into account all assets and the second specification (columns 5 to 8) only considers assets that are traded by more than one PFA at a moment in time. These regressions are presented over all asset classes and by asset class. Standard errors are presented in parenthesis. T-tests are two-tailed. One asterisk indicates statistical significance at the ten-percent level and two asterisks indicate statistical significance at the five-percent level.

||_Using Lagged Ret_|_urn and Return A_|_lternatively as Independ_|_ent Variables_|_Using Both Lag_|_ged Return and_|_Return as Independe_<br>Assets Traded b|_nt Variables_<br>More than One|
|---|---|---|---|---|---|---|---|---|
||All As|sets|Assets Traded by Mo|re than One PFA|All As|sets|y<br>PF|<br>A|
||Lagged Return|Return|Lagged Return|Return|Lagged Return|Return|Lagged Return|Return|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**All Asset Classes**|0.10|1.97**|-0.12|1.70**|0.28**|2.06**|0.00|1.63**|
||(0.11)|(0.10)|(0.15)|(0.13)|(0.10)|(0.10)|(0.14)|(0.13)|
|**Domestic Assets**|||||||||
|Former Pension System Bonds|1.88**|2.53**|1.34**|0.93**|2.58**|3.06**|1.83**|1.48**|
||(0.69)|(0.52)|(0.52)|(0.33)|(0.69)|(0.51)|(0.57)|(0.38)|
|Corporate Bonds|0.32*|0.15|0.07|1.19**|0.35*|0.17|0.15|1.34**|
||(0.17)|(0.17)|(0.27)|(0.56)|(0.18)|(0.17)|(0.33)|(0.55)|
|Financial Institutions|-0.28|0.06|0.82*|-0.14|-0.29|-0.04|0.81**|-0.08|
||(0.23)|(0.28)|(0.43)|(0.49)|(0.23)|(0.26)|(0.45)|(0.48)|
|Government Paper|0.34**|0.49**|0.49**|0.49**|0.38**|0.47**|0.55**|0.47**|
||(0.08)|(0.08)|(0.12)|(0.11)|(0.08)|(0.08)|(0.12)|(0.11)|
|Investment and Mutual Funds|-0.57|0.55|1.22|1.94|-0.85|0.83|0.80|1.23|
||(0.69)|(0.62)|(1.49)|(1.79)|(0.70)|(0.67)|(1.39)|(1.73)|
|Equity|0.26**|-0.09*|0.27**|-0.19**|0.26**|-0.10*|0.27**|-0.20**|
||(0.06)|(0.05)|(0.05)|(0.05)|(0.06)|(0.05)|(0.05)|(0.05)|
|Mortgage Bonds|-1.70**|1.53**|-2.67**|0.69**|-1.31**|1.30**|-2.41**|0.55**|
||(0.09)|(0.10)|(0.36)|(0.08)|(0.09)|(0.09)|(0.37)|(0.06)|
|**Foreign Assets**|||||||||
|Fixed Income|-0.03|0.09|0.03|0.01|0.42|-0.09|-0.03|-0.38|
||(0.35)|(0.25)|(0.81)|(0.84)|(0.38)|(0.42)|(0.80)|(0.98)|
|Investment and Mutual Funds|0.98**|0.61**|0.88**|0.73**|0.93**|0.57**|0.83**|0.64**|
||(0.11)|(0.15)|(0.11)|(0.21)|(0.10)|(0.15)|(0.10)|(0.21)|
|Equity|0.39*|-0.53**|0.38|-0.31|0.26|-0.55**|0.43|-0.24|
||(0.21)|(0.10)|(1.91)|(0.31)|(0.23)|(0.11)|(2.00)|(0.39)|
<u>Note</u> : Heatwave days are defined as days where the highest temperature exceeds the 90<sup>th</sup> percentile of the temperature distribution for a present-day reference period. Shared Socioeconomic Pathways (SSPs) are standardized scenarios developed in connection with the Intergovernmental Panel on Climate Change. SSP11.9 represents a scenario in which global emissions peak early; SSP3-7.0 is a scenario in which emissions remain high until after mid-century.

Looking to future periods, the projections point to significant intensification in heat stress metrics that will affect human health and the economy. Figure 5(A) depicts the expected change in the number of tropical nights (defined as nights where temperature do not fall below 25°C). In Surat, roughly 65% of nights per year currently fall into this category, but this would rise to 82% of nights. In the most densely built neighborhoods, the frequency of hot nights will be even higher with nocturnal temperatures <mark>rarely falling below 25°C. F</mark> igure 5(B) depicts the number of hours per year at high heat stress (>30°C WBGT) across all three cities, where high heat stress is defined using the WBGT metric. At present, we observe that some 25% of working hours in the year meet the high heat stress threshold for the three cities combined. Under a high emissions scenario, the number of high heat stress hours per year would rise by more than two-thirds for all cities amounting to some 40% of annual working hours.

_Figure 5: Projected change in key heat stress metrics for Chennai, Surat and Lucknow (2050 vs. present-day)_ <u>Note: Panel A depicts the expected number of tropical nights (defined as nights where temperatures remain</u> above 25°C). Panel B depicts number of hours per year with very high heat stress (defined as temperatures exceeding 30°C on the Wet Bulb Globe Temperature scale) for Chennai, Surat and Lucknow. SSP1-1.9 refers to a moderate scenario for global greenhouse gas emissions while SSP3-7.0 refers to a high emissions scenario.

# (ii) Impacts under a ‘no intervention’ scenario

# _Health impacts_

Heat-related excess mortality is, at present, estimated to range between 0.2 to 0.4 per 1,000 people for the cities of Lucknow, Chennai and Surat according to our modeling. This translates to approximately 1,000 deaths per year for both Surat and Lucknow and around 2,500 excess deaths per year for Chennai. The heat-related excess mortality rate is roughly 20% higher for individuals aged 70 or above. By 2050, under a low-emissions scenario, our modeling estimates a 30% increase in annual heat-related deaths per 1,000 residents for Chennai and Surat and a 38% increase in the case of Lucknow. The increase is greater in the high-emissions scenario, with each 14