rafmacalaba/datause-encoder-data huggingface dataset:
change scenario: The more severe the climate change, the more severe is the decline. There is a significant decline in rainfed and livestock activities. Value added created by rainfed activities deteriorates as a result of a declining yield in rainfed activities. Livestock production falls due to increasing crop prices and costs in livestock production. However, increasing production in irrigated land compensates for the decline in other sectors.

Value added created by agriculture increases in all scenarios in nominal terms. However, real agricultural value added declines. This implies, that in all three cases the effects working on increasing domestic price dominates the effects working on the cost of production.

Sectoral value added is highly sensitive to the shock given in the climate change scenario when compared with the impact of other scenarios.

**Table 4.1. Effects on GDP**

||**Base**<br>**Level**|||**% C**|**hange**|||
|---|---|---|---|---|---|---|---|
||**(1000 TRY)**|**Sc. 1 **|**Sc. 2a **|**Sc. 2b **|**Sc. 3a**|**Sc. 3b**|**Sc. 3c**|
|Total|335,699,820|-0.79|0.12|-0.12|-5.82|-7.00|-5.90|
|Agriculture|48,935,094|5.24|1.54|2.04|1.34|1.91|1.63|
|Rainfed Agriculture<br> <br>|25,688,482|8.34|4.16|7.34|-6.48|-0.18|2.65|
|Irrigated Agricultural<br>sts<br>nal|18,001,061|4.58|-1.44|-3.77|28.89|24.20|16.87|
|Other Agriculture<br>Co<br>mi|5,245,551|-7.68|-1.01|-3.97|-54.92|-64.36|-55.69|
|Non-Agricultural<br>ctor<br>No|286,764,725|-1.81|-0.13|-0.48|-6.96|-8.42|-7.11|
|Services<br>Fa|168,529,416|-1.38|1.36|1.43|-6.86|-8.24|-6.88|
|at<br>Industry|118,235,309|-2.42|-2.25|-3.21|-7.10|-8.68|-7.43|
|at Market Prices|379,686,752|-1.13|0.07|-0.17|-5.56|-6.74|-5.69|
|Total<br>|335,699,820|-0.31|0.25|0.35|-2.66|-3.47|-3.02|
|Agriculture<br>osts|48,935,094|-0.05|-1.66|-3.02|-15.90|-20.02|-17.37|
|Rainfed Agriculture<br>C|25,688,482|1.34|1.34|2.73|-37.67|-35.03|-27.16|
|Irrigated Agricultural<br>tor<br>l|18,001,061|-1.30|-6.04|-11.37|15.60|1.73|-3.19|
|Other Agriculture<br>Fac<br>Rea|5,245,551|-2.55|-1.36|-2.55|-17.44|-21.21|-18.13|
|at<br>Non-Agricultural<br>|286,764,725|-0.35|0.58|0.92|-0.37|-0.59|-0.53|
|Services|168,529,416|-0.19|0.08|0.07|-0.29|-0.49|-0.41|
|Industry|118,235,309|-0.59|1.28|2.14|-0.47|-0.75|-0.69|
|at Market Prices|379,686,752|-0.57|0.32|0.45|-1.94|-2.54|-2.23|

_Source: Results from TACOGEM-W._ _Note: Sc1: Agricultural prices are increased according to FAO and OECD 2008; Sc2a: 30 percent water transfer from urban to rural areas; Sc2b:50 percent water transfer from urban to rural areas; Sc3a: Yield of rainfed crops falls by 30 percent; Sc3b: Yield of rainfed crops falls by 30 percent and irrigated crops falls by 10 percent; Sc3c: Regional yields of all crops falls at various degrees._ 35
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# **Appendix B. Data sources**

_Corruption cases_ . The main sources of data are Trace International Compendium ́s, a database on international anti-bribery enforcement (http://www.traceinternational.org/compendium), US DOJ and SEC documents, and OECD reports (various years). Other databases and publications, such as Shearman and Sterling 2013, Transparency International 2009 and 2013, and Cheung _et al_ . 2012 were consulted. Various news sources, such as the Wall Street Journal Risk and Compliance Journal - - (http://www.wsj.com/news/risk <u>compliance journal), and anti-corruption blogs like the FCPA</u> Blog <u>(http://www.fcpablog.com) were considered. Cases reported in multiple sources were</u> laboriously consolidated to avoid double counting. The reference period for each case is the year when the bribe was allegedly paid. However, in some instances, this date had to be presumed from the available data. A small number of cases could not be considered for the computation of the _PACI_ because of lack of information on the identity of one of the two countries involved. All cases linked to the United Nations Oil-for-Food Programme, which allowed Iraq to sell oil in exchange for food, medicine and other humanitarian goods, were excluded because of their peculiar origin. All the coding reflects information available on April 1, 2015. Raw data are available at http://www2.dse.unibo.it/picci/measure_corruption.html _Exports_ : Barbieri and Keshk 2012.

_Gdp_cap_ : Gross domestic product based, per capita. Expressed in PPP dollar per person. Source: IMF-World Economic Outlook, October 2014 (variable name: PPPPC) (http://www.imf.org/external/Pubs/ft/weo/2015/01/).

The secondary source of the following variables is the “Standard data” of the Quality of Government Institute dataset (Teorell et al. 2013) which assembles various sources. The May 2014 35
In fact there is not a single country which did not see an increase in co-dependence during this period. However we do see variation in the magnitude of the increase across countries and to some extent regions. For the time period leading up to the crises (January 2004 to June 2007), we see much greater variation. There was an increase in co-dependence throughout most of the developed world. Banks in United States, Japan and especially European Union have seen a significant rise in co-dependence. Banks located in developing countries on the other hand have seen a decline in co-dependence over the same time period. As with the crisis period, we again see much variation across countries. It is these cross-sectional differences that we explore next.

# **3.4 Cross-country Analyses**

In this section we examine the cross-country differences in default risk co-dependence. A number of papers have linked commonality in asset returns and asset liquidity to financial and trade liberalization.<sup>4</sup> We are interested in whether policies that lead to financial and economic openness and greater integration also increase co-dependence. We are also interested in the extent to which banking de-regulation and banking supervision has led to changes in codependence. The empirical model we use to test these relationships is the following: Our dependent variable is the variance ratio, , calculated for each country _i_ for each year _t._ We obtain similar results using the co-movement measure or the quantile betas described in Section 3.1. Since correlations increase during crises periods, we include a dummy variable, , that takes on a value of one if a country in our sample has experienced a banking crises in a given year. We use the banking crisis definition and the data provided in Leaven and Valencia (2010). is a vector of country level variables that measure economic/financial openness and banking regulation/supervision. is a vector of country level controls. We use GDP per capita, GDP per capita growth to control for levels of economic and financial development. Previous literature has shown that more developed countries tend to have lower commonality in asset prices and liquidity (see for instance Karolyi, Lee and Van Dijk 2009). To

> 4 See for instance Karolyi, Lee and Van Dijk (2009) and Bekaert and Wang (2009).

12
|||(0.037)|||||
|---|---|---|---|---|---|---|
|Share of manufacturing-working hours-1|||-0.429<sup>***</sup><br>(0.018)||||
|Change in share of provincial manufacturing<br>workers between 2002 and 1998|||0.024<br>(0.084)||||
|Share of service-working hours-1||||-0.349<sup>***</sup><br>(0.011)|||
|Change in share of provincial service<br>workers between 2002 and 1998||||0.003<br>(0.046)|||
|Share of mining-working hours-1|||||-0.454<sup>***</sup><br>(0.067)||
|Change in share of provincial mining<br>workers between 2002 and 1998|||||0.051<br>(0.043)||
|Share of utility and construction working||||||-0.463<sup>***</sup>|
|hours-1||||||(0.023)|
|Change in share of provincial utility and<br>construction workers between 2002 and 1998||||||0.156<sup>**</sup><br>(0.069)|
|Constant|0.297<sup>***</sup>|0.390<sup>***</sup>|-0.109<sup>***</sup>|-0.075<sup>*</sup>|0.013<sup>**</sup>|0.016<sup>**</sup>|
||(0.052)|(0.031)|(0.015)|(0.039)|(0.006)|(0.008)|
|N|18957|18957|18957|18957|18957|18957|
|adj. R2|0.299|0.325|0.204|0.163|0.226|0.202|

Notes: - Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

- Control variables are Household head (HH)’s ethnicity, HH age, HH gender, HH vocational education dummy, HH high school dummy, HH middle school dummy, HH primary school dummy, spouse vocational education dummy, spouse high school dummy, spouse middle school dummy, spouse primary school dummy, household size, and number of working age members. All of these household characteristics are reported in 2002. Standard errors are clustered at province level.

The results suggest a reallocation of labor from agriculture to manufacturing and services in provinces more exposed to tariff declines, leaving aggregate employment unchanged. The finding that manufacturing employment goes up after the BTA is not surprising because manufacturing directly benefits from higher growth of exports to the United States. What is interesting is an increase in employment in services, the sectors that do not directly benefit from higher growth of exports. The increase is significant at the 5% level in both Panels A and B.

The finding reveals a spillover of job gains from the manufacturing sector to service sectors. Note that the magnitude of employment increase in services is not much smaller than the employment increase in manufacturing. This suggests a sizable equilibrium effect, or the 19
used ex post. The second dimension pertains whether the oversight tools are established inside or out side the Parliament, that is whether they are internal or external oversight tools. Questions, question time, interpellations, hearings, public account committees are internal tools, while ombudsmen and auditors general are external tools.

The IPU, in collaboration with WBI, conducted a survey on Executive-Legislative relations; some 180 Parliaments were surveyed, and 83 responded (82 national parliaments plus the European Parliament). Respondents were asked questions concerning the accountability of the government to the parliament, the impeachment procedure, the dissolution of parliament, the oversight of government, budgetary oversight, oversight of implementation of the budget, oversight over foreign policy, oversight over national defense policy, the parliament and the state of emergency, the verification of the constitutionality of laws, and, finally, oversight over the application and evaluation of laws. In this note we will focus only on the oversight of the government.

Respondents were asked several questions: whether the government in their country was considered an institution that must report to Parliament, how does the parliament exercise oversight, whether parliamentarians could question government officials, whether time for questions was allocated in Parliament, whether interpellations were foreseen and, finally, whether there was an ombudsman in the country<sup>13</sup> .

> 12 Roberta Maffio, “Quis Custodiet Ispos Custodes? Il Controllo Parlamentare dell’Attivitá di Governo in Prospettiva Comparata”, _Quaderni di Scienza Politica_ , ..., pp.333-383. The quote is taken from p. 348. 13 A discussion for why questions, question time, interpellations should be considered as instruments of parliamentary control can be found in Roberta Maffio, “ _Quis custodiet ipsos custodes?_ Il controllo parlamentare dell’ attivitá di governo in prospettiva comparata”, _op. cit._ , pp. 333-383. See also David G. McGee, _The Overseers. Public Accounts Committees and Public Spending_ , London, Commonwealth Parliamentary Association in Association with Pluto Press, 2002.

5
- -----. 1985. "Sensitivity Analyses Would Help." American Economic Review 75, (June) p.308-313.

- -----. 1988. *Measures of Openness" in R.Baldwin ed. Trade Policy Issues and Empirical Analysis. NBER.

- -----. and Herman Leonard. 1983. "Reporting the Fragility of Regression Estimates." Review of Economics and Statistics. 65 (May) p.306-317.

- Levine, Ross. 1991. "Stock Markets, Growth, ar’ Tax Policy.* forthcoming Journal of Finance.

- -----. and David Renelt. 1990. "Cross Country Studies of Growth and Policy: Some Methodological, Conceptual, and Statistical Problems." World Bank.

- Mankiw, N. Gregory, David Romer, and David Weil. 1990. “A Contribution to the Empirics of Economic Growth." Harvard University.

- McAleer, Michael, Adrian Pagan, and Paul A. Volker. 1985. "What Will Take the Con Out of Econometrics?" American Economic Review 75 (June) p.293-307.

- Pritchett, Lant. 1990. "Measuring Outward-Orientation in LDCs: Can It Be Done?" The World Bank.

- Psacharopoulos, George and Ana-Maria Arriagada. 1986. "The Educational Attainment of the Labor Force: An International Comparison." World Bank ETD WP#38.

- Quah, Danny. 1990. "Galton’s Fallacy and Tests of the Convergence Hypothesis." Working Paper, MIT Economics Department.

- Ram, Rati. 1986. "Government Size and Economic Growth: A New Framework and Some Evidence from Cross-Section and Time Series Data." American Economic Review 76 (March) p.191-203.

- Rivera-Batiz, Louis and Paul Romer. 1989. “International Trade with Endogenous Technological Change." University of Chicago.

- Romer, Paul. 1986. "Increasing Returns and Long-Run Growth." Journal of Political Economy. 94 (October) p.1002-1037.

- ----+. 1987. “Crazy Explanations for the Productivity Slowdown." in S. Fischer ed. NBER Macroeconomics Annual Cambridge MA: MIT Press.

- -----. 1989. "Human Capital and Growth: Theory and Evidence." NBER WP#3173.

- er---. 1990a. "Capital, Labor, and Productivity." Brookings Papers on Economic Activity: Microeconomics.

-----. 1990b. "Endogenous Technological Change.* Journal of Political Economy 98 (October), $1:71-102.

30
spillover effects on demand for investments in the economy generated by the liquidity and capital availability of the GFSI could also benefit high-carbon activities and lead to unintended higher GHG emission intensity in the economy.

Those indirect effects pose several challenges when aiming to quantify the impact of GFSI on macroeconomic variables and GHG emission reductions. In particular, the quantification of the impact of each GFSI on GHG emissions depends on the conditions of GFSI implementation, which in turn might vary across time and space. Then, access to and availability of affordable finance represent important obstacles in several countries. Moreover, there are substantial uncertainties about the value of key parameters such as the elasticity of the investment demand to interest rate.<sup>35</sup> Finally, there is a large number of confounding factors and policies whose effects need to be disentangled. More broadly, our analysis shows that transmission channels can be extremely complex and involve several feedback loops.

Our results highlight that because of those indirect feedback loops the quantification of GFSIs and of their transmission channels is important to better understand the full GHG emission impacts. Yet, challenges remain given the level of uncertainty associated (e.g., due to the presence of feedback effects) and the limited availability of the required data/models for the quantification. In this context, climate financial risk disclosure and risk assessment is a precondition for a successful application and tailoring of the ToC and the effective implementation of the GFSI at the country level, to reduce the risk of greenwashing and allow for the sustainable development of green finance markets.

Finally, the policy complementarity of GFSI needs to be highlighted. GFSI can support GHG emission reductions only if the country engages on a decarbonization path by means of economic policies. A country’s credible commitment on a NetZero path, by means of phasing out fossil fuel subsidies and measures to support firms and workers in affected sectors, implies that a higher cost of capital for high carbon firms make economic sense and is in line with financial valuation. Thus, the adjustments in cost and availability of capital entailed by GFSIs can work best if they complement climate economic policy packages that ensure persistence over the years of the transition. In contrast, in the absence of a country’s credible commitment on a NetZero path, GFSI could lose effectiveness, because eventually interest rates, cost of capital and risk scores for low carbon firms would revert to the levels of the high-carbon ones.

> 35 See in this respect the concluding section of the 2DII report on “Assessing the impact potential of financial products supporting the energy transition”, available at https://2degrees-investing.org/wpcontent/uploads/2021/11/Impact-Potential-Green-Finance-Products.pdf

37
destination per firm varies from a low of two destinations in Malawi to a high of four destinations in Senegal. The average number of exported products also ranges from 6 in Burkina Faso to 16 in both Malawi and Senegal. On average, a higher number of unique products are exported per firm in the agriculture sector relative to the manufacturing sector. Whereas the number of destinations served per firm remained rather stable over time, the number of products exported per firm increased over time.

Table 1: Firm-level characteristics across countries, economic sectors, and years

||_N_|Firms|HS6|Dest.|HI Dest.|Value|Quantity|Per frm||
|---|---|---|---|---|---|---|---|---|---|
||||||||Dest.|HS6||
|Total sample|233,922|16,021|3,632|233|79|214,812|340,802|3|29|
|_Countries_||||||||||
|Burkina Faso|17,091|1,579|1,664|156|54|508,819|299,290|3|6|
|Malawi|62,666|8,015|2,598|193|69|226,952|17,7068|2|16|
|Senegal|154,165|6,428|3,300|225|79|177,283|411,966|4|16|
|_Product sectors_||||||||||
|Agriculture|66,665|5,171|549|220|75|336,710|282,125|3|34|
|Manufacturing|167,257|12,729|3,083|223|76|166,226|364190|2|28|
|_Years_||||||||||
|2000|2,206|320|737|62|23|82,482|589,905|3|2|
|2005|7,974|1,041|1,437|144|49|138,385|336,039|3|4|
|2010|13,943|2,432|1,894|170|61|263,830|431,296|2|5|
|2015|17,881|2,621|1,982|177|65|173,551|292,172|3|6|
|2019|16,242|2,407|2,007|189|63|244,241|349,700|3|5|

Notes: N is the total number of observations. HS6 denotes the number of products defined at the HS6 digit level. Dest. denotes the number of unique destinations while HI Dest. denotes the number of unique high-income destinations. For brevity, the data across years are only reported for every fourth year.

We also depict trends in exports over time, aggregated across the origin countries and sectors (Figure 3). Overall, we observe growth in export values over time in Senegal and Burkina Faso, whereas Malawi displays fluctuations without any discernible change over time. What becomes obvious is that specialization patterns in production are different across the three countries. Malawi exports mostly products in the agriculture sector. Burkina Faso exports mostly products from the manufacturing sector. Senegal, on the other hand, while exporting mainly products in the agriculture sector also shows a lot of exports in manufacturing.

9
census data and estimate regional childhood exposure effects using migrants. To the best of my knowledge, this is the first paper to document IGM at a very disaggregated regional level for almost the entire population in LAC.

The paper is organized as follows. Section II describes data and methodology. Section III reports the main descriptive results at country level and the geography of mobility. Section IV looks at correlates of intergenerational mobility. Section V concludes with final remarks.

# **II Data and Methodology**

Three sources of data have been typically used to estimate intergenerational mobility: (1) cross-sectional samples of adult populations with retrospective questions about parental education, for example, Narayan et al. (2018) use household survey data that covers the 96% of the world population; (2) panel data long enough in its time dimension to include the socioeconomic or educational attainment of two generations, for example, Celhay, Sanhueza, and Zubizarreta (2010) use the Chilean CASEN to estimate mobility in schooling and income; and (3) administrative/registry data with linked information for parents and adult children, for example, Chetty et al. (2014) use tax records in the U.S. to estimate income mobility.

In the case of Latin America, most of the literature has used household survey data or public opinion surveys (see for example, Hertz et al., 2007; Narayan et al., 2018; Neidh ̈ofer et al., 2018) given that long panel data as well as administrative/registry data that allow the researcher to link generations are rare. In contrast, in this paper, I use census data obtained from IPUMS International (Integrated Public Use Microdata Series, IPUMS, 2019), hosted at the University of Minnesota Population Center, which reports harmonized representative samples (typically 10%) of full census micro data sets for a large number of countries. In particular, I use 91 samples of population and housing censuses from 24 countries, which are run to compute the total population and contain an educational attainment question in their questionnaire.<sup>4</sup> The key advantage of this data set is that it contains the entire population

> 4Because the individuals are not organized into households, I do not use Chile 1960, Colombia 1964,

5
2003; Hall et al., 1992; Kahsay et al., 2018; Kalogirou, 2002). This method is also applicable to the plot-level MAPS data, however, as the data includes precise measures of soil parameters that are often extrapolated from lower resolution geospatial data. In this paper, the unit of analysis is the _plot_ rather than the _pixel_ as in geospatial analysis.

The fuzzy membership method, drawn heavily from Ahamed et al. (2000) and Hall et al. (1992), begins with an identification of the similarity, or Euclidean distance, between the vector of soil properties on each plot, _x_ , and the representative vector for a given suitability class. After normalizing values over the interval [0,10] for each property to eliminate unit-sensitivity (following Hall et al., 1992), the “distance measure” is constructed as follows: where: ~~x~~ 1 2 ) is the vector of soil parameters on a given plot; and ~~μ~~ nn = (xx , xx , ... , xx suitability class, _c_ . cc<sup>= (μμcc1, μμcc2, ... , μμccnn) is the representative vector of soil properties that corresponds to</sup> Equation 1 results in a _distance measure_ for each suitability class, where a higher score reflects greater divergence (less similarity) between the properties on a given plot and the respective suitability class. Subsequently, a _membership grade_ is computed for each suitability class, which indicates the relative fit of a given plot to the specific class, ranging from zero to one, allowing for comparison of fit across suitability classes.

Equation 2 results in a plot-level membership grade for each suitability class based on a given crop’s representative vectors for each class. Each plot is then assigned the overall suitability class of that with the highest membership grade. It is important to note that the method above assumes equal weights for each of the soil properties, which may be a strong assumption considering agronomic needs. However, in the absence of literature upon which to anchor unequal weighting of soil properties for the Ugandan context, we utilize the equal weighting approach and leave exploration of alternative weighting schemes to future work.

11