rafmacalaba/datause-encoder-data huggingface dataset:
chain and increase its overall value added, it will have to diversify its production, and move out of traditional primary commodities into manufactured goods.

# **2. Asia’s Growing Interest in Sub-Saharan Africa**

- _A. Analytical Framework: Trade, Investment, Aid_

Although Sub-Saharan Africa’s total trade with Asia is relatively small by comparison with its trade with Europe and the United States (see figure 2 above), it has grown at an incredibly rapid pace, especially from 2001 onwards (figure 5). Indeed, Asia has become Sub-Saharan Africa’s fastest growing export market destination almost overnight, growing more than 10% on average during the last fifteen years. Since 1995, we have witnessed a “genuine reorientation” of Africa’s export towards the Asian Drivers, with falling importance of its traditional markets in the West.<sup>6</sup> <u>Figure 5</u> Figure 5: Growth Rates of African Exports by Destination (1991-2003 average) <!-- Start of picture text --> 12.0% 10.3%<br>10.0%<br>8.0% 7.0%<br>6.1%<br>5.3% 5.3%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>E.U. U.S. Asia Africa Overall<br><!-- End of picture text --> <u>Source: World Bank Group, Africa Region, Private Sector Unit,</u> _Summary of Patterns of AfricaAsia Trade and Investment_ , Note Number 1, September 2005 China, for a variety of reasons, catches the eye when looking at Africa-Asia trends over the last five years. Although in 2004 only 2% of Chinese trade was with Africa, the continent has done particularly well as China opened up to the world, with Africa-China bilateral trade growing more than 700% between 1991 and 2002. Since the first China-Africa Forum in Beijing in 2000, bilateral trade has grown to more than $20bn over the four years to the end of 2004. Chinese imports from Africa concentrate on primary commodities; extractive mining and forestry in particular make up the bulk of African exports to China. The picture in India is very different: India’s share in Sub-Saharan Africa’s exports has actually fallen and remains at low levels of 2%. Apart from crude oil, metals and woods, the other significant Chinese imports from Africa are cotton from Cameroon and

> 6 Andrea Goldstein, Nicolas Pinaud, Helmut Reisen and Xiaobao Chen, “The Rise of China and India: What’s in it for Africa?” OECD, Development Centre Studies, May 2006, pp. 45-47

4
**Table 14 Panel Data Analysis for Firms in Manufacturing Sector**

|VARIABLES|I|II|III|
|---|---|---|---|
|JobCr/Firm|‐0.210|‐0.366|‐0.123|
||(0.101)**|(0.096)***|(0.068)*|
|Log(labor)|0.165||0.055|
||(0.017)***||(0.012)***|
|Foreign|0.183|0.330|0.130|
||(0.059)***|(0.049)***|(0.077)*|
|Age|‐0.002|0.003|‐0.001|
||(0.001)*|(0.001)***|(0.001)|
|Growth||0.134||
|||(0.054)**||
|Observations|922|916|446|
|R2(p)|0.308|0.221|0.175|

Robust standard errors clustered by country, industry, year are in parentheses. In the regressions, I also control for industry, survey year, and country fixed effects. All regressions use net job creation at firm level instead of industry average. (I) main result (II) controls for past employment growth (III) restricts the set to firms that didn't export three years ago. Coefficients for the probit regressions show the marginal effects at their mean values. *** p<0.01, ** p<0.05, * p<0.1.

31
# **Box 1: From one shock to another: The 2011 earthquake in Japan rattled the auto industry worldwide**

Supply chain management, backed by tight vertical connections among enterprises, has resulted in a high level of competitiveness for the automobile industry. Car makers at the top of a chain can procure meticulously customized, high-quality components from firms further down the chain (resulting in differentiated, high-quality cars), collect information to continuously predict the appropriate amount of outputs, and minimize inventory and associated costs.

The high degree of customization and just-in-time production practice, two key drivers of success, also expose the automobile industry to worldwide shocks (Canis, 2011). In March 2011, an earthquake struck eastern Japan. The disruption of production of automotive parts generated immediate impacts. Since automotive parts are highly customized, replacement from other suppliers is almost impossible. In April 2011, Nissan closed plants in Mexico for five days and plants in the United States for six days<sup>.</sup> Output at eight of Honda’s Canadian, Indian, U.K., and U.S. plants was cut by half.<sup>3</sup> The U.S. car maker, General Motors, closed its assembly plant in Louisiana because of a shortage of vehicle parts, which in turn led to short layoffs at its New York, plant, where the engines are made. Ford closed assembly plants in Belgium and the United States for one week, and plants in China, the Philippines, Taiwan, China, and South Africa for two weeks.<sup>4</sup> Multinational companies’ internal capital markets and investment flows from parents to subsidiaries can lower subsidiaries’ dependence on host-country credit conditions and hence lower their performance volatility when host countries experience credit crunches (Antras et al., 2009). In Poland, for example, during the recent global economic crisis, foreign ownership appears to have provided a higher degree of resilience to affiliates facing external credit constraints through intra-group lending mechanisms (Kolasa et al., 2010).

> 3 “Quake Update,” Automotive News, April 4, 2011.

> 4 “Piecing Together a Supply Chain,” _New York Times_ , May 12, 2011.

8
_Table E.5. Preference function characteristics_

||**A.1.1**|**A.1.2**|**A.1.3**|**A2.1**|**A2.2**|**A2.3**|**A2.4**|**A2.5**|**A2.6**|**A2.7**|**A2.8**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**q**|1|2|1|2|2|1|1|1|1|1|1||
|**p**|5|5|2|5|5|2|2|2|2|2|2||
||**B.1.1**|**B.1.2**|**B.1.3**|**B.1.4**|**B2.1**|**B2.2**|**B2.3**|**B2.4**|**B2.5**|**B2.6**|**B2.7**|**B2.8**|
|**q**|1|1|1|1|2|2|1|1|1|1|1|1|
|**p**|5|5|2|2|5|5|2|2|2|2|2|2|

_Table E.6. Net flows of each alternative using PROMETHEE II for each DM_

||**DM1**|**DM2**|**DM3**|**DM4**|**DM5**|**DM6**|**DM7**|**DM8**|**DM9**|**DM10**|
|---|---|---|---|---|---|---|---|---|---|---|
|**M1**|0.024|0.128|0.153|-0.027|-0.319|-0.050|0.142|0.044|0.060|-0.008|
|**M2**|0.193|0.163|0.105|-0.001|-0.104|-0.055|0.148|-0.047|-0.054|-0.020|
|**M3**|-0.185|0.070|0.128|-0.022|-0.125|-0.076|-0.103|0.054|0.019|-0.108|
|**M4**|-0.066|-0.181|0.093|-0.318|0.050|-0.122|0.085|-0.431|0.068|0.031|
|**M5**|0.011|0.032|0.241|0.174|0.002|0.193|-0.059|-0.090|0.142|0.271|
|**M6**|-0.165|-0.180|-0.192|0.216|0.161|-0.059|-0.111|0.348|-0.019|-0.177|
|**M7**|-0.182|-0.018|0.054|0.208|0.056|0.022|-0.110|0.388|0.089|-0.096|
|**M8**|0.160|0.058|-0.467|0.011|0.104|-0.020|-0.056|0.037|-0.100|0.124|
|**M9**|0.147|0.032|-0.110|-0.038|0.015|-0.162|-0.075|-0.042|-0.237|0.126|
|**M10**|-0.266|-0.156|0.080|0.127|0.078|-0.074|-0.121|-0.378|-0.120|-0.226|
|**M11**|-0.162|-0.125|0.165|-0.143|-0.139|-0.025|-0.143|-0.022|0.018|-0.150|
|**M12**|0.156|0.144|0.069|-0.126|-0.160|-0.115|-0.018|-0.309|0.032|-0.124|
|**M13**|0.335|0.034|-0.320|-0.061|0.382|0.542|0.421|0.449|0.102|0.357|

59
<!-- Start of picture text --> 2.5 Number of rules<br>2<br>1.5<br>1<br>0.5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> <!-- Start of picture text --> Number of rules<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->
Table A7. Sensitivity analysis of unbobserved heterogeneity for health expenditure

||||Hodge|s-Lehman|point estim|ates|
|---|---|---|---|---|---|---|
|Gamma||p-value+<br>pvalue-|t-hat+<br>t|-hat-<br>C|I+<br>C|I-|
||1|0|0<br>5.33638|5.33638|5.26003|5.40651|
||1.1|0|0<br>5.28894|5.37847|5.21834|5.45554|
||1.2|0|0<br>5.24708|5.42161|5.17574|5.4959|
||1.3|0|0<br>5.21123|5.45833|5.13966|5.53914|
||1.4|0|0<br>5.17812|5.4959|5.10726|5.56989|
||1.5|0|0<br>5.14932|5.52522|5.07783|5.598|
||1.6|0|0<br>5.11715|5.55183|5.04249|5.63387|
||1.7|0|0<br>5.09221|5.58771|5.01694|5.66192|
||1.8|0|0<br>5.06484|5.60747|4.99287|5.69026|
||1.9|0|0<br>5.03861|5.63388|4.96627|5.71148|
||2|0|0<br>5.01821|5.66106|4.94073|5.73865|

Reported P-values are the Wilcoxon sign-rank test of significance under hidden bias. Results are based on stata ado routine "rbounds". Calculation is done based on Rosenbaum bounds for ATT; nearest neighbour matching with common support. The outcome variable is the log of health expenditure.

28
- Galal, Ahmed, Leroy Jones, Pankaj Tandon, and Ingo Vogelsang, _Welfare Consequences of Selling Public Enterprises._ Oxford: Oxford University Press, 1994.

Greene, W. _Econometric Analysis 3rd ed._ New York: Macmillan, 1997.

- Hausman, J. "Specification Tests in Econometrics." _Econometrica,_ 1978, pp. 12511271.

- Kalbfleisch, J and Ross L. Prentice, _The Statistical Analysis of Failure Time Data._ New York: John Wiley and Sons, 1980.

- Kikeri, Sunita, John Nellis, and Mary Shirley, _Privatization: The Lessons of Experience._ Washington, DC: The World Bank, 1992.

- Laffont, Jean-Jacques and Jean Tirole, "Privatization and Incentives," _Journal of Law, Economics, and Organization,_ VII, September, 1991, pp. 84-105.

- La Porta, Rafael and Florencio L6pez-de-Silanes, "The Benefits of Privatization - Evidence from Mexico," The World Bank, _Viewpoint,_ note 117, 1997.

- Levine, Ross, "Financial Development and Economic Growth: Views and Agenda," _Journal of Economic Literature,_ forthcoming, 1997.

- L6pez-de-Silanes, Florencio, Andrei Shleifer, and Robert W. Vishny, "Privatization in the United States," _RAND Journal of Economics,_ Autumn, 1997, pp. 447-71.

- L6pez-de-Silanes, Florencio, "Determinants of Privatization Prices," _Quarterly Journal of Economics,_ forthcoming.

- Martinelli, Alberto, "The Italian Experience: A Historical Perspective," in Raymond Vernon and Yair Aharoni, eds., _State-Owned Enterprise in Western Economies,_ London: Croom Helm, 1981, pp. 85-98.

- Megginson, William L., Robert C. Nash, and Mathias van Randenborgh, "The Financial and Operating Performance of Newly Privatized Firms: An International Empirical Analysis," _Journal of Finance,_ XLIX, 1994, pp.<sup>4 03</sup> -<sup>5 2</sup> .

- Mueller, Dennis C., _Public Choice II._ Cambridge, UK: Cambridge University Press, 1989.

- Ministry of Economics and the Central Bank of the Republic of Argentina, "Experience and Lessons from Financial Market Instability: The Argentine Experience," Paper prepared for G10 Working Party on Emerging Market Instability, December, 1996.

- Shleifer, Andrei and Robert Vishny, "Politicians and Firms," _Quarterly Journal of Economics,_ November, 1994, pp. 995-1025.

25
Table S2: Robustness Check with Different Price Specification

||OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|
|Log(Price - Incentive)|-0.666<sup>_∗∗∗_</sup>|-1.561<sup>_∗∗∗_</sup>|-1.464<sup>_∗∗∗_</sup>|-1.132<sup>_∗∗∗_</sup>|-1.182<sup>_∗∗∗_</sup>|
||(0.080)|(0.150)|(0.140)|(0.398)|(0.388)|
|Log Charging Ports|0.423<sup>_∗∗∗_</sup>|0.454<sup>_∗∗∗_</sup>|0.232|0.266<sup>_∗_</sup>|0.907<sup>_∗∗_</sup>|
||(0.073)|(0.065)|(0.145)|(0.144)|(0.443)|
|Range (miles)|0.006<sup>_∗∗∗_</sup>|0.006<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|
||(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|
|1(PHEV) _×_ Range|0.021<sup>_∗∗∗_</sup>|-0.011<sup>_∗∗_</sup>|0.007|0.008|0.008|
||(0.004)|(0.005)|(0.006)|(0.007)|(0.007)|
|Vehicle size (m3)|-0.025|0.020|0.015|-0.005|-0.002|
||(0.017)|(0.016)|(0.015)|(0.025)|(0.025)|
|Indicator of Non-fnancial Incentives|0.483<sup>_∗∗∗_</sup>|0.470<sup>_∗∗∗_</sup>|0.216|0.219|0.070|
||(0.159)|(0.137)|(0.147)|(0.147)|(0.186)|
|Brand FE||✓|✓|✓|✓|
|Fuel Type FE||✓|✓|✓|✓|
|Country FE|||✓|✓|✓|
|Year FE|||✓|✓|✓|
|First Stage F-stats for Price||||105.23|121.44|
|First Stage F-stats for Charging Station|||||36.34|
|Underidentifcation Test||||53.38|62.30|
|Weak Identifcation Test||||36.49|24.08|
|Overidentifcation Test||||58.89|66.87|
|Observations|4528|4528|4528|4528|4528|

_Notes:_ The regressions are based on data for 13 countries from 2013 to 2020. Observations for China in 2020 are excluded due to the impact of COVID19. The dependent variable is log(sales). _Price - Incentive_ variable is constructed from tax inclusive price subtracting total incentive received. Column (4) shows 2SLS estimates using instruments for consumer prices using the battery supplier dummy interacted with battery capacity as well as BLP instruments including the number of models and models’ size, battery capacity, and range for both own brand and rival brands. Column (5) in addition instruments for the number of charging ports using heavy duty EV stock, construction labor costs, and their interactions. Standard errors are clustered at the country by year level and reported in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 S7
The unobserved year effects are coefficients on dummy variables for each year included in the panel of data. As with the country fixed effects regressions, we repeat these regressions using one-year lagged values of the independent variables on the right hand side of the equation.

# **_Discussion of Results_**

The regressions of economic statistics were conducted to attempt to identify whether there is evidence of a climate signal on the economic activity indicators of the nations of the world. The results are reported in Tables 1 – 8 and are discussed below. The tables show the regression coefficient and the standard error in parentheses for each climate variable. Statistically significant regression coefficients are indicated with asterisks. Table 1 presents the summary results for each of the dependent variables for the most robust model specification, fixed country and year effects.

GDP per capita growth is the most widely used and available measure of economic growth at the national level. Here the analyses were conducted with data from approximately 180 nations. The most striking result of the analysis of GDP growth is the negative influence of the WASP(-1) (moderate drought) statistic at the 99% level statistical significance. This result was consistent in all regression specifications. Other results varied by specification.

12
# B.1.3 Final Goods and Services

We assume that technology indices in food and timber processing, energy, and other goods and services sector is described by the following equation: where the parameters _θ_ 0<sup>_i_and</sup><sup>_κi_reecttheinitiallevelandannualgrowthrate</sup> in technologies of these sectors. We assume that the technology growth rates are the same across these sectors and are equal to the global economy's total factor productivity growth rate, _κ_<sup>_o_</sup> .

## B.1.3.1 Processed Food

We calculate the level of technology index of the food processing in 2004 using GTAP v.7 database, by dividing the output of services from processed grains and crops (GTAP sectors 21, 23-25) by the output of grains and cereals (GTAP sectors 1-8). We obtain the food processing costs from GTAP v.7 database (GTAP sectors 21, 23-25), and normalize them by the total expenditures on other primary resources. To compute the normalized average cost of producing services from processed food, _c_<sup>_o,f_</sup> _,_ we divide normalized expenditures by the output of services from processed grains and crops produced in 2004.

## B.1.3.2 Energy Services

We obtain the initial values for total consumption of liquid fossil fuels and rst generation biofuels from the BP Statistical Review of World Energy database (2013). The elasticity of substitution of fossil fuels for rst generation biofuels is based on Hertel et al. (2010) econometric estimates for the US biofuel industry over the 2001-2008 period. Energy eciency index, and the value shares of biofuels and fossil fuels in energy production in 2004 are calibrated as described in Rutherford (2002).

## B.1.3.3 Processed Timber

We calculate the level of technology index of the timber processing sector in 2004 using GTAP v.7 data, by dividing the output of timber products (GTAP sectors 30-31) by the output of commercial forestry sector (GTAP sector 13). We obtain the timber processing costs from GTAP v.7 database (GTAP sectors 3031), and normalize them by the total expenditures on other primary resources.

17
# **5 Concluding remarks**

A considerable literature is concerned with assessing the extent to which countries share their consumption risk. Much of it, however, uses empirical models designed to test the null hypothesis of perfect consumption risk sharing. Once such hypothesis is rejected – as is almost invariably the case in practice – those models do not offer a rigorous basis for inferences about the degree of imperfect risk sharing present in the data. Drawing such inferences requires an empirical model of partial risk sharing. Furthermore, unless one is willing to assume that the extent of consumption risk diversification is the same across all countries in the world, the model needs to allow for cross-country heterogeneity in the degree of risk sharing.

This paper extends the existing literature by developing an empirical model that fits those two requirements. In the model, countries contribute possibly different fractions of their income to a common pool, in exchange for a claim on the aggregate income contributed to the pool by all countries. The fraction of income contributed to the global pool by each country can be naturally interpreted as its respective degree of risk sharing. Solution of the model yields a system of equations in which each country’s consumption path depends on the innovations to the present discounted value of income growth of all countries. Moreover, the system features nonlinear parameter restrictions across equations.

The model is implemented empirically using panel data for industrial and developing economies. Estimation results show that consumption risk sharing varies significantly across countries. On the whole, rich countries exhibit higher degrees of risk sharing than developing countries, and the gap between both country groups has widened over the period of financial globalization. Moreover, the pattern of consumption risk sharing across countries is significantly related to their degree of financial openness. Countries possessing larger stocks of international assets and/or liabilities, and more developed domestic financial sectors exhibit larger degrees of risk sharing. In contrast, the size of the economy and its degree of trade openness (after controlling for financial openness) seem to hinder consumption risk sharing.

30
that entrepreneurs are willing to undertake _negative_ social value projects. The social return to the entrepreneur who is indifferent between undertaking his project, or not, is _p*R-r. p*R-r_ is negative since using (1) and (2) and the fact that _p_ * _<_ p . Thus, the good types draw in the bad to the financial market.

# D. _The equilibrium with a discrete distribution of types_

In order to better show the intuition behind the results of this paper, in the text I will focus on the case of a discrete distribution of types. In the appendix, I will present the case of a continuous distribution of types.

I now assume that the probability of success for projects is either _pL_ or _pH_ , with 0 < _pL_ < _pH_ < 1, and that the two types exist in proportions λ and 1-λ, respectively. I will refer to an entrepreneur with a project success probability _pL_ (respectively, _pH_ ) as the low (high) ability type.<sup>3</sup> Figure 1 depicts the _banks’ break-even locus_ (equation (2)). With discrete types, the locus is a step function. For _p_ * < _pL_ , the interest rate is _r_ /E( _p_ ), where E( _p_ ) is the unconditional expectation. For _p*_ > _pL_ , the interest rate declines to _r_ / _pH_ . The banks‘ break-even locus shows how individual behavior aggregates up to the market interest rate.

Figure 1 also depicts the _entrepreneurs’ reservation success probability_ (RSP in equation (1)). The curve is upward sloping because at a higher interest rate, the entrepreneur requires a higher success probability to be willing to invest in his project.

FIGURE 1. Equilibrium with two types of entrepreneurs. In panel (A), the success return _R_ is low and the marginal borrower is high ability. In panel (B), the success return is _Rʹ_ (with _Rʹ > R_ ) and

> 3 The case of two types generalizes fully to an arbitrary number of discrete types.  This can be seen by considering the ‖ average success probability of inframarginal types in equilibrium as the composite type ― _pH_ and by considering the success probability of the next worst type as ― _pL_ .‖

7
strategies for reducing these costs, and highlighting sources of information for comparing costs and learning about new remittance products. This included explaining the fixed fee and exchange rate commission components of the cost and illustrating how much they vary across different providers, showing how the transaction costs fall with the amount sent so that bundling smaller transactions into one large transaction saves costs, and providing information about the _sendmoneypacific_ website for comparing costs and about the new Westpac direct debit card as a new product.

The remittance material was then adapted for the East Asian and Sri Lankan populations. Since _sendmoneypacific_ doesn’t cover remittance transactions for these remittance corridors, both groups were given instructions and shown how to get rates and the expected amount received on the Western Union website, plus ANZ Bank online and _fxcompared.com_ for the Sri Lankans, and _MoneyBookers_ for the East Asians in New Zealand.

Initially we had planned that the financial literacy training would focus just on remittances, but inspection of the baseline survey data from the Pacific Island group indicated that very few of the participants had credit cards, and a lack of awareness of this credit channel was corroborated by qualitative discussions with participants. For the Pacific Island group only, we therefore expanded the material covered in the financial literacy training to also include comparisons of sources of credit, especially in terms of their annual percentage rate of interest. The teaching material was based on examples of advertisements for payday lenders and other short term finance companies that used prominent sports stars to target Pacific Islanders (e.g. Figure 1a). The participants were taught how to calculate credit interest rates so that they could compare the annual cost of a payday or short-term loan or hire purchase with the cost of obtaining the same funds either as a cash advance or a purchase with a credit card.

The randomly chosen survey respondents were then invited to the financial literacy training sessions, which were held at multiple times and venues to ensure the maximum participation from those who were invited. These were typically held at churches, community centers, and sports clubs and were usually for groups of about 30 at a time and took about two hours. In addition to a presentation of about 25 powerpoint slides, written material was handed out and there was use of worked examples and continuous discussion with the community. As 12