rafmacalaba/datause-encoder-data huggingface dataset:
better groundnut seed. Insurance premiums were substantial, amounting to a mark-up in interest of 6–10% of the insured loan value, depending on the location. This is in addition to the 33% annual interest rate charged on the loan (Malawian inflation rate is about 15% and low-risk, treasury bonds offer 26% interest). In the 2006 season, the renewal rate of policies was about 100%, and additionally a number of other farmers joined the scheme bringing up the total number of policies sold to 3,000 (which is still a small number for any insurance scheme). The scheme has not been put to test in a major drought event, but some localized payouts were triggered in the 2005/06 season.

# **3 Methodology**

# **3.1 Overview**

Climate change impacts on the Malawian microinsurance scheme can be assessed from two perspectives: From the view of the insurers (or _supply-side_ perspective), it is important to analyze the financial robustness and risk of insolvency of the insurance scheme. From the view of the clients (or the _demand-side_ perspective), it is important to consider financial robustness as it affects their livelihoods and the extent to which insurance reduces their financial vulnerability. Because of the fragility of nascent insurance systems in developing countries, such as the pilot program in Malawi, this analysis takes a supply-side perspective. If the systems cannot withstand shocks from increasing weather variability, they will not only default on claims, but generally discredit insurance as an adaptive risk management option. Because commercial reinsurance will greatly raise premiums to clients who can ill afford any additional costs, the international development community is considering options for providing backup and pooling the risks of small-scale microinsurers offering catastrophe cover (Gurenko, 2006; Linnerooth-Bayer and Mechler, 2006). A supply side analysis can inform these options.

This paper makes use of _dynamic financial analysis_ to assess the financial robustness of the Malawi insurance scheme under dynamic weather and climate conditions as potentially altered by climate change. Dynamic financial analysis makes use of stochastic simulations of key insurance variables, such as surplus, loss ratios and solvency (or the risk of insolvency) based on inputs on insurance conditions and premium income (for example, see Lowe and Stanard, 1997 or Ho, 2005). In this case, claim payments are contingent on current and predicted rainfall data. The analysis is based on observed changes in rainfall characteristics and derived downscaled climate change scenarios from both Regional Climate Models (RCMs), as well as statistical downscaling for one site (Chitedze) in Malawi (Tadross et al., 2007). Information on actuarial calculations was obtained from D. Osgood (International Research Institute, Columbia University), who conducted the original calculations that informed the Malawi scheme. The modeling approach is schematically represented in Figure 2.

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We apply the Arnold et al. (2006a) approach to 9 ECA countries – Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Poland, Romania, Slovenia, and the Slovak Republic - to examine the effect of liberalization in services sub-sectors on downstream manufacturing productivity. Our dependent variable is labor productivity for 2-digit NACE manufacturing sub-sectors in those 9 countries obtained from the _WIIW Industrial Database Eastern Europe_ from 1997 to 2004. For each country and year, labor productivity in a manufacturing sub-sector is expressed as a ratio to the average labor productivity in manufacturing overall.<sup>27</sup> Since labor productivity is measured in relative terms, problems of deflation of real value added analogous to those discussed in Section 3 for services sub-sectors are avoided. However, the use of labor productivity in relative terms implies that the comparison of levels across countries is not meaningful and thus that controlling for country and year dummies in the regressions is essential.

Our independent variable is a measure of liberalization in services sub-sectors weighted by the reliance of a given manufacturing sub-sector on inputs from each services sub-sector. Input-output matrices for each of the 9 countries are used to capture the inter-sectoral dependencies between services sub-sectors and manufacturing subsectors. More specifically, the independent variable is given by _services_ link itc_ − 1 = ∑ _aik_ * _EBRDktc_ − 1 , where _aik_ is the quantity of inputs sourced by _k_ manufacturing sub-sector _i_ from services sub-sector _k_ as a fraction of the total inputs used by manufacturing sub-sector _i_ and _EBRDktc_ − 1 is the EBRD index of progress in policy reform in services sub-sector _k_ in year _t-1_ .28 Considering lagged values of the EBRD index allows time for the effects of liberalization in the services sector to materialize.

Interestingly, we find that the usage of inputs from services sub-sectors by the manufacturing sector varies greatly across ECA countries. Figure 15 shows the intensity of usage of each of the services sub-sectors (for which the EBRD index is available) by the manufacturing sector overall and by two manufacturing sub-sectors: textiles and

> 27 For example, labor productivity of 0.8 indicates that the sub-sector is 80% as productive as the manufacturing sector overall.

> 28 The input-output tables provide information on input usage by 2-digit NACE manufacturing sub-sectors from each 2-digit NACE services sub-sector as well as from all sectors in the economy, which allow us to

> calculate _aik_ .

35
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to do transactions with either a branch or an agent. In that sense, they have a choice between at least two alternatives. Still, agents are likely to be physically closer to many potential clients, have longer opening hours and transactions may take less time and be less formal than at a branch. Lacking client survey data, however, we again rely on indirect tests of these hypotheses. Controlling for population and population density (both of which should be positively associated with the frequency and volumes of agent transactions), potential clients in less commercially developed municipalities might benefit less from the convenience of, or have less need for, agents. That is, clients in commercially active municipalities might have greater appreciation for the timesaving convenience of an agent, and thus agent transactions might be more frequent in those areas.<sup>20</sup> While agent transactions are likely to be heavier in commercially developed municipalities, how the general level of local financial development affects agent transactions is harder to predict. On the one hand, the level of financial development may simply be an indication that the local demand for financial services is high, and thus we would expect more transactions from both agents and other providers in financially developed municipalities. But the demand for financial services is likely to be strongly linked to the level of local commercial activity. Since we already control for the level of local commercial development in our regressions, financial development could be negatively related to agents’ transactions to the extent that the financial services provided by agents are substitutes for those from other providers.

> 20 We are assuming that population density serves as a proxy for the number of potential clients that are near an agent. In that sense, density provides information on the convenience associated with reduced travel time to agents rather than branches. In a separate ongoing field experiment, we study how the density of the local FINCA agent network (i.e., the number of agents in close proximity) affects the financial behavior of clients and the profitability of the agents themselves.

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About 76 percent of the entrepreneurs in our sample were banked at the time they were screened by the EFL tool.

**Table 2. Descriptive Statistics**

||Accepte<br>Mo|d by Both<br>dels|Rejecte<br>Mod<br>Accept|d by Trad.<br>el and<br>ed by EFL|Accepte<br>Mod<br>Rejecte|d by Trad.<br>el and<br>d by EFL|Rejecte<br>Mo|d by Both<br>dels|
|---|---|---|---|---|---|---|---|---|
||Banked|Unbanked|Banked|Unbanked|Banked|Unbanked|Banked|Unbanked|
|EFL Score (Old Model)|423.61|421.57|421.54|415.44|347.67|344.01|345.06|342.40|
|EFL Score (New Model)|444.89|446.06|439.09|435.63|432.60|430.38|428.76|421.36|
|Age|43.820|44.338|40.465|40.000|37.338|34.190|35.485|33.200|
|Female|0.489|0.471|0.514|0.375|0.501|0.486|0.526|0.600|
|log_sales|10.187|10.028|10.517|10.392|9.920|9.574|9.754|9.365|
|Debt to sales ratio|1.724|0.101|1.495|0.065|1.507|0.029|1.499|0.000|
|Marital Status|||||||||
|Divorced|0.033|0.022|0.035|0.000|0.020|0.023|0.026|0.000|
|Living with partner|0.023|0.044|0.035|0.063|0.076|0.074|0.057|0.000|
|Married|0.361|0.375|0.289|0.375|0.217|0.162|0.175|0.133|
|Separated|0.013|0.022|0.014|0.000|0.009|0.005|0.026|0.067|
|Single|0.558|0.507|0.627|0.563|0.674|0.731|0.706|0.800|
|Widowed|0.011|0.029|0.000|0.000|0.003|0.005|0.010|0.000|
|Business sector|||||||||
|Agriculture|0.006|0.000|0.000|0.000|0.009|0.005|0.005|0.000|
|Commerce|0.753|0.838|0.718|0.938|0.716|0.750|0.737|0.733|
|Other Services|0.124|0.103|0.176|0.000|0.157|0.130|0.149|0.133|
|Manufacturing|0.117|0.059|0.106|0.063|0.118|0.116|0.108|0.133|
|Classified as "Normal" at the SBS|0.939|N.A.|0.718|N.A.|0.964|N.A.|0.737|N.A.|
|Number of Observations|523|136|142|16|644|216|194|15|

_Note:_ The table displays averages of the variables in each group.

Table 2 lists descriptive statistics for the four possible scenarios described in Table 1, separating banked and unbanked entrepreneurs.

# **_3.2 Repayment Behavior and Access to Credit Indicators_**

To assess loan repayment behavior, we used the status of the entrepreneur at various intervals in the public credit registry managed by the SBS.<sup>16</sup>

> 16 The public credit bureau managed by the SBS receives the classification given to the entrepreneurs by the various financial institutions with which they maintain credit. The classification we are using for this analysis corresponds to the highest risk classification provided by any institution; the classification given by the implementing institution based on the entrepreneurs’ days in arrears may differ from the classification based on days in arrears with other financial institutions.

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**Figure 4. Scale Factors and Income Mobility, RLMS 1994-2017**

<!-- Start of picture text --> 1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 More upward mobility .2<br>Mixed<br>.1 More downward mobility .1<br>0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1<br>Child parameter b Child parameter b<br>Adult parameter e Adult parameter e<br><!-- End of picture text --> 35
# **Reference**

Angrist, Joshua, Guido Imbens and Donald Rubin, 1996, ―Identification of Causal Effects Using Instrumental Variables,‖ _Journal of the American Statistical Association_ , Vol. XCI, pp. 444-455.

- Bloom, Howard S. 1984. ―Accounting for No-shows in Experimental Evaluation Designs,‖ _Evaluation Review_ 8: 225-246.

Heckman, James. 1992. ―Randomization and Social Policy Evaluation.‖In _Evaluating Welfare and Training Programs_ , ed. Charles F. Manski and Irwin Garfinkel, 201–30. Cambridge and London: Harvard University Press.

Heckman, James, Serio Urzua and Edward Vytlacil. 2006. ―Understanding Instrumental Variables in Models with Essential Heterogeneity,‖ _Review of Economics and Statistics_ 88(3): 389-432.

Heckman, James and Edward Vytlacil. 2005. ―Structural Equations, Treatment Effects and Econometric Policy Evaluation,‖ _Econometrica_ 73(3): 669-738.

________________________________._ 2007. ―Econometric Evaluation of Social Programs, Part ii: Using the Marginal Ttreatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments,‖ in James Heckman and Edward Leamer (eds) _Handbook of Econometrics Vol. 6B_ , Amsterdam: North Holland.

Imbens, Guido and Joshua Angrist. 1994. ―Identification of Local Average Treatment Effects,‖ _Econometrica_ 62(2): 467-475.

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As shown in figures 2 to 5, plotting the results, reveals an intriguing pattern that is similar for all indicators. First, longer official time requirements correlate with higher percentages of bribe payments. Second, higher percentages of bribe payments are associated with lower actual time.

**Figure 2:    Electricity connections: Fitted values of corrupt payments vs. official and actual time**

<!-- Start of picture text --> 0 200 400 600 0 50 100 150<br>DB_electrcity_time (days) Delay in Obtaining an Electrical Connection (days)<br>30<br>20<br>10<br>0<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Figure 3:    Operating licenses: Fitted values of corrupt payments vs. official and actual time**

<!-- Start of picture text --> 0 50 100 150 200 0 50 100 150 200<br>Time to start business days to get an operating license<br>30<br>20<br>10<br>0<br>-10<br>30<br>20<br>10<br>0<br>-10<br><!-- End of picture text --> 8
<!-- Start of picture text --> Current export profile<br>Intensive margin Growth decomposition Extensive margin<br>Product Market<br>Deepening Survival diversification diversification<br>RFI analysis<br>Specific Specific Comparative‐ Comparative‐<br>products across markets across advantage advantage<br>all markets? all products? defying? supporting?<br>X<br>Specific Specific<br>products across markets across<br>all markets? all products?<br>Product Market<br>Quality Cost Market access Trade promo / proactive policy Transport / facilitationtrade<br>Standards & Labor and Inputs /<br>certification Innovation productivity backbone services Tariffs NTBs Policy Administration Location Transport / logistics Customs /borders<br>Skills / Institutions Cost Quality<br>capacity<br>Business climate (micro‐ Trade policy and macro‐<br>economic environment) incentive framework<br><!-- End of picture text -->

- Are the main challenges to export outcomes (growth, diversification, quality) occurring at the _intensive_ margin or the _extensive_ margin?

- If the intensive margin, is the problem with _deepening_ trading relationships or export _survival_ ? If the extensive margin, is the problem with new _markets_ or new _products_ ?

- If the problem is with deepening, is it a problem with _products_ or _markets_ ? If it is about survival is it in sectors that are _aligned with comparative advantage_ or in those that _defy_ it? If comparative‐advantage supporting is it a problem with _specific products_ or _specific markets_ ?

- If the problem is with products, is it an issue of _quality_ or _cost_ ?

- If quality, is it an issue with _innovation_ or with _standards_ and certification? What is the role of _skills_ and _institutions_ in both of these?

- If cost, is it an issue of _labor and productivity_ or _of inputs (components and capital equipment) and backbone services (cost and quality of utilities, business services, industrial infrastructure, etc.)_ ?

- If the problem is with markets, is it an issue of _market access_ , _transport/trade facilitation_ , or _proactive trade promotion policies_ ?

- If market access, is it an issue of _tariffs_ or _non‐tariff barriers_ ?

- If transport and trade facilitation, is it an issue of _location_ , _transport & logistics_ markets/services, or _customs and border‐related issues_ ?

- If proactive trade promotion, is it an issue of the _policies_ in place or the _administration_

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enters negatively and significantly in all but one regression. On the other hand, banks are more stable in countries with more independent and more powerful bank supervisors (columns 3 and 4). Controlling for the generosity of deposit insurance and the role of the deposit insurer in bank failure resolution, we cannot find a significant relationship between the importance of private monitoring in supervision and bank z-scores (column 5). Our measures of the independence and power of bank supervisors and on the degree of private monitoring of banks are from Barth, Caprio and Levine (2001) – see Table A1 for a more detailed definition of these variables.

Turning to the bank- and country-level control variables, we find that banks are significantly more fragile in countries with more volatile GDP per capita growth rates. The other bank- and country-level variables do not significantly and robustly enter the regressions.

Overall, these results confirm the hypothesis that a greater role for the deposit insurer in bank failure resolution can strengthen bank stability by aligning incentives within the financial safety net properly. Deposit insurers have most at stake when banks fail; therefore, giving them higher responsibility within the financial safety net can reduce the negative moral hazard effects of deposit insurance. We next investigate whether this positive impact differs across different institutional settings.

Table 3 shows that the role of deposit insurance in bank failure resolution interacts critically with other elements of the financial safety net. The results in columns 1 and 2 indicate that the positive impact of the intervention power of deposit insurers is strengthened if the deposit insurer is politically independent. While DI Independence enters negatively, but insignificantly, its interaction with DI Intervention enters positively. It is more, as the DI Intervention does not enter significantly anymore, this suggests that the power to intervene only has a positive impact on bank stability if exercised by politically independent deposit insurers.

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Ministry of Commerce, PRC 2010, “2010 Statistical Bulletin of China’s Outward Foreign Direct Investment” (Beijing, MOFCOM, 2010) Moyo, Dambisa 2012, “Winner Take All: China’s Race for Resources and What It Means for the World”, Basic Book, New York.

Renard, Mary-Françoise 2011, “China’s Trade and FDI in Africa” African Development Bank Group, Working Paper series No. 126.

<u>http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/Working%20126.pdf</u> Rosen, D. H. and Hanemann, T. 2009, “China’s Changing Outbound Foreign Direct Investment Profile: Drivers and Policy Implications,” _Policy Brief_ , Number PB09-14, June 2009, Peterson Institute for International Economics, Washington DC.

Samaké, I. and Yang, Y. 2011, “Low-Income Countries' BRIC Linkage: Are There Growth Spillovers?” IMF Working Paper, Nov. 2011 Sauvant, K. 2011, “China, Inc. Goes Global,” Project Syndicate, 2011-11-30. - <u>(http://www.project syndicate.org/commentary/sauvant5/English)</u> Shen, Xiaofang 1990a, ““Foreign Direct Investment and Economic Development: a Study of the Chinese Experience, 1978-88”, (Doctoral dissertation, unpublished) Johns Hopkins University, School of Advanced International Studies, Washington DC.

Shen, Xiaofang 1990b “A Decade of Foreign Direct Investment in China,” _Problems of Communism_ , March 1990, Washington, DC.

Shinn, D.H. and Eisenman, J. 2012, _China and Africa: A Century of Engagement_ , (Penn Press, University of Pennsylvania University) UNCTAD 2012, _World Investment Report 2012_ , (United Nations Conference on Trade and Development, Geneva.) UNCTAD 2007, _Global Players from Emerging Markets: Strengthening Enterprise Competitiveness through Outward Investment_ (United Nations Conference on Trade and Development, New York and Geneva) Wang, J. Y. 2007, What Drives China’s Growing Role in Africa. Washington DC: IMF. IMF Working Paper WP/07/21 1.

Wells, T. L. 1983, _Third World Multinationals: The Rise of Foreign Investments from Developing Countries_ , MIT Press Book Series, 1983 46
The marginal effect of an improvement in country risk has a positive effect on the level of PPI investment in Infrastructure. The model of column 5 shows that an improvement of one point in the country risk rating produces on average an increase of 3 percent in the level of private investments in infrastructure. In other words, an one-standard-deviation improvement in the country risk (13.8 points) will imply a 41 percent more PPI commitment.

# **_5.2 Private-participation investments per sector_**

The PPI database provides data on commitments into private infrastructure projects separated by sectors: energy, telecom, transport, and water and sewerage.

In every sector, the effects of country risk are present and statistically significant. The lower the country risk, the greater is the likelihood of an investment. Table 5 summarizes these results.

The sensitivity is very high and statistically significant in the sectors of energy, transport, and water and sewerage (at the level of 0.01). In the case of Telecom the marginal effect of the country risk is lower than the other sectors and not as statistically significant as the other sectors. This may be explained by the nature of many telecommunications investments— particularly in the mobile sub-sector—which have high rates of return and shorter cost recovery periods than traditional forms of infrastructure.

**Table 5: Probability of Undertaking PPI Investment by Sector**

|**VARIABLES**|**PPI Energy**|**PPI Telecom**|**PPI Transport**|**PPI Water**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Country Risk|0.0265***|0.0154**|0.0296***|0.0214***|
||(0.00573)|(0.00615)|(0.00604)|(0.00696)|
|Log GDP ppp|0.359***|0.943***|0.410***|0.414***|
||(0.0527)|(0.112)|(0.0556)|(0.0626)|
|Log GDP Growth|0.0491|-0.0236|0.115*|-0.00944|
||(0.0596)|(0.0565)|(0.0682)|(0.0878)|
|Log Inflation|-0.126***|-0.219***|-0.0288|-0.129**|
||(0.0431)|(0.0420)|(0.0495)|(0.0618)|
|Log Openness|0.000283|0.754***|-0.139|-0.166|
||(0.156)|(0.183)|(0.156)|(0.177)|
|Constant|-10.32***|-25.15***|-11.88***|-11.90***|
||(1.484)|(2.752)|(1.579)|(1.788)|
|Observations|1,753|1,753|1,753|1,753|
|Number of countries|121|121|121|121|

*** p<0.01, ** p<0.05, * p<0.1

Source: Authors’ calculations.

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