rafmacalaba/datause-encoder-data huggingface dataset:
17 _per contact_ , for either outpatient or inpatient care; indeed, there is a hint that it may have increased the cost per inpatient episode. The impact on average household out-of-pocket spending tells us nothing about the impact on the incidence of large payments. In the event, NCMS appears to have _increased_ the incidence of catastrophic household out-of-pocket payments, at least where the catastrophic threshold is 20% or less of income. This is consistent with the increase the scheme has had on utilization and with recent research pointing to a positive impact of China’s urban insurance scheme on catastrophic health spending (Wagstaff and Lindelow 2005).

Table 11 points to some interesting differences across income groups in the impact of NCMS on household out-of-pocket spending. The program seems to have _increased_ average outof-pocket spending among the poorest decile, but to have _reduced_ the incidence of catastrophic spending among this group. It appears, in other words, to have compressed the distribution and shifted it rightwards. By contrast, NCMS appears to have _increased_ the incidence of catastrophic spending among deciles 3-10, leaving average spending unaffected.

# VII. **DISCUSSION AND CONCLUSIONS**

This paper has reported findings on the impact of a new health insurance scheme for rural areas in China, focusing on both the demand and the supply sides. Impacts are estimated by combining differences-in-differences and matching methods. Short of a fully randomized evaluation, this approach is arguably the most effective way of dealing with the potential problem of biases arising from observed and unobserved heterogeneity in estimating the impact of health insurance.

The results suggest that, despite its relatively short life and limited financing, the NCMS has had substantial impacts. It has resulted in an increase of over 20% in both outpatient visits and inpatient episodes. In the case of outpatient care, the household data suggest that most of the increase has been at THC level, while the increase in inpatient episodes is mainly accounted for by county hospitals. Given high coinsurance rates, it is perhaps not surprising that there has been no significant increase in utilization among the poorest quintile. Narrow coverage and high coinsurance rates also go some ways towards explaining why we find no evidence of the NCMS reducing either out-of-pocket spending or the incidence of catastrophic expenditures. The results
Policy Research Working Paper 7179

# **Abstract**

This paper investigates the link between competitive, wellfunctioning food markets and consumer welfare. The paper explores two key food markets in Kenya—sugar and maize— and argues that a variety of factors conspire to distort market prices upward. Distortionary factors include import tariff policy, nontariff barriers, potential anticompetitive conduct by firms, and direct state intervention in markets. Changes in sugar and maize prices are shown to have significant welfare effects on consumers. Equivalent income effects are estimated using the most recent available representative household survey data—the Kenya Integrated Household Budget Survey 2005/06. The paper shows that relaxing trade barriers to allow sugar prices to fall by 20 percent could reduce poverty by 1.5 percent. Similarly, adjusting government interventions in the maize market, which have been shown to inflate maize prices by 20 percent on average, could reduce poverty by 1.8 percent. The magnitude of the estimated income effects may vary based on updated household-level consumption data, assumptions regarding demand elasticities, and estimates of import parity prices for these staples. However, in all the scenarios, more competitive prices have a larger average effect on the poorest households in urban and rural areas, supporting the relevance of effective competition policies for poverty reduction strategies.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tbegazo@worldbank.org and jargent@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
equal, would produce no pours, reducing the correlation. Figure 9, however, indicates that the difference in the correlations (0.29 in good times and 0.23 in bad times) is not significantly different from zero. Summarizing our findings for macroeconomic factors: Stylized fact # 3 The relationship between fiscal procyclicality and key macrovariables is not always as expected. Procyclicality is higher when (i) capital controls are present and (ii) under fixed/predetermined exchange rates. Procyclicality, however, does not seem to be associated with the cyclical stance or the level of external debt.

Clearly, further analytical work on the relationship between fiscal procyclicality and key macro-variables is needed. In contrast, as discussed in Section 4, analytical models based on institutional variables (particularly political-economy variables) are much more established.

# 2.3 Institutional variables

We now turn to institutional variables.<sup>15</sup> Our first institutional variable is political risk.<sup>16</sup> As expected, and shown in Figure 10, high-political-risk CE are more procyclical (i.e., have a higher correlation between the cyclical components of real GDP and real government spending) than low-political-risk CE. The correlation is 0.49 for high-risk and 0.28 for low-risk, with the difference significant at the 10 percent level. When it comes to bureaucracy quality, Figure 11 shows that CE with low bureaucracy quality have an average correlation of 0.47 compared to just 0.33 for countries with high bureaucracy quality. While large, the difference is not significantly different from zero (p-value = 0.16). As to corruption, Figure 12 shows that countries with low corruption control have an average correlation of 0.50 compared to 0.31 for countries with high corruption control, with the difference being statistically significant at the five percent level. Another institutional variable is government stability. Again, as expected, countries with high government stability have a correlation of 0.39 compared to countries with low government stability (0.45), as illustrated in Figure 13. The dif-

> 15Section 4 will look at institutional variables in a formal econometric set-up.

> 16The source for the first five institutional variables (political risk, bureaucracy quality, control of corruption, government stability, and law and order) is the International Country Risk Guide (ICRG). The political risk index is based on 12 components with varying weights: government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, and bureaucracy quality.

9
An example of such an abbreviated social welfare function is one that I used in Fields (2005b) to evaluate alternative labor market policies in one of the theoretical models reviewed below, the Harris-Todaro labor market model. The welfare function chosen was (1) SW = f(Total labor earnings, unem, ineq, pov), f1 > 0, f2 < 0, f3 < 0, f4 < 0 – that is, social welfare is a function of total labor earnings, unemployment, inequality, and poverty, with the judgments that other things equal an increase in total labor earnings increases social welfare and increases in unemployment, inequality, or poverty lower social welfare. Three policies were evaluated: modern sector enlargement, which increases the number of modern sector jobs holding wages in the two sectors constant; modern sector enrichment, which increases wages in the modern sector holding the number employed in the modern sector and wages in the traditional sector constant; and traditional sector enrichment, which increases wages in the traditional sector holding wages and employment in the modern sector constant. For the class of social welfare functions given by (1), a policy of traditional sector enrichment was shown to be unambiguously welfare-improving, whereas policies of modern sector enrichment and modern sector enlargement were shown to be welfare-ambiguous.<sup>2</sup> In similar fashion, in analyzing labor market policies, we can move to what might be called an “abbreviated labor market well-being function.” What should be included in such a function? Today, four components would be regarded as essential.

First, following traditional practice, an abbreviated labor market well-being function would presumably include the unemployment rate. Even though the unemployment rate should probably receive some weight, how much weight it should receive is open to discussion (Peek, 2006).

> 2 Abbreviated social welfare functions have been used both explicitly and implicitly in World Bank research. As one example of explicit criteria, de Ferranti et al. (2000) assess social protection options in Latin America using coverage, cost, incentives, and insurance. As another example of explicit criteria, Vodopivec (2004) identifies two main classes of performance criteria: distributive effects and efficiency effects. Distributive effects include coverage, adequacy of support, and income distribution. The efficiency effects include job-search efforts, post-unemployment wages, equilibrium labor market outcomes (employment, unemployment, labor force participation), restructuring and overall economic adjustment, labor supply of other family members, taking jobs in regular versus informal sector, and aggregate output and growth.

13
Financial Regulation and Government Revenue We advance this literature and contribute to the calculation of government revenue gains by tracking the micro-level evidence through which financial regulation leads to government revenue gains. We propose a direct calculation of such savings based on credible counterfactuals. In this paper, we focus on a specific case study: the introduction of a financial regulation policy in April 2011 by the National Bank of Ethiopia (NBE) on all private banks. This regulation is ideal for the purpose of our study for a variety of reasons. First, the policy was announced and implemented with short notice (in mid-March 2011 and April 2011, respectively) and banks were largely surprised by this. Second, the magnitude of the regulation was substantial, as it imposed the purchase of 0.27 bonds issued by the central bank (NBE bills) for every unit of loans extended to the private sector. Third, banks would not be willing to hold these bills in absence of the regulation, because these present a fixed nominal rate of 3% and deliver an effective negative net return<sup>1</sup> . Therefore, both the regulated mandatory quantity of bond purchase and their price are fixed.

By having access to data on monthly balance sheets of all Ethiopian banks, to a disaggregated survey of branch installation costs, and to annual reports, we are able to verify how the policy affects bank behavior and to calculate the government revenue gains induced by the policy. This paper advances this body of literature through two fundamental innovations. First, we provide a countryspecific government revenue calculation based on a clear policy, for which we can verify its effect on the regulated entity (banks and their balance sheets) and calculate the revenue gains. Secondly, we benchmark this policy against three alternative and credible ways of raising revenue through the banking system. Despite methodological changes, our results are essentially in line with the literature<sup>2</sup> , however rather than a macroeconomic approach we follow a micro-oriented calculation as suggested by Siegel (1981).

We find that this policy creates a moderate revenue gain: depending on the counterfactual scenarios, such gain lies between 1.5% and 2.6% of the total tax revenue (this corresponds to 20% of all Personal Income Tax revenue in Ethiopia). In addition, we track the mechanism through which revenue is generated, and verify that diminished bank profits is the only aggregate affected, with an 1In fact, while on the asset side each NBE Bill provides a 3% remuneration, the core liability of Ethiopian banks (deposits) are remunerated, on average, at 5.5% and at least 5% by law. Therefore every NBE Bill delivers to the bank a net negative return between 2% and 2.5%.

> 2In fact, in Appendix D we show that our results are consistent with the methodologically alternative calculations of the financial repression revenue introduced by Giovannini and De Melo (1993).

3
**Figure 3. 10  Requests for informal lending services**

<!-- Start of picture text --> 100% 90.7%<br>86.4%<br>90% 79.8%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30% 20.2%<br>20% 9.3% 13.6%<br>10%<br>0%<br>Banked Unbanked Total Population<br>Requested an informal loan during the past 3 years<br>Did not request an informal loan during the past 3 years<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 23. **The unbanked have less access to credit sources with a higher degree of formality.** As Figure 3.11 shows, the unbanked mostly use friends or family and money lenders as sources of credit. In contrast, the banked are more frequent users of credit services provided by commercial stores, employment funds or informal savings funds. This suggests that access for the unbanked to these sources of lending is limited.

**Figure 3. 11  Source of informal lending**

<!-- Start of picture text --> 100%<br>88.8% 90.5%<br>90%<br>80% 74.7% 76.8%<br>70%<br>60% 51.3% 54.0%<br>48.7% 46.0%<br>50%<br>40%<br>30% 25.3% 23.2%<br>20%<br>11.2% 9.5%<br>10%<br>0%<br>Friends or Money Commercial Employment Informal Other<br>family lenders store fund savings<br>fund<br>Banked Unbanked<br><!-- End of picture text --> _Source: DANE household study of Bogotá , 2003_ 24. **While reasons to request a loan vary by income level, personal expenses and housing finance are key demand drivers** . In particular, up to the 40<sup>th</sup> income percentile, demand to borrow for personal expenses rates higher than any other reason. In the “middle class” (41<sup>st</sup> to 80<sup>th</sup> percentile) housing finance is the most important reason for borrowing. At all income levels, demand for personal expenses plus health and education is greater than demand for either housing or business expenses. This range of demand could also help explain why 55
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licensing, quotas and nominal exchange rate overvaluation, and the indirect effect of real exchange rate appreciation, are factored in.

Direct evidence on the implications of increased openness for exports of manufactures is provided by a wide range of empirical studies using traditional CGE models. A recent econometric study by Elbadawi, Mengistae and Zeufack (2001) builds on recent economic geography models developed by Redding and Venables (2001) and concludes that increasing openness in African countries would considerably expand exports of manufactures.

Overall, it seems highly likely that the sharp reductions in developing country trade distortions since the early 1980s have played a vital role in allowing developing countries to so sharply increase their exports of manufactures, and hence reduce their dependence on resource-based products.

# **Technological Change**

Technological change is a very important determinant of changes in both resource dependence and economic growth and development. Unfortunately, it is relatively poorly understood because of the complexity of many of the processes that lead to technological change, and because of the problems involved in measuring technical change.

Much thinking on the role of technological change in promoting structural change has been confused by a failure to distinguish between open and closed economies. The oft-encountered argument that technical advance in agriculture promotes industrialization by freeing up resources formerly used in agriculture is, as pointed out by Matsuyama (1992) likely to be relevant only in a closed economy. In an open economy, technical change that increases productivity in agriculture, or any other sector, will generally increase the size of that sector by drawing additional resources into the sector because of induced increases in the profitability of production.

25
# **Table 9:  Regression Results: Transmission and Distribution Losses (% of output)**

||Log of Transmi|ssion and Distribution Losses||
|---|---|---|---|
|Procedures Getting Electricity|0.095**|0.095**<br>0.020|0.020|
||(0.032)|(0.033)<br>(0.030)|(0.030)|
|Private utility||<br> <br>0.015|<br>0.038|
|||(0.105)|(0.086)|
|Ln GNI per capita||-0.234***<br>|-0.234***<br>|
|||(0.04)|(0.04)|
|Constant|1.850***<br>|1.845***<br>4.300***<br> <br>|4.293***<br>|
||(0.197)|(0.206)<br>(0.427)|(0.435)|
|R-squared|0.052|0.052<br>0.311|0.311|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||
||Log of Transmi<br>|ssion and Distribution Losses<br> <br>||
|Time Getting Electricity (Log)|0.168|0.168<br>0.079|0.077|
||(0.089)|(0.090)<br>(0.068)<br>|(0.069)<br>|
|Private utility||-0.006|0.03|
|||(0.112)|(0.088)|
|Ln GNI per capita||-0.235***<br>|-0.236***<br>|
|||(0.04)|(0.04)|
|Constant|1.632***|<br>1.633***<br>4.077***|<br>4.080***|
||(0.399)|(0.400)<br>(0.492)|(0.493)|
|R-squared|0.027|0.027<br>0.314|0.315|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||
||Log of Transmi|ssion and Distribution Losses||
|Cost as % of GNI pc|0.104**|0.106**<br>0.030*|0.032*|
|Getting Electricity (Log)|(0.036)|(0.035)<br>(0.013)|(0.013)|
|<br>Private utility||<br> <br>0.079|<br>0.054|
|||(0100)|(0088)|
|Ln GNI per capita||.<br>-0.204***|.<br>-0.203***|
|||(0.04)|(0.04)|
|Constant|1.796***|1.765***<br>3.991***|3.958***|
||(0.213)|(0.207)<br>(0.387)|(0.402)|
|R-squared|0.191|0.194<br>0.318|0.319|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||
||Log of Transmi|ssion and Distribution Losses||
|Country rank on the ease of getting|0.005***|0.005***<br>0.001|0.001|
|<br>electricity|(0.001)|(0.001)<br>(0.001)|(0.001)|
|Private utility||0.001<br>(0102)|0.032<br>(0087)|
|Ln GNI per capita||.<br>-0.215***|.<br>-0.216***|
|||(004)|(004)|
|Constant|1.974***|.<br>1.974***<br>4.148***|.<br>4.146***|
||(0.089)|(0.094)<br>(0.428)|(0.432)|
|R-squared<br>|0.151<br>|0.151<br>0.315<br> <br>|0.316<br>|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||

40
_E_ [  _ijl_  _t_ 1 _D jl_ ]  0 , implying that disaster was observed in _t_  1 and actions taken in _t_  1 are conditioned on this information.

Including area fixed effects in the above specification may underestimate the impacts of disaster if shocks are perfectly correlated within an area. However, there is a cost of not including area fixed effects since unobserved area-specific time-varying factors often jointly affect child schooling in the same area. For example, change in school availability and local wage (due to increased labor demand in the local labor market) affect changes in human capital investments. Actual costs of flood and drought are not evenly distributed in an area.

Note that the labor market (substitution) effect is relatively short in time. During a natural disaster, the wage decreases due to the reduction of labor demand. However, it is also expected that the wage will eventually go back to the normal level after the disaster. Therefore, if our panel data are collected with the interval of several years, we cannot capture the labor market effect. We can only observe the total effect, that is, the income effect net of the substitution effect.

To clarify our theoretical insight, we also estimate pre-disaster asset allocation equations.

where Pr[ _Dl_ ] is the estimated village-level disaster probability conditional on the information from _t_ to _t_  1, and _K jl_ is landholding of household _j_ in village _l_ . We focus on human capital and livestock allocation in the analysis. For human capital, we use the maximum level of schooling (years) achieved among the household members. If agents correctly perceive the future disaster probability and pre-disaster asset allocation is effective to mitigate potential disaster impacts, they will adjust asset portfolio prior to the actual occurrence of disasters.

To construct a measure of Pr[ _Dl_ ], first, we use time series data of disaster incidences at the household level. This first-stage estimate of the disaster probability contains idiosyncratic errors. We then take the average within village to average out the idiosyncratic errors.

Comparison of Eqs (1) and (2) provides some integrated hypotheses on ex-ante actions and disaster impacts. That is, if<sup></sup> 1<sup>and/or</sup>  2 are positive for _k_ in Eq (2), we should expect positive  2 _k_ in Eq (1). In other words, if some assets play a role in mitigating the impacts of disasters, agents would try to allocate more to those assets before the actual disaster happens: a higher future probability of disasters increases incentives to do so.

# **4. Data**

This section describes the data from Bangladesh, Ethiopia and Malawi that we use to test our hypotheses. The International Food Policy Research Institute has conducted panel household surveys in the three countries with corresponding local collaborators. The period covered in the panel data includes the occurrence of major natural hazards such as flood and drought.

In Bangladesh, the initial survey round was fielded in late 1998, immediately after the onset of the 1998 flood, followed by two subsequent rounds until the middle of 9