rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

As in any other public policy-making field, scarcity of resources means that the nature of publicly financed health care provision is affected by political economy dynamics. These ‘generic’ underlying dynamics determine the relative bargaining power of different stakeholders, and therefore, who gets what, when and how. Given the (partially) conflicting priorities of different stakeholders, it becomes possible to anticipate that perceptions of the utility of the public health care system vary across stakeholders in predictable ways. Furthermore, emanating from this general premise, it is possible to anticipate the different reactions of stakeholders to institutional change, which alters the attributes of the public health care system.

One major institutional change that has occurred/may occur in many countries is the increased use of health care related litigation. The substantive effect of increased health litigation, on the overall utility of the public health care system, is controversial and much disputed in the literature. On the one hand, increased litigation can improve the protection of the individual right to health, as well as generate positive externalities in policy design, implementation and monitoring. Thus, health litigation can, potentially, correct inefficiencies in the public health system and ensure that litigant’s rights are not ignored. On other hand, the enforceability of the individual right to health care can also have a negative impact on health systems by distorting the allocation of resources towards the priorities of those who can litigate (not necessarily representatives of the typical patient), as well as adversely affecting the fundamentals of priority setting, equity, and the financial sustainability of the benefit plan. What is the net effect of litigation? Does it promote/diminish the equity and/or efficiency of public health benefit plans?

This paper aims to contribute to this growing corpus of work by formally developing and testing the implications of a model of the political economy of public health resource allocation. Specifically, we test: (1) whether the agendas of stakeholders do diverge significantly along efficiency and equity dimensions as the _generic political economy literature_ would anticipate; and (2) given the substantive assumptions of the governance literature, how increased health litigation is perceived to affect the welfare of different stakeholder groups. These empirical implications, of the model, are tested using data collected from key healthcare stakeholders in Uruguay. This research agenda is realized via the first section. The next section, Section II, provides a brief literature review- thereby, providing a rationale for modeling and critically evaluating, via empirical hypothesis testing, the political economy of public 2
counted 5,371,785 Christians, 80 percent of whom were Roman Catholics. The Protestants were always outsiders. With the exception of one small Belgian group, the missionary force was composed of British, American, and Scandinavian nationals, whose loyalty to the Belgian colonial enterprise was somewhat suspect. Although Belgian administrators were far from unanimous in their own religious attitudes, the Belgian administration itself was strongly committed to evangelization as an integral part of its objective.

The final element in the trinity of power, the large companies, was no less remarkable in the scale of its operation. In part these companies were a survival of the concessionary regime by which King Leopold II sought quickly to make effective the occupation of the company by the Free State or by its surrogates. The colonial administration was expected to give full support to the companies in recruiting labor and seeing that food was produced for the labor camps.

Although the tripartite alliance tended to display a monolithic face to the external world and to the Congolese, it would be misleading to underestimate the continual bargaining and dialogue that went on among them. Their interests were not in fact the same: the administration was, in the final analysis, most concerned with an effective scheme for governing the country, the mission in evangelical work, and the companies in expanding their enterprises.

The Belgian colonial system was a specific variant of the colonial model of accumulation, a model which introduced a systematic political bias in the distribution of the economic surplus in favor of the Belgian colonizers. Management of the economy was directed to providing raw materials for industry in Belgium and to support financially the Belgian cities, infrastructure and economy, which were affected and damaged by the World Wars, as well as to support Belgian settlers in the Congo who came to work for their own profits.

The colonial agricultural policy was highly authoritarian in its methods, directing farmers when and how to cultivate; it also means that farmers had few skills to draw on 3 1 after the abrupt departure of the Belgian administration . The Belgian colonial system did not have much room for the welfare of the Congolese people.

The colonial period achieved significant growth, however. This "economically efficient" outcome may be interpreted as _resulting from a bargaining equilibrium among the members of the trinity,_ which was able to consistently produce decisions which furthered growth. The brunt of the taxation in kind, in cash, and in the form or labor was borne by the Congolese people in general, and the peasant populations in particular.

> 31 Country Profile: ZaYre, (London: The Economist Intelligence Unit, 1996-1997), 18.

> 31 Crawford Young, Politics in the Congo. Decolonization and Independence, 64-66.

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important. The general strong performance of agriculture, and not specific circumstances in coffee regions, seem to have helped coffee farmers to compensate the losses from falling coffee prices.

Such an interpretation is substantiated by a closer look at the agricultural sector. Table 9 shows the market integration of non-coffee farmers in both coffee and non-coffee regions as well as the number of crops planted by different farm types including coffee farmers. We interpret the number of planted crops as a proxy of more productive and more diversified farming systems. The figures for both market integration and diversification illustrate the strong growth dynamics between 1995 and 1999. Maybe somewhat surprisingly at first sight, market participation of non-coffee farmers in coffee regions as well as the number of crops planted drops significantly during the boom phase. Given the purchasing power generated by the rise in coffee prices one might have expected the reverse, but two factors help explain this phenomenon: first, it is likely that the more market-integrated farmers moved into the coffee sector (which also explains the drop in the number of crops planted by coffee farmers); second, the increased specialization into non-agricultural activities was possibly associated with a retreat from agricultural markets.

Table 9: Participation in agricultural markets and number of crops planted, by farm type, 1992-1999

|_Farmtype_|_Share of f_<br>_pro_|_armers partic_<br>_duct markets_|_ipating in_<br> _(%)_|_N_|_umber_|_of cro_|_ps_|
|---|---|---|---|---|---|---|---|
||_1992_|_1995_|_1999_|_1992_|_19_|_95_|_1999_|
|Coffee farmers||||_5.8_<br>_6.3_<br>6.0|_4.9_<br>5|_5.8_<br>.3|_6.7_<br>_6.9_<br>6.8|
|Non-coffee farmers in|54|39|63|4.5|3|.1|4.2|
|coffee regions|_50_<br>_57_|_32_<br>_46_|_58_<br>_67_|_4.3_<br>_4.7_|_2.7_|_3.5_|_4.0_<br>_4.4_|
|Non-coffee farmers in|63|68|79|4.8|5|.6|5.7|
|non-coffee regions|_58_<br>_68_|_59_<br>_77_|_75_<br>_83_|_4.5_<br>_5.1_|_5.1_|_6.2_|_5.4_<br>_5.9_|

Source: Authors’ calculations.

Note: Market participation is defined as the share of farmers with positive sales; 95 % confidence intervals in italics (standard errors corrected for survey design).

Finally, there is also some evidence that coffee farmers responded to falling coffee prices by selling assets as a means of smoothing consumption. The share of farmers owning cattle, arguably the asset that can most readily be sold, increased over the period 1995-1999 for coffee and non-coffee farmers alike, but the mean value of cattle in constant prices went down for coffee farmers, while it went up quite dramatically for non-coffee farmers (Table 10). Looking at which coffee farmers might have sold cattle does not produce very clear-cut results, but two tentative conclusions can be drawn: first, poorer coffee farmers do not 26
# IV. Analytical extensions

Based on the latest household expenditure survey available for Mali, we have analyzed the determinants of poverty, food poverty and of vulnerability to poverty in Mali up prior to the March 2012 coup.

Poverty, food poverty and vulnerability in Mali are mostly concentrated among the rural farmers. One region stands out with relatively high rates of poverty and vulnerability despite being a rich agricultural area where cotton represent the main cash cropping activity. This so-called “Sikasso-paradox” could be further analyzed either through qualitative methods, or through additional data collection combining agricultural information with household data. A first round of LSMS-ISA survey was scheduled to be conducted in Mali just before the coup of March 2012. This type of dataset could inform in more depth about the factors driving the Sikasso paradox.

Were additional and comparable household surveys be conducted in Mali after the situation is stabilized, small area based methods using repeated cross sections could extend the present analysis of vulnerability which is based on a single cross-section only.

Finally, poverty measurement can be improved in Mali through (1) strengthening the calculations of regional consumer price indices, (2) ensuring household survey comparability by keeping questionnaires and seasonal timing of surveys constant and (3) reviewing calculations of regional poverty lines.

24
# **Table 4. Descriptive Statistics of Variables Used in WTP Function**

(Number of observations: 485)

|**Variable**|**Description**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**_Social, economic a_**|**_nd demographical information_**|||||
|Male|Male=1, female=0|.70|.45|0|1|
|Age|Age (years)|38.76|11.50|17|75|
|Income|Household income last year (yuan)|12,572|12,291|250|70,000|
|Secondary<br>Education|Have secondary education or above=1, other=0|.73|.44|0|1|
|Married|Marital situation : married=1, unmarried=0|.89|.30|0|1|
|Donation|In 2006, did you make any donation for pubic<br>goods? Yes=1. No or do not know=0|.60|.48|0|1|
|**_Perception about_**|**_Environment_**|||||
||Respondent list water quality as one of the three|||||
|Water important|most urgent problems of Qiubei County that need<br>to be coped with by the government. Yes=1, no=0<br>In 2006, did you hear through media about the|.45|.49|0|1|
|Press discussion|water quality in Puzhehei? Yes=1, not or don’t<br>know=0|.56|.49|0|1|
||Do you think it is solely the local government’s|||||
|Government job|responsibility to address environmental issues? Yes<br>=1, not sure or no = 0|.30|.45|0|1|
|**_Current use of Pu_**|**_zhehei lake_**|||||
||Did you or your household member visit Puzhehei|||||
|Visit lake|<br>in 2006? Yes=1, no or do not know=0|.79|.40|0|1|
||Are you aware that the water quality in Lake|||||
|Notice change|Puzhehei has degraded dramatically in the past?<br>Yes=1, no or not sure=0<br>Did you generate any income from the lake such as|.61|.48|0|1|
|Generate income|providing meals, lodging, transportation and<br>performance services to tourists, etc. in 2006?<br>Yes=1, no or not sure=0|.059|.23|0|1|
|Irrigation|Did your household use lake water irrigating|.10|.30|0|1|

21
9 The relations (3), (4), and (5), respectively, are the estimating equations for the rural wages, agricultural output, and rural employment (farm and nonfarm).

The ordinary least squares (OLS) estimation with a cross-section data (i.e., for a given t) is both biased and inconsistent, because the unobserved district-specific characteristics may be correlated with the included right-hand v2riables such as government infrastructure (B). Also, because government infrastructure variables (Ej) are not randomly distributed as often hypothesized (i.e., they are determined partially by the district's permanent factors, ij), the OLS estimates with cross-section data do not tell us whether it is the government infrastructure or the district's permanent attributes that matter most in explaining variation in agricu.ltural output, wage, rural emplo7ment, and rural labor supply.

We can circumvent both the endogeneity and unobserved variable problems using a panel dataset with either a fixed or a random effects technique. If the unobserved ability characteristics are time-invariant and specific to each district, then a fixed effects procedure (i.e., dummy variable or differencing out methods) will yield consistent estimates. In contrast, the random effects procedure accounts for the existence of both the time-invariant and time-varying error components. This procedure, however, ignores any correlation between the persistent errors and timevarying observed variables. The fixed effects procedure, on the other hand, does not estimate the influence of the measured but time-invariant variables (e.g., soil moisture capacity) on the dependent variables. We shall use Hausman-Wu specification test to determine whether fixed or random effects technique is appropriate for the given data and present results accordingly.
**<u>Table 6: Regression Results on Market Shares of Leading Companies</u>**

|Data Set<br>Average|Market Size and<br>_a_(constant)|Development<br>_b_(_ln(P):_<br>Premium)|Adjustment<br>_c_(Insurance<br>Penetration)|_R_<sup>_2_</sup>|
|---|---|---|---|---|
|_Top Company Market Share_|||||
|Life<br>34.141|15.297|-2.738|0.379|37.74%|
|Non Life<br>24.789|10.474|-1.668|-0.399|40.01%|
|_Top Three Companies’ Market Share_|||||
|Life<br>61.616|33.619|-5.706|2.045|44.01%|
|Non Life<br>49.343|23.641|-3.903|0.784|34.03%|
|_Top Five Companies’ Market Share_|||||
|Life<br>74.983|36.377|-6.169|2.503|45.48%|
|Non Life<br>62.525|27.865|-4.704|1.641|32.43%|

Source: Staff Analysis

# **Observations on Numbers of Market Participants**

## **_What Could Be the Natural Number of Sector Participants?_**

The above discussion on the Herfindahl Index can provide focus on the direction and pressures for change however, it may also be possible to consider the ultimate potential number of companies that the sector may see. As the index changes toward the natural level, the number of companies can be expected to change. Figure 9 presents charts of company numbers against the index in each sector and suggests that a curve could be fitted to the data. As a result, it would be possible to postulate that, for example, a country with 60 non life companies (as defined for the data set) would have an index around 400 and, as the index increases toward the natural level closer to 1,500 then this would suggest the company numbers should fall by around 25, ie a change of around 35 participants.

18
- 17. -

Table 1 <!-- Start of picture text --> Impact of Environment on Adjustment Performmnee<br><!-- End of picture text --> <!-- Start of picture text --> YGR DINF CAR IR<br>Intercept# 4.070 -1.62 -8.60 28.00<br>USINF -0.561*<br>YASHR 0.037 0.755** 0.082 -0.225**<br>TOTB 0.881** 1.015 1.645** 0.385**<br>RR -O.180** -0.845** 0.061 -0.102*<br>LTDPC -2.402* 18.567** 8.393** -8.473**<br>STDPC -8.015** 3.916 2.708 -3.599<br>D7 -0.581 1.345**<br>D8 -0.979 2.048**<br>D9 -0.524 1.877**<br>DO -1.803** 2.189**<br>Dl -2.149** 2.319**<br>D2 -2.421** 1.367**<br>D3 -0.498 -0.233<br>D4 -0.308 -0.070<br>D5 0.099 -0.505<br><!-- End of picture text --> <!-- Start of picture text --> Summary statistics:<br>R2 - 0.46 0.64 0.63 0.90<br>F value 7.12** 14.25** 12.09** 63.47**<br><!-- End of picture text -->

-

- Intercept was not estimated. It was chosen to center fixed-effect terms around a median of zero.

- ** - Significant at 95 percent level of confidence.

- - Significant at 90 percent level of confidence.
**Figure 7. Use of a mobile phone or the Internet to access an account varies widely within Europe and Central Asia**

<!-- Start of picture text --> Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herzegovina<br>Bulgaria<br>Croatia<br>Georgia<br>Kazakhstan<br>Kosovo<br>Kyrgyz Republic<br>Republic of North Macedonia<br>Moldova<br>Montenegro<br>Romania<br>Russian Federation<br>Serbia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%<br>Percent of adults with account using mobile phone or the Internet to access their<br>account<br><!-- End of picture text -->

## _Source_ : Global Findex database.

_Note_ : Length of the bars represents percentage of adults with a financial account who used mobile phone or internet to access their account.

# _Use of mobile phones for financial services_

In some regions of the world – such as Sub‐Saharan Africa – use of mobile money is widespread and a significant share of adults have a mobile money account only rather than a financial institution account. In the ECA region, where ownership of financial institution accounts is higher than in other developing regions, using mobile phones only instead of bank accounts is not common. Account owners are more likely to use online payments from their bank accounts than mobile accounts only.

Mobile phones can be used for financial services in at least two ways (Demirgüç‐Kunt and others 2018), for which China and Kenya serve as examples. In China mobile financial services are provided mainly through third‐party payment service providers, such as Alipay and WeChat, using smartphone apps linked to a bank account or other type of financial institution account. By contrast, in some African countries, such as Kenya, mobile financial services are offered primarily by mobile network operators, and mobile money accounts do not need to be linked to an account at a financial institution. In ECA, 17
TAILE 7 DRAZIL: CINPOSITION Of THE DONESTIC RESORMCE SAP (per cent of SDP)

|Tears|Net Import<br>of6<br>NFS<br>(1)|NutIn<br>s Public<br>Sector<br>(2)|ternst<br>Pa<br>Privat<br>Sector<br>(3)|ments<br>e Total<br> <br>(4)|Other<br>factor<br>Income<br>(5)|Current<br>ccount<br>Defici<br>(6)|Operational<br> <br>PSBR<br>t<br>(7)|Private<br>Sector<br>Dalance<br>(8)|
|---|---|---|---|---|---|---|---|---|
|1981|<br>0.40|2.08|<br>1.79|3.87|<br>0.14|4.41|<br>5.20|-0.79|
|1992|<br>-1.05|2.49|<br>1.75|4.24|<br>*.22|<br>3.41|<br>**7.00**|-3.59|
|1983|<br>-1.97|3.22|<br>1.45|4.67|<br>0.37|3.07|<br>3.50|-4.43|
|1984|<br>-5.36|3.14|<br>1.68|4.82|<br>0.38|-A.16|2.70|-2.86|
|1985|<br>-4.79|2.97|<br>1.29|4.26|<br>0.47|<br>-0.06|<br>4.30|-4.36|
|1986|<br>-2.29|2.51|<br>*.84|3.35|<br>0.46|<br>1.52|<br>3.50|-1.98|
|1987|<br>-2.71|2.10|<br>0.42|2.52|<br>0.28|<br>-0.49|<br>5.50|-5.59|

Note: Column (8) obtained as a residual. Source: Balance of pasnents statistics and Table 4.

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|Bought assets last year Y:1 N:0||||0.127|0.128|
|---|---|---|---|---|---|
|||||(0.121)|(0.121)|
|GDP per capita growth rate (%,<br>annual)|||||0.003<br>(0.030)|
|Primary enrollment gender parity|||||-0.146**|
|index|||||(0.070)|
|% of women in 35-64 years age|||||0.540***|
|group|||||(0.195)|
|Total population (logs)|||||-2.230<br>(1.390)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Number of observations|15,460|15,460|15,460|15,460|15,460|
||**Panel B: M**<br>|**arginal effe**<br>|**ct**<br>|||
|Bank credit (ratio of GDP)|0.264***|0.265***|0.272***|0.248***|0.271***|
||(0.046)|(0.052)|(0.059)|(0.063)|(0.062)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|
|All coefficient values are log odds<br>standard errors clustered on countr|ratios obtaine<br>y in brackets.|d from logit<br>*** (1%), *|estimation.<br>* (5%), * (1|Huber-White<br>0%).|robust|

37
<!-- Start of picture text --> 1 SF<br>09 ———-— OptionSet ae<br>NewAS ny<br>08 ra<br>/<br>07 £<br>i}<br>0.6 iF<br>iris=> 05 re4<br>04 LQ<br>(<br>03 fe<br>02 _ty<br>7<br>0.1<br>=<br>0o4 0.2——— 0.3 0.4 0.6 0.6 0.7 0.8 0.9 1<br>d<br><!-- End of picture text --> <!-- Start of picture text --> 1 —--—— OptionSet Fae a =<br>0.9 | |—— NewAS 4<br>08<br>07<br>0.6<br>=raS) 05<br>04<br>03<br>=<br>0.2<br>0.1<br>0 _<br>0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>d<br><!-- End of picture text -->

<!-- Start of picture text --> 1 <==<br>—--=— OptionSet a<br>0.9 + | NewasS aa<br>a<br>08 ie<br>07 ><br>+Q<br>06 os<br>Ke)= 11<br>205 A<br>t<br>04 i<br>03 if<br>”<br>02 =<br>1<br>0.1 aa<br>_- ia<br>0 aaa<br>0 0.2 0.4 0.6 0.8 1<br>d<br><!-- End of picture text -->

<!-- Start of picture text --> 1 - —<br>—--=— OptionSet we<br>0.9 + | NewAS ie<br>a<br>08 2<br>07 -<br>| i<br>06 2<br>Ko)= -’.<br>Los<br>1<br>04 ra<br>03 i<br>¢<br>0.2<br>a<br>01 C<br>. a<br>0 i<br>0 0.2 0.4 0.6 0.8 1<br>d<br><!-- End of picture text -->
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE it via protected wells and boreholes to access water. For sanitation, the statistics close to half of the population relies on improved latrines.

**Figure 9. Half of Mali’s population has access to improved water and sanitation**

<!-- Start of picture text --> a. Water<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1987 1996 2001 2006<br>improved unimproved<br>b. Sanitation<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1987 1986 2001 2006<br>improved unimproved<br>% of population<br>% of population<br><!-- End of picture text --> Source: WHO – Joint Monitoring Program 2010, from Demographic and Health Surveys for 1987,1996, 2001 and 2006 Mali’s achievements in water and sanitation are even more remarkable given the country’s demography and geography. Mali’s population has been growing at an annual rate of 2.5 percent, and people continue to move to the most urbanized areas of the country, creating enormous pressure on existing infrastructure. In the late 1990s, Mali’s expansion strategy focused on water provision through standposts. During the 2000s, however, the emphasis has been on increasing the use of wells, boreholes, and, to some extent, piped water for improved water provision (table 8a). An average of 0.8 percent of the population gained access to piped water annually between 1996 and 2001, and 0.6 percent gained access between 2001 and 2006. Use of standposts increased by 2 percent of population per year between 1996 20