rafmacalaba/datause-encoder-data huggingface dataset:
service organization, human resources, access rules, and governing structures seeking correlation between decision space and social service outcomes.

A first step to analyzing decentralization as a governance determinant in public health care provision would be to measure:

- share of public health care expenditures made by local governmental authorities

- share of public health workers hired by local governmental authorities

These measures would approximate the level of decentralization in public health care provision, but would be difficult to use for cross-country comparisons since countries vary significantly in size and in the subnational structures of public administration. Furthermore, these broad measures do not make it possible to distinguish the degree of decentralization which may vary across different health service functions. For example, a public health service might decentralize personnel management while retaining highly centralized drug procurement and distribution systems.

Interpreting any measured associations between the share of local government responsibility for public health care provision and performance would require data that distinguishes different aspects of health care provision, such as share of supplies purchased centrally and share of drugs purchased centrally; more refined measures of the scope of decision-making authority by local offices, such as the share of local government expenditures financed by local tax revenues; and contextual information such as the average and range of population sizes served by local government unit.

Additional measures that have been used to assess decentralization which could also be considered include:

- share of user fees retained by facility or local government unit,

- local authority to determine user fees,

- local authority to hire and fire workers,

- share of funds transferred to local health systems and autonomous regions (Mozambique and Nicaragua)

# _Formal procedures_

Formal procedures have been the focus of many efforts to improve public services in developing countries, with the goal of rationalizing public sector policy- and decisionmaking. The presumption behind this work is that the existence of formal procedures can improve public service provision by clarifying expectations and tasks assigned to different staff and units; by keeping those with vested interests from unduly influencing decisions; and by increasing accountability to higher-level authorities and citizens.

Assessing whether or not formal procedures are in place makes up a large part of the governance literature, outside as well as within the health sector. For example, the Public Expenditure Framework Assessments (PEFA) contain numerous questions related to the existence of different procedural arrangements (e.g. separation of authorization and expenditure functions, provisions for selecting procurement committees). As noted above, most of the indicators in WHO‟s draft proposal for monitoring health sector governance are such rules-based measures.

______________________________________________________________________________________

-24-
(and related, age) especially important. Table 5 already suggests this since the percentage of large firms with complaints is less than that for the smallest firms, on average some eight-percentage points, but sometimes as much as 10 to 20 percentage points. Across a large sample of countries and controlling for other factors, it has also been found that size strongest affects access to credit (Beck, Demirgüç-Kunt, and Maksimovic, 2005). (This analysis used the World Bank Economic Survey data; the ICA-data are currently being analyzed to further determine what drives (lack of) firm access across countries.) For a specific country, Brazil, size was found to be more important than performance and other variables, suggesting quantitative limitations to credit access (Francisco and Kumar, 2004). The impact of size on credit was found to be greater for longer-term loans in case of Brazil (as well as for many other countries). As an aside, public financial institutions in Brazil are actually found more likely to lend to large firms, thus negating the idea that public banks necessary fill market gaps.

Size may, however, reflect not only profitability, financial and legal collateral but also political collateral. This is particularly so in developing countries where lending is often done on the basis of relationship and connections, often political. Indeed, in countries with well-developed financial systems size can be overcome. Many banks in developed countries lend to many small, single proprietary firms, sometimes without requiring collateral, financial statements, or other requirements. Thanks due to the spread of technological advances such as automated credit scoring, banks in developing countries are also becoming active in these forms of financing. And in the most developed financial markets, universal access to basic financial services, including some forms of credit, is essentially assured for households, as the data above showed.

Access can thus be based on opportunities and needs. Since in many countries it does not seem so, the question to ask is what constrains access in general and in some countries in particular? In other words, before asking whether there is a need for intervention, one needs to address the question what the barriers to access are, and whether there is a market failure and, if so, which.

11
Results from the zero-inflated negative binomial model (ZINB) for households suggest that separating the participation and quantity decisions is significant in modeling household behavior. In this application, explanatory variables such as age, education and income were found to be significant factors in the binary decision to recreate at Lake Sevan. The quantity of trips was determined by travel costs, income, household size and education. Expected trip demand was found to be 0.58 trips per individual per annum, and the welfare measure calculated from the underlying demand function reveal a per trip consumers surplus of $8.82. From the on-site sample the TRNB and TRNBES models yielded expected trip demands of 5.9 and 7 trips per person per year with consumers surplus values of $8.79 and $8.21 per person per year, respectively. Expected trip demand from the on-site models is higher than the household sample due to the difference in sampling casual versus more avid users of the site. However, an even higher trip demand is found in the TRNBES model due to a higher estimated overdispersion parameter, α used in the calculation of expected trip demand.

All three models appear to yield similar welfare measures, but it appears that accounting for endogenous stratification in the TRNES model did not yield a significantly different estimate from the TRNB model. In fact, consumers surplus from the TRNB model is slightly closer to the household result than the TRNBES model. One possible explanation is that individual characteristics of the on-site sample are not correlated with higher trip frequencies (arguing against the precise reason we factor in ES). This does not imply that ES is not an important consideration in modeling on-site behavior, rather the results found here suggest that the on-site sample was merely representative of the population-based household survey. This finding is quite contrary to other studies where the ES bias in welfare measurement has been found to be quite significant (Shaw, 1988; Englin and Shonkwiler, 1995; Loomis, 2003; MartinezEspineira and Amoako-Tuffour, 2005).

Although we did not find any significant difference in accounting for ES, this does not negate the main result that when comparing household and on-site samples, either can be used to derive a consistent welfare measure of access to the site after 18
- Litchfield, J., and P. Justino (2004). ‘Welfare in Vietnam during the 1990s: Poverty, Inequality and Poverty Dynamics’. _Journal of the Asia Pacific Economy_ . 9(2): 145–169.

- Liu, A. Y. C. (2001). ‘Markets, Inequality and Poverty in Vietnam’. _Asian Economic Journal_ . 15(2): 217–235.

- Minot, N. (2000). ‘Generating Disaggregated Poverty Maps: An Application to Vietnam’. _World Development_ , 28(2): 319–331.

- Minot, N., B. Baulch, and M. Epprecht (2003). ‘Poverty and Inequality in Vietnam: Spatial Patterns and Geographic Determinants’. Research Report 48. Washington, DC: International Food Policy Research.

- Niimi, Y., P. Vasudeva-Dutta, and L. A. Winters (2004). ‘Storm in a Rice Bowl: Rice Reform and Poverty in Vietnam in the 1990s’. _Journal of the Asia Pacific Economy_ , 9(2): 170–190.

- Ravallion, M. (2001). ‘Growth, Inequality and Poverty: Looking Beyond Averages’. _World Development_ , 29(11): 1803–1815.

- Rodriguez, F., and D. Rodrik (2001). ‘Trade Policy and Economic Growth: A Sceptic’s Guide to the Cross-national Evidence’, in B. S. Bernanke and K. Rogoff (eds), NBER Macroeconomics Annual 2000, vol. 15. Cambridge, MA: MIT Press.

- Tarp, F., D. Roland-Holst, and J. Rand (2002). ‘Trade and Income Growth in Vietnam: Estimates from a New Social Accounting Matrix’. _Economic Systems Research_ , 14(2): 157–184.

- Thanh, V. T. (2005). ‘Vietnam’s Trade Liberalization and International Economic Integration: Evolution, Problems, and Challenges’. _ASEAN Economic Bulletin_ , 22(1): 75–91.

Winters, L. A. (2002). ‘Trade Liberalisation and Poverty: What are the Links?’ _World Economy_ , 25(9): 1339–1367.

- Winters, L. A., N. McCulloch, and A. McKay (2004). ‘Trade Liberalization and Poverty: The Evidence so far’. _Journal of Economic Literature_ , 42(1): 72–115.

32
**Figure 4: Income per capita and U5M rates**

<!-- Start of picture text --> 260<br>240<br>Mal<br>220<br>Ni<br>200<br>180 Gui<br>160 Moz<br>140<br>Ug Sen<br>120 Ke<br>Mad<br>100<br>80<br>60<br>300 500 700 900 1100 1300 1500 1700<br>GNI pc, PPP, current international $<br>U5M<br><!-- End of picture text --> Source: Authors’ calculation from DHS surveys. GNI per capita comes from the World Development Indicators. Note: The arrows point from the first survey year to the second for each country.

**Figure 5: Income per capita growth and U5M rates**

<!-- Start of picture text --> 2<br>Ke<br>0<br>-1 0 1 2 3 4 5 6<br>Mal<br>-2<br>-4 Gui Sen<br>Ug Moz<br>-6<br>Mad<br>-8<br>Ni<br>-10<br>-12<br>Annual GDP pc growth<br>Annual % point change in U5M<br><!-- End of picture text --> Source: Authors’ calculation from DHS surveys. GDP per capita comes from the World Development Indicators.

21
<!-- Start of picture text --> 2, Spectral , Max Share<br>OG _—— —= Va<br>><br>Wo 0<br>10 20 30 40 10 20 30 40<br>© 4 Long Run 4 NAMS<br>g eS!<br>a 0.5 0.5<br>Wo ————EEE l<br>10 20 30 40 0 10 20 30 40<br>© ‘ Limited Spectral<br>coy f<br>cofi7)© 0 lay |<5<br>10 20 30 40<br><!-- End of picture text -->

<!-- Start of picture text --> 2, Spectral , Max Share<br>isu ae ae<br>> / I<br>Wo 0<br>10 20 30 40 10 20 30 40<br>© 4 Long Run 4 NAMS<br>© Ke a<br>a 0.5} 0.5 y<br>ae<br>Wo 10 20 30 40 0 10 20 30 40<br>© ‘ Limited Spectral<br>coy<br>eofi7)© 0 la |<br>10 20 30 40<br><!-- End of picture text -->
**Table 1: Can Human Experts Predict Business Survival After 3 Years?**

|||Non-Winners|||Wi|nners||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Judges|McKenzie|FaWo|Judges|Judges|McKenzie|FaWo|
|Business Plan score|-0.0002|-0.0009|-0.0009|0.0000|-0.0003|-0.0006|-0.0007|
||(0.0014)|(0.0015)<br>|(0.0015)<br>|(0.0006)|(0.0006)|(0.0008)|(0.0008)|
|Female||-0.1519<sup>***</sup>|-0.1250<sup>**</sup>|||-0.0009|0.0082|
|||(0.0384)<br>|(0.0385)<br>|||(0.0214)|(0.0220)|
|Age||0.0109<sup>***</sup>|0.0087<sup>**</sup>|||0.0034|0.0037|
|||(0.0032)|(0.0031)|||(0.0021)|(0.0019)|
|Graduate Education||-0.0208||||-0.0028||
|||(0.0631)||||(0.0288)||
|Worked abroad||0.0249||||-0.0397||
|||(0.0589)||||(0.0248)||
|Lottery choice||-0.0201||||-0.0145||
|||(0.0291)||||(0.0161)||
|Wealth||-0.0031||||0.0020||
|||(0.0057)||||(0.0027)||
|Grit||-0.0367||||-0.0049||
|||(0.0285)||||(0.0154)||
|Ability||0.0507<sup>**</sup>||||-0.0080||
|||(0.0169)||||(0.0088)||
|Agriculture||-0.0481||||-0.0157||
|||(0.0351)||||(0.0194)||
|IT||-0.0563||||-0.0295||
|||(0.0510)||||(0.0243)||
|Manufacture||<br>0.0028|0.0326|||<br>-0.0303|-0.0218|
|||(0.0394)|(0.0352)|||(0.0199)|(0.0177)|
|Retail|||0.0635||||0.0185|
||||(0.0800)||||(0.0457)|
|Self-confidence|||0.0229<sup>*</sup>||||0.0032|
||||(0.0093)||||(0.0040)|
|Motivation|||0.0211<sup>*</sup>||||-0.0060|
||||(0.0107)||||(0.0047)|
|Happiness|||-0.0009||||-0.0083|
||||(0.0119)||||(0.0070)|
|Optimism|||0.0336<sup>*</sup>||||0.0095|
||||(0.0135)||||(0.0075)|
|Ability|||0.0334<sup>*</sup>||||-0.0090|
||||(0.0162)||||(0.0083)|
|Credit|||<br>0.0150||||<br>-0.0140|
||||(0.0173)||||(0.0074)|
|Existing|0.2140<sup>***</sup>|0.1692<sup>***</sup>|<br>0.0721|0.0386<sup>*</sup>|0.0364<sup>*</sup>|0.0199|<br>0.0108|
||(0.0386)|(0.0409)|(0.0465)|(0.0159)|(0.0157)|(0.0195)|(0.0202)|
|Log(Total award paid)|||||<br>0.0250|<br>0.0223|<br>0.0203|
||||||(0.0147)|(0.0142)|(0.0152)|
|Regional Indicators|No|Yes|Yes|No|No|Yes|Yes|
|Sample Size|1,078|1,078|1,078|1,062|1,062|1,062|1,062|
|P-Value||0.000|0.000|||0.671|0.091|

Robust Standard Errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. See Appendix A.1 for variable definitions. Dependent variable is having a business in operation 3 years after applying. Marginal effects from logit shown. P-value is for testing that set of controls added by MacKenzie (2015) or Fafchamps and Woodruff (2017) (abbreviated as FaWo) are jointly zero.

31
Classifying countries into income groups according to their GNI per capita ignores the substantial overlap that exists in their distributions of consumption expenditure. In Figure 9 we have split the African distribution of consumption expenditure in 2008 into 20 ventiles, each representing 5% of the African distribution. For each ventile, the chart shows the share of the population that comes from low, lower middle, and upper middle/high income countries.<sup>49</sup> In 2008, 54% of the population in the top 5% of the African distribution come from upper middle/high income countries, 36% from lower middle income countries and 10% from low income countries. Not surprisingly, the share of population from upper middle/high income countries increases as one moves up the distribution, while the share of the population from lower income countries declines. In other words, better-off Africans are more likely to live in upper middle/high income countries. However, it is clear that there is a lot of overlap across these country classifications.

In Figure 10 we focus on the top 10% of the African distribution in 2008, which of course could be repeated for other deciles or other benchmark years. A positive (negative) value implies that the share of a particular country in the African top 10% exceeds (is less than) the share of that country in the African population, i.e. it is over-(under-)represented in the top 10% relative to its population size.<sup>50</sup> Countries are ranked according to their GDP per capita, so one would expect the bars to become more positive as one moves to the right. But the extent to which countries with a similar rank of GDP per capita are represented at the very top shows some interesting variation. For example, Swaziland has a higher GDP per capita than Cabo Verde, but its population share at the top is lower. Similarly, Nigeria is richer than Côte d’Ivoire, but it is underrepresented in the regional top 10%, controlling for its population. While the three African countries with highest GDP per capita are very small in terms of population (0.5% of the regional population), their citizens represented almost 3% of the top African decile.<sup>51</sup>

> the conclusions from the Stiglitz-Sen-Fitoussi commission, and the emerging work on Distributional National Accounts (DINA).

> 49 We use the World Bank’s income groups from Fiscal Year 2014. Countries are classified according to their GNI per capita in 2012 using exchange rates based on the Atlas method.

> 50 In this comparison, population shares are defined relative to the total population covered by the surveys included in the database.

> 51 By comparison, the population from the top three East Asian countries accounted for about 88% of the regional population, and their citizens made up about 77% of the top East Asian decile.

20
# **Table 13: Estimated annual value added (in million LCU at 2000 prices) based on UBI 2002 and 2009**

||<br>Annu|Sampl<br>al value|e values o<br>% share|f<br>in||Weighted e|stimate of indu|strytotal<br>% shar|<br>e in||
|---|---|---|---|---|---|---|---|---|---|---|
|**One Digit Industry (ISIC Rev. 3.1)**|added<br>(mill|ion LCU)|total||Growth<br>rate|Estimated|total|total||Growth<br>rate|
||2002|2009|2002|2009|(%pa)|2002|2009|2002|2009|(%pa)|
|**Traded sector:**|||||||||||
|A ‐ Agriculture, hunting and forestry|27,642|64,532|19.1|1.9|1.1|78,186|185,151|2.3|0.9|19.5|
|B ‐ Fishing|2,135|335|‐12.0|0.1|0.0|4,076|31,983|0.1|0.2|97.8|
|C ‐ Mining and quarrying|493|35,231|1007.2|0.0|0.6|3,960|97,992|0.1|0.5|339.2|
|D ‐ Manufacturing|508,739|1,673,992|32.7|34.3|27.3|683,988|2,800,640|19.8|13.3|44.2|
|_Traded sector total_|539,009|1,774,090|32.7|36.3|29.2|770,209|3,115,766|22.3|14.8|43.5|
|**Non‐traded sector:**|||||||||||
|**_Other industry:_**|||||||||||
|E ‐ Electricity, gas and water supply|107,543|145,193|5.0|7.3|2.4|197,595|993,210|5.7|4.7|57.5|
|F ‐ Construction|37,805|1,880,337|696.3|2.5|30.6|83,507|6,381,847|2.4|30.3|1077.5|
|**Services:**|||||||||||
|G ‐ Wholesale/retail trade and services|158,980|496,129|30.3|10.7|8.1|750,000|6,278,287|21.7|29.8|105.3|
|H ‐ Hotels and restaurants|37,493|137,985|38.3|2.5|2.2|170,502|1,200,459|4.9|5.7|86.3|
|I ‐ Transport/storage/communications|169,300|386,902|18.4|11.4|6.3|254,261|623,627|7.4|3.0|20.8|
|J ‐ Financial intermediation|237,843|667,343|25.8|16.0|10.9|537,083|829,805|15.5|3.9|7.8|
|K ‐ Real estate, renting business|60,238|101,799|9.9|4.1|1.7|144,352|642,130|4.2|3.1|49.3|
|L ‐ Public administration and defense|25,411|36,762|6.4|1.7|0.6|53,946|36,762|1.6|0.2|‐4.6|
|M ‐ Education|38,978|116,357|28.4|2.6|1.9|276,099|149,365|8.0|0.7|‐6.6|
|N ‐ Health and social work|31,651|307,668|124.6|2.1|5.0|80,592|558,714|2.3|2.7|84.8|
|O ‐ Other social and personal|30,284|34,447|2.0|2.0|0.6|84,173|237,146|2.4|1.1|26.0|
|Q‐ Extraterritorial organizations|8,550|||0.6|0.6|52,579||1.5|0.0||
|_Non‐traded sector_|||||||||||
|_total_|944,075|4,310,922|50.9|63.7|70.8|2,487,094|10,556,295|72.0|80.7|46.3|
|**Grand total**|1,483,084|6,085,012|44.3|100.0|100.0|3,454,898|21,047,118|100.0|100.0|72.7|

47
||High productive frms|Low productive frms|
|---|---|---|
|ln(_TFPi,t−_1)|-0.149*|-0.103*|
||(0.069)|(0.042)|
|ln(_Ki,t−_1)|-0.067***<br>(0.016)|-0.084***<br>(0.014)|
|_Serv.Lib.i,t−_1|0.082|0.215***|
||(0.054)|(0.052)|
|_Exporteri,t−_1|-0.194*<br>(0.087)|-0.271**<br>(0.084)|
|_Serv.Lib.i,t−_1_× Exporteri,t−_1|-0.020<br>(0.090)|0.063<br>(0.134)|
|N|6300|8103|
|Log Likelihood|-773|-1608|

* p<0.05, ** p<0.01, *** p<0.001

Table 8: Exit split the sample of firms according to their productivity into quartiles and report estimates of probit for firms in the first productivity quartile (low productive firms) and the fourth productivity quartile (high productive firms) in Table 8. Indeed, low productive firms are more likely to exit, when the services sector liberalizes. All other variables influence the probability of exit in the expected direction. More productive firms and firms with more capital are less likely to exit. Exporters are less likely to exit.

# **Alternative methods**

There is inherent difficulty of and methodological debates on estimating the production function (Ackerberg et al., 2005; Van Biesebroeck, 2007), which is the crucial element of our empirical procedure. We try several alternative methods of estimation of the production function to reassure the robustness of our results, presented in Table 9. The estimation results of the two-stage Olley-Pakes procedure estimated on the pooled manufacturing sample (columns (1) and (2) of the table) are compared with the estimation results by several other methods. The one-stage Olley-Pakes procedure, analogous to the method presented in panel B of Table 6, but estimated on the pooled manufacturing sample, is presented in column (3). Similarly to the results by manufacturing industries, the one-stage method estimate 31
Table 1: Calibration

|Targets and parameters|Notation|Developed|Emerging|Source|
|---|---|---|---|---|
|Calibration targets|||||
|Formal employment|_lF /_(_lF_ +_lI_)|93%|70%|ILO, Trends Econometric Models|
|Informal employment|_lI/_(_lF_ +_lI_)|7%|30%|ILO, Trends Econometric Models|
|Unemployment rate|_U_|6%|8.7%|ILO, Trends Econometric Models|
|Share of exporting rms|_Nx/Nd_|21%|18%|World Bank and Bernard et al. (2003)|
|Final good Market|||||
|Sunk entry costs|_fe_|0.4|0.4|Ebell and Haefke (2009)|
|Fixed export costs|_fx_|0.0062|0.0090|Calibration targets|
|Iceberg trade costs|_τ_|1.7|1.7|Anderson and van Wincoop (2004)|
|Pareto shape|_κp_|3.4|3.4|Ghironi and Melitz (2005)|
|Plant exit|_δ_|0.026|0.026|Haltiwanger et al. (2008)|
|Elasticity of substitution|_θ_ =_φ_|3.8|3.8|Bernard et al. (2003)|
|Taxation|||||
|Consumption tax|_τ _<sup>_c_</sup>|7.8%|17%||
|Income tax|_τ _<sup>_w_</sup>|18%|14%||
|Payroll tax|_τ _<sup>_f_</sup>|7.65%|28%||
|Labor market|||||
|Bargaining power|_η_|0.4|0.4|Flinn (2006)|
|Matching function elasticity|_ε_|0.4|0.4|Petrongolo and Pissarides (2006)|
|Vacancy costs, formal|_κF_|2.5|2.5|Calibration targets|
|Vacancy costs, informal|_κI_|1.5|1.5|Calibration targets|
|Matching eciency, formal|_χF_|0.30|0.28|Calibration targets|
|Matching eciency, informal|_χI_|0.35|0.38|Calibration targets|
|Separation rate, formal|_λF_|0.032|0.055|Calibration targets|
|Separation rate, informal|_λI_|0.27|0.15||
|Disutility of search, scale|_θ_|2|2||
|Disutility of search, elasticity|_ρ_|1.3|1.3||
|Unemployment benets, formal|_b_|13|15.2|Aleksynska and Schindler (2011)|
|Other parameters|||||
|Risk aversion|_γc_|2|2||
|Discount factor|_β_|0.99|0.99||
|Bond adjustment cost|_ψ_|0.0025|0.0025|Ghironi and Melitz (2005)|

17