rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2.** Definition of CEQ Income Concepts

<!-- Start of picture text --> Pre-fiscal income<br>• Income from work (wages/salary + in-kind benefits)<br>• Income from capital<br>• Self-provision of goods/services<br>• Private transfers/remittances/alimony<br>• Private pensions<br>• Imputed rent<br>• Include Contributory social insurance old-age pensions, exclude<br>contributions to social insurance old-age pensions<br>-<br><!-- End of picture text --> <!-- Start of picture text --> Direct taxes and contributions<br>• Personal income tax (IRPF)<br>• IASS, FRL<br>• Contributions to FONASA<br><!-- End of picture text --> <!-- Start of picture text --> Direct transfers<br>• Plan de equidad (AFAM-PE)<br>• Non-contributory pensions<br>• Food (TUS and INDA)<br>• Other direct transfers***<br><!-- End of picture text --> <!-- Start of picture text --> Net market income<br><!-- End of picture text --> <!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> Disposable income<br><!-- End of picture text --> <!-- Start of picture text -->
-<br>Indirect taxes*<br><!-- End of picture text -->

<!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> Subsidies**<br><!-- End of picture text --> <!-- Start of picture text --> Consumable income<br><!-- End of picture text --> <!-- Start of picture text --> • Copayments and user fees for<br>education and health services ****<br><!-- End of picture text --> <!-- Start of picture text -->
-<br><!-- End of picture text -->

<!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> In-kind transfers<br>• Monetized value of education<br>and health services<br><!-- End of picture text --> <!-- Start of picture text --> Adjusted income<br><!-- End of picture text --> _Source:_ Adapted from Lustig 2018. * No information on household consumption was available for 2017. Thus, none of the indirect taxes such as the Value Added Tax are incorporated in this exercise. ** In Uruguay, subsidies per se are nonexistent. However, some products have fixed prices (such as a specific kind of milk, fuels, and others), which could have welfare effects. Since there is no information on purchases for 2017, these are also not considered. *** These are benefits of the social security system that require some conditions to be eligible: unemployment insurance, maternity and family benefits, disability and sickness allowances. **** In some cases, FONASA services require the payment of an “orden” or “ticket” that varies depending on the service. However, there is no information available on this expenditure for 2017.

14
# **4 Empirical Analysis**

# **4.1 Data**

The variables required are measures of employment, output, and value added, and indicators for protection, labor-market rigidity and industrial deregulation. Our source for production related information is the Annual Survey of Industries (ASI)<sup>8</sup> from 1980 to 2000 which, among other things, reports for each industry-state combination values of gross output produced, intermediate inputs, wage bill, the book value of capital stocks and the number of workers.<sup>9</sup> Since the ASI reports monetary values in current prices, appropriate price deflators are needed to convert the nominal values into real ones. We use industry specific wholesale price index (WPI) series to deflate output to constant 1981 rupees. The WPI for machinery, transport equipment and construction is used to deflate the book value of capital stock. Dividing the total wage bill by the number of workers is used to arrive at wages. Our materials price deflators are those constructed by Trivedi, Prakash and Sinate (2000).<sup>10</sup> As regards our trade policy variable, we use industry-year specific tariff rates, for the period 1988-2000 (summarized in table 1). These variables are based on the calculations for the 18 twodigit industries made by Hasan, Mitra and Ramaswamy (2003, 2006).<sup>11</sup> 8 ASI data covers establishments registered under the Factory Act and employing ten or more workers (with power and 20 or more workers without power). It provides information on 18 manufacturing industries disaggregated by their location across India’s states.

9 The term “workers” refers to production workers (permanent, contract, and temporary). The ASI also reports the number of “total employees”, i.e., production and non-production workers. Unfortunately, the ASI uses different definitions for reporting payments to “workers” (called "wages") and “total employees” (called "total emoluments"). Total emoluments include not only “wages” paid to production and nonproduction workers (not reported separately), but also the imputed value of benefits in kind provided to production and nonproduction workers (once again, not reported separately). This prevents us from computing a meaningful wage rate for non-production workers. Nevertheless, if we ignore this and compute a wage rate for nonproduction workers (["total emoluments"- workers’ "wages"]/[“total employees”- “workers”]) and include it in our labor demand regressions, the key results of our paper regarding the relationship between trade liberalization and labor demand elasticity go through. Additionally, the results are also unchanged if we estimate "total" labor demand equations (i.e., for total employees with the wage rate now being computed as total emoluments divided by total employees).

10 Details of mapping from their product groups to the two-digit classification are provided in Hasan, Mitra and Ramaswamy (2003). We thank Pushpa Trivedi for providing us with the data.

11 In order to be able to use all our data from 1988 onwards, we use linear interpolation/extrapolation to fill in the years for which the data are missing.

13
3 OVERVIEW of urban households lack access to piped water (house connections or standpipes) and over half lack adequate sanitation. As in most countries, the situation is worse in rural areas. In general, according to these aggregate data the proportion served in urban areas has been static while service in rural areas has improved somewhat. While international comparisons of this type are not precise, Ghana appears to have made some limited progress at a time when the services in other African countries were deteriorating rapidly (perhaps because of faster urban growth in some of the comparator countries).

**Table 1.1: Housing** Infrastructure Indicators, Selected Countried

||Pet Ur<br>**v.**Acce<br>Wate|ban<br>ss to<br>r|Pct Ru<br>**v.**Acces<br>Wate|ral<br>s to<br>r|Pct Urban<br>**w.**Access to<br>Sanitation|Pct Rural<br> **w.**Access to<br> <br>Sanitation|GNP Per<br>**&apit&**|Popu-<br>lation|**Pct**Urban<br>Popu-<br>lation|
|---|---|---|---|---|---|---|---|---|---|
||(1970)|(1980)|(1970)|(1980)|<br>(1980)|(1980)|(1985)|(1985)|(1985)|
|Benin|94|26|15|20|48|4|S260|**4.0**|**35**|
|Burkina Faso|<br>68|27|31|30|38|5|$150|**7.9**|8|
|Burundi|77|90|20|24|40|35|$230|**4.7**|2|
|**GHANA**|**73**|**72**|**33**|**47**|**47**|**17**|**$380**|**12.7**|32|
|Kenya|97|85|15|26|89|19|$290|20.4|20|
|Lesotho|100|37|11|14|13|14|$470|**1.5**|17|
|**Mali**|29|37|0|6|79|0|$150|**7.5**|20|
|Senegal|98|77|25|42|100|2|$370|**6.6**|36|
|Sierra Leone|<br>75|50|2|16|31|6|$350|**3.7**|25|

Source: Habitat Global Report on Human Settlements, 1987 These low levels and adverse trends have important implications for development generally as well as for people's housing consumption. Shelter is one of two capital assets applicable across the entire income distribution and in every geographic location, rural and urban (the other is human capital, in other words, peoples' skills and innate productivity). For many, particularly those with lower incomes, housing is inseparable from other fixed capital, for example, shops and workrooms. Also much productive infrastructure is shared by housing and other productive activities (for exaple, water, transport, waste disposal). In short, housing has many spillovers and many forward and backward linkages with other productive sectors.

It is important that, as the economy recovers, policies encourage efficient housing markets. Regulatory reform can be a very powerful tool to reduce unnecessary costs, ultimately improving affordability. While the focus of this paper will be on effects and possible changes in rent control, reforms in other areas will also be discussed.
# **Figure 8. FDI flows and investment growth**

_Since 2010, weak investment growth has partly reflected shrinking FDI inflows among both EMDE commodity importers and exporters._

## **A. FDI inflows to GDP in EMDEs**

<!-- Start of picture text --> Percent of GDP<br>Commodity importers<br>6<br>Commodity exporters<br>4<br>2<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**B. Investment growth in EMDEs with high and low FDI inflows, 2010‐15**

<!-- Start of picture text --> Percent<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Low High<br><!-- End of picture text --> Sources: International Monetary Fund, World Bank.

A. Gross FDI inflows as ratios to GDP. Weighted averages. Includes 75 EMDEs.

B. “Low” and “High” indicate annual change in the FDI to GDP ratio in the bottom and top one‐thirds of the distribution, respectively. Difference in medians between “high” and “low” subsamples is significant at the five percent level. Group medians for 120 EMDEs during 2010‐15.

# **Figure 9. Political stability and investment growth**

_Weak investment growth compared to pre‐crisis rates partly reflects reduced political stability since the global financial crisis._

## **A. Political stability in EMDEs**

<!-- Start of picture text --> Index Commodity importers<br>68 Commodity exporters<br>64<br>60<br>56<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **B. Investment growth in EMDEs with strong and weak improvement in political stability, 2010‐15**

<!-- Start of picture text --> Percent<br>6<br>4<br>2<br>0<br>Weak improvement Strong improvement<br><!-- End of picture text --> Sources: Political Risk Services International Country Risk Guide (ICRG).

A. Lines show unweighted annual average, as measured by the ICRG index, for each group. A decrease in the index denotes deteriorating political stability. Includes 95 EMDEs.

B. “Strong improvement” and “Weak improvement” indicate improvement in political stability from 2010‐15 in the top and bottom one third of the distribution, respectively. Difference in medians between “strong improvement” and “weak improvement” subsamples is significant at the ten percent level. Group medians for 61 EMDEs during 2010‐15.

32
Scientific Assessment.” _Journal of Geophysical Research Atmospheres_ , 118: 5380–5552. doi: 1002/jgrd.50171. Available at http://onlinelibrary.wiley.com/doi/10.1002/jgrd.50171/pdf

- Burwen, J. and Levine, D. I. 2012. “A rapid Assessment Randomized-Controlled Trial of Improved Cookstoves in Rural Ghana,” _Energy for Sustainable Development_ . 16:328-338.

- Charness, G. and U. Gneezy. 2009. “Incentives to exercise.” _Econometrica_ . 77: 909-931.

- Cohen, J., and P. Dupas. 2010. “Free distribution or cost-sharing? Evidence from a randomized malaria prevention experiment.” _Quarterly Journal of Economics_ . 124: 1-45.

- Cooke, P. 1995. _Household Heterogeneity, Time Allocation and the Use of Environmental Products: Responses to Deforestation by Rural Nepali Households_ , Unpublished Ph.D. Dissertation, University of Washington, Seattle, WA.

- Cooke, P., G. Köhlin, W. F. Hyde. 2008. “Fuelwood, Forests and Community Management – Evidence from Household Studies.” _Environment and Development Economics_ . 13: 103-135.

- Duflo, E., R. Glennerster, and M. Kremer. 2008. Using Randomization in Development Economics Research: A Toolkit, in: _Handbook of Development Economics_ , edited by Paul Schultz and John Strauss, 3895-3962. North Holland.

- Economist. 2010. “Not a Small Problem: Will REDD Trample on the Rights of Traditional Forest Folk?” _The Economist_ , September 25, 2010.

- Gebreegziabher, Z, A. Beyene, R. Bluffstone, S. Dissanayake, P. Martinsson, M Toman. 2015. “Can Improved Biomass Cookstoves Contribute to REDD+ in Low-Income Countries? Results from a Randomized Control Trial in Ethiopia.” World Bank Working Paper.

- Gifford, M. L. 2010. _A Global Review of Cookstove Programs_ , Unpublished Masters Thesis, UC-Berkeley, downloaded 2/28/14 from mlgifford.wordpress.com

- Gil, J. 1987. “Improved Stoves in Developing Countries: a Critique.” _Energy Policy_ , April: 135-144.

- Grieshop, A. P., P. J. D. Marshall and M. Kandlikar. 2011. “Health and Climate Benefits of Cookstove Replacement Options.” _Energy Policy,_ 39: 7530-7542.

- IEA 2006. _World Energy Outlook 2004: Energy for Cooking in Developing countries (ch. 15_ ). International Energy Agency _._ Paris: IEA.

- IEA 2012. _World Energy Outlook 2010_ . International Energy Agency _._ Paris: IEA.

- Jeuland, M. A. and S. Pattanayak. 2012. “Benefits and Costs of Improved Cookstoves: Assessing the Implications of Variability in Health, Forest and Climate Impacts.” _PloS one,_ 7: e30338.

- Johnson, M, R. Edwards, A. Ghilardi, V. Berrueta, D. Gillen, C. A. Frenk and O. Masera. 2009. “Quantification of Carbon Savings from Improved Biomass Cookstove Projects. _Environmental Science and Technology._ 43: 2456-2462.

- Jones, D. 1988. “Some Simple Economics of Improved Cookstove Programs in Developing Countries.” _Resources and Energy_ , 10: 247-264.

- Kar, A., I. Rehman, J. Burney, S. P. Puppala, R. Suresh, L. Singh, V. K. Singh, T. Ahmed, N. Ramanathan and V. Ramanathan. 2012. “Real-Time Assessment of Black Carbon Pollution in Indian Households Due to Traditional and Improved Biomass Cookstoves.” _Environmental Science & Technology._ 46: 2993-3000.

- **29 |** P a g e
**Figure 2. Deficit, debt and debt service indicators**

<!-- Start of picture text --> 5 35<br>30<br>4<br>25<br>3 20<br>2 15<br>10<br>1<br>5<br>0 0<br>GDF/GDP RD/GDP Debt/GDP IP/RR<br>Debt/GDP and IP/RR<br>GDF/GDP and RD/GDP<br><!-- End of picture text --> Source **:** RBI Handbook of Statistics on State Finances, various issues. Note: GFD: Gross fiscal deficit, IP: Interest payments, RR: Revenue Receipts, GDP: Gross domestic product.

Until the mid-1990s, states‘ finances were relatively stable, characterized by low current and fiscal deficits, averaging below 1 percent and 3 percent of GDP. Debt levels remained moderate at about 20 percent of GDP, and the interest burden hovered close to 15 percent of revenue. The turning point came during 1998–99, with a significant deterioration in the current account, which became a key driving force for the declining fiscal health of the states, with increased spending on administrative services and interest payments.

The next phase, 1998–99 to 2003–04, saw the steep rise in the fiscal deficit as a ratio of GDP—from 2.8 percent to 4.2 percent; the revenue deficit more than doubled from 1.1 percent of GDP to 2.5 percent. As a result, the states‘ outstanding debt to GDP grew from 21.7 percent during 1997–98 to its peak of 32.8 percent during 2003–04. Interest payments as a share of revenue receipts (repayment burden) rose from 17.9 percent to 26.0 percent over the same period, and the primary deficit grew from 0.9 percent to 1.5 percent. This period, until 2003, was also characterized by higher interest rates, with the interest rates being gradually liberalized; the average market interest rate on states‘ borrowing was over 10 percent during this period. Concomitantly, the average interest burden, at 23.4 percent, was significantly higher than the 15 percent considered tolerable for a sustainable debt level (Dholakia, Mohan, and Karan 2004).<sup>21</sup>

> 21 Interest payments as a ratio of revenue receipts provide an explanation of the interest burden and the level of ―tolerable‖ debt. Debt is said to be tolerable if servicing it does not impose an intolerable burden on the fiscal

12
<!-- Start of picture text --> 50<br>rs<br>32 4540<br>a ~2° —— Zambia<br>9 35 = -<br>= ——— Mozambique<br>3 30 =s=F<br>5 25 =-- --- — Tanzaniai<br>X20 — Kenya<br>s 15 —— Ethiopia<br>id<br>2 10 = == Madagascar<br>as=<br>a)<br>0<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>‘6& 35 Sek —— Zambia<br>z= 30 Sow aoee ~ ——Tanzania<br>2s<br>% 825 ——— Kenya<br>bo<br>22xS —— Uganda<br>E = 15 —— Ethiopia<br>5 = —— Mozambique<br>% 10<br>= === Madagascar<br>aS 5<br>ie)<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->
Table 2: Prevalence of institutional care by barriers. Three-delay model.

|Women Characteristics.|At least four ANC|Inst’l delivery|
|---|---|---|
|Decide to seek care based on socioeconomic and cultural fac|tors||
|Has job|||
|No<br>|61.0 (0.016)<br>|39.1 (0.017)<br>|
|Yes<br>|71.2*** (0.013)|44.4** (0.016)|
|Has secondary education<br>|||
|No<br>|56.2 (0.017)<br>|25.9 (0.014)<br>|
|Yes<br>|80.0*** (0.013)|62.5*** (0.014)|
|Has access to information<br>|||
|No<br>|56.0 (0.019)<br>|27.0 (0.017)<br>|
|Yes|72.6*** (0.012)|50.4*** (0.016)|
|Is independent - does not need permission|||
|No|54.1 (0.031)|29.0 (0.027)|
|Yes|67.9*** (0.013)|43.3*** (0.014)|
|Lives in household from bottom 40|||
|No|76.8 (0.012)|58.1 (0.017)|
|Yes|52.8*** (0.019)|19.9*** (0.013)|
|Identify and reach care|||
|Lives in rural households|||
|No|76.0 (0.016)|60.2 (0.022)|
|Yes|61.0*** (0.017)|31.1*** (0.017)|
|Has no transport means|||
|No|82.1 (0.017)|63.1 (0.024)|
|Yes|64.0*** (0.014)|38.4*** (0.014)|
|Lives in mountainous cluster|||
|No|70.0 (0.012)|45.0 (0.015)|
|Yes|56.6*** (0.032)|33.0** (0.032)|
|Lives in service area where 50% of facilities with ANC|18 days/month||
|No|61.3 (0.043)<br>|23.1 (0.064)<br>|
|Yes|66.7 (0.013)|42.3** (0.014)|
|Lives in service area where 50% of facilities with inst’l|delivery available||
|No|65.7 (0.014)<br>|39.8 (0.015)<br>|
|Yes|72.3* (0.027)|54.4** (0.042)|
|Receive adequate care|||
|Lives in service area where share of facilities with ANC|with medications is||
|Low (_<_25%)|66.4 (0.017)|46.6 (0.019)|
|Medium (25%_<_HC_<_40%)|69.8 (0.026)|39.1* (0.029)|
|High (_>_40%)|64.7 (0.031)|32.3*** (0.033)|
|Lives in service area where share of facilities with ANC|with equipment is||
|Low (_<_30%)|56.8 (0.041)|25.1 (0.031)|
|Medium (30%_<_HC_<_50%)|68.7** (0.017)|52.0*** (0.024)|
|High (_>_50%)|67.7* (0.020)|39.0*** (0.020)|
|Lives in service area where share of facilities w/ deliveries|with equipment is||
|Low (_<_35%)|62.5 (0.028)|33.2 (0.026)|
|Medium (35%_<_HC_<_70%)|69.1* (0.015)|48.6*** (0.020)|
|High (_>_70%)|69.8 (0.026)|39.1 (0.033)|

Note: Linearized standard errors into brackets. Adjusted Wald test except for receive adequate care barriers where we use linear regression coefficients. New population weights for each year to reflect the total women population in Haiti. *** _p <_ 0 _._ 001; ** _p <_ 0 _._ 01; * _p <_ 0 _._ 05. Source: Authors’ estimates with EMMUS 2017 distribution of assets) are more likely to receive institutional care. Thus, 80 percent of women who delivered in the previous five years and had a secondary education had at least four ANC visits, compared to 56.2 percent of women with lower or no education. Better educated women 11
to be higher, and by moving to a system where money follows the patient, thereby providing incentives for quality improvements in both public and private health facilities. Both the fact that health insurance is layered on existing networks of subsidized public care and that it is used as a mechanism for improving quality, either by reducing the cost to the patient of care in the private sector or providing incentives for the public sector is again common across LMICs as no country has chosen to defund its publicly provided system as health insurance has grown. Of course, the relative importance of these two mechanisms depends on the context. In Latin America, for instance, the share of the private sector is smaller and insurance schemes tried to address differences in quality between clinics run by the social security system (accessible only to those employed in the public sector) and those run by the public health system, rather than between private and public care.

# Provider Reimbursements

Interestingly, the convergence that we see across countries in how health insurance is financed breaks down when we look at how providers are reimbursed. The London School of Hygiene and Tropical Medicine’s RESYST study documents 19 different purchasing mechanisms in 10 LMICs (Hanson et al. 2019). These include, inter alia, fee-for-service (the hospital is reimbursed for each service given as part of the stay), capitation (physicians agree to a fixed amount per patient under their care for a given duration), diagnostic-related groups (bundle all goods and services for one hospitalization episode into a single price depending on patient characteristics), line-item budgeting (health ministry pays for each item according to a budget) and global budgeting (a hospital signs a contract for a sum over a period to cater to the population in its catchment), used either by themselves or in combinations. Prices may be set through individual negotiations, group negotiations or administratively. Administrative prices themselves vary in their degree of sophistication, ranging from average accounting costs of procedures to more sophisticated, risk-adjusted marginal cost pricing.<sup>6</sup> Reimbursement rates often seem to involve political processes and large discontinuous jumps. In Kenya, prices paid to hospitals for surgeries were revised upward by 50 to 100 percent in some cases in 2016 (Barasa et al. 2018). In Vietnam, hospitals are reimbursed according to a pre-determined fee-forservice schedule. Tien et. al (2011) noted that, at the time of their article, the price of services on the original list had not been updated since 1995, although an additional 992 services were added in 2006. In Ecuador, prices have not been updated since 2012, even in nominal terms. In Colombia, prices for

> 6 Barber, Lorenzoni and Ong (2019) document similar variation in remuneration schemes for providers in their study of OECD countries, Thailand and Malaysia.

7