rafmacalaba/datause-encoder-data huggingface dataset:
income inequality is associated with health inequality, and only weak support is found for the hypothesis that inequality is linked to the share of health spending financed publicly. What _does_ come through is a strong positive relationship between health inequality and average income-richer _countries invariably have higher health inequalities._ Corroborating evidence is presented from data on trends in health inequalities, which have tended to be rising in both the developing and developed worlds, especially during periods of economic growth. The paper suggests that this strong link between average income and health inequality is in part the product of growth going hand-in-hand with technological change in the production of health, and the fact that technological change tends to be assimilated faster by the better-off.

How should one respond to this evidence? How does one swim against the tide of rising incomes pushing up health inequalities? For a start the health costs of rising incomes (higher inequality) need to be traded off against the health benefits (higher average health levels), and the paper suggests a way of quantifying this trade-off. But it also argues that policies aimed at reducing health inequalities-or at least containing their increasecontinue to need development and implementation, but that their success should not be measured in terms of headline statistics on inequalities in health outcomes. Rather, methods need to be employed that separate out the effects of anti-inequality policies and the often offsetting effects of other changes. These methods include cross-country comparative analysis, country-based evaluation studies with controls, benefit-incidence analysis, and decomposition analysis. Examples for each are presented. Some of these studies suggest that policy measures could be much more effective at reducing health inequalities, while other studies point to successes.

# **2. International Differences in Socioeconomic Inequalities in Health**

A number of comparative studies have recently been undertaken on socioeconomic inequalities in maternal and child health (MCH). Some of these studies have looked at inequalities in child health across the distribution of income (or consumption) using data from surveys like the Living Standards Measurement Survey (LSMS) (Wagstaff 2000; Wagstaff and Watanabe 2000). The largest and most complete study, however, has been the recent World Bank 42-country study (Gwatkin and others 2000), based on the Demographic and Health Survey (DHS). This section focuses on the results from this study. It presents the key findings relating to socioeconomic inequalities in child health outcomes, and then goes on to speculate on the causes of the international differences in health inequality.

# _The data_

The dataset on socioeconomic inequalities in child health includes three measures-mortality, malnutrition (measured by anthropometric scores (Alderman 2000)), and prevalence of disease. These variables are defined in **Table 1.** The typical DHS does not contain information on income or consumption. Socioeconomic status was instead defined by means of a synthetic measure of household _3_
- 28 -

at one price range would become "tradable" at another); and practical problems of data availability. In fact, we have equated here, as is often done for lack of a better practical alternative, the production of "tradables" with that of goods; whereas services constitute the "non-tradables". Once in a while, this pragmatic solution may distort the outcome.'<sup>4</sup> Next, data of structure of production is classified differently than trade (using the SIC-Standard Industrial Classification- rather than the SITC). Moreover, unlike the richness of trade data-which allows, inter alia, a selection of the degree of fineness of classification-production data are available at best at one classification level, which is generally less detailed than the three-digit SITC level. Indeed, sometimes data which are generally available do not go beyond the one-digit level: minerals are the most important case in point. At this level, comparisons of structures of production and trade flows would yield inferences of very little value. We have therefore constructed ourselves a more finely classified data when this was required, ending up with a classification into 75 items-a less satisfactory scheme than that applied for trade data.

# G. Trade Compatibility: Potential **for Trade Diversion**

The index of compatibility of trade flows of the country and its partner is, we have argued, an indicator of the potential for trade diversion; as such, it also is an indicator of the 14. An obvious case in point is that of Panama, in which most "tradables" would actually be found in the services sectors.
For the estimate of _β_ , we use the result in column (2) in table 4. Regression equation (9) yields a point estimate of _γ_ of 0.0157, with t-statistic 4.01 and significance at the 1 percent level.<sup>22</sup> We estimate var( _z_ ), var( _y_ ), and cov( _y, z_ ) directly from the sample. With those estimates, we arrive at an optimal weight of 0.67. The first row in table 6 presents from left to right the intermediate calculation results of equations (12)-(14). The second row presents some useful statistics. Notably, the majority of the variance of nighttime light growth can be attributed to noise.

Table 6: Optimal Weight on Nighttime Lighted-Predicted GDP Growth

||_β_<br>_γ_|cov(_y, z_)|var(_y_<sup>_∗_</sup>)|var(_y_)|var(_ε_<sup>_y_</sup>)|var(_z_)<br>var(_ε_<sup>_z_</sup>)|
|---|---|---|---|---|---|---|
|Point estimate|1.55<br>0.0157|4.05|2.62|7.90|5.29|259<br>252|
||optimal weight||var(_y_) de|composition||var(_z_) decomposition|
||||signal|noise||Signal<br>Noise|
|Point estimate|0.67||33%|67%||2%<br>98%|

_Note_ . This table presents detailed calculations of the optimal weight based on equation (11). The sample includes 84 EMDEs between 2013Q2 and 2020Q4. _β_ is estimated in column (2) in table 4 and _γ_ in column (4) in table G.1.

# **6.2 Shedding Light on National Accounts**

# _Long-Run Differences between National Accounts and Nighttime Lights_

Following Henderson, Storeygard, and Weil (2012), we start by analyzing long-run differences between GDP growth as reported in national accounts and adjusted GDP growth incorporating information from nighttime light growth. To do so, let _yi_<sup>_L_and</sup> _zi_<sup>_L_belong-runaverageofficialGDPgrowthandnighttimelightgrowth,respectively,</sup> between 2012Q4 and 2020Q4 for country _i_ . We first construct a linear prediction of long-run average GDP growth by long-run average nighttime light growth: Analogous to equation (10), we then construct a new measure of long-run GDP growth using the predicted long-run average GDP growth from equation (15), _y_ ̃ _i_<sup>_L_, and</sup> the optimal weight obtained in table 6:

> 22The regression table and further explanation are included in appendix G.

20
least one cash payment during the study period. Consequently, 75 percent of the households have direct experience with both cash and food payments. Reselling of food transfers is rare. In 2016, 93 percent of the households that received food transfers reported that they never sold any of them, and most of the remaining 7 percent did so rarely (Hoddinott, Stifel, Hirvonen, Minten, 2018).<sup>11</sup> Starting in 2010, the household surveys began collecting more detailed information on beneficiaries’ experiences associated with obtaining their payments. In order to receive their payments, beneficiaries are instructed – typically with 1‐2 days notice – to go to a “pay point”, nearly always a roadside location. These are supposed to be sited in such a way that beneficiaries do not have to travel more than three hours (one way) from their home. In practice, however, logistical considerations – most notably road conditions which determine where and how far vehicles carrying payments can travel – play a role in the siting of these pay points. Table 2 describes the transactions costs associated with receiving food and cash payments by year.<sup>12</sup> While there is some variability in these data from round to round, the following features emerge. First, generally pay points for food are farther away than those for cash, reflecting the difficulties that heavy goods vehicles face when carrying several tons of grains to remote areas. Second, irrespective of transfer modality, virtually all beneficiaries walk

> 11 Across all rounds, PSNP payments were equivalent to approximately 15 percent of household consumption.

12 Total transactions costs would also be affected by the number of trips made to receive payments. Looking at payment data covering the five months prior to each survey round, there are always more cash payments than food payments, but the magnitude of the difference in most years is 0.3‐0.8 trips over a five‐month period. The exception is 2012 when there were 1.6 more cash payments.

13
system than Chile and Brazil (but not Mexico), it is currently not very different in overall bank financial performance. In particular, relatively higher revenue-generating capacity helps to offset higher operating expenses (relative to assets), potentially indicating scope for operational improvements arising from greater consolidation and scale economies. However, the extent to which such improvements can be attained solely by changes to the current financial system structure (as opposed to the macroeconomic environment or individual financial group strategies) remains unclear.

**Table 4: Comparative Performance of Financial Systems in Selected Countries** **<u>(latest available)</u>**

||**Colombia**|**Chile**|**Brazil**|**Mexico**|**Year**|
|---|---|---|---|---|---|
|**SIZE / DEPTH**||||||
|Total Bank Assets (US$ Billion)|59|119|715|209|2005|
|Total Bank Assets (% of GDP)|42%|95%|86%|27%|2005|
|Stock Market Capitalization (% of GDP)|26%|124%|55%|25%|2004|
|Number of Listed Firms|114|239|357|152|2004|
|Amount Outstanding of Domestic Private Bonds (% of GDP)|8%|23.3%|12.6%|3.4%|2004|
|**ACCESS**||||||
|Credit to the Private Sector (% of GDP)|23%|62%|36%|17%|2004|
|Branches per 100,000 People|8.7|9.4|14.6|7.6|2002-03|
|ATMs per 100,000 People|9.6|24.0|17.8|16.6|2002-04|
|**SOLVENCY**||||||
|Bank Capital to Assets|11.4%|6.8%|16.0%|12.0%|Sep-Dec. 2005|
|Bank Capital Adequacy Ratio|12.5%|14.2%|18.2%|14.4%|Sep. 2005|
|**PROFITABILITY**||||||
|Bank Return on Assets|2.5%|1.4%|2.0%|1.9%|Sep-Dec. 2005|
|Bank Return on Equity|25.7%|18.7%|20.8%|15.8%|2005|
|**EFFICIENCY**||||||
|Bank Cost-Income Ratio (Operating Expenses/Gross Income)|51%|52%|58%|60%|2005|
|Bank Operating Expenses/Total Assets|4.6%|2.3%|6.9%|4.6%|2005|
|**ASSET QUALITY**||||||
|Bank Non-Performing Loans/Total Loans|3.2%|1.0%|4.1%|2.0%|Jun-Dec. 2005|
|Bank Provisions/Non-Performing Loans|144%|166%|153%|232%|Mar-Sep. 2005|
|**FINANCIAL INFRASTRUCTURE**||||||
|Legal Rights Index|4|4|2|2|2005|
|Credit Information Index|4|6|5|6|2005|
|Investor Protection Index|5.7|5.7|5.3|3.7|2005|
|Cost of Enforcing Contracts (% of Debt)|18.6%|10.4%|15.5%|20.0%|2005|

_Source: Superintendencies and Central Banks, IMF, World Bank, Standard and Poor’s, BIS and own analysis. Note: Some financial performance ratios are based on reported Financial Soundness Indicators (FSIs) that are not fully comparable across countries. The ratios for Colombia include all credit institutions and might differ from those estimated using figures provided by the Superintendencia Financiera (previous Table). The size of the private sector debt market in Colombia is an estimate._

**Colombia’s financial system is dominated by financial conglomerates, the most important of which belong to domestic mixed-activity economic groups** . Using a variant of the Joint Forum definition to account for the presence and importance of AFPs in Colombia’s financial system<sup>11</sup> , it can be seen in the Figure and Tables below that

11 The Joint Forum was established in 1996 under the aegis of the Basel Committee on Banking Supervision, the International Organization of Securities Commissions (IOSCO) and the International 13
# **_Question 2: What factors contribute to the high dispersion across the four dimensions of the SPI in low-performing countries and the slow convergence to the fully capable systems in place in high-performing countries?_**

Donor coordination in statistical capacity building is weak. A 2020 report by the OECD Development Assistance Committee (DAC) noted that “challenges relating to donor coordination and alignment with country priorities can often be explained by a combination of weak in-country demand for data and statistics, unclear priorities, along with tensions created by strong donor demand for specific data and statistics for program design, targeting, monitoring and results reporting.” Only half of DAC members make the country’s national strategy for the development of statistics the basis for their engagement. Donors are more likely to invest in point-in-time data sources, such as household surveys, that provide ready-to-use data quickly, with minimal cost overruns. Household surveys are not necessarily conducive to developing sustainable statistical capacity, however, because “the role of the NSO is often reduced to recruiting and fielding enumerations while questionnaire designs are standardized and analyzed by development agencies.” In addition, only 16 percent of low-capacity NSOs cited these surveys as a strategic priority.

Donors’ poorly coordinated and survey-centric approach to country support increases scores in one dimension of the SPI (Census and Surveys) but pays little regard to the other three dimensions. It also means that NSSs accept external support if it brings additional funding. As countries become better at understanding (and stimulating) user demands, they are more likely to increase domestic funding for their work, which should strengthen all four SPI dimensions.

# **_Question 3: Should capacity-building strategies better reflect the fact that many countries are providing too little data given users’ needs, as highlighted in Figure 4?_**

An important implication of our theoretical framework is the existence of a steady-state equilibrium of statistical capacity that is determined by the supply of and demand for data. This equilibrium shifts in response to changes in the demand for data or changes in the technology of data production (Solow 1956).

Similar to the turnpike theorem (McKenzie 1986), we assume that there exists an efficient and optimal path for statistical capacity building. If the goal of statistical capacity building is far from the current state of the NSS, it is optimal for a country to expand its statistical capacity along the 17
# **_For individual-level risk reduction, a core task for cities is to provide the information for land, housing and insurance markets to operate efficiently._**

Consider the city government’s role in supporting these activities. A core task of the urban public sector is simply to coordinate the production and wide dissemination of credible information on disaster risk. Such information is often produced by private firms as well, but selective disclosure creates information asymmetries that put households at a disadvantage. Data on hazard probabilities and vulnerability of structures and people feed into comprehensive risk assessments. These should be considered a public good, accessible to all.<sup>9</sup> Such information then allows residents to make informed location choices, enables markets to price hazard risk appropriately, provides the basis for the emergence of private insurance markets, and it serves as a sound basis for transparent zoning decisions and other land use restrictions.

Disaster risk varies across space, so information on hazard event probabilities, exposure and vulnerability needs to be collected and disseminated spatially. New technologies, such as satellite images with a resolution high enough to replace far more expensive air photos and global positioning systems that facilitate field data collection, have made it easier and cheaper to collect geographic data. Easy-to-use geographic data browsers (such as Google Earth) make spatial data available to everyone. Most importantly, while hazard mapping has been performed for many decades, new technologies allow constant updating of information at relatively low cost. Making these technologies accessible to cities—not only the largest, but also smaller and medium sized cities with limited local capacity—should be a priority for national governments and donors.

# **_For large-scale collective hazard risk reduction investments, the costs and benefits depend in large part on the dynamics of the urban economy._**

The above portfolio is something cities _should_ do to support risk reduction. The decision to invest in larger protective infrastructure is more complex. For instance, if parts of a city are at risk of flooding from river overflows or storm surges, large scale infrastructure

> 9 Robert J. Murnane, “New Directions in Catastrophe Risk Models”, Baseline Management, October 9, 2007. Risk modeling companies typically use proprietary models and require non-disclosure agreements with licensees.

27
|**Table 6: Exports by Product and by Fed**|**eral Okrug **(in|billions of 2001|rubles)|||||Sector|
|---|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|total|
|Railway transportation|8.8|13.9|5.5|14.7|8.8|14.1|3.9|69.7|
|Truck transportation|1.2|2.2|0.8|1.6|1.0|1.3|0.6|8.7|
|Pipelines transportation|1.1|0.1|2.3|6.4|19.6|0.7|0.4|30.6|
|Maritime transportation|7.3|11.2|5.8|16.1|18.0|9.7|3.1|71.2|
|Air transportation|6.1|25.1|6.4|10.8|7.9|7.2|3.1|66.5|
|Other transportation|1.2|2.8|1.0|2.9|1.9|1.6|0.5|11.9|
|Telecommunications|0.9|3.2|0.9|1.7|1.4|1.1|0.4|9.5|
|Science & science servicing|0.6|1.7|0.5|1.3|1.0|0.6|0.3|6.0|
|Financial services|0.7|2.5|0.6|1.3|1.1|0.8|0.4|7.4|
|Ferrous metallurgy|25.2|39.4|7.1|10.9|46.9|15.3|0.7|145.5|
|Non-ferrous metallurgy|21.2|15.3|7.7|19.8|51.6|116.3|36.7|268.6|
|Chemical & oil-chemical industry|13.6|25.3|8.3|54.6|5.0|20.4|0.3|127.5|
|Mechanical engineering & metal-working|27.4|87.1|15.5|54.2|20.3|31.6|10.5|246.7|
|Timber & woodworking & pulp & paper industry|27.0|2.8|0.9|6.3|1.1|19.2|9.0|66.3|
|Construction materials industry|0.6|2.1|0.6|1.3|1.1|1.0|0.1|6.7|
|Food industry|9.4|22.0|13.6|5.4|1.6|3.4|10.3|65.8|
|Other industries|2.1|12.0|1.7|4.5|0.6|1.3|0.3|22.6|
|Public services, culture and arts|0.3|1.0|0.2|0.5|0.4|0.3|0.1|2.8|
|Agriculture & forestry|0.6|2.4|2.1|2.7|0.6|1.6|0.4|10.4|
|Coalmining|1.5|0.1|0.8|0.0|0.5|15.3|2.2|20.5|
|Housing and communal services|0.3|1.1|0.3|0.5|0.3|0.3|0.1|3.0|
|Construction|2.5|11.3|5.0|7.1|2.7|2.7|0.6|31.8|
|Electric industry|0.9|2.7|0.8|2.0|1.4|1.2|0.4|9.4|
|Gas|0.2||0.7|1.3|25.0|0.1|0.2|27.5|
|Crude oil extraction|14.4||28.7|80.2|227.8|7.5|4.0|362.5|
|Oil refining and processing|16.8|31.7|17.8|76.5|7.5|33.3|9.1|192.7|
|Other goods-producing sectors|1.3|2.5|0.7|2.5|2.2|1.3|0.6|11.1|
|Post|0.3|1.2|0.3|0.7|0.6|0.4|0.2|3.8|
|Wholesale and retail trade|63.7|81.2|46.0|124.4|414.7|93.7|32.4|856.1|
|Textiles and apparel|1.5|6.2|1.5|2.5|1.6|1.7|0.7|15.7|
|Okrug Total:|258.8|410.4|183.9|514.5|874.0|405.3|131.7||

40
# **Table 3: The Impact of Migration on School Attendance**

||Probit<br>Males|IV-Probit<br>12 to 15|Probit<br>Males 1|IV-Probit<br>6 to 18|Probit<br>Females|IV-Probit<br>12 to 15|Probit<br>Females|IV-Probit<br>16 to 18|
|---|---|---|---|---|---|---|---|---|
|Child is in a Migrant Household|-0.036|-0.162|-0.042|-0.217|0.011|-0.090|-0.020|-0.205|
||(2.04)**|(1.69)*|(1.67)*|(2.21)**|(0.44)|(1.11)|(0.75)|(2.49)**|
|Age of Child|-0.108|-0.080|0.383|0.389|-0.348|-0.333|0.431|0.524|
||(0.66)|(0.52)|(0.64)|(0.63)|(1.70)*|(1.60)|(0.48)|(0.55)|
|Age of Child Squared|-0.001|-0.002|-0.014|-0.015|0.008|0.008|-0.016|-0.018|
||(0.13)|(0.32)|(0.82)|(0.81)|(1.10)|(1.01)|(0.59)|(0.65)|
|Mother's Years of Education|0.031|0.031|0.034|0.033|0.045|0.046|0.053|0.051|
||(14.52)***|(13.66)***|(10.67)***|(9.53)***|(11.76)***|(12.47)***|(20.25)***|(12.29)***|
|Proportion of rural households owning land in 1910|-0.015|-0.010|0.008|0.017|-0.007|-0.004|-0.008|0.001|
||(2.31)**|(1.55)|(0.86)|(1.64)|(1.26)|(0.67)|(0.87)|(0.08)|
|School Attendance in 1930 (6 to 10 year olds)<sup>1</sup>|-0.003|-0.002|-0.002|-0.001|-0.002|-0.001|-0.003|-0.001|
||(1.56)|(0.83)|(0.89)|(0.42)|(1.00)|(0.53)|(1.03)|(0.28)|
|Gini of Income in 1960|-0.267|-0.222|-0.278|-0.180|-0.088|-0.028|-0.719|-0.587|
||(1.60)|(1.34)|(1.31)|(1.10)|(0.38)|(0.11)|(2.68)***|(2.05)**|
|Number of Schools per 1000 population in 1930|0.149|0.099|0.148|0.082|0.135|0.097|0.096|0.016|
||(4.12)***|(1.76)*|(3.77)***|(1.41)|(4.72)***|(2.41)**|(2.58)**|(0.35)|
|Gini of Years of Schooling for 15-20 year olds in 1960<sup>1</sup>|0.068|0.085|0.375|0.438|0.005|0.020|-0.844|-0.754|
||(0.18)|(0.23)|(0.84)|(0.98)|(0.02)|(0.05)|(1.83)*|(1.50)|
|Average Years of Schooling in 1960<sup>1</sup>|-0.004|-0.007|-0.016|-0.008|0.028|0.027|-0.123|-0.118|
||(0.10)|(0.18)|(0.29)|(0.15)|(0.56)|(0.50)|(2.28)**|(1.88)*|
|Observations|6454|6454|4094|4094|6108|6108|3430|3430|

# Notes:

1. These variables are for historic male schooling in columns 1-3, and historic female schooling in columns 4-6. T-statistics are in parentheses with standard errors clustered at the state level. Instruments are 1924 state-level migration rate

* significant at 10%; ** significant at 5%; *** significant at 1%

35
**Chart 1: Average costs of natural disasters per reported event (1961-2005)**

|**Disaster Type**|**Number of Events***|**Total Affected**|**Economic Damage **|**Total Affected 2** **|<sup>**Economic Damage /**</sup><br>**Total Affected 2**|
|---|---|---|---|---|---|
|Drought|717/216|3,583,535|$321,346,900|6,572,660|$48.89|
|Flood|756/367|1,190,734|$328,332,200|2,406,117|$136.46|
|Earthquake|2545/1107|142,374|$977,841,000|263,830|$3,706.32|
|Storm|2279/1074|330,873|$513,861,100|514,482|$998.79|

* Number of Events / Number of events for which Economic Damage is reported ** Total Affected 2 is average of Total Affected for events where Economic Damage is reported Source: author’s own calculations using data from CRED- EMDAT.

From Chart 1 it becomes clear that each type of disaster leaves a very different impression on the economy and its population. For example, the number of people affected by earthquakes (about 142,000 per event) pales in comparison with the number of people affected by droughts (almost 3.6 million per reported event). However, the picture reverses when looking at the estimated economic damage. Earthquakes are by far the most devastating of all the disaster types considered (almost one billion dollar estimated damage per event) compared with US$ 321,000 per drought<sup>14</sup> . The contrast is even sharper when expressed in terms of damage per person affected (dK/dL) which is 75 times larger for earthquakes (estimated at $3,706 per person affected) than for droughts (estimated at $49 per person affected). In light of the Solow-Swan model, these figures suggest that it is quite plausible to expect a positive effect of earthquakes (and also Four types of disasters will be considered: droughts, floods, storms and earthquakes. In particular, for each of these disasters the log of the sum of the total number of people affected in each event over the five -year period, divided by the total 14 This is likely even an overestimate as economic damage has only been reported for 25 percent of the droughts, arguably the more damaging ones.

17