rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1:** **<u>Summary statistics for heads of households by gender</u>**

||Ma|le|||Fem<br>|ale<br>|||
|---|---|---|---|---|---|---|---|---|
||||Al|l|Wid|owed|Not w|idowed|
||Rural|Urban|Rural|Urban|Rural|Urban|Rural|Urban|
|Age of head|52.94|49.41|48.12|50.28|50.75|52.86|43.39|42.57|
||[14.18]|[13.14]|[14.48]|[12.45]|[14.36]|[11.39]|[13.62]|[12.41]|
|Head married monogamous|0.52|0.71|0.08|0.11|-|-|0.24|0.46|
||[0.50]|[0.45]|[0.28]|[0.32]|-|-|[0.43]|[0.50]|
|Head married polygamous|0.46|0.27|0.22|0.04|-|-|0.61|0.16|
||[0.50]|[0.44]|[0.41]|[0.20]|-|-|[0.49]|[0.37]|
|Head divorced|0.001|0.003|0.04|0.07|-|-|0.10|0.28|
||[0.03]|[0.06]|[0.19]|[0.26]|-|-|[0.30]|[0.45]|
|Head widowed|0.01|0.01|0.64|0.75|1.00|1.00|-|-|
||[0.11]|[0.09]|[0.48]|[0.43]|[0]|[0]|-|-|
|Head chron. ill last year|0.57|0.51|0.47|0.51|0.48|0.58|0.46|0.32|
||[0.49]|[0.50]|[0.50]|[0.50]|[0.50]|[0.50]|[0.50]|[0.47]|
|Others chron. ill|0.06|0.06|0.06|0.07|0.06|0.08|0.05|0.03|
||[0.11]|[0.11]|[0.13]|[0.12]|[0.13]|[0.13]|[0.12]|[0.07]|
|Head‘s schooling yrs|0.78|3.43|0.32|1.99|0.22|1.56|0.50|3.26|
||[2.30]|[4.34]|[1.61]|[3.52]|[1.24]|[3.13]|[2.12]|[4.28]|
|Household size|13.22|10.05|7.92|8.76|7.68|9.21|8.34|7.40|
||[8.11]|[5.85]|[4.59]|[5.11]|[4.53]|[5.37]|[4.72]|[3.98]|
|Share women 56+|0.03|0.02|0.01|0.01|0.01|0.01|0.01|0.01|
||[0.06]|[0.06]|[0.06]|[0.05]|[0.07]|[0.05]|[0.04]|[0.03]|
|Share men 56+|0.004|0.004|0.002|0.002|0.002|0.001|0.003|0.004|
||[0.02]|[0.03]|[0.01]|[0.02]|[0.02]|[0.01]|[0.01]|[0.04]|
|Share women 15-55|0.27|0.29|0.18|0.27|0.19|0.28|0.14|0.23|
||[0.12]|[0.15]|[0.16]|[0.18]|[0.18]|[0.17]|[0.10]|[0.22]|
|Share men 15-55|0.14|0.16|0.21|0.26|0.24|0.28|0.16|0.21|
||[0.13]|[0.17]|[0.21]|[0.23]|[0.22]|[0.23]|[0.18]|[0.20]|
|Share girls 0-6|0.14|0.12|0.13|0.10|0.12|0.09|0.15|0.13|
||[0.12]|[0.13]|[0.19]|[0.15]|[0.19]|[0.12]|[0.18]|[0.20]|
|Share boys 0-6|0.15|0.14|0.15|0.10|0.14|0.07|0.19|0.19|
||[0.13]|[0.16]|[0.18]|[0.15]|[0.15]|[0.10]|[0.21]|[0.23]|
|Non-ag land ‘00 m2|6.57|1.76|1.80|0.93|1.82|0.95|1.77|0.87|
||[38.38]|[5.78]|[9.74]|[2.05]|[11.97]|[1.92]|[3.03]|[2.41]|
|Ag land (hectares)|7.55|1.36|4.11|0.63|4.62|0.70|3.19|0.44|
||[12.93]|[3.12]|[6.32]|[1.92]|[7.39]|[2.03]|[3.61]|[1.55]|
|No. cows/large livestock|9.55|2.36|5.94|0.72|6.30|0.62|5.31|1.00|
||[16.91]|[8.13]|[15.94]|[2.56]|[17.55]|[2.26]|[12.67]|[3.32]|
|No. small livestock|10.53|4.08|7.12|1.43|5.19|1.33|10.59|1.73|
||[18.24]|[11.79]|[14.33]|[4.37]|[12.89]|[4.61]|[16.2]|[3.600]|
|House exterior cement/ tile|0.06|0.59|0.05|0.62|0.03|0.63|0.09|0.59|
||[0.23]|[0.49]|[0.22]|[0.49]|[0.17]|[0.49]|[0.29]|[0.50]|
|Log consumption per cap|11.78|12.32|11.91|12.36|11.88|12.31|11.96|12.51|
||[0.59]|[0.66]|[0.58]|[0.62]|[0.48]|[0.6]|[0.73]|[0.66]|
|Log consumption per equiv.|12.99|13.40|12.85|13.37|12.81|13.35|12.94|13.43|
||[0.55]|[0.63]|[0.53]|[0.58]|[0.45]|[0.57]|[0.65]|[0.60]|
|Observations|2,779|1,405|131|179|89|115|42|64|

Note: Standard deviations in parentheses. All statistics above are population weighted. Log consumption per equivalent person is defined as the log of household consumption normalized by the square root of household size to allow for scale economies. Source: 2006 ELIM.
district-level outcomes (the unemployment, inflow, outflow, and vacancy rates, and U/V ratio) between a given year in early and late transition. A coefficient close to 1 indicates that the shocks and restructuring affect district-specific labor markets in different ways in the sense that for the given indicator the ranking of districts changed little between the two periods, a coefficient of 0 indicates that the ranking of districts is completely unrelated in the two periods and a negative coefficient would indicate reversal patterns in the ranking. In Table 2, we show the rank correlation coefficients for 1992-96 and 1992-99 in the left hand side panel, and correlations between 1999-2002 and 1999-2005 in the right hand side panel.<sup>23</sup> The correlation coefficients suggest that there were shocks and restructuring (in terms of district rankings) taking place in the transition economies during the early-to-mid 1900s and that the speed of this restructuring slowed down during the late 1990s and early-to-mid 2000s. Except for the outflow rate, the extent of restructuring was less pronounced in Poland than in the other transition countries in the 1990s, and in this sense Poland resembles West Germany, which also generates correlation coefficients of less than unity but higher than in the other economies. East Germany represents the other extreme with the lowest values of the correlation coefficients, especially in the 1990s. The correlation coefficients also indicate that in all six economies the shocks and restructuring affected most the vacancy rates – suggesting that there were major inter-regional changes on the demand /job creation side of these economies over time. This pattern holds in the 1990s as well as 2000s and is also to a lesser extent reflected in the other potential measure of demand, namely the outflow rate.<sup>24</sup> Interestingly, the results in Table 2 indicate that shocks and restructuring have not affected in a major way the district ranking for unemployment, inflows and U/V rates, with East Germany again standing out and showing the smallest rank correlation coefficients for these variables over time. Hence, both in the benchmark market economy

> 23 The year 1996 is the last year for which we have Slovak data based on the initial classification of districts. Similarly, 1999 is the first year for which reduced structure of Polish districts is available. 24 The outflow rate measure measures total outflows but in some countries (especially East Germany) it contains also significant outflows into training programs.

− 21 −
Table 2: Baseline Quality Association with OOP, Market Share, and Facility Exits By Facility Ownership

|||Pri|vate||Pu|blic|
|---|---|---|---|---|---|---|
||OOP (USD<br>PPP) at<br>Baseline|OOP (USD<br>PPP) at<br>Baseline|Market Share<br>at Baseline<br>(x100)|Exit by<br>Endline<br>(Control<br>Facilities)|OOP (USD<br>PPP) at<br>Baseline|Market Share<br>at Baseline<br>(x100)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|JHIC Score at Baseline|0.155***|0.146***|0.036|-0.008**|-0.006|0.308**|
||(0.049)|(0.046)|(0.044)|(0.004)|(0.007)|(0.149)|
|Unlicensed at Baseline|0.216|0.300|0.849|0.103**|||
||(0.632)|(0.641)|(0.868)|(0.051)|||
|Patient Wealth Index||0.394**<br>(0.189)|||||
|Patient Years of Education||0.061<br>(0.042)|||||
|Patient Health Status (Bad or Very Bad)||2.680***<br>(0.667)|||||
|Distance from Home (in Km)||0.683***<br>(0.152)|||||
|Observations|3201|2938|648|189|5260|367|
|R<sup>2</sup>|0.07|0.09|0.77|0.08|0.21|0.79|
|Dependent Variable Mean|8.16|8.13|9.66|0.15|0.70|53.05|
|Mean (SD) JHIC Score at Baseline|36.63 (12.14)|36.68 (12.22)|33.55 (11.28)|32.66 (9.56)|42.90 (10.31)|41.18 (10.20)|
|Total Controls Selected by PDF (out of 23)|6|8|12|2|3|6|

_Notes_ . Robust standard errors reported in parentheses and clustered at the market level. *** (**) (*) denotes significance at 1% (5%) (10%) level. Controls are selected by PDSLASSO out of a list of 23 variables. Market size, facility opening year, facility levels and strata FE at baseline are partialled out for all regressions (imposed as controls in the regression) so not included in the list of 23 variables. The indicator for unlicensed at baseline is partialled out for the private facilities regressions. In Column 2, patient wealth index, years of education, health status, and distance from home are partialled out. HF = health facility; JHIC = Joint Health Inspection Checklist; OOP = out-of-pocket payments; PPP = purchasing power parity.

the JHIC score and (b) that there was at least some (perhaps substantial) information about this score available to consumers. As we will see in Section V, this is consistent with a diminished role for information as the mechanism for the improvements we observe. These patterns also suggest that regulating facilities at the low-end may be very costly given their high rates of churn and low patient loads, an observation we return to in the conclusion.

# **III.4 Design Integrity**

## **III.4.1 Balance, Attrition, and Accretion**

There are no systematic differences across treatment and control groups in baseline main outcome variables and key covariates with the exception of out-of-pocket payments at the facility level and the test of joint significance yields an F-stat of 1.020 (p=0.425) (Table A2 of the Appendix). Response rates were 93% at baseline and 97% at endline and non-response is balanced between treatment and control at endline with an estimated null difference (p-value = 0.974). At baseline there is a small 4 percentage point higher response rate among facilities in treatment markets 15
<mark>Merschmann, U. and Thonemann, U.W., 2011. Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms.</mark> _<mark>International Journal of Production Economics</mark>_ <mark>,</mark> _<mark>130</mark>_ <mark>(1), pp.43-53.</mark> <mark>Mills, D.E., 1984. Demand fluctuations and endogenous firm flexibility.</mark> _<mark>The Journal of Industrial Economics</mark>_ <mark>,</mark> _<mark>33</mark>_ <mark>(1), pp.55-71.</mark> <mark>Mills, D.E. and Schumann, L., 1985. Industry structure with fluctuating demand.</mark> _<mark>The American Economic Review</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(4), pp.758-767.</mark> <mark>Morikawa, M., 2012. Demand fluctuations and productivity of service industries.</mark> _<mark>Economics Letters</mark>_ <mark>,</mark> _<mark>117</mark>_ <mark>(1), pp.256-258.</mark> <mark>Nor, N.M., Nor, N.G.M., Abdullah, A.Z. and Jalil, S.A., 2007. Flexibility and small firms’ survival: further evidence from Malaysian manufacturing.</mark> _<mark>Applied Economics Letters</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>(12), pp.931-934.</mark> <mark>Ortega, B. and Chicón, C.G.B., 2013. Determinants of regional labour productivity in the hospitality industry in Spain, 1996–2004.</mark> _<mark>Tourism Economics</mark>_ <mark>,</mark> _<mark>19</mark>_ <mark>(2), pp.393-414.</mark> <mark>Oster, E., 2019. Unobservable selection and coefficient stability: Theory and evidence.</mark> _<mark>Journal of Business & Economic Statistics</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>(2), pp.187-204.</mark> <mark>Parrilla, J.C., Font, A.R. and Nadal, J.R., 2007. Accommodation determinants of seasonal patterns.</mark> _<mark>Annals of Tourism Research</mark>_ <mark>,</mark> _<mark>34</mark>_ <mark>(2), pp.422-436.</mark> <mark>Park, S., Yaduma, N., Lockwood, A.J. and Williams, A.M., 2016. Demand fluctuations, labour flexibility and productivity.</mark> _<mark>Annals of Tourism Research</mark>_ <mark>,</mark> _<mark>59</mark>_ <mark>, pp.93-112.</mark> <mark>Parte-Esteban, L. and Alberca-Oliver, P., 2015. Determinants of technical efficiency in the Spanish hotel industry: regional and corporate performance factors.</mark> _<mark>Current Issues in Tourism</mark>_ <mark>,</mark> _<mark>18</mark>_ <mark>(4), pp.391-411.</mark> <mark>Pérez-Rodríguez, J.V. and Acosta-González, E., 2023. The impact of ownership and size heterogeneity on hotel efficiency in the Canary Islands (Spain).</mark> _<mark>Tourism Economics</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(1), pp.329.</mark> <mark>Perrigot, R., Cliquet, G. and Piot-Lepetit, I., 2009. Plural form chain and efficiency: Insights from the French hotel chains and the DEA methodology.</mark> _<mark>European Management Journal</mark>_ <mark>,</mark> _<mark>27</mark>_ <mark>(4), pp.268-280.</mark> <mark>Pulina, M., Detotto, C. and Paba, A., 2010. An investigation into the relationship between size and efficiency of the Italian hospitality sector: A window DEA approach.</mark> _<mark>European journal of operational research</mark>_ <mark>,</mark> _<mark>204</mark>_ <mark>(3), pp.613-620.</mark> 32
|Reduced non-food spending|0.603||0.489|
|---|---|---|---|
|Loans from moneylenders|0.217||0.412|
|Sell assets/savings|0.703||0.457|
|Work more|0.413||0.492|
|Num. obs||6,813||
|**B. Unexpected shocks**||||
|None|0.164||0.370|
|Reduced food consumption|0.174||0.379|
|Loans from moneylenders|0.242||0.428|
|Social network|0.192||0.394|
|Other help|0.229||0.421|
|Total|1.000|||
|Amount of costs (median)||BDT 10,000||
|Num. obs<br>Notes: The survey asks coping strategies durin|g regular lean seasons a|2,990<br>nd in response to|unexpected shocks.|

Notes: The survey asks coping strategies during regular lean seasons and in response to unexpected shocks. The coping strategies for unexpected shocks were asked only for those who reported experiencing unexpected shocks. The figures presented in the table are the share of respondents who reported each item. While households were allowed to report multiple strategies during the lean seasons (Panel A), but they were asked for only one major strategy that was used to respond to the most severe shock (Panel B).

# **3.2.5 Potential role of EGPP participation**

EGPP participation, like other workfares, provides both employment and income generating opportunities to poor households. According to the EGPP rule — which allows the wage rate of BDT 200 per day, 40 days per cycle, and up to 2 cycles a year — participating households should be able to receive BDT 8,000 up to 16,000 as EGPP benefits. The average EGPP benefits among the surveyed households are about BDT 7,924. When plotted, many are clustered around BDT 7,000–8,000, and also around BDT 15,000–16,000 (See Figure 1). Unfortunately, the survey does not ask households about the number of cycles they participated in EGPP; thus, it is not clear whether the benefits are taken from one or two cycles. Also, there is a non-negligible share of individuals who are receiving much smaller amounts than BDT 8,000. There is a possibility that many individuals opted to participate one cycle even if they were allowed to participate twice, as many of the respondents are new participants who had only one opportunity yet at the time of the survey, or individuals may drop out without filling the full 40 days per cycle.

**Figure 1: Distribution of EGPP Benefits among the Main Sample Households**

17
needs or operational constraints. This would require a partnership between AIS data providers and an international organization.

The connection between rate spikes and disruptions warrants deeper investigation across sectors, regions, and transport modes (bulk shipping or other modes, e.g., air cargo). The primary takeaway from the proposed framework is that market mechanisms explain the observed significant increases in rates. The latter should not be immediately interpreted as a sign of monopolistic behavior.

Mitigating maritime supply chain disruptions requires multi-faceted support:

1. Enhancing port-hinterland infrastructure resilience including IT systems, contingency planning for infrastructure services and customs, and other initiatives (as outlined in existing toolkits by the World Bank and others (World Bank, 2003)).

2. Increasing end-to-end shipment visibility through real-time tracking to empower shippers and supply chain actors to make agile decisions. Visibility solutions are generally led by the private sector, but public-private collaboration unlocking data-sharing (e.g., API to port community system or customs) could facilitate visibility solution development.

3. Ensuring more flexibility in the utilization of available capacity is crucial for mitigating impacts during periods of supply chain stress. Systems that facilitate capacity sharing across operators can yield substantial benefits, including positive benefits for end consumers. This principle extends to the alliance framework in global container shipping, which most industry experts see as having played a constructive role in the recent crisis. However, the European Commission’s stated intent<sup>8</sup> to reexamine exemptions granted to shipping alliances under competition policy may mean that new forms of operational collaboration may be required in the future.

> 8 <u>https://ec.europa.eu/commission/presscorner/detail/en/ip\_23\_4742</u>

18
An additional point of departure for our work is to consider the impact of services liberalization on the structure of Tunisia’s economy relative to that of trade liberalization. The Tunisia input-output table allows us to disaggregate services into 15 sectors. We consider the regulatory environment at a disaggregated level and are able to compare the relative impact of liberalization in individual service sectors. With data on the manner in which services act as intermediate inputs, our simulations are indicative of how service liberalization may restructure the domestic economy.

# **3.  The Model and Benchmark Data**

Services play a significant role in the Tunisian economy, as is evident from Table 1. Nearly half of all output (excluding tourism) is comprised of services. Output of commercial services has been growing at a faster rate than that of goods. Services make up one-third of household consumption and eighteen percent of intermediate demand according to the 1995 input-output table (Institute National de la Statistique, 1998). Of the tradable services, tourism plays the most significant role, representing over fifteen percent of imports and more than 19 percent of all exports. Tourism export services are considered to be rather open.<sup>3</sup> There is no statistical information on the importance of FDI in Tunisian services trade, as the earnings and sales of foreign-owned subsidiaries are not reported separately from those of domestic firms. However, foreign participation clearly is minimal in the main service activities in Tunisia. This is due to a variety of investment measures, exchange controls and limits on movement of foreign personnel. The most important restriction is the Foreign Commercial Activities Law of 1961 (Decree Law Number 61-14), by which non-Tunisians are required to obtain a trader’s permit, _carte de commercant_ , to engage in any form of commercial activity. This permit, granted on a discretionary basis, has effectively precluded foreign participation in most wholesale and retail markets. The Investment Code of 1993 is also an important barrier to

> 3 Tourism may be exported without being produced because tourists are treated as “foreign consumers” in

7
# **Table 14: Correlates of Other Flows**

|||**Other I**<br>|**nflows**<br>|||**O**<br>|**ther Outflows**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Federal Funds Rate|0.037*|0.053**|0.037*|0.031|0.023|0.033*|0.026|0.034*|
||[2.01]|[2.68]|[1.84]|[1.28]|[1.51]|[1.91]|[1.51]|[1.87]|
|Log(VIX)|-0.415***|-0.455***|-0.428***|-0.506***|-0.306***|-0.328***|-0.334***|-0.362***|
||[4.02]|[4.46]|[4.18]|[4.86]|[3.00]|[3.06]|[3.34]|[3.15]|
|GDP growth|0.063***|0.058***|0.062***|0.063***|0.016**|0.013**|0.012**|0.019***|
||[5.69]|[5.89]|[5.74]|[4.97]|[2.67]|[2.09]|[2.37]|[2.95]|
|Investment environment||0.078**||||0.048**|||
|||[2.53]||||[2.29]|||
|Chinn-Ito index of capital account openness|||0.044||||0.056||
||||[1.05]||||[1.60]||
|Bank assets, % of GDP||||0.007*||||0.008**|
|||||[1.73]||||[2.61]|
|Country fixed-effects|Yes|Yes|Yes|Yes|Yes|Yes|Ye|Yes|
|Observations|2,232|2,210|2,128|1,980|2,229|2,207|2,125|1,977|
|R-squared|0.123|0.134|0.126|0.124|0.022|0.026|0.025|0.035|
|# of countries|29|29|29|29|29|29|29|29|

_Notes:_ The dependent variable is “other” inflows as % of trend GDP in Columns 1-4 and other outflows as % of trend GDP in Columns 5-8. The dependent variables are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

24
Table 6. Average cumulative damages due to rain events, by road alternative

|Rain Event<br>Frequency|Design Scenario|Gradient<br>Profile|% Affected<br>Length|Cumulative<br>Segment<br>Length(km)|Affected<br>Length (km)|Total Affected<br>Length (km)|
|---|---|---|---|---|---|---|
||SQ|9-15%|10%|7.33|0.733|1653|
|||>15%|20%|4.6|0.92|.|
||MD|-|-|-|-|0|
|3-year Rain|HD|-|-|-|-|0|
|(10 times)|Gl|9-15%|5%|3|0.15||
||SLS<br>rave|>15%|10%|1.77|0.177|0399|
||<br>Chip<br>Seal|>15%|2.5%|2.87|0.072|.|
|||6-9%|5%|7.19|0.36||
||SQ|9-15%|15%|7.33|1.10|2.84|
|||>15%|30%|4.6|1.38||
|||6-9%|5%|7.19|0.36||
||MD|9-15%|5%|7.33|0.367|0.727|
|10-year Rain<br>3 i|HD|9-15%|5%|7.33|0.367|0.367|
|( tmes)||6-9%|3%|3.49|0.105||
||Gravel|9-15%|7.5%|3|0.225||
||SLS|>15%|15%|1.77|0.266|1.099|
||Chip|9-15%|5%|4.33|0.217||
||Seal|>15%|10%|2.87|0.287||
|||<6%|2.5%|22.36|0.558||
||S|6-9%|10%|7.19|0.719|4584|
||Q|9-15%|20%|7.33|1.466|.|
|||>15%|40%|4.6|1.84||
|||<6%|2.5%|22.36|0.558||
||MD|6-9%|10%|7.19|0.719|2471|
|||9-15%|10%|7.33|0.733|.|
|||>15%|10%|4.6|0.46||
|||<6%|2.5%|22.36|0.558||
|30-year Rain||6-9%|2.5%|7.19|0.180||
|(1 time)|HD|9-15%|10%|7.33|0.733|1.932|
|||>15%|10%|4.6|0.46||
|||<6%|2.5%|17.06|0.427||
|||6-9%|5%|3.49|0.175||
||Gravel|9-15%|10%|3|0.3||
||SLS|>15%|20%|1.77|0.354|2.950|
|||6-9%|2.5%|3.7|0.093||
||Chip<br>Seal|9-15%|10%|4.33|0.433||
|||>15%|20%|2.87|0.574||

# _2.3 Linking Road Disruptions to Access_

While the available information allows us to estimate the share of the road in a particular gradient that is likely to fail during a specific rainfall event, the exact locations of the disruptions remain unknown. Given that there are nearly 6,000 segments in the road, 100 random simulations were conducted to test the impact of various road segment closures (based on specific gradient levels) on the overall access to the road’s western-most point.

21
tutions in that region. Institutions in turn in�uence current levels of economic development. While we show that colonial activities are correlated with current economic development, it remains to investigate the channel connecting them in more detail. For the lack of measures of institutions at the region level, we use interactions with country level data on institutions to investigate the link. The results suggest that institutions are the channel. However, for future research we plan to construct region-level measures of institutions and elite dominance. This will allow us to study the link between colonial activities and current levels of development more extensively.

# 8 Appendix A: Variable Definitions

- PPP GDP per capita: Gross state product for each state divided by the contemporaneous population of that state and converted to PPP values using the 2000 value from the World Development Indicators. Due to data limitations, the data for Venezuela corresponds to household income.

- Poverty rate: Percentage of the population living below the poverty line, according to each country's de�nition of the poverty line.

- Gini index: Gini measure of income inequality for households.

- Health index: The health index measures the quality-adjusted-life-years (QALY) based on the health status attributed to skeletal remains, which display chronic health conditions and infections. The health index is adjusted for the age distribution of the population and is a simple average of seven health indicators: stature, hypoplasias, anemia, dental health (teeth and abscesses), infections, degenerative joint disease, and trauma.

- Pre-colonial population density: The ratio of the estimated pre-colonial population to the area of modern states.

- Colonial activities: Predominant economic activity performed during the colony in the region that matches the current day state.

- Average temperature: Average yearly temperature (<sup>�</sup> C)

- Total rainfall: Total yearly rainfall (mm)

- Altitude: Elevation of capital city of the state (kms)

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