rafmacalaba/datause-encoder-data huggingface dataset:
**Definition.** The Gini coefficient of a conditional distribution _G_ [ _Fj_ ], _j ∈{_ 1 _,_ 2 _}_ , is defined as:

where _μ_ 1 _≡ E_ [ _Y |Y ≤ b_ ] and _μ_ 2 _≡ E_ [ _Y |Y > b_ ].<sup>2</sup>

These Gini coefficients are simply the standard Gini coefficients computed on each of the two distributions.

**Proposition 2.** _The full-population (unconditional) Gini coefficient can be written as:_

_where_ _Proof._ See Appendix.

This proposition tells us that we can estimate the overall Gini coefficient as a linear combination of the Gini coefficients of the conditional distributions, plus an adjustment term that takes into account that the two conditional distributions are obtained from different underlying portions of the unconditional distribution.

# **2.3 Implementation and Relation to the Literature**

A crucial prerequisite in the derivation of Propositions 1 and 2 is that we can obtain a consistent estimate of the conditional distribution of income above and below a threshold _b_ . The specific case of combining household surveys with tax records provides a natural example in which to apply this methodology.

Household surveys tend to be poorly representative of the top of the income distribution because high income individuals tend to underreport or simply not report their

> 2Note that given the definitions of _F_ 1 and _F_ 2 above, _F_ 1( _y_ ) is 1 for _y > b_ , so  ́ _∞_ 0 _F_ 1( _y_ )(1 _− F_ 1( _y_ )) _dy_ = _b ∞ ∞_  ́0<sup>_F_1(</sup><sup>_y_)(1</sup><sup>_−F_1(</sup><sup>_y_))</sup><sup>_dy_.Viceversa,</sup><sup>_F_2(</sup><sup>_y_) is 0 for</sup><sup>_y≤b_, so</sup>  ́0 _F_ 2( _y_ )(1 _− F_ 2( _y_ )) _dy_ =  ́ _b F_ 2( _y_ )(1 _− F_ 2( _y_ )) _dy_ .

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tors. For example, income, wealth and education tend to be influenced by initial circumstances (Ferreira and Gignoux 2014, Brock 2020). Our paper improves upon this literature by holding access to investment and interest rates equal across groups, and isolating the impact of the unfair inequality separate from the other ways in which exogenous shocks impact other aspects of life.

Our experimental design is informed by two strands in the experimental economics literature. The first examines investing when the income of a partner/peer is higher or lower than one’s own. The consensus is that people will take more risk to avoid having less income than a peer (Linde and Sonnemans 2012, Lindskog et al. 2022, Gamba et al. 2017). The result extends to how incentives are structured, with rank incentives increasing risk taking as much as tournament incentives (Kirchler et al. 2018). With the exception of (Kirchler et al. 2018), these papers evaluate behavior of pairs of subjects, such that income inequality is defined relative to one other person. Fafchamps et al. (2015) take this one step further by analyzing behavior across multiple rounds in six people groups. Between rounds, they provide information to all participants on the earnings of all group members. Consistent with the literature, they find that low endowment subjects risk higher shares of income than high endowment subjects, on average (controlling for past winnings). Since the concern is to precisely describe how subjects react to having less (or more) than a peer, they do not consider scenarios where rank is not precisely known. This literature informs our work by showing the importance of rank on risky decision making. It thus paves the way for us to consider this rank-risk relationship as the operative link between IOp and risk taking.

More fundamentally, our paper uniquely compares the impact of this rank information across income generating processes. In other words, we include a control group where subjects are not exposed to randomness in the income generating process. In real life, the counterfactual of good luck is not necessarily bad luck. Ideally these studies would include a comparison group where everything is the same, except the exposure to random luck in the income generating process. Including a suitable comparison group, or control, is particularly important in studying inequality and peer comparison since the literature shows people respond differently to different income generating processes.

The second strand of experimental literature to which we contribute examines how IOp impacts preferences for redistribution (Krawczyk 2010, Brock et al. 2013, Akbas ̧ et al. 2019, 7
the poorest quintile in Sumatra (World Bank 2006). At the junior secondary school level, the level of access varies even more widely across provinces. Indonesia has almost universal enrollment at the primary level across provinces. However, vast differences in enrollment rates emerge for children between 13–15 years. While Jakarta and Yogyakarta achieved enrollment levels over 90 percent, the majority of provinces considered in this analysis fall below 80 percent. South and Central Sulawesi fall below 70.

Even for primary enrollment rates, the geographical differences are stark, particularly within provinces. The province of Kalimantan Tengah, ranks highest among all provinces, with an enrollment rate above 95 percent but enrollement rates of its districts fluctuate a lot. For example, the district Kota Palangkaraya has an enrollment rate of 88.4. This is lower than the overall enrollment rate of Gorontalo, which ranks second lowest of all provinces with an enrollment rate of 88.5 percent (Figure 6).

Figure 6 Primary Education: District Enrollment Rates within Provinces <!-- Start of picture text --> 1<br>0.95<br>0.9<br>0.85<br>0.8<br>0.75<br>0.7<br>0.65<br>0.6<br>0.55<br>0.5<br>Papua Gorontalo Sulawesi Utara Kepulauan Bangka Belitung Sulawesi Tenggara Sulawesi Selatan Maluku Nusa Tenggara Timur Sulawesi Tengah D I Yogyakarta D K I Jakarta Sumatera Selatan Papua Barat Nusa Tenggara Barat Kalimantan Barat Maluku Utara Lampung Jawa Barat Sumatera Barat Jawa Tengah Bali Kalimantan Selatan Kalimantan Timur Jambi Jawa Timur Kepulauan Riau Sumatera Utara Riau Banten Bengkulu Nanggroe Aceh Darussalam Kalimantan Tengah<br><!-- End of picture text --> _Source:_ Susenas 2005.

However, inequality in net enrollment rates is decreasing in all levels of education. In primary schools, the variance enrollment gaps were reduced substantially from a variance of 31.9 (1998) to only 13.2 (2004). Also in junior and senior secondary education, the difference in enrollment gaps variance was reduced by almost half. Most of the geographical differences are driven by inequality within provinces, which is consistently explaining inequality by a factor of more than 2:1 compared to between province inequality.19 This means that inequality in net enrollment is every time less due to provinces lagging behind, and more due to specific districts lagging behind across all provinces (Table 9).

> 19 The “Between” mean square estimates the population variance based on the sum of squares of the province means multiplied by the number of districts (size of the samples). The “Within” mean square estimates the population variance based on the average of all sum of squares within the several provinces.

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FoMign investment played a smaller role in China and Japan. I China it was oncentratedin British-owned businesses, mainly trading companies, in tie Shanghai area. Only one-seventh of Brits funds went into transport, most of it was devoted to shipping. The French contribution to infrastructure was more important: French capital financed railways connecting French Indo-China with China and penetrating into China's inteior. About one-tird of foreig investment in China as of 1913 was devoted to transportation. Japan was essenfially cosed off to foreign investment prior to 1900. Only therafter did the government begin to float bonds on the London markt to defay the cost of its wars with China and Russia and to help finance public utilities, including railroads.

Europan investment in Afica was driven by strtegic as well as commercial motives. ITe fonmer, eptomi by the French-financed Suez Canal, dominated in North Africa, while the latter was the driving force to the continent's south. There, Europan money financed railways and ports to open up the interior, with its diamonds and gold, and to exploit opportunities for plantation agcultue. French fimds, attrsted by government guarantees, financed the construction of railways and porU in Tunisia, Algeria, and Morocco (where Spanish money was also important).6" Exena funds also loomed large in Austrlia and New Zealand, both bause the counis wer sparsey populated and because the colonial connection helped to overcome information problems and other rsk Bnrtsh investors financed rually all of New Zealand's pre-1913 pubLic worls, including the railway and harbor systems. Population increased more rapidly in Australia, fcilitating some domestic fiance of pubLic works. But much infrastructre investment, in railways, ports, and urban public works, was financed through borrowing in London, particularly before 1850 and after 1880. Recent studies of latin Ameica (for example, Stone 1977) have produced a deailed picture of I A few loans wero mised directly from govenmet agencieasuc dae Caim del Depots. For dtails, see KEuak (197).

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**Figure 1. Overall determinants of investment location decisions (% of respondents)**

**Question:** How important were the following factors in your parent company's decision to invest in this country?

<!-- Start of picture text --> Political stability 49% 35%<br>Macroeconomic stability 49% 36%<br>Legal & regulatory environment 42% 42%<br>Talent/skills 41% 43%<br>Low taxes 41% 35%<br>Market size 39% 43%<br>Physical infrastructure 39% 41%<br>Ability to export 38% 32%<br>Intellectual property protections 35% 35%<br>Investor protections 35% 38%<br>Low labor & input costs 33% 41%<br>Supply chain coordination 30% 44%<br>Local input sourcing 27% 42%<br>Resource endowments 18% 32%<br>Local acquisition targets 14% 36%<br>0% 20% 40% 60% 80% 100%<br>Critically important Important<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

More export-intensive respondents place lower importance on host countries’ legal and regulatory environments. In the preferred ordered logistic specification, the coefficient on export percentage of sales is negative and statistically significant at the p<0.05 level. This finding is robust to the exclusion or inclusion of country and subsector fixed effects as well as alternative specifications using OLS (Table 11, Column 8). A 10-percentage point change in the portion of sales from export is associated with a 1- percentage point increase in the likelihood of considering legal and regulatory environments to be ‘critically important’ (Table 12). Although further research is required to explain this pattern, this coefficient may reflect exporters being less subject to regulations governing sales within their countries of operations (e.g., local consumer protection laws) relative to market-seeking investors.

**Table 7. Overall determinants of investment decisions: Coefficient signs and significance levels for heterogeneity analyses**

||**Legal/regulatory**<br>**environment**|**Inv. protection**<br>**guarantees**|
|---|---|---|
|**Services**|||
|**Export % of sales**|-**||
|**Share of sourcing via imports**||+***|
|**High-income source country**|+*||

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**Table 10: Gender Wage Gap by Economic Activity in Nepal (2008)**

|**S.No.**|**Economic activity**|**Men**|**Women**|**Wage Gap**<br>**(%)**|
|---|---|---|---|---|
|||**Nominal**<br>**earnings**<br>|**annual**<br>**(in US$)**<br>||
|1|Agriculture, hunting and forestry|816|492|60.3|
|2|Fishing|600|||
|3|Manufacturing|1056|||
|4|Electricity, gas and water supply|1164|768|66.0|
|5|Construction|1848|876|47.4|
|6|Wholesale and retail trade; repair of motor vehicles, etc.|1020|708|69.4|
|7|Hotels and restaurants|768|672|87.5|
|8|Transport, storage and communications|1632|756|46.3|
|9|Financial intermediation|1116|936|83.9|
|10|Real estate, renting and business activities|2316|1260|54.4|
|11|Public administration and defense; social security|1176|888|75.5|
|12|Education|1500|972|64.8|
|13|Health and social work|1308|1044|79.8|
|14|Other community, social and personal service activities|1644|744|45.3|
|15|Activities of private households as employers|672|372|55.4|
|16|Extraterritorial organizations and bodies|1956|1920|98.2|

Source: ILOSTAT database, International Labor Organization The existing literature and the results above suggest that gender differences in the labor market in South Asia are highly prevalent. Gender differences loom large in female labor force participation and earnings across different sectors and jobs in South Asia. Though the combined effect of economic development, rising education among women, and declining fertility to an extent explains increase in female participation rates in some South Asian countries, there still remains several prominent constraints to increasing female labor force participation such as women’s domestic responsibilities, limited mobility, which restrict the subset of jobs considered appropriate for women, and also impede their access to information.

# **3. Development of the GTAP-Gender Data Set for South Asia**

Data on the wages and number of employees for different sectors were first obtained, separately for men and women. These sectors were then mapped for which data were available to the 57 sectors in the standard GTAP database. In the GTAP-Power data set, electricity is further broken down into several types of renewable and non-renewable sectors, so the male and female wage bill shares were used corresponding to ISIC sector 35 to all the electricity sectors in GTAP-Power.

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locally weighted regressions. These are local linear regressions that weigh the data using kernel methods. We follow Fan (1992) and Pagan and Ullah (1999). We estimate a different nonparametric regression using data for 1996 and 1998.<sup>7</sup> The inspection of figure 6.1 provides details of the evolution of cotton as a cash crop in rural Zambia.

Both in 1996 and 1998, the cotton income shares increase at the very bottom of the income distribution and then decline with income. The maximum share is roughly over 10 percent. At the upper tail, the average share is quite small, of around 2 percent. As regards cotton dynamics, the average share is higher in 1998 at the bottom of the distribution (mostly poor households) and at its upper tail. In the middle of the income distribution, the cotton income share is higher in 1996 than in 1998.

It is instructive to look at the evolution of cotton shares across the different cottonproducing provinces. Figures 2 to 4 plot the non-parametric averages for the Central, the Eastern, and the Southern provinces, respectively. In the Central province, for instance, cotton income shares in 1998 track the shares in 1996 at the bottom of the distribution, but become smaller at the middle. The Central province resembles the pattern at the national level, with higher average shares in 1998 at to bottom and at the top of the distribution, and lower shares at the middle. In contrast, cotton shares in the Southern province are higher across the entire income distribution in 1998 than in 1996.

The results in the previous figures indicate a regionally differentiated effect of the reforms on the pattern on income sources in rural Zambia. In particular, the increase in cotton shares in the Southern province is remarkable. Since this is the region that is perhaps closest to the capital, the result indicates that access to markets and infrastructure are key variables in adoption and in the deepening on cotton production.

> 7  Unfortunately, the 1991 Priority Survey does not include data on cotton income separately.

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regression errors.<sup>6</sup> This systematic heteroskedasticity produces log-linear estimates that are driven by the disproportionate influence of observations with high expected bilateral trade flows, which leads to biased estimates. Indeed, Monte Carlo simulations suggest that the application of log-linear estimators to this type of data-generation process tends to produce substantial biases in the coefficients compared to the Poisson estimator, which controls for a constant correlation between the conditional mean of each observation and its regression-error variance (see Santos Silva and Tenreyro 2006).

Furthermore, if the data-generation process is characterized by over-dispersion (a rising ratio of variance over conditional mean) then the Negative Binomial estimator is preferable as it down weights even more the observations with large conditional means. Silva and Tenreyro (2006) argue that the Negative Binomial estimator might not be desirable if the trade data of country pairs with little bilateral trade are more prone to measurement errors than the observations with large bilateral trade. They further argue that this may be the case in a sample of both developed and developing countries, as data from larger countries (measured in terms of GDP) is less likely to be subject to measurement error. However, in our sample composed of LAC exporters and importers, there is no reason a priori to believe that trade flows associated with small countries like Uruguay are more likely to be subject to measurement error than the trade flows of large countries like Venezuela.<sup>7</sup> We therefore present results from the Negative Binomial estimator along with OLS and Poisson estimates of equation (2). Since this estimator does not fully account for the heteroscedasticity in the model we use the Eicker-White correction by reporter to obtain a robust covariance matrix.

**1.2 The effect of China and India’s trade flows on LAC exports to third markets** There are four potential channels through which Chinese and Indian trade could affect LAC exports to third countries: China (or India) exports to the rest of the world, China (or India) imports from the rest of the world, China (or India) exports to LAC, and China (or India) imports from LAC.  Thus, in a sample of Latin American importers and

> 6 The expected variance falls with the expected level of bilateral trade.

> 7 Venezuela’s trade flows are approximately 10 times larger than those of Uruguay.

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# **Table 2:  Rare Cases of Catch-Up**

**(Economies with a greater than .10 increase in relative GDP per capita with respect to the United States)**

||**1950**|**1980**|**2008**|**Change**<br>**1950-2008**|
|---|---|---|---|---|
|Hong Kong SAR,|||||
|China|0.23|0.57|1.02|0.78|
|Singapore|0.23|0.49|0.90|0.67|
|Equatorial Guinea|0.06|0.08|0.71|0.65|
|Taiwan, China|0.10|0.28|0.67|0.58|
|S. Korea|0.09|0.22|0.63|0.54|
|Ireland|0.36|0.46|0.89|0.53|
|Japan|0.20|0.72|0.73|0.53|
|Spain|0.23|0.50|0.63|0.40|
|Austria|0.39|0.74|0.77|0.39|
|Norway|0.57|0.81|0.91|0.35|
|Finland|0.44|0.70|0.78|0.34|
|Greece|0.20|0.48|0.52|0.32|
|T. & Tobago|0.38|0.67|0.68|0.30|
|Israel|0.29|0.59|0.58|0.28|
|Italy|0.37|0.71|0.64|0.27|
|Germany|0.41|0.76|0.67|0.26|
|Puerto Rico|0.22|0.44|0.48|0.26|
|Portugal|0.22|0.43|0.46|0.24|
|Mauritius|0.26|0.24|0.47|0.21|
|Oman|0.07|0.22|0.27|0.20|
|Thailand|0.09|0.14|0.28|0.20|
|Belgium|0.57|0.78|0.76|0.19|
|France|0.54|0.79|0.71|0.17|
|China|0.05|0.06|0.22|0.17|
|Malaysia|0.16|0.20|0.33|0.17|
|Netherlands|0.63|0.79|0.79|0.16|
|Botswana|0.04|0.09|0.15|0.12|
|Bulgaria|0.17|0.33|0.29|0.11|

_Memo/_ Former Soviet Union/E. European countries with data only since 1990

||1990|2008|Change|
|---|---|---|---|
|Estonia|0.47|0.64|0.17|
|Slovenia|0.47|0.58|0.11|
|Armenia|0.26|0.37|0.11|

Source: Authors‘ calculations based on Maddison data.

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The financial costs of energy subsidies are not treated in a transparent manner in the government’s budget and are therefore not straightforward to estimate. Nevertheless, recent estimates suggest that these costs are substantial. For example, it has been estimated that between 2005/06 and 2009/10, the costs of petroleum subsidies alone increased by 65%, from E£40 billion ($7.2 billion) to E£68 billion ($11.9 billion).<sup>2</sup> The dynamic CGE model used in the analysis presented here suggests that in 2013/14, the total budgetary cost of all energy subsidies was approximately E£150 billion ($21 billion) or about 8.5% of GDP.

The economic costs of energy subsidies go well beyond the direct budgetary costs, as a result of distorted consumption and investment decisions and the adverse economic impacts that these distortions have on productivity and growth. It has been estimated that the just the direct economic cost of these subsidies could have been as much as 11.9% of GDP in 2009/10.<sup>3</sup> To put Egypt’s experience in some context, the IMF has estimated that for the MENA region as a whole, energy subsidies cost about $237 billion in 2011, which amounted to about 8.6% of regional GDP, or 22% of aggregate government revenue, and accounted for 48% of global energy subsidies.<sup>4</sup> (These costs would have been higher in 2013/14 as a result of higher international energy prices). In addition, energy subsidies far exceeded the value of other subsidies provided in many MENA countries, (e.g., food subsidies are estimated to have amounted to 0.7% of GDP in 2011).

# 3. The Impacts of the July 2014 Reforms

In July 2014 the Egyptian government introduced a range of reforms through increases in official prices in order to significantly reduce fuel subsidies (Table 1.) The largest prices

> 2   “Reforming Energy Subsidies in Egypt”, African Development Bank, Vincent Castel, March 2012.

> 3    The estimate for the full economic cost of energy subsidies is derived from the gap between the actual prices charged and the corresponding reference prices for different sources of energy, taking into account transport costs, etc.  (See Castel [2012].)  The comparison for natural gas in that study was drawn from “Natural Gas Pricing in the countries of the Middle East and North Africa” by Hossain Razavi, The Energy Journal, 2009.

> 4   IMF: ‘About one-half of total energy subsidies in MENA are accounted for by petroleum products, while the remainder represents subsidies on electricity and natural gas. There is a wide dispersion of subsidies in the region, with subsidies being more prevalent in oil exporters. Energy subsidies exceeded 5%.

6
Table A35: Robustness of impact on mental health index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|0.28***<br>(0.10)|0.33***<br>(0.09)|0.25***<br>(0.09)|0.27***<br>(0.10)|0.27***<br>(0.10)|0.27***<br>(0.10)|0.28***<br>(0.10)|
|T1|+Leaders|-0.00|0.23**|-0.16|-0.04|-0.03|-0.02|-0.00|
|||(0.11)|(0.09)|(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|
|T2|(Safe Spaces + IGA)|0.27**|0.43***|0.17*|0.23**|0.24**|0.25**|0.27**|
|||(0.10)|(0.10)|(0.10)|(0.10)|(0.10)|(0.10)|(0.10)|
|T2|+Leaders|-0.00|0.26***|-0.19*|-0.06|-0.03|-0.01|0.00|
|||(0.11)|(0.09)|(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|
|T3|(Safe Spaces + Husbands Clubs)|0.11|0.36***|-0.05|0.04|0.06|0.08|0.11|
|||(0.13)|(0.10)|(0.12)|(0.13)|(0.13)|(0.13)|(0.13)|
|T3|+Leaders|0.27**|0.37***|0.22**|0.25**|0.26**|0.26**|0.27**|
|||(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|
|T4|(control spillover)|0.04|0.05|0.04|0.04|0.04|0.04|0.04|
|||(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|N||3003|3002|3002|3004|3004|3004|3003|
|Con|trol Mean|0|0|0|.002|.001|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

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