rafmacalaba/datause-encoder-data huggingface dataset:
of the urban population. Of the surveyed women 4.9% identified as lesbian and 1.4% as bisexual, resulting in a total of 6.3% lesbian and bisexual women. The National SocioEconomic Characterization Survey (CASEN) from Chile provides one of the few population estimates for transgender people (in this case specifically the gender category ‘other’). When comparing the responses for ‘sex assigned at birth’ and ‘gender identity’, 2.7% of the Chilean population can be considered transgender.<sup>32</sup> Because of its medical nature, population estimates for intersex people are somewhat more reliable. Widely accepted estimation by Anne Fausto-Sterling in 2000, estimates that 1.7% of all live births are intersex.<sup>33</sup> These examples highlight the broad range of population estimates for LGBTI people in different countries and even within a given country estimations can vary significantly over time as the example from the UK shows. To fully understand the development outcomes of LGBTI people it is important for future research to address this knowledge gap.

# **Nepal’s 2011 census**

In 2011 the Nepalese government included “third gender”, along with male and female in its <u>census. “Third gender” was thought to be a locally resonant umbrella term for sexual</u> orientation, gender identity, and expression. Unfortunately, a number of challenges were encountered which prevented a reasonable estimation of the population size.<sup>34</sup> The biggest problem turned out to be definitional ambiguity. Neither respondents nor enumerators had sufficient information on what the term “third gender” meant or who was supposed to be covered under it. Further, there was insufficient training provided to enumerators on how to properly ask the question and respond to questions from citizens. While some of the challenges could have been avoided by providing a guidebook and/or better training there was also a bigger conceptual problem, revealed through a follow-up survey conducted by UNDP and the Williams Institute in 2014. As mentioned above, the respondents to the later survey – which allowed open-ended identification - used 21 terms to describe their sexual orientation and gender identity. Only 51.4 % self-identified as ‘third gender’, posing the question whether other respondents would have chosen to identify as ‘third gender’ if just that option was provided like it was the case in the census.<sup>35</sup> After the Nepalese census attempt, censuses in <u>India and Pakistan</u> have moved forward with similar approaches to include transgender populations. The next census in the UK will

> 32 Valfort, Marie-Anne. 2017. LGBTI in OECD Countries: A Review. OECD Social, Employment and Migration Working Papers, No. 198.

> 33 Fausto-Sterling, Anne. 2000. Sexing the Body.

> 34 Park, Andrew. 2015. An inclusive Approach to Surveys of Sexual and Gender Minorities, Report of Meeting.

> 35 UNDP; Williams Institute. 2014. Surveying Nepal’s Sexual and Gender Minorities: An Inclusive Approach.

11
16 nuisance and sweeps them under the carpet. By contrast, the other two approaches explicitly assume that fees paid tell us something about the cost of care, and that particularly high fees indicate that the care received has been especially high‐cost.

The second set of results in Table 3 assume that unit costs and unit fees are proportional. There is no need to set negative subsidies to zero here; indeed, in this approach subsidies are always nonnegative assuming >1. In this case the concentration index for subsidies is simply the concentration index for fees, which is the same irrespective of whether the grossed‐up survey data are used or the NHA data. Given the heavy weight on hospital care in government spending and the highly pro‐rich distribution of fees for these types of care, the distribution of subsidies emerges as distinctly pro‐rich under the proportionality assumption— far more pro‐rich than under the constant cost assumption, even in the most pro‐rich scenario where the out‐of‐pocket spending data are scaled to gross up to the NHA figure and where negative imputed subsidies are set to zero.

The third set of results in Table 3 relate to the general assumption, where unit costs and fees are linearly related. Table 3 explores two assumptions for _a_ , and by implication  (once _a_ has been set,  emerges by solving the cost equation, i.e. eqn (6)). In the first, _a_ is set in terms of a fixed monetary amount, subject to being at least 1, i.e. fees cannot exceed unit costs at the margin. In the absence of any estimates of the basic unit cost of each type of care, illustrative values _a_ were chosen. For CHC visits and polyclinic visits the value of _a_ was set as close to (the Vietnamese Dong equivalent of) $ US 1.00 without  falling below 1. For hospital outpatient visits, _a_ was set at the equivalent of $2.00, and for inpatient visits _a_ was set at the equivalent of $10.00 Under these assumptions, is 100% for CHC and polyclinic care, but larger than 100% for the other two types of care, i.e. units of care costing in excess of the cost
a large margin—from those in other transition countries and countries with significant natural resource base such as the Russian Federation (see Table 1). For the last three years for which we have data, the Gini coefficients of consumption expenditures were in the range of 16—18 percent. These are among the lowest Gini coefficients ever observed in any country. These are extremely low even with the standard of countries generally considered as most equal in the world such as Denmark, where the Gini coefficient was about 25 percent in 2004. It is very unlikely that Azerbaijan is the most equal country in the world. This oddly low inequality figures has cast serious doubts on the quality and the reliability of the Azerbaijan HIES data.

**Table 1.** Most recent <u>Gini indices of Azerbaijan and neighboring countries</u>

|**Country**|**GINI Coefficient**|**Year**|
|---|---|---|
|**Azerbaijan**|**18.4**|**2002**|
|**Azerbaijan**|**18.2**|**2003**|
|**Azerbaijan**|**16.3**|**2004**|
|Armenia|33.8|2003|
|Estonia|35.8|2003|
|Georgia|40.4|2003|
|Latvia|37.7|2003|
|Lithuania|36.0|2003|
|Macedonia, FYR|39.0|2003|
|Moldova|36.9|2002|
|Poland|34.5|2002|
|Russian Federation|39.9|2002|
|Tajikistan|32.6|2003|
|Turkey|43.6|2003|

Source: Azerbaijan HIES data; WB Poverty Assessment Reports; WB Development Data Platform (DDP).

The objective of this paper is to investigate why inequality figures in the Azerbaijan household survey data are so low to extent of putting the quality and reliability of the data in question. Is it plausible that Azerbaijan has such a low inequality and that the data are merely reflecting the underlying reality in the country? Is the data generating mechanism systematically biased rendering inequality artificially low? Is the variability in the data suppressed due to disproportionately less representation of households in the various income and consumption echelons? Have lack of or poor supervision of the data collection and entry processes caused unnaturally high homogeneity in the data? Is a bad sampling design, which led to unrepresentative sample, to blame for the extremely low inequality? Are respondents, especially in the upper income echelon, concealing their incomes and expenditures, and by so doing depressed income and consumption expenditure disparity in the data? In this paper, we explore the data of the 2002-2004 HIES and seek answers to some or all of the above questions.

The paper puts forward various hypotheses why inequality is low in the Azerbaijan data and empirically tests them. We first employ ANOVA and cluster analysis to investigate if poor supervision of the data collection process had led to increased homogeneity in the resulting data. Secondly, we test the impact of public and private transfers on inequality by calculating 2
35

# C. Firnancial Paolicy & Monetary Pol icy

Financial irnstruxnents anid financial institut:ions have been integral parts of economic activity for over two hundred years. The most importarnt financial instruments have probably been nonmetallic<sup>money and demand</sup> deposits, but bonds, equities, options, and forward<sup>contracts also play</sup> important economic roles. Similarly, banks have been the<sup>most ubiquitous</sup> financial institutions, but mutual funds, investment banks,<sup>and insurance</sup> companies are becoming increasingly important in many<sup>countries. To the</sup> - extent that an economy's financial structure its composition<sup>of financial</sup> instruments and institutions - affects economic growth,<sup>financial and monetary</sup> policy may affect growth by altering the financial<sup>structure. Theoretically,</sup> however, the profession is only beginning to integrate<sup>financial markets into</sup> modern growth theories. And, econometrically, it is<sup>very difficult to obtain</sup> good measures of financial policy - or even financial<sup>market performance-for</sup> a broad cross-section of countries to include in cross-country<sup>growth studies.</sup> The role of money in economic activity is one of the most frequently studied issues in economics [see: the extensive survey<sup>by Orphanides and Solow</sup> in the Handbook of Monetary Economics). Early rational<sup>expectations models by</sup> Lucas (1972, 1973) and Barro (1976, 1980) predicted<sup>a neutral relationship</sup> between anticipated money growth and output. But, in recent models,<sup>high</sup> money-growth, high inflation environments can elicit<sup>behaviors that reduce</sup> growth. For example, talented agents may transfer out<sup>of productive</sup> enterprises and into rent-seeking activities, agents<sup>may substitute out of</sup> simple money exchange and into transactions technologies<sup>that require more</sup> time and effort, or capital accumulatinn may be discouraged<sup>[Stockman (1981)].</sup> Consequentl;. rapid money-growth could retard growth. On the other hand, the
Figure 4.10 Share of Asian FOI Outflows - 2001 <!-- Start of picture text --> Ilsa,iluiid Q:Isur C11<br>1% 3% 6<br>18%<br>HF.ioj Kosiqc<br>2.9%<br>33% p I.sppillwics 3%<br>1 *7<br><!-- End of picture text --> _Note In_ this figure _Asia_ Includes South, Easg _and Southeast Asia._ Source Compiled from UNCTAD, World Investment Report 2002 Annex table B 2, 307-309 Unsurpnsingly, the largest share of the region's outward investment **is** from the NIEs. Contranly, the ASEAN4 countries provide very little of these outflows. However, China's share is significant - 6 percent, up from a share of only 1 percent **in** 2000.

On a global level, the region's (excluding Japan) share of FDI outflows was nearly 5 percent **in** 2001 with that of Singapore, Hong Kong and Taiwan (around 1.0 to _1 5_ percent each) being the largest among individual countnes (See Table S. 12 **in** appendix.) Wiule this may not appear to be much compared to the EU's 59 percent, it is very close indeed to Japan's 6 percent. Furthennore, while a small amount of FDI poured into an economy the size of the U.S. would not have much effect, if the same amount is invested in a country the size of Malaysia or Indonesia, it could have a significant impact on the local economy Given that TNCs play such an important role in the intemational production network, a look at the number of East Asian TNCs and their share in _total_ foreign assets will give some indication of the region's firm-level capacity for contributing to regional production networks. In addition, UNCTAD has developed an index called the "transnationality index" (TNI),<sup>96</sup> which gives some idea of the extent to which host countries and TNCs (separately calculated indices for each) are involved **in** international production. Table 4 9 shows these indicators for the largest 50 non-financial developingcountry TNCs, based on their foreign assets 35
- An architecture firm that has developed an innovative luxury “boatel” that runs on an electric motor and can be used on lakes

- A firm that has developed solar-powered benches for public spaces that can charge phones and also monitor air and noise quality.

A number of the firms were developing apps for the Balkan and global markets, covering a wide range of activities such as making it easier to use public transport, a local version of Uber, an app to connect consumers with producers of organic products, online sports coaching, and an app to manage freight logistics. But there are also firms involved in physical manufacturing of products, such high-end electrical bicycles, smart vending machines, indoor pet houses, and a USB charger that charges while bicycling.

# **3.2 Random Assignment**

Applications closed on September 6, 2015 and were then screened to ensure they met the eligibility requirements. All applicants which met the formal eligibility criteria were accepted into the study. Eligible applications were then scored on four criteria to measure their initial level of investment readiness: market attractiveness, product technology, traction, and team. Appendix 3 describes the scoring methodology. The top 10 proposals overall in terms of score were then randomly assigned to 5 in treatment and 5 in control, in order to ensure that some of the very top proposals were in both groups. Then the remainder of firms were divided into strata based on country (Serbia, Croatia, or the rest), and on whether or not they already have a private investor. Within these six stratum firms were ranked into groups of four on the basis of their investment readiness score. Within these quartets two firms were randomly allocated by computer to treatment and two to control. This was done for an initial batch of 333 firms, allocating 167 to treatment and 166 to control. An additional batch took longer to verify their eligibility requirements and were received after this assignment, these were then also randomly allocated and form a separate strata. This resulted in 346 firms, with 174 treatment and 172 control. A pre-analysis plan was registered with the AEA trial registry on October 2, 2015 to pre-specify the initial outcomes of interest.<sup>8</sup> This process resulted in treatment and control groups that are evenly balanced and comparable in terms of their initial characteristics. This is seen in Table 1. Figure 1a shows that the two groups

> 8 <u>https://www.socialscienceregistry.org/trials/895</u>

11
**Group Regressions**

**Table A1. Meta Regressions by Outcome Group** **<u>(between publication corr.)</u>**

|Independent Variables|Group 1<br>Paper Corr|Group 2<br>Paper Corr|Group 3<br>Paper Corr|Group 4<br>Paper Corr|Group 5<br>Paper Corr|
|---|---|---|---|---|---|
|Effect size (ES)|0.166**<br>(0.0674)|0.0599***<br>(0.0132)|0.0970**<br>(0.0433)|0.198***<br>(0.0133)|0.130***<br>(0.0118)|
|Standard error of study|0.0305|‐0.0235|‐0.0428|‐0.423***|0.193***|
||(0.0848)|(0.0547)|(0.125)|(0.0633)|(0.0410)|
|Journal=1, 0=Otherwise|0.0231|0.0182**|0.00797|0.000547|0.0107|
||(0.0193)|(0.00751)|(0.0159)|(0.00470)|(0.00753)|
|Number of studies|0.0352|‐0.0285**|0.0339|‐0.0536***|‐0.00566*|
||(0.0267)|(0.0124)|(0.0426)|(0.0106)|(0.00326)|
|Sample size|‐0.0514***|‐0.00338|‐0.0180**|‐0.00697**|0.0155***|
||(0.0177)|(0.00385)|(0.00755)|(0.00295)|(0.00295)|
|Urban dummy|0.00187|0.00173|0.0248|‐0.00241|0.000599|
||(0.0313)|(0.00998)|(0.0279)|(0.00770)|(0.00776)|
|Government implementation||||||
|dummy|0.0414|0.0159|0.0641|0.0664***|‐0.0183|
||(0.0318)|(0.0211)|(0.0503)|(0.0170)|(0.0133)|
|Quasi‐experimental dummy|‐0.0457|‐0.00377|0.00299|‐0.149***|‐0.000528|
||(0.0327)|(0.0139)|(0.0548)|(0.0343)|(0.0131)|
|LAC region dummy|0.120*|‐0.0274|‐0.0576|‐0.00376|‐0.0197|
||(0.0618)|(0.0395)|(0.0855)|(0.0121)|(0.0156)|
|MENA region dummy|0.129|0.0225|‐0.0223|0.143***|0.000652|
||(0.0850)|(0.0471)|(0.0628)|(0.0361)|(0.0232)|
|SA region dummy|0.116*|‐0.0312|‐0.0298|0.00241|‐0.0287**|
||(0.0643)|(0.0490)|(0.0591)|(0.0117)|(0.0142)|
|SSA region dummy|0.0663|0.0142|‐0.0103|‐0.0110|‐0.0234|
||(0.0454)|(0.0564)|(0.0539)|(0.0122)|(0.0145)|
|Constant|0.0761<br>(0.0977)|0.0123<br>(0.0806)|0.0202<br>(0.0999)|0.166***<br>(0.0384)|‐0.0771**<br>(0.0343)|
|R‐squared|0.43|0.21|0.07|0.58|0.72|

_Source_ World Bank GWSP‐SIEF WASH Impact Evaluation Database.

_Note:_ Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1. LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SA = South Asia, SSA = Sub‐Saharan Africa.

25
year (1960-2000; 2001-2050; 2051-2100) to one of nine cells in a 3-3 matrix (reflecting all possible combinations of low, medium and high values for temperature and rainfall). Now we are ready to develop a simple template for calculating joint incidence probabilities for the two variables in each period. The first step draws on the reliability weights calculated in Step 2.1 of the analysis. In fact, the 8 GCMs are far from equal in their ability to reproduce local climate patterns in Ethiopia. To quantify this difference, we have calculated the R<sup>2</sup> -based scores that were previously described. Now we use these scores in an expectational calculus. The intuition here is straightforward: In the two future periods, we cannot assign the same confidence to predictions from different GCMs. Before we assign a GCM-based forecast to one of the nine cells for a grid square, we weight it with the R<sup>2</sup> -based score that we have computed for the GCM. In effect, we count an observation from the best-fitting GCM as occurring more frequently than an observation from the numeraire (worst-fitting GCM). The relative frequency is the ratio of a GCMs R<sup>2</sup> score to the numeraire score (for the worst-fitting GCM).

Once the weighted observations are all assigned to the nine cells for each grid square, we calculate the weighted probability for each cell (total cell score/total of all cell scores). We do this for both future periods, as well as for the historical CRU data (here each observation gets unit value in the count, since all observations are deemed equally reliable). The result is an easily-interpreted template for each grid square, which shows the transition in expected Low-Medium-High incidence of temperature/rainfall pairs from actual recent history, to the relatively near future, and then to the distant future. This provides decision-makers with a clear, simple picture of our best evidence on how the 7
In contrast, in Henan and Sichuan, a good proportion of in-migrants worked as technician. For example, in Henan, about 20 percent of in-migrants worked as technician, higher than the percentage of town local population, which had only 12.55 percent of them working as technicians. The pattern also holds true in Sichuan. In addition, there were 28 percent and 34 percent of in-migrants working in service sector in towns in Henan and Sichuan provinces respectively, which were also higher than that for town general population.

Furthermore, among the town in-migrants in Henan and Sichuan, data of Table 12 do not suggest a severe labor market segregation, which exists between those inmigrants from same province and those from outside provinces as we observed in Zhejiang data. For example, generally there are more in-migrants from other provinces working in the blue-collar production sector than in-migrants of same province in Henan (37.89% vs. 22.60%) and Sichuan (23.13% vs. 26.53%); however the difference was much smaller than that we observed in Zhejiang (83.31% vs. 43.24%) For the same token, although the percentage of workers engaging in the whitecollar occupations such as manager, technician, and sale-related jobs was lower among inter-province in-migrants than among the intra-province in-migrants, the gap is much smaller. All of this suggests that labor market is more flexible in Henan and Sichuan, whereas it is more segregated in Zhejiang.

Table 11 Occupation composition of towns in-migrants (15 years old or above) by migration status: 2000

|__________<br>province|__________________<br>type of population|_______<br>1|______<br>2|______<br>3|______<br>4|______<br>5|______<br>6|_______<br>7|
|---|---|---|---|---|---|---|---|---|
|__________|__________________|_______<br>|______<br>(%)|______|______|______|______|_______|
|Zhejiang|town in-migrant|<br>1.38|<br>7.71|3.80|19.59|4.02|63.46|0.04|
||<br>local population|2.57|8.84|4.93|23.38|17.22|43.00|0.05|
|Henan|town in-migrant|3.90|20.48|11.25|28.38|11.85|23.95|0.18|
||local population|3.19|12.55|7.65|17.45|43.01|16.00|0.14|
|Sichuan|town in-migrant|3.62|19.41|8.59|34.22|10.47|23.43|0.26|
||local population|3.14|14.50|6.57|24.41|31.81|19.38|0.20|

|________________________________________________________________________|
|---|

Source: calculated from Tabulation on 2000 Population Census of the People’s Republic of China, Table 4- 2b page 1167-1201, and Table L7-1b and L7-2b.

note: occupation title: 1, manager and administrative personals, 2. professionals and technician, 3. staff and other auxiliary workers, 4. salesperson, 5. agriculture, animal husbandry, and fishery, 6. production workers, 7. unclassified.

13
that the relationships of employment variations between size and age classes appear right. Thus, small and younger firms have higher job creation and destruction than older and larger firms within the country, when considering total employment. Therefore, statistical methods that remove country level effects are appropriate to draw conclusions within countries on the difference of the impact of investment climate on different types of firms.

The construction of the investment climate variables _ICikc_ requires some explanation. A number of previous studies assess the investment climate experienced by firm i, using firm level subjective measures. Such studies rely on answers to questions of the form “how much (the dimension _k_ of the business climate) is an obstacle for the growth of your firm?” with possible answers ranking between 1 and 5 (Ayyagari et al. 2008; Beck et al. 2005; Pierre and Scarpetta 2006; Johnson, McMillan and Woodruff 2002). Subjective assessments, while insightful, have a number of possible drawbacks. There is a concern that firms’ perceptions of the business environment reflect idiosyncratic differences in the degree of optimism or pessimism of the individuals responding to the survey. Answers are also likely to be strongly influenced by the experience and performance of the firm (see HallwardDriemeier and Aterido 2007).

Our analysis uses objective measures to gauge the relative importance of the investment climate to firms. While, in principle, objective measures may be less likely to be influenced by firms’ performance than subjective measures, the results presented in Table 3 suggest that individual firms ́ reported objective investment climate conditions are likely to be endogenous to firm performance, a feature that we need to take into account when estimating the effect of investment climate variables on firm performance.

To account for endogeneity, we construct a measure of the _IC_ conditions faced by firm _i_ on dimension _k_ in country _c_ , by averaging the responses of firms in the same countrylocation-sector-size cell, excluding the observation from firm _i_ in computing that average.<sup>15</sup> To ensure adequate numbers of firms in each location-sector-size cell average, we drop one

> 15 In some specifications we also assess whether results vary if we instead compute IC conditions by averaging the responses of firms in the same country-location-sector bins (i.e. excluding size as a dimension). Here we use a finer disaggregation of location.  Above, locations are aggregated by city-size (e.g. capital cities and those with a population above one million, and cities below 1 million people).  In the country-location-sector averages, we construct the cells based on the actual cities or towns the firms are located in.  If the representation is low, locations are aggregated based on 5 size groups (capital cities or more than 1 million; 500,000-1 million; 250,000-500,000; 50,000-250,000; less than 50,000).

16