rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 22. Access to urban infrastructure and services, by employment type**

<!-- Start of picture text --> Electricity access Improved water Improved sanitation<br>Total 80.3 Total 78.6 Total 41.3<br>Total Inf 75.5 Total Inf 77.2 Total Inf 35.8<br>Unpaid 68.8 Unpaid 71.8 Unpaid 34.1<br>Paid Info 81.4 Paid Info 80.3 Paid Info 36.5<br>LE-SE 74.2 LE-SE 77.0 LE-SE 35.9<br>HE-SE 87.4 HE-SE 79.3 HE-SE 44.1<br>Paid Form 91.9 Paid Form 83.3 Paid Form 57.2<br>0 20 40 60 80 100 0 20 40 60 80 0 20 40 60<br>Internet access Cellphone access<br>Total 45.2 Total 81.5<br>Total Inf 40.6 Total Inf 80.3<br>Unpaid 35.3 Unpaid 83.2<br>Paid Info 52.6 Paid Info 85.6<br>LE-SE 34.7 LE-SE 76.4<br>HE-SE 58.5 HE-SE 92.3<br>Paid Form 58.9 Paid Form 80.0<br>0 20 40 60 0 20 40 60 80 100<br><!-- End of picture text --> Source: Own elaboration based on GMD. Notes: Cross-country averages using population weights. Countries included in electricity, water, sanitation, and cellphone averages: Angola, Burundi, Benin, Burkina Faso, Chad, Cote d’Ivoire, Cameroon, Gabon, Ghana, Guinea, Guinea Bissau, Kenya, Liberia, Mali, Malawi, Niger, Nigeria, Senegal, Sierra Leone, Sao Tomé and Principe, Eswatini, Togo, Uganda, Zambia and Zimbabwe. Internet average includes Cameroon, Benin, Burkina Faso, Chad, Cote d’Ivoire, Gabon, Ghana, Guinea, Guinea Bissau, Kenya, Mali, Senegal, Sierra Leone, Sao Tomé and Principe, Togo and Zambia.

**<u>Table 4. Urban Infrastructure and Services Correlates with Urban Informal Firms</u>**

||Low self-e<br>|mployed<br>|High self-e<br>|mployed<br>|Empl<br>|oyer<br>|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|Has grid electricity|-0.405|-0.242|0.050|0.01|0.355|0.232|
||[0.084]***|[0.112]**|[0.026]*|[0.016]|[0.085]***|[0.115]**|
|Has non-grid electricity|-0.234|-0.306|0.013|-0.005|0.221|0.311|
||[0.174]|[0.203]|[0.024]|[0.027]|[0.180]|[0.204]|
|Full water access|0.105|0.01|-0.064|-0.084|-0.041|0.074|
||[0.126]|[0.154]|[0.029]**|[0.058]|[0.128]|[0.168]|
|insufficient water access|0.026|0.195|-0.040|-0.024|0.014|-0.171|
||[0.080]|[0.085]**|[0.015]***|[0.015]|[0.082]|[0.084]**|
|Use computer|-0.371|-0.371|-0.049|0.036|0.421|0.335|
||[0.074]***|[0.157]**|[0.025]*|[0.036]|[0.077]***|[0.164]**|
|Demographic controls|no|yes|no|yes|no|yes|
|Firmcontrols|no|yes|no|yes|no|yes|
|Constant|0.823|0.634|0.017|-0.016|0.160|0.382|

Page **40** of **60**
**16 |** P a g e

**Table 9: Exports of BiH and other SEE‐5 economies in terms of factors’ intensity in 1996, 2000, and 2007 (in percent)**

||Natu|ral Resou|<br>rces|Share of T<br>Uns|otal Fact<br>killed Lab|or Intens<br>or|ity (%)<br>Capit|al and Sk<br>Labor|illed|Unskilled<br>Labor<br>2000|Capit<br>Skilled Lab|al and<br>or in 2007|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1996|2000|2007|1996|2000|2007|1996|2000|2007|1996=100|1996=100|2000=100|
|Albania|40.1|27.9|39.0|51.6|59.3|48.2|8.4|12.8|12.8|115|153|100|
|BiH|50.0|56.4|41.3|22.5|25.5|19.8|27.5|18.0|38.9|113|141|216|
|Croatia|33.0|30.9|39.8|31.1|30.0|16.5|35.9|39.1|43.7|96|122|112|
|Macedonia, FYR<br>Serbia &|43.9|30.6|43.6|33.5|32.5|24.9|22.6|36.9|31.5|97|139|85|
|Montenegro|37.0|46.8|39.0|11.5|17.2|12.2|51.5|36.0|48.8|149|95|136|
|SEE‐5|37.1|37.3|40.3|27.2|28.4|18.1|35.6|34.3|41.7|104|117|121|

Notes: The classification of factor intensity categories is based on SITC 4-digit products in revision 1. Indexes calculated for shares of skilled labor and capital intensive products in total exports Source: Computations based on world data from UN COMTRADE database.

During the current expansion phase, the relative position of natural resource‐intensive product has declined indicating shift in a causal chain linking factor endowments, comparative advantage and trade patterns. The post‐war recovery phase was characterized by a growing gap between a country’s large pool of skilled labor force and labor intensive products being the major driver of export expansion. Skills of workers employed in metalworking and mechanical and automotive engineering “... considered to be among the highest skilled workforce in the FSR of Yugoslavia” (FIPA 2003, p. 11 and 12) remained untapped until the 2000s. The share of unskilled labor intensive products increased in total exports between 1996 and 2000. It fell significantly in 2000‐07. So did the share of natural resource intensive products falling by 15 percentage points in this period. In contrast, BiH experienced the largest increase among SEE‐economies in the share of capital and skilled labor intensive exports in 2000‐07.

FDI has been critical to closing the gap between endowments in skilled labor and the dominance of unskilled labor intensive exports in Central European transition economies. Foreign firms have brought capital, marketing and technological knowhow; have employed local skilled labor; and have taken advantage of unlimited access to EU single market. Put differently, BiH is well endowed in skilled labor force but not in capital: in other transition economies that experienced similar transformation in factor intensities, capital came from abroad. Since BiH has had both skilled labor and preferential access to EU markets, an interesting question is whether the same has happened in BiH.

# **A. FDI and change in BiH export basket**

There is considerable evidence pointing to FDI as drivers of the current expansion in exports. Their impact on export was larger than relatively low FDI inflows might suggest, as foreign firms have
**Table 4.4b: Overall bias in Ethiopia HFPS by Urban-Rural domain**

||**E**|**thiopia- Urban**|||**Ethiopia- Rura**|**l**|
|---|---|---|---|---|---|---|
||**ESS**|**HF**|**PS**|**ESS**|**HF**|**PS**|
||**Urban**|**Intervi**|**ewed**|**Rural**|**Interv**|**iewed**|
||||_Overall_|||_Overall_|
||_Mean_|_Mean_|_bias†_<br> _(2) -(1)_|_Mean_|_Mean_|_bias†_<br> _(5) -(4)_|
|**Characteristic**|(1)|(2)|(3)|(4)|(5)|(6)|
|**Sample size(number of households)**|_3,655_|_2,270_||_3,115_|_1,013_||
|Average household size|3.647|3.695|0.048|4.885|5.131|0.063|
|Dependencyratio|0.651|0.615|-0.036|1.103|1.085|-0.028|
|Female head(%)|0.346|0.324|-0.022|0.223|0.178|-0.029**|
|Age of head|38.728|38.359|-0.369|44.924|43.337|-0.995|
|**_Religion of head_**|||||||
|Orthodox|0.574|0.607|0.034|0.431|0.458|0.027|
|Muslim|0.207|0.178|-0.030|0.335|0.347|0.012|
|Christian|0.213|0.210|-0.003|0.210|0.180|-0.030|
|**_Marital status of head_**|||||||
|Married|0.618|0.641|0.023|0.798|0.828|0.031|
|Formerlymarried|0.220|0.209|-0.011|0.174|0.137|-0.037*|
|Never married|0.162|0.150|-0.012|0.029|0.035|0.006|
|**_Education level of head_**|||||||
|Head Literate|0.753|0.813|0.06**|0.408|0.495|0.088***|
|None|0.287|0.225|-0.061**|0.637|0.542|-0.094***|
|Primary|0.296|0.306|0.010|0.296|0.340|0.045|
|Secondary|0.215|0.243|0.028|0.047|0.074|0.027|
|Tertiary|0.202|0.226|0.023|0.020|0.043|0.022**|
|**_Working status of head_**|||||||
|Any|0.592|0.605|0.013|0.804|0.827|0.022|
|Wage|0.302|0.325|0.023|0.051|0.076|0.025|
|Agriculture|0.124|0.105|-0.019|0.756|0.749|-0.007|
|Nonfarm enterprise|0.219|0.220|0.001|0.066|0.083|0.017|
|**_Consumption_**|||||||
|Mean expenditure(LCUper capita)|22875.803|22976.492|100.690|11130.133|12155.499|1025.366|
|**_Quintiles_**|||||||
|Quintile 1 -poorest|0.059|0.041|-0.018|0.268|0.213|-0.055|
|Quintile 2 -poorer|0.107|0.096|-0.011|0.245|0.224|-0.021|
|Quintile 3 - middle|0.158|0.158|0.001|0.221|0.254|0.034|
|Quintile 4 - richer|0.274|0.299|0.024|0.164|0.194|0.029|
|Quintile 5 - richest|0.402|0.406|0.004|0.103|0.115|0.013|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

30
the survey following the disaster, the empirical analysis focuses on the change in IPV prevalence in the Southern Highlands relative to the rest of the country.

In this analysis, we first focus on the men’s survey, which includes a range of questions about the acceptability of intimate partner violence (IPV). The question states ”in your opinion, is a husband justified in hitting or beating his wife in the following situations,” and then presents a range of situations. The situations are ”if she goes out without telling him,” ”if she neglects the children,” ”if she argues with him,” ”if she refuses to have sex with him,” and ”if she burns the food.” And respondents are able to respond ”yes,” ”no,” or ”don’t know.” The question only asks about a hypothetical scenario about a husband and wife and does not ask if the respondent would hit or beat his wife in each situation.

In addition to the men’s survey, we also focus on a subset of the women’s survey that administered a domestic violence module. In a random subset of the households that were selected to be administered the men’s questionnaire, one woman who was eligible for the women’s survey was randomly selected to respond to the added module. The module was only fielded in households in which privacy from other household members could be secured, resulting in a sample of 4,873 women (DHS 2019). The questions about abuse on which we focus are the questions about the incidence of IPV, which are asked only of the 3,945 women in the sample that had either been married or had lived with a man at one point in their lives.

The enumerator began fielding the module with a statement that guaranteed the anonymity of the respondent and that nobody else in the household would be informed of the responses. The extensive module asked whether the respondent’s husband or partner engaged in three different types of emotional abuse (e.g., humiliate or threaten, etc.) and ten different types of physical abuse (e.g., slap or punch, etc.) directed at the respondent.<sup>21</sup> For each type of abuse that the respondent experienced, they were then asked if the abuse happened ”often,” ”sometimes,” or ”not in the past 12 months.” The module further asked additional relevant questions about IPV. Specifically, the module asks whether the respondent is often afraid of her husband or partner, sometimes afraid, or never afraid; and the module further asks the respondent if she had physically harmed her husband or partner at times when he was not harming her.

The respondent in the women’s survey is explicitly told that they are allowed to not answer specific questions if they so choose. However, given the very unique circumstances of PNG and the careful fieldwork ensuring the safety of respondents, the rates of non-response are very low. In each

> 21An exhaustive list of types of abuse covered can be provided upon request.

10
|Table 1: Attributes of manuf|Non-exporters<br>acturing firms by|Exporters<br>export status|All firms<br>|
|---|---|---|---|
|Log employment|1.888|4.160|1.997|
||(1.231)|(1.711)|(1.349)|
|Average log hourly wage|1.724|2.253|1.749|
||(0.371)|(0.493)|(0.394)|
|Share with schooling less than high school|0.515|0.389|0.509|
||(0.360)|(0.271)|(0.357)|
|Share with schooling equal to high school|0.437|0.451|0.438|
||(0.352)|(0.236)|(0.348)|
|Share with schooling more than high school|0.046|0.158|0.052|
||(0.129)|(0.178)|(0.134)|
|Share of production workers|0.352|0.366|0.353|
||(0.348)|(0.254)|(0.344)|
|Share of workers in skill level 4|0.040|0.069|0.041|
||(0.135)|(0.116)|(0.134)|
|Share of workers in skill level 3|0.034|0.101|0.038|
||(0.122)|(0.126)|(0.123)|
|Share of workers in skill level 2|0.813|0.667|0.806|
||(0.279)|(0.249)|(0.280)|
|Share of workers in skill level 1|0.111|0.161|0.113|
||(0.229)|(0.213)|(0.228)|
|Observations||||
|2009|224,546|11,761|236,307|
|2010|242,206|11,545|253,751|
|2011|221,807|11,331|233,138|
|2012|219,135|11,114|230,249|
|Total<br>Notes: Table reports means and standard dev<br>manufacturing sector over the period 2009-201<br>the firm had export revenue in that year. Wage<br>to the number of workers employed by the fi<br>employment. Skill levels 1 to 4 are based on <br>Occupations (see Appendix Table A1 for a det|907,694<br>iations (in parenth<br>2. A firm-year obse<br>s are in 2010 Braz<br>rm, and employm<br> the 1988 Internati<br>ailed description).|45,751<br>eses) for firms <br>rvation is defin<br>ilian Reais, em<br>ent shares are <br>onal Standard|953,445<br>operating in the<br>ed as exporter if<br>ployment refers<br> relative to total<br>Classification of|
ularly the tax administrative data) only have a limited number of variables and there are likely to be other factors, such as the determination of the firm manager/owner, that may be impacting firm performance. These caveats need to be kept in mind when examining the findings of the econometric analysis. For both data sources we conduct an ordinary least squares (OLS) regression in the form of a linear probability model.<sup>4</sup> Specifically, we have four main outcomes (Y) that are in the form of a dummy variable. These are: (1) whether a firm experienced a decline in sales compared to pre-pandemic levels in June 2020; (2) whether a firm experienced a decline in employment compared to pre-pandemic levels in June 2020; (3) whether a firm experienced a complete recovery in sales compared to pre-pandemic levels in July 2021; and (4) whether a firm experienced a complete recovery in employment compared to pre-pandemic levels in July 2021. The econometric model can be expressed formally as follows: whereby, _β_ 1 captures the extent to which the size of a firm (small, medium or large) is associated with an outcome, _β_ 2 captures the extent to which the age of a firm (in years) is associated with an outcome, _β_ 3 captures the extent to which the activity of a firm (manufacturing, retail and wholesale services or other) is associated with an outcome, _β_ 4 captures the extent to which the location of a firm (Lusaka or elsewhere) is associated with an outcome, _β_ 0 is the intercept and _ε_ is the model error term.

For the survey data we conduct further analysis to fully utilize the richer information available on respondents. First, we rerun the regression above and include additional baseline variables captured in the baseline ES (top manager’s years of experience, >5 competitors entering the market in the last two years, new products introduced over the last three years, access to finance is a very severe/major obstacle, training is offered to permanent

> 4As a robustness check we rerun the regressions as a logit model and the results are qualitatively similar.

16
and sharia-compliant loans are less authentic than banks that specialize in sharia-compliant products. Tathmeer was not a lender known in the market, and was the intended name of the new Sharia-compliant subsidiary to be opened by Tamweelcom. The word “Tathmeer” means “to be fruitful” and does not have any religious connotation. Jordan Microfinance Company, on the other hand, is a known brand in the community.

In a separate, auxiliary marketing experiment, we tested whether the name brand (Jordan Microfinance Company) generated higher demand than the unknown brand (Tathmeer), but strictly for conventional loans. The name brand generates higher demand than the unknown brand. Section 4 discusses the ways in which this can affect the interpretation of the results.

# _Baseline Balance_

Table 1 displays the basic demographics of those recruited in the sample. Column 1 shows that individuals average 36 years old, and 57 percent are male. A third of the sample lives in Amman, the capital of Jordan, and a bit more than half the sample have a high school education or less. Sixty-five percent are married, 98 percent are Jordan nationals, and 11 percent plan to use the loan for home repair. Because it was important that respondents were not aware that they were part of a study we were unable to implement a large baseline survey which could have allowed us to collect detailed data on religiosity and financial literacy. Instead, we asked individuals whether or not they watch religious television programming<sup>15</sup> , in an effort to find a simple proxy for religiosity: 80 percent of the sample claim to watch any religious TV<sup>16</sup> . Half way through the study we updated our religiosity question to allow for a response on a 5 point scale. This allows us to identify individuals who claimed to “mostly” or “always” watch religious TV as “very religious”, which about 20% of our sample claim to do. Our results are qualitatively similar using either

> 15 We considered other types of proxies such as whether women wore the head-scarf or men had a beard, but these are often considered to be more cultural artefacts than religious. Asking about actual religious adherence (e.g. “How many times a day do you pray?”) is considered culturally inappropriate by many.

> 16 World Bank data report that 97% of households had a TV in 2002. We were unable to find more recent data as there seems to be a shift towards collecting data on internet connectivity. The near universal ownership of TVs makes us less worried that TV watching is proxying for other important characteristics.

13
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# <u>Appendix Table 1: Effects of Cash on Behaviors</u>

||<br>Parenting<br>(I)|<br>Behavior<br>(II)|Interaction<br>(I)|with Children<br>(II)|<br>(III)|Preparing D<br>(I)|iverse Meals<br>(II)|
|---|---|---|---|---|---|---|---|
|Treatment Effect (Cash)|0.19**|0.18**|0.33*|0.25*|0.33**|0.02|0.02|
||(0.09)|(0.09)|(0.17)|(0.14)|(0.16)|(0.05)|(0.04)|
|Intercept|2.17**|2.31**|2.27**|0.84**|0.59|0.32**|0.51**|
||(0.07)|(0.11)|(0.12)|(0.27)|(0.36)|(0.04)|(0.08)|
|N|1840|1840|2404|2404|1268|2163|2163|
|R-squared|0.01|0.03|0.00|0.38|0.39|0.00|0.01|

_Note: Standard errors in parentheses. '*' denotes significance at p<.1 level, '**' at p<.05. Model (I) is a simple regression with clustered standard errors, model (II) adds demographic and explanatory control including household size, gender of household head, age of household head, education of household head, age of youngest child, weeks since last payment, and distance of the household from the nearest school, and model (III) adds additional baseline controls_

# <u>Appendix table 2: Effects of Cash on Proximate Outcomes</u>

||<br>Food Div|<br>ersity||<br>Number|<br>of Meals Pre|<br>pared|Food Ins|ecurity, 7-D|ay|Food Ins|ecurity, 12-M|onth|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(I)|(II)|(III)|(I)|(II)|(III)|(I)|(II)|(III)|(I)|(II)|(III)|
|Treatment|||||||||||||
|Effect|||||||||||||
|(Cash)|0.30*|0.27*|0.32 *|-0.06|-0.03|0.00|0.21|0.14|0.08|-0.36|-0.4|-0.60**|
||(0.15)|(0.15)|(0.17)|(0.10)|(0.10)|(0.07)|(0.24)|(0.21)|(0.23)|(0.31)|(0.29)|(0.29)|
|Intercept|8.03**|8.01**|6.99**|2.71**|2.72**|2.07**|0.94**|0.37|0.19|4.89**|3.79**|3.64**|
||(0.11)|(0.20)|(0.34)|(0.07)|(0.11)|(0.14)|(0.14)|(0.31)|(0.39)|(0.20)|(0.39)|(0.50)|
|N|2404|2404|1268|2404|2404|1268|2343|2343|1155|2404|2404|1268|
|R-squared|0.02|0.04|0.06|0.00|0.05|0.09|0.01|0.05|0.08|0.00|0.02|0.04|

_Note: Standard errors in parentheses. '*' denotes significance at p<.1 level, '**' at p<.05. Model (I) is a simple regression with clustered standard errors, model (II) adds demographic and explanatory controls including household size, gender of household head, age of household head, education of household head, age of youngest child, weeks since last payment, and distance of the household from the nearest school, and model (III) adds additional baseline controls_ 29
**Table 3 Percentage of Exporters in Sectors in 2005**

||Construction|Manufacturing|Transport,<br>Communication|Wholesale,<br>Retail|Real Estate,<br>Business|Hotels,<br>Restaur.|Other<br>Services|
|---|---|---|---|---|---|---|---|
|Albania|0.05|0.39|0.55|0.06|0.18|0.60|0.33|
|Armenia|0.00|0.29|0.00|0.00|0.13|0.00|0.17|
|Azerbaijan|0.03|0.14|0.40|0.03|0.13|0.10|0.20|
|Belarus|0.15|0.47|0.50|0.09|0.15|0.22|0.00|
|Bosnia|0.27|0.34|0.79|0.11|0.14|0.19|0.20|
|Bulgaria|0.13|0.41|0.40|0.09|0.12|0.08|0.27|
|Croatia|0.00|0.46|0.65|0.16|0.13|0.55|0.00|
|Czech|0.04|0.49|0.18|0.13|0.14|0.04|0.15|
|Estonia|0.23|0.50|0.39|0.16|0.15|0.12|0.22|
|FYROM|0.14|0.59|0.36|0.12|0.23|0.00|0.08|
|Georgia|0.10|0.45|0.06|0.03|0.17|0.00|0.06|
|Hungary|0.07|0.39|0.23|0.14|0.08|0.38|0.10|
|Kazakhstan|0.07|0.11|0.17|0.07|0.00|0.00|0.00|
|Kyrgyzstan|0.13|0.37|0.20|0.08|0.12|0.00|0.08|
|Latvia|0.15|0.41|0.44|0.08|0.06|0.20|0.00|
|Lithuania|0.04|0.58|0.46|0.24|0.04|0.08|0.00|
|Moldova|0.00|0.29|0.36|0.08|0.33|0.00|0.04|
|Poland|0.05|0.27|0.30|0.14|0.13|0.00|0.06|
|Romania|0.00|0.27|0.31|0.06|0.09|0.29|0.00|
|Russia|0.07|0.21|0.22|0.14|0.02|0.00|0.00|
|Slovakia|0.14|0.68|0.31|0.13|0.22|0.06|0.07|
|Slovenia|0.15|0.81|0.65|0.28|0.13|0.07|0.32|
|Tajikistan|0.00|0.29|0.00|0.12|0.00|0.00|0.00|
|Turkey|0.20|0.52|0.37|0.29|0.31|0.22|0.24|
|Ukraine|0.06|0.23|0.36|0.07|0.14|0.15|0.12|
|Uzbekistan|0.03|0.31|0.22|0.02|0.10|0.05|0.00|
|Average|0.09|0.40|0.34|0.11|0.13|0.13|0.10|
|Std Dev|0.08|0.16|0.19|0.07|0.08|0.17|0.11|

22
<!-- Start of picture text --> x2<br>pathH<br>U H<br>U L<br>pathL<br>xH<br>xL<br>achievement-LIM<br>(w A = p)<br>x1<br><!-- End of picture text -->

**Figure 13:** Equating weights to market prices needs not yield an optimal identification of the poor. Budget lines for bundles xH and xL are in green, the two market dimensions (shown) are not separable from the third non-market dimension, which admits values L or H.

be excluded from total consumption. Such exclusion would correspond to setting the weights associated to health expenditures to zero. Again, the rationale being that health expenditures signal low unobserved health achievements.

# **Identifying low well-being from non-monetary data**

We consider the case of a specialized survey covering the non-monetary domain. We explain that, in oder to minimize (expected) mis-identification errors, the weight of a non-market dimension should depend on the correlation that is expected between achievements in this dimension and monetary aggregates.

Non-market achievements are not chosen, but they may still signal something useful about market achievements. This is at least the case when the strength of the correlation between the monetary aggregate and non-market dimensions varies across non-market dimensions. Assume for instance that the correlation between education and income is perfect while health and income are uncorrelated. Under this assumption, someone who has low education also has low income, but an individual with bad health may have low income or high income.

The signal that non-market achievements provide on the ignored monetary aggregate may help improve identification in expected terms. For simplicity, assume that there are four dimensions, where the market domain is {1, 2}. Dimension 3 is perfectly correlated with the monetary aggregate while dimension 4 is not correlated with the monetary aggregate. Assume that all dimensions are cardinally measured and all are equally important to welfare, e.g., U(x) = x1 + x2 + x3 + x4.<sup>43</sup> Without data constraints, it would be possible to perfectly identify the poor with an achievement-LIM

> 43 This assumption violates both of our requirements on the indifference curve U . One can construct a more complicated example that satisfies these requirements, although it will be a bit more involved.

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