rafmacalaba/datause-encoder-data huggingface dataset:
# **9. References**

‐ ‐ Abadie A., Athey S, Imbens G.W., Wooldridge J.M., 2020. Sampling Based versus Design Based Uncertainty in Regression Analysis. Econometrica. 88(1):265–96.

Abadie, A., Imbens, G., 2006. Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica 74(1):235-267.

<mark>Akresh, R. (2009). Flexibility of household structure child fostering decisions in Burkina Faso. Journal of Human Resources, 44(4), 976-997.</mark> Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., Sanghi, A., 2018. Do refugee camps help or hurt hosts? The case of Kakuma, Kenya. J Dev Econ. 130:66–83.

<mark>Altindag, O., O'Connell, S.D., Sasmaz, A., Balcioglu, Z., Cadoni, P., Jerneck, M. and Kunze Foong, A., 2019. Targeting humanitarian aid using administrative data: model design and validation.</mark> _<mark>Available at SSRN 3444974</mark>_ <mark>.</mark> Anderson, M.L., 2008. Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. J Am Stat Assoc. 103(484):1481–95.

Angrist, J.D., Imbens, G.W., Rubin, D.B., 1996. Identification of Causal Effects Using Instrumental Variables. J Am Stat Assoc 1;91(434):444–55.

<mark>Aronow, P.M., Samii, C. and Wang, Y., 2019, July. Design-based inference for spatial experiments with interference.</mark> Baird S, Bohren JA, McIntosh C, Özler B., 2018. Optimal design of experiments in the presence of interference. Rev Econ Stat. 100(5):844–60.

Beegle K, De Weerdt J, Friedman J, Gibson J., 2012. Methods of household consumption measurement through surveys: Experimental results from Tanzania. J Dev Econ. ;98(1):3–18.

BFA Global., 2018. Refugees and Their Money: The Business Case for Providing Financial Services to Refugees.

Chaaban, J., Salti, N., Ghattas, H., Moussa, W., Irani, A., Jamaluddine, Z., Al-Mokdad, R., 2020. MultiPurpose Cash Assistance in Lebanon: Impact Evaluation on the Well-being of Syrian Refugees. Economic Research Forum, No. ERF26_84.

34
3 Moderate Substantial government influence on banks; government owns or controls some banks; government controls credit; domestic bank formation may face significant barriers 4 High Heavy government involvement in the financial sector; banking system in transition; banks tightly controlled by government; possible corruption; domestic bank formation virtually nonexistent 5 Very high Financial institutions in chaos; banks operate on primitive basis; most credit controlled by government and goes only to state-owned enterprises; corruption rampant Guide 19 ; U.S. Department of State, Country Reports on Economic Policy and Trade Practices for 2001; and official government publications of each country.

|**CONTRACT ENFORC**|**EMENT**|||||
|---|---|---|---|---|---|
|**Variable name in**<br>**corresponding**<br>**database**||**Scale**|**Description**|**Years**|**Source**|
|**(name in our**<br>**database)**||||||
|Number of<br>procedures<br>(db_contr_proc)|Actual|number|Number of procedures mandated by law or court regulation<br>that demands interaction between the parties or between<br>them and the judge or court officer. The questionnaire<br>covers the step-by-step evolution of a debt recovery case<br>before local courts in the country’s most populous city.|Survey<br>conducted<br>in 1999,<br>updated<br>to 2003|**Doing**<br>**Business,**<br>**The**<br>**World**<br>**Bank**<br>**Group.**<br>See Simeon Djankov, Rafael La Porta, Florencio Lopez-<br>de-Silanes, and Andrei Shleifer, "Courts", Quarterly<br>Journal of Economics, May 2003.|
|Bureaucracy quality<br>(icrg_bureau)|0 to 4||High points are given to countries where the bureaucracy<br>has the strength and expertise to govern without drastic<br>changes in policy or interruptions in government services.<br>Countries that lack the cushioning effect of a strong<br>bureaucracy receive low points because a change in<br>government tends to be traumatic in terms of policy<br>formulation and day-to-day administrative functions.|1990-<br>2000|**International Country Risk Guide - PRS Group**<br>http://www.prsgroup.com/icrg/icrg.html|
|**FISCAL**<br>**REGULATION**||||||
|**Variable name in**<br>**corresponding**<br>**database**||**Scale**|**Description**|**Years**|**Source**|
|**(name in our**<br>**database)**||||||
|Fiscal burden<br>(ief_taxation)|1|Very low|Individual Income Tax Grading Scale<br>Top income tax rate 0 percent. Marginal rate for the<br>average taxpayer 0 percent.|1995-<br>2003<br>(annual)|**The Index of Economic Freedom, The Heritage**<br>**Foundation**|

40
# **_5.  Concluding Remarks_**

Poverty reduction is now considered a fundamental objective of development, and therefore has become a metric for assessing the effectiveness of development interventions. Given the fundamental role economic growth plays in reducing income poverty, there has been an increased interest in measurement frameworks to assess the pro-poorness of a growth process. This is an exercise in social evaluation, the outcome of which hinges on the underlying value judgments.

This paper revisits elasticity-based methods and offers a _calculus of pro-poorness_ based on three key elements: (1) the definition of a growth pattern in terms of the point elasticity of individual incomes with respect to total income; (2) the use of individual poverty contributions and members of the class of additively separable poverty measures in the construction of a social evaluation criterion; and (3) the selection of the amount of poverty reduction obtainable under distributional neutrality as the threshold that must be crossed in order to declare a growth pattern pro-poor.

What emerges from this framework is a new measure of the pro-poorness of a growth pattern which can be expressed as a weighted average of the deviations of that growth pattern from the benchmark case. A growth pattern is pro-poor if this average deviation is positive. This measure can be used to assess pro-poorness both in terms of aggregate poverty _and_ at percentiles – unlike the poverty equivalent growth rate ( _PEGR_ ) measure and associated correction factor of Kakwani et al (2004) – and for income or expenditure components separately. The Kakwani et al measures are also elasticityrelated, as our analysis has shown, but they are not explicitly constructed from the same fundament – namely, from the function _q(x)_ which fully defines an income growth pattern. Nevertheless, each methodology is consistent with the conceptual framework set out by Osmani (2005), and both rest upon the same additively separable class of deprivation-based poverty indices.

An application of this methodology to data for Indonesia for the period of 19932002 shows that the amount of poverty reduction achieved over that period remains far below what distributionally neutral growth would have produced. This conclusion is robust to the choice of both a poverty measure among members of the additively separable class, and a poverty line up to about 2 dollars a day. Also, the behavior of five 27
the expected discounted value of utility over an infinite horizon by making decisions on consumption, savings, and on whether to take advantage of the investment opportunity, subject to borrowing constraints. Kaboski and Townsend (2007) derive a steady state solution to the model.

The behavioral model is estimated using five years of ”pre-program” data, in particular, information from households and local businesses on their consumption, income, investment, credit, liquid assets, and interest income from these assets in addition to village population data. The validity of the estimated model is assessed by comparing the model‟s predictions of the effects of the Thai Million Baht program on consumption, investment and the probability of investing to the actual effects observed under the social experiment.<sup>16</sup> Impact estimates obtained using the simulated model are close and not statistically different from impact estimates obtained from reduced form regressions based on post-experiment data, lending support to the model.

One of the notable predictions of the model that is borne out in the data is that the impact of the program on consumption exceeded one million baht and that consumption increased more than credit did because the credit was used toward productive activities. Kaboski and Townsend (2007) use the estimated model to compare the costs of the microfinance program to the costs of a direct cash transfer program that would have provided the same utility benefit. They find that the cost of the microfinance program was 33 percent less, due to the fact that the microfinance program relaxed borrowing constraints that the transfer program did not.<sup>17</sup> In summary, Kaboski and Townsend (2007) demonstrate both theoretically and empirical that microfinance programs are an effective means of increasing the liquidity of credit constrained households and that they positively impact both investment and consumption.

# **4.2  Relaxing Consumer Credit in South Africa**

Many microcredit programs focus on providing credit for entrepreneurs rather than consumers. Providing credit to consumers is a more controversial policy initiative, because of concerns that behavioral biases may lead consumers to take on too much debt. Karlan and Zinman (2007)

> 16 The program is introduced into the model as a reduction in borrowing constraints in an amount that would increase the amount of total expected credit (as calculated from the model) in the village by one million baht. This implies a larger reduction in credit constraints in smaller villages (because all size villages received the same amount).

> 17 Interestingly, even households that do not use credit can be affected by the relaxation in borrowing constraints, as it lowers their need for a buffer stock of liquidity and allows them to increase consumption.

35
-48-

table, based on Fullerton's study, describes the characterist4 - of these four different tax packages in terms of the their provisions fey indexing depreciation, interest, capital gain, and inventory, etc. Fullerton's study considered the effect on the METR of fairly moderate rates of inflation (O to 10 percent) but even at these levels the effect on the METR is clear.

TABLE 12: U.S. TAX REFORM PROPOSALS, INFLATION, & THE NETR

|Item|1985 Tax<br>(Original|1986 Tax<br>) (New Law)|1984 Treasury<br>Proposal|1985 President's<br>Proposal|
|---|---|---|---|---|
|CorpTax<br>Rate (%)|46<br>|34|33|33|
|Deprecia<br>tion|- Unindexed<br>Acceleratd|Unindexed<br>Accelerated|Indexed<br>Economic Dep.|Indexed<br>Accelerated|
|Interest|Unindexed|Unindexed|Indexed<br>(approximate|Unindexed<br>)|
|Capital<br>Gain|Unindexed<br>60t Long<br>Term Gain<br>Excluded|Unindexed<br>Lower Pers.<br>Tax (15,28%<br>No Exclusio|Indexed<br>Lower Person-<br>) al Tax, No<br>n Exclusion|Unindexed<br>Lower Personal Tax<br>50% Exclusion|
|Inventory|LIFO but<br>financial<br>conformity<br>required|LIFO but<br>financial<br>conformity<br>required|LIFO w/o<br>conformity<br>&<br>Indexed FIFO|LIFOw/oconformity<br>& Indexed<br>FIFO|
|Invest.<br>TaxCredit|Allowed on<br>Equipment|Repealed|Repealed|Repealed|
|Dividend|No Deduct.|No Deduction|50% deduction|10% deduction|
|System<br>Characte|No<br>r Indexation|No<br>Indexation|Full<br>Indexation|Partial Asset<br>Indexation<br>(Dep. and Inventory)|
|Inflatio<br>Effect on<br>METR|n METR rises<br>with<br>inflation|METR rises<br>but less<br>rapidly due<br>to lower tax<br>rates|METR almost<br>unaffected<br>by inflation<br>|METR falls with<br>inflation due to<br>unindexed interest<br>but more slowly due<br>to lower tax rates|
method, survey respondents provide their perceived minimum income for attaining certain subjective-qualitative levels of living (Van Praag, 1968; Danziger, et al., 1984; Kapteyn et al., 1988). The “consumption adequacy” question of Pradhan and Ravallion (2000) is an alternative approach with advantages when “income” is not well-defined. More general “subjective wellbeing” questions have also been used; probably the most common example is the satisfaction with life (SWL) question.<sup>13</sup> The role of such subjective welfare data is not to decide who is better off, but to identify the parameters of an objective welfare metric. SWL is assumed to depend on true objective welfare plus other extraneous factors such as personality traits, “mood effects” and so on. Econometric methods are then used to extract the signal on how objective welfare varies with relevant covariates (see, for example, Kapteyn, 1994, and Ravallion and Lokshin, 2002).

A body of research along these lines has suggested that there are RD effects on welfare. Past econometric tests have regressed self-rated welfare (satisfaction with life or happiness) on both own income and an estimate of comparison group income, which is typically the mean income of people living in the same area or working in the same industry or with similar characteristics (age, education, occupation).<sup>14</sup> Such studies have suggested that welfare-consistent poverty lines would need to have a high income elasticity in developed countries. In a recent example, Luttmer (2005) regressed self-assessed happiness in the US on log income and log mean ‘neighbors’ income, and found that the two coefficients add up to roughly zero, implying that an equal proportionate increase in all incomes (leaving relative inequality unchanged) would have no impact on average happiness. This suggests a welfarist justification for setting the poverty line at a constant proportion of mean income. An equi-proportional increase in all incomes would then leave most poverty measures unchanged. Other studies for developed countries have found lower income elasticities of the poverty line than suggested by Luttmer’s results. Kilpatrick (1973) found that subjective poverty lines over time in the US have an elasticity of about 0.6 to mean income. Hagenaars and van

> 13 A common form is: “Overall, how satisfied (content, happy) are you with your life?  Are you (1) very unsatisfied; (2) unsatisfied; (3) neither unsatisfied nor satisfied; (4) satisfied; (5) very satisfied?”

> 14 Examples include Stadt et al. (1985), Clark and Oswald (1996), Pradhan and Ravallion (2000), Ravallion and Lokshin (2002), McBride (2001), Blanchflower and Oswald (2004), Senik (2004), Kingdon and Knight (2004), Ferrer-i-Carbonell (2005) and Luttmer (2005).

7
74 because the technology necessary is basically the same as that needed to achieve 1.0 gpm NOx standard, although calibrations must be modified and precious metal loadings increased in catalytic converters in some cases. CARB has also determined that a 0.25 gpm standard is feasible and cost effective for HC. The incremental cost per vehicle to achieve this standard is estimated at US$25, with an overall cost effectiveness of about US$ 1600 per ton reduction in emissions.

As noted by CARB, the technology, which automobile manufacturers are expected to utilize for lowering non-methane hydrocarbons (NMHC) emissions, includes more durable and efficient close-coupled catalytic converters. These single bed converters are located in or close to the engine exhaust manifold and "light off" quickly to effectively reduce cold start HC [and CO] emissions. The advanced technology fostered by the tighter standards should make it easier to achieve low in-use emission levels. Several organizations have made estimates of the costs associated with these advanced controls as shown in Table 38.

# **Table 38. Costs associated with advanced vehicle emission controls in the United** States **(Beyond 0.41 HC and 1.0** NOx **standards for 50,000 miles)**

|||Source of Estim|ate||
|---|---|---|---|---|
||EPA|CARB<br>(in US$ per ve|OTA'<br>hicle)|CRS<sup>2</sup>|
|Automobiles|||||
|0.25 HC|46.75|25|60||
|0.40 NOx|36.75|25-30|80||
|Light Duty Veh. Full Life<sup>3</sup>|Minor||||
|Idle Standard<sup>4</sup>|Minor||||
|10% Air Quality Level'|Minor||||
|No Averaging5|Minor||||

Total 100

- **U.S.Congressional Office of Technology Assessment**

- **2** U.S. **Congressional Research Service**

- 3 **Requires standards to be achieved for 100,000 miles instead of** 50,000 **miles.**

- **4Requires the addition of an idle test along with the Federal Test Procedure.**

- _5_ **Requires all vehicles to meet standards as they come off the assembly lines.**

- **6 Prohibits any averaging of emissions results for individual cars to determine compliance.**
are taken into account, FHH ─ whether headed by widows or not ─ have significantly lower per capita consumption expenditures. FHH have per capita expenditures that are on average 16 percent lower than male headed households; for households with a widowed female head mean per capita consumption is 19 percent lower than that of all other urban households. Again as in rural areas the significant disadvantage associated with female headship vanishes when marital status is taken into account. For households with widowed female heads in urban areas, the negative effects are substantially attenuated only when controls are added for education and physical capital, reducing the mean difference to a smaller but still significant 6 percent.<sup>16</sup> In sum, allowing for their smaller household size, I find that FHH are on average poorer than MHH only before controlling for marital status. The analysis suggests that it is FHH headed by widows who are the most impoverished, echoing the findings for Uganda and Zimbabwe (Appleton, 1997; Horrell and Krishnan, 2007). Given the very small sample of rural and urban divorced female heads, I am unable to say anything about this group, though there are indications that they may also be relatively disadvantaged.

A considerable drawback to examining the condition of women with this type of data and analysis is that it is limited to households with female heads. A majority of widowed and divorced women get reabsorbed into male headed households either through remarriage or residence. (The ELIM data indicate that 7 percent of women aged 15 and older who are not household heads are current widows.) Thus, an examination of FHH does not allow me to say anything about a large share of ever-widowed or ever-divorced women. To better investigate the welfare of these women, I now turn to the individual data in the DHS.

# **4. Evidence from individual welfare indicators**

We have seen the difficulty of making assessments of women‘s welfare with standard household level data sources. The paper now turns to a source of data rarely used in poverty measurement but with some advantages.

We know that widows and divorced women usually remarry in Mali, typically into polygamous households as second, third or fourth wives. As a result of large age gaps between

> 16  Here, the 2% difference with the estimate in Table 3 is due to the omission of urban male divorcee headed households from the comparison group.  I also tested effects for divorced FHH of which there are 19 in the ELIM‘s urban sample.  The estimated coefficients are negative and large (-0.20) but vastly attenuated once demographics are included in the regressions.  However, they are never statistically significant, possibly due to the limited sample size.

15
Ditk . ~~e; e+~~ <u>. .</u> =] if ~~i -~~ it-1eit <u>4]</u> > mean ([{<i#4-*is=1))Cit—-1 ) 42% std ~~[Sissi]~~ ej<sup>it-1</sup> FOMC,large change FOMC"? large change t Prob(Ditx = 1) = P(a + B,FOMC 79° MP9" + BoFOMCLE Arg" change +
development. One of the explicit objectives of the JNNURM is to ensure basic services to the urban poor, and an important strategy to do this would be to integrate the poor with service delivery systems. Presumably, these improvements are likely to improve the welfare of poor households.

However at this stage it is unclear what (package of) services are demanded by poor households, and whether pro-poor improvements should be targeted in the form of cash transfers / subsidies or if citywide improvements in services and public goods (such as extending water and sewer networks, reduction in crime and air pollution) are likely to accrue disproportionate benefits to poor households. In this paper, we outline a general methodology to assess the value of public services and apply this to examine demand for public services in the Indian city of Pune. While the empirical application focuses on household data collected for Pune, the estimation strategy outlined in the paper has broader applicability and can be used to examine similar issues across cities.

Our main finding is that the value of publicly-provided services accruing to the poor is greater than that going to wealthier households.<sup>5</sup> The services examined (water, transport, education, safety) are worth roughly 29 percent of the rental value of the dwelling among the wealthy, but 67 percent of the rental value among the poor. The list of services examined in our analysis is clearly not comprehensive – wealthier households may benefit from different public services that we have not included. But the results suggest that even untargeted, across-the-board investment in these services can be progressive.

The remainder of this paper is organized into five sections. In Section 2, we discuss the role of hedonic models in estimating the net benefits from local property taxes 5