rafmacalaba/datause-encoder-data huggingface dataset:
collected as part of the APS, we can only provide cross sectional analysis of the impact of social protection programs on poverty outcomes.

Following Dehejia and Wahba (2002), we assume Y1 _i_ to be the outcome for household _i_ when it is subject to treatment and Y0 _i_ to be the outcome for the same household _i_ in the absence of the program. The treatment effect for a single unit, and the expected treatment effect for the treated population can be defined as τ _i_ = Y1 _i_ - Y0 _i_ and τ = E(Y1 _i_ | Ti = 1) – E(Y0 _i |_ T _i =_ 1), respectively, where Ti is equal to 1 if the i _th_ unit is a program participant, and 0 otherwise. Since we do not observe outcomes that would have materialized if the participants had not been assigned treatment, E(Y0 _i |_ T _i =_ 1) is not estimable. As noted above, when treatment is randomly assigned to households, treatment status is independent of potential outcomes (Y1 _i_ , Y0 _i_ ⊥ T _i_ ) such that E(Y0i | Ti = 0) = E(Y0i | Ti = 1) = E(Yi | Ti = 0). This implies that the average treatment effect for the treated population is E(Y _i_ | Ti = 1) – E(Y _i |_ T _i =_ 0). If program assignment is not random but based on a vector of observable covariates, X _i_ , the distribution of X _i_ will not be independent of treatment status. However, conditional on observable covariates, we can assume assignment to treatment to have been random, such that Y1 _i_ , Y0 _i_ ⊥ T _i_ | Xi (conditional independence assumption). The average treatment effect is then expressed as τ = E(Y _i_ | T _i_ = 1, Xi) – E(Y _i_ | Ti = 0, Xi).

The basic idea behind PSM is to construct a control group with observable characteristics similar to attributes of program recipients. Rosenbaum and Rubin (1983) have shown that if outcomes are independent of program participation conditional on X, then outcomes are also independent of program participation conditional on the propensity score; P(Xi), which is the predicted probability of program participation given X.

adequate, (vi) household is satisfied with life, (vii) life has improved in the past 3 years, and (viii) life is expected to improve in the next 12 months.

18
various equations we specified for estimating relationship [2], and the hypothesized directions of impact, based on the theory developed earlier.

**Table 4: The estimated equations of the decentralization process**<sup>**7**</sup>

||**InstCreatd**|**WuasInvlv**|**PltclCost**|**YrsDecent**|**InstDismntld**|
|---|---|---|---|---|---|
|%BgtBsn|NI|NI|NI|NI|**+**|
|%BgtExtr|-|NI|NI|NI|NI|
|%BgtSpnt|NI|-|NI|NI|-|
|%BgtSrcs|NI|-|NI|NI|NI|
|%UsrPay|NI|NI|-|-|-|
|Facilities|NI|NI|+|NI|NI|
|FormsDisput1|NI|+|NI|NI|NI|
|FormsDisput2|+|NI|NI|NI|NI|
|GovrBdy|-|-|NI|-|NI|
|MainObj|NI|NI|NI|+|NI|
|MinrObj|+|NI|-|NI|NI|
|PrblmsBfr|-|NI|-|NI|NI|
|Scarcity1|NI|NI|NI|-|NI|
|SectrComposit|+|+|NI|NI|+|
|ShareSw|+|+|+|NI|NI|
|TypesDisput|NI|NI|NI|NI|-|
|WuasInvlv|+||+|||
|Functional form|GLM|OLS|GLM|POISSON|GLM|

Note: NI=Not Included

# **5. Results**

Due to the large number of variables in the dataset, and the high level of association among many of them (leading to high auto-correlation), we administered principal component (PC) analyses that helped us group these correlated variables and reduce the number of variables with inter-correlation on the right hand side of the estimated equation. We constructed a working dataset that includes the PC variables and some other variables that were incorporated in our regression analyses. The PC variables are described in Annex 3, but the results of the PC analysis are presented below. Following the PC results, we present the descriptive statistics of the variables in the working dataset. Finally, the Results section is concluded with the various regression outcomes and their interpretation.

> 7 Some of the variables presented here are explained in the section “Principal Component Results”.

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3

# Table 1. 1992 Noutariff Measure (NTM) Coverage Ratios for OECD Imports from Developed and Developing Countries.

|____________________________|1992Impo|rts(Smillion)|NTM! Covc|rage Ratios|
|---|---|---|---|---|
|Product Group (SlTC)|Developing<br>Countries|Industrial<br>Countries|Developing<br>Countries|industrial<br>Countries|
|_ALL_NON-FUEL ITEMS (0 to 9- 3)|530,740|1,910,710|18.4|10.4|
|All Foods (0+1+22+4)<br>|77,083<br>|192,590<br>|18.3<br>|28.0<br>|
|Food_and_Live Anmbal (0)<br>|67,732<br>|154,280<br>|19.2<br>|29.8<br>|
|ailseeds and Nuts (22)|2.502|_5.856_|9.8|4.2|
|Aninmal-Vegetable<br>Oils (4)|2,829|_5,081_|7.8|8.3|
|Agricultural Materials (2-22-27-28)|20,076|_53.580_|_10.9_|_5.0_|
|Ores and Metals (27+28 +67+68)<br>|41,627<br>|117,073<br>|10.0<br>|13.7<br>|
|Ferrou Metals (67)|11,063<br>|_55.555_<br>|35.6<br>|38.3<br>|
|_Non-fmorros_<br>Metal (68)|15,116|37,859|0.0|0.0|
|Mineral Fuels (3)|164,420|86,205|16-5|21.5|
|Chemicals_(5)_|21,806|216,939|3.4|_5.9_|
|Other Manufactures (6_to_8-67-68)<br>|354,370<br>|1,290.212<br>|21.6<br>|912<br>|
|Leather (61)|3,741|5,013|_56.5_|29.6|
|Textile Yarn & Fabric (65)|18.375|_50,651_|52.1|9.0|
|Clothing (84)|75,293|47,615|64.3|7.0|
|Footwear (85)|15,844|12,163|34.0|16.8|
|ALL ITMS (0 to 9)|1695,160<br>_1_|_1,966,916_<br>|1<br>18.0|10.9|

Source: World Bank-UTNCTAD SMIART Dat-abas. Trade data from UN COMfTRADE Records.

Notes: (1). Trade and NTMd coverage ratios are for the following markets: Australi, Austria, Canada, EEC(12), Finland, Japan, New Zealand, Norway, Sweden, Switzerland and the United State.

(2).Tbe following tys of measures have been included in the computation of the NIl trade coverage index: surcharges, variable levies, quantitative restrictions (mncluding prohibitions, quotas, non-automatic licensing, 'voltuny' export restAnts and restaints under the MFA and similar xtie arrangements, and st monopolies), price control measures (inluding minimum, reference or basic import price systems, price survelce and voluntary export price restraints), additional custm formalities and er entry control measures, and local content requirements.
Table 10: Variable selection through lasso across simulations

||Weather|NDVI|LC|Population|Pollution|Distances|GPS|Regions|
|---|---|---|---|---|---|---|---|---|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.99|0.54|0.99|0.00|1.00|0.97|0.29|1.00|
|Madagascar|0.77|0.79|1.00|0.77|1.00|0.99|0.68|1.00|
|Malawi|0.99|0.99|0.74|0.53|0.99|0.96|0.14|0.99|
|Mozambique|0.81|1.00|1.00|0.72|0.98|0.99|0.97|0.99|
|Sri Lanka|0.74|1.00|1.00|0.23|0.88|1.00|0.98|1.00|
|Tanzania|0.99|0.97|1.00|0.56|1.00|0.99|0.02|1.00|
|Vietnam|1.00|0.90|1.00|0.81|1.00|0.90|0.99|1.00|
|Average|0.90|0.88|0.96|0.52|0.98|0.97|0.58|1.00|
|**Panel B: Poverty**|||||||||
|Malawi|0.37|1.00|0.74|0.52|1.00|0.71|0.05|1.00|
|Tanzania|0.93|0.79|1.00|0.07|1.00|0.98|0.00|1.00|
|Average|0.65|0.90|0.87|0.30|1.00|0.84|0.03|1.00|

Note: The columns list the different types of candidate variables used in estimation. For each country, we calculate the proportions of simulations in which lasso selects at least one of each variable type for use in EBP. GPS variables include the longitude and latitude of each subarea centroid, while region is defined separately by country, but is generally dummy variables for the strata used in sampling (usually admin2, sometimes also stratified by urban/rural).
attempt to target subsidy resources to specific customer groups. Although interrupted in 2001, following the economic crisis, they were reinstated and substantially increased during 2002.

4.25 Moreover, in May 2002, the government introduced a new supply-side subsidy for metropolitan bus services. This is controversial given the substantial excess capacity that exists in this sector, as a result of a sustained decline in the demand for these services during the last decade. These new subsidies are financed via a surcharge of 18.5% on the net price of diesel nationwide. The total cost is estimated at US$91.6 million, and translates into an implicit subsidy per passenger-trip of US$0.038 for the bus service, US$0.097 for the train service, and US$0.051 for the metro service.

4.26 Based on the pattern of commuting journeys for heads of household in the Metropolitan Area obtained from the OPSM survey, it is estimated that each commuter receives an implicit average weekly subsidy of US$0.64. Crossing commuting patterns against income, it becomes evident that the absolute value of the subsidy is highest for the middle-income quintiles, and for those using the suburban rail network. The concentration coefficients show a regressive distribution, and range from +0.36 for metro rail, +0.16 for the buses, and +0.09 for suburban rail (Figure 12 and Table 6).

**Figure 11: Distributional incidence of metropolitan public transport subsidy**

<!-- Start of picture text --> 35%<br>Monthly<br>30% Average<br>Subsidy<br>25%<br>20% Beneficiaries<br>Percentage<br>15%<br>10%<br>Total Subsidy<br>5% Percentage<br>0%<br>1 2 3 4 5<br>Income Quintile<br>Source: OPSM, May 2002<br><!-- End of picture text -->

**Figure 12: Distributional incidence by mode of public transport in the Metropolitan Area**

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**Map 9 : Employment densities for each census tract (Radio censal) unit within the Buenos Aires region in 2012 (data source: projections based on Censo Nacional Economic 04/05 (CNE 04/05) and Encuesta Permanente de Hogares, 2011 (EPH, 2011))**

Map 9 shows that job dispersal exists along the major public transport lines, the railway network (represented as black lines), but is nearly non-existent elsewhere. This suggests that the radial public transportation network plays a strong role in the location of employment centers which is characteristic of dominantly mono-centric urban forms.

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Similarly, the Central African Republic and Ethiopia, countries that find themselves at the bottom of the borrowing rate rankings, are also among the 10 with the lowest per capita GDP in all of Africa. However, caution is required in interpreting this relationship, as the same factors that drive gains in income could be leading to an increase in borrowing rates. For example, one such factor might be local institutions. For instance, Mauro (1995) has shown that corruption has a pernicious effect on economic growth by lowering investment. In fact, corruption is an endemic problem in SSA. The “Corruption Perceptions Index” published by Transparency International has consistently ranked SSA as one of the most corrupt regions in the world and in 2010, 16 of the world‟s 30 most corrupt nations were in SSA.<sup>3</sup> The region also ranks dismally in the World Bank‟s Doing Business index<sup>4</sup> , which rates countries based on how conducive their regulatory environment is to starting and operating a local firm. The rating incorporates several parameters like ease of registering property, getting credit and enforcing contracts. A low ranking in this index is indicative of weak institutions. It is highly likely that corruption could be one of the driving forces behind the low per capita GDP as well as the low borrowing rates in this region.

Although it is difficult to glean any other causes behind low usage of formal financial services from the Gallup survey, another potential candidate is “trust.” The trust explanation is closely related to the problem of corrupt institutions that we just discussed. There are two ways in which trust, or social capital, can have an impact on the adoption of microfinance. Under the first mechanism, which is specific to microcredit, people are less likely to borrow under joint liability if there is low level of trust within their community. Cassar and Wydick (2010) provide laboratory evidence indicative of support for this hypothesis. They find a positive correlation

> 3 Complete data is available at: http://www.transparency.org/policy_research/surveys_indices/cpi/2010/results. 4 Complete data is available at: http://doingbusiness.org.

15
vulnerability to changes in fish species in areas where salinization will significantly alter habitats.<sup>13</sup> Vulnerability will in turn depend on the relative abundance, average size, commercial value and dietary status of local fresh- and brackish-water fish species. If the aquatic intensity (yield per unit volume) of fish biomass, commercial value and dietary status were always identical for fresh- and brackish-water species groups, then salinization would have no impact on the welfare of poor households. Tropical field research on habitat salinity and fish biomass has revealed diverse patterns in different regions and ecosystems, but no clear, robust relationship between biomass yields in fresh and brackish water bodies (see for example Welcomme, et al. 2010; Nixon 1988; Marten and Polovina 1982). In addition, we have only spotty information about the relative abundance, commercial value and dietary status of the 83 fish species consumed by the poor in the Sundarbans region.

Given the lack of robust research results and species-specific data, we cannot project the ultimate impact of salinization on fish consumption by poor households with any confidence. However, it does seem reasonable to assert that transitional risks for poor households will be higher in areas where the greatest changes in fish species will occur. And collective risks will be greater in areas where the settlement density of poor households is also high.<sup>14</sup>

> 13 An example is provided by Bombay Duck ( _Harpadon nehereus_ ), a low price fish that is still caught in abundance and preferred by poor and middle class consumers all along the Bangladesh coast.  On average, Bombay Duck accounts for 14 percent of daily fish sales.  Using the IPCC A1B emissions scenario, Farnandes et al. (2015) have predicted a 35 percent reduction in production of this species in Banglades’s exclusive economic zone.

> 14 This is an issue of major concern as fishery experts in Bangladesh indicated that significant gain of brackish fish species in the study region is unlikely to occur in a changing climate by 2050. Salinity is only one of the multiple determinants of brackish fish behavior and habitats. Wild marine and brackish fish species prefer coastal ecosystems to river systems because of their feeding habits and biology; and are expected to move slowly over time to inland river systems, if at all. On the contrary, many freshwater fish species have low swimming speed, prefer local habitats and will cease to survive with increase in salinity (Robin et al. 2010). Gain et al. 2008 also reported significant decline in fish diversity with increase in salinity in _Sibsa_ River near _Paikgacha_ . In 1975, fresh water fish species near Paikgacha were abundant, but in 2005 the field sampling could not locate 17 fresh water species, including _Labeo rohita, Catla catla, Anabas testudineus_ and _Clarius batrachus._ Experts also indicated that with change in aquatic salinity, a

16
common scale according to their consumption, we can obtain the relative distribution of household welfare, or measures of inequality.

We next provide an overview of data situations and imputation methods before discussing the analytical framework and data.

# **II.1. Overview Data Situations**

For presentation purposes, we group all situations of missing data into three broad categories as follows (Table 1): A. the cross-sectional household consumption data are _completely_ missing B. the cross-sectional household consumption data are _partially_ missing, and C. although the cross-sectional consumption data are available, the panel household consumption data are (completely or partially) missing These categories can also be thought of as being ranked, in a roughly decreasing order, according to their severity of data scarcity. Put differently, we assume that the ideal data scenario is one where we have (high-quality) panel consumption data, the second-best scenario is one where we have cross sectional consumption data (but no panel data), and the least desirable scenario is one where we have no consumption data.

Group A thus represents the most data-scarce situation, where no (cross-sectional) data are collected on household consumption, although some data may be collected on other household characteristics.<sup>8</sup> Clearly, a typical example of the surveys that belong to Group A is nonconsumption surveys where no consumption data are collected by design. Group A remarkably covers quite a few surveys that are commonly implemented. For example, these surveys include the popular Demographic and Health Surveys (DHS), (most) Labor Force Surveys (LFS), and other surveys such as school-based surveys. In fact, implementing a household consumption

> 8 Unless otherwise noted, for brevity we hereafter follow the common practice and do not use the term “cross sectional” when referring to such data, but we explicitly mention the term “panel data” when discussing these data.

8
analysis is supposed to have. Researchers, therefore, make efforts to minimize attrition and follow a rigorous procedure to locate previously surveyed households. As mentioned before, our survey data were subject to attrition during the two follow-ups: 7.4 percent from 1991/92 to 1998/99 (131 households out of 1,769 from 1991/92 were not located in 1998/99) and 7.9 percent from 1998/99 to 2010/11 (129 households out of 1,638 from 1998/99 were not re-interviewed in 2010/11). Overall, the attrition rate is 14.7 percent from 1991/92 to 2010/11, which is less than one percent a year.

However, what is important is not the extent of attrition but whether attrition is nonrandom. To estimate the determinants of attrition, a probit regression is run using the 1991/92 data with attrition dummy (1 for households that were lost, 0 otherwise) as the dependent variable, and the outcome variable (income, expenditure, school enrollment, etc.), household- and village-level characteristics as explanatory variables. This is done separately for the 9 outcome variables. Results of the regressions (not reported) show that attrition is positively correlated with households with less land or non-land assets, absence of adult males or females, and those in villages with poor road conditions, a proxy for infrastructure. That is, attrition is more likely to occur among households with low socio-economic status and in less developed villages. These findings are consistent with other studies on household attrition (Alderman and others 2000; Fitzgerald, Gottschalk and Moffitt 1998; Thomas, Frankenberg and Smith 1999; Ziliak and Kniesner 1998). For example, Fitzgerald, Gottschalk and Moffitt (1998) found from the Michigan Panel Study of Income Dynamics (PSID) that households with lower earnings, lower educational levels and lower marriage propensities are more prone to attrition. Overall, these variables explain only 7 to 10 percent of the probability of household attrition, which implies that up to 93 percent of the attrition cannot be explained by the explanatory variables and may be random. We also perform the Wald joint significance test to see whether the explanatory variables are jointly equal to zero, and the results of the tests are reported in 9
36 eamings, plus average program cost per participant. Expected<sup>weekly eamings are calculated as wages</sup> (for those who are employed) times the employability measure which<sup>is the percentage of calendar time</sup> employed. Except for males in the General Projects and females<sup>In the Individually Subsidized Jobs</sup> option, program participation is seen to enhance employability<sup>by between 13 and 20%. Nevertheless,</sup> only for males In the Individually Subsidized Jobs option is there<sup>evidence of a positive net impact on</sup> eamings. Indeed, for males In the General Projects and females<sup>in the Individually Subsidized Jobs</sup> options the net impact estimates suggest that they would have<sup>done better without the program. These</sup> generally negative resuits for expected eamings must be regarded<sup>as particularly discouraging for the</sup> General Projects option in view of Its high cost per participant.

Table 7 Net Impact and Cost Estimates for the Canadian Job Development Program, by Program Option and Gender

|Program option and gender|Employed/calendar<br>time|Expected<br>eamings/week'|Cost/participant|
|---|---|---|---|
|General Projects:||||
|Female|16.1%|-$1|$9,346|
|Male|-12.7|-109'||
|Individually Subsidized Jobs:||||
|Female|-5.5%0|-$17*|$3,914|
|Male|13.1|41||
|Severely Employment<br>Disadvantaged:||||
|Female|14.5°|$16|$5,914|
|Male|19.9"|-21||

Source: Goss, Gilroy & Associates (1989: Tables 1.1 and 4.10).

Defined as the product of the employability measure and wages. and' signify that the program effect Is statistically significant at<sup>the 1% and 5% levels, respectely.</sup>
- **52** -

investment. When straight-line depreciation is allowed, the expression for _rg_ has to be amended as indicated earlier. 0 _(Wit) Personal Tax Returns (m, 0,_ c). Typically, very little disaggregation occurs on the saving side. The personal tax rate on interest income is calculated as an average of marginal tax rates on capital income across all income classes. In the case of dividends, this must be corrected for any dividend tax credit that exists. The capital gains tax rate is somewhat more difficult to calculate since c is an accrued tax rate whereas capital gains are actually taxed on realization. The accrued tax rate c is calculated such that the present value of capital gains tax payment based on realized taxation is equal to the present value of taxes levied on accrued gains discounted by the shareholders' after-tax cost of equity finance. The average holding period of shares can be taken as the ratio of shares floated to volume of shares traded. The realized capital gains tax rate itself may differ from the personal tax rate on other forms of capital income.

# B. Using **MEIRs** to Evaluate Investment Incentives

In principle, it should be relatively straightforward to use the MEMT methodology to determine the size of incentive offered by various types of measure design to encourage investment. Once can calculate the METR in the presence and the absence of incentives and see explicitly by how much the incentive changes the marginal tax rate on investment decisions of various sorts. As mentioned there are limitations to using METRs and they apply equally well here. For one thing, there will be a large number of potential METRs corresponding with the many types of capital decisions undertaken by the many different agents in the economy. Most METR studies show considerable dispersion of rates across the economy. The same incentive can have very different effects on different types of investment decisions. Thus, it is difficult to characterize the effects of investment incentives in a simple and general way. Instead, one may be left with presenting a series of essentially illustrative calculations of the effects of investment incentives in different circumstances. It is naturally quite important for evaluative purposes to select the appropriate sample calculations for illustrating the effects of incentives.

For another thing, the information content of MEIR effects is limited. While it shows the size of the incentive imposed at the margin on a particular sort of investment decision, it does not show the magnitude of response of investment to the incentive. Nor does it show the tax revenue effects to the