rafmacalaba/datause-encoder-data huggingface dataset:
Households that received $350 in a small neighborhood of the cutoff were 17 percent points more likely to own a house, 29.8 percent points more likely to have been issued legal documentation for their household, and 40.4 percent points less likely to have spent more than half their reintegration assistance on food (table 5).

## **Table 5 Nonparametric regression discontinuity design estimate of impact of reducing UNHCR cash assistance from $350 to $150 per household member**

|_item_|_(1)_<br>_Own house_|_(2)_<br>_Employed_<br>_household_<br>_Member_|_(3)_<br>_All household_<br>_members_<br>_have_tazkira|_(4)_<br>_Spent most of_<br>_the assistance_<br>_onfood_|_(5)_<br>_All children_<br>_in school_|
|---|---|---|---|---|---|
|Cash amount|–0.170***|0.076|–0.298***|0.404***|-0.010|
|(1=$150, 2=$300)|(0.054)|(0.095)|(0.109)|(0.107)|(0.109)|
|Observations|4,302|4,302|4,299|2,979|2,184|
|Kernel type|Triangular|Triangular|Triangular|Triangular|Triangular|
|BW type|MSERD|MSERD|MSERD|MSERD|MSERD|
|Observations left|956|610|371|358|456|
|Observations right|2110|1897|1818|571|958|
|Days on each side|74.81|54.90|42.86|42.26|61.80|
|Conventional_p_-value|0.00169|0.423|0.00620|0.000152|0.929|
|Order Loc. Poly. (p)|1|1|1|1|1|
|Order bias (q)|2|2|2|2|2|
|Covariates|Yes|Yes|Yes|Yes|Yes|

_Note_ : Robust standard errors are in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ <0.1.

We find no significant effect of change in cash assistance on the likelihood of having an employed household member approximately 16 months after receiving the grant. The impact on the likelihood of enrolling children in school also becomes insignificant after controlling for confounding factors in the neighborhood of a 62-day bandwidth.

# **Robustness Test: Parametric Regression Discontinuity Design Model**

To ensure the robustness of results, we estimate the effects using a parametric strategy in which all returnee household-level observations are used. While the local linear regression approach is more likely to be unbiased in terms of estimating average treatment effects, the parametric estimation strategy, by using all available observations, provides a more precise estimate of treatment effects. The robustness check uses the following parametric model: (3) where yyii is the reintegration outcome of interest (education, house ownership, etc.); = αα+ ββDDii + ff(xxii ) + εεii indicates whether the household received a reduction in assistance ( = 1 is the reduction of assistance from $350 to $150); ii ii and yy ) is a flexible function of the running variable, the week of return (centered on zero at the cutoff). DD ii If the function is correctly specified, we can interpret DD as the average treatment effect on the treated ii ff(xx ff ββ 18
earlier findings suggested that CIMO appears to attract into the program firms that are, on average, less productive, than others in the larger population of SMEs. These productivity differentials persist despite efforts to closely match CIMO beneficiaries with non-participant firms with similar observable characteristics. If self-selection of weaker firms into CIMO were the issue, then simple comparisons of post-program outcomes of the treatment and control groups would not yield accurate estimates of program impacts; rather, any improvements in performance due to the program would be confounded by any existing pre-program differences in productivity levels between the two groups.

The solution, which is investigated here, is to frame the impact analysis in terms of changes over time in firm-level performance, in effect separating out pre-program initial productivity differences from possible impacts of program participation on productivity growth over time. This approach, which is called “difference-in-differences,” essentially compares preand post-program changes in performance (first difference) of the treatment group with changes over time in the performance of the control group (difference between groups in first differences). By abstracting from initial productivity level differences between the two groups attributable to self-selection into CIMO, the analysis can then focus on measuring the unbiased (by self-selection) impacts of the program on performance.

To see this, consider a simple Cobb-Douglas production function: where _VA_ is value-added, _K_ is capital assets, _L_ is total employment, ε is a regression error term, and θ is the parameter that shows the impact of CIMO on labor productivity. If the regressions were cross-sectional, as in the first CIMO study, and focused only on the post-program year, 1993, the large pre-program productivity level differences would almost certainly be reflected in a **negative** estimate of the θ parameter. Pooling all three years of data for 1991, 1992 and 1993 would not address the large pre-program productivity level differences between the two groups, since the θ parameter would still be negative, reflecting the lower overall productivity levels of CIMO firms over the three-year period.

The difference in differences (DID) approach eliminates the level differences in productivity, which may be caused by unobserved factors such as managerial ability. All three years of data are used and the production function is estimated using **changes over time** in each 9
Because the levels of income/consumption are likely to have diminishing marginal effects on the quantity of durable goods owned, we assume a log-linear relationship between those variables. This produces the following baseline regression specification: where _εit_ is the residual with the usual properties (zero mean, uncorrelated with Xs, uncorrelated with _vi_ , and homoskedastic).<sup>27</sup> Note that in this model (Xit/Xt) is the variable that changes across both time and units (wealth classes), while (Xt) is the variable that changes over time, but not across units.<sup>28</sup> Equation (4) can be estimated using the fixed-effects, or the ”within” estimator, which amounts to performing OLS estimation of equation (4) expressed as deviations from the group means over time.<sup>29</sup> Given the assumed properties of the residuals, OLS is the best linear unbiased estimator (Hsiao, 1986). The fixed-effects estimator is well suited for estimating equation (4) since it allows one to focus on how changes in within-group (wealth class) consumption are related to changes in withingroup average quantity of assets. In other words, the fixed-effects estimator ignores the variation across groups.

To estimate the model, we construct a four-year pseudo panel of wealth classes by calculating the mean values of the respective variables for 20 wealth viciles (from the bottom 5% to the top 5% of wealth groups) based on the distribution of household per capita consumption. For instance, the 1st (poorest) wealth class will correspond to the bottom 5 percent of the dsitribution, while the 20th (richest) class will correspond to the top 5 percent of the distribution. All data come from the Bulgaria Integrated Household Surveys (BIHS) that were carried out in 1995, 1997, 2001 and 2003. Although the surveys were relatively small (about 2500 sample households per survey), the fact that the panel was formed using only 20 wealth groups allows for sufficient number of observations (about 125 households) in each vicile group. Since we have

> 27 Here we express (Xit/ Xt) in percents.

> 28 Assuming that the rate of increase in real consumption is the same as the rate of increase in real GDP, one can use the real GDP index as (Xt).

> 29 M M M _M_ In general form the corresponding equation that is estimated is: (yit-yi _)_ = _β1*(_ xit- xi _) + β2*(_ xt-x _) + ( εit- εi where_ yiM is the mean of yi over time; xiM is the mean of xi over time, and xM<sup>is the mean of x</sup> t<sup>over time.</sup>

- 14 -
- 21 -

<!-- Start of picture text --> FIGURE 3: MEXICO - EX-POST REAL INTEREST RATES<br>(in percent)<br>10<br>i<br>UNITED<br>MEXICOX<br>-25 ,,, W I,,, . I, . I * | . .<br>1960 1965 1970 1975 1980 1985<br><!-- End of picture text -->
goods and services that households use. One drawback, however, is the difficulty in pinning down the exact minimum level of energy required for basic needs, owing to the significant country and regional differences in cooking practices and heating requirements. Energy consumption is often location-specific due to the vast differences in climatic conditions worldwide. Furthermore, the minimum needs for physical quantities of energy are chosen somewhat arbitrarily.

Among the methods based on physical energy requirements, an important one was proposed by Bravo et al. (1979). The Bravo measure quantifies a household’s direct energy needs in considerable detail including those for cooking and lighting. The average household in a tropical country such as Bangladesh requires about 27.4 kilograms of oil equivalent (KgOE) per capita per month to meet its essential direct energy needs. A second interpretation of physical energy requirements, proposed by Goldemberg (1990), includes a wider range of energy-using activities, placing that estimate at 32.1 KgOE per capita per month. A third, more universal interpretation, which examines the physical needs of daily cooking and lighting based on various surveys from around the world, results in a much lower minimum household energy requirement (50 KgOE per year) (Modi et al., 2005). Thus, varying assumptions yield vastly different results.

A second approach defines energy poverty as the level of energy used by households below the known expenditure or income poverty line (Foster et al., 2000).<sup>4</sup> Because the expenditure-based poverty line is well-defined in most countries, this approach is fairly attractive since it is unnecessary to measure how much energy people are actually using. One can simply assess the average fuel used by the households at the expenditure poverty line based on a household energy survey. But the drawback of defining energy poverty based on such general criteria, as opposed to an energy basket

> 4 The terms _expenditure poverty_ and _income poverty_ are used interchangeably throughout this paper, although the measurement of such poverty involves expenditure only.

6
states of Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh, along with states in the eastern region, achieved relatively little poverty reduction between 1993–94 and 1999–2000.

Secondly, in both countries, the most rapid growth did not occur where it would have had the most impact on poverty. This is evident if one compares growth rates across provinces with the growth elasticities of poverty reduction weighted by the initial shares of total poverty. (The weights assure that this gives the impact on national poverty of growth in a given province.) Had the pattern of growth favored provinces where growth would have had the greatest impact on poverty, we would find a negative correlation between the growth rate and the share-weighted elasticity. However, for neither country does one find any relationship, one way or the other (see Ravallion and Chen, 2006, for Chin and Datt and Ravallion, 2002, for India.)

# **Growth has been sectorally uneven**

A second dimension of uneven growth in both countries is found across sectors. Growth rates in the primary sector (agriculture) have not only lagged behind those in the secondary (industry) and tertiary (services) sector, but have actually declined over the last quarter century (figure 3).

# **...and the gap between rural and urban incomes has increased**

In nominal terms, urban incomes and expenditures have clearly increased faster than rural incomes over the past quarter century in both countries. In the case of India, this has been reflected in a steady increase in the ratio of urban to rural mean real consumption levels from just below 1.4 in 1983 to about 1.7 in 2000. Even in 1981, the urban-to-rural ratio of nominal mean incomes in China was around 2.5—much higher than it has ever been in India. And since then, while there have been periods when the ratio of urban-to-rural mean incomes fell, the overall trend has been upward.

**_Figure 3: Sectoral GDP growth rates in China and India, 1980–2005_**

<!-- Start of picture text --> 1980-1985 1985-1990 1990-1995 1995-2000 2000-2005<br>16.0<br>14.0<br>12.0<br>China<br>10.0<br>India<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>Agriculture Industry Services Agriculture Industry Services<br>Average annual growth rate (%)<br><!-- End of picture text -->

**_Source:_** China Statistical Yearbook (various years); Central Survey Organization, Government of India

8
population have acceptable diets; however recent work suggests that the cutoff points for the FCS classifications may be too low (Weismann, Basssett, Benson, & Hoddinott, 2009).

# _Sample_

The effective sample size for our analysis is 20,483 households.<sup>9</sup> Approximately 80 percent of households reside in rural areas. On average, households have 8.6 members living in about 3.6 rooms (or tents for Kuchi populations). The typical household consists of 2.1 men, 2 females, and 4.5 children (under 16). Heads of households are about 45 years old; nearly all are married and most are illiterate.

In the empirical work, we estimate the effects of the wheat flour price increases at the national level, as well as based on a household‘s access to agricultural land (defined hereafter as agricultural households). This categorization broadly relates to the net seller – net buyer distinction in the literature that is discussed in detail below. The vast majority (95 percent) of agricultural households are situated in rural areas. About 67 percent of households in rural areas and 15 percent of households in urban areas have access to agricultural land.

Table 1 displays population averages for the national sample, and for agricultural and nonagricultural households. Afghan households spend about 60 percent of their budgets on food, with agricultural households spending about 65 percent and nonagricultural households spending about 55 percent. Nonagricultural households are better off in terms of total consumption and caloric intake. These patterns are consistent with the typical ruralurban divide observed in many countries. Most nonagricultural households are situated in urban areas which are, on average, richer than rural areas. Agricultural households have statistically higher levels of dietary diversity, though the differences are small; these differences may be due to access to a larger variety of foods through home production.

> 9 The household response rate was 99.8%, and the PSU replacement rate was 3%.  Thirty-two households were dropped due to missing female questionnaires; all of these households are located in four communities, suggesting systematic errors in field operations.  Fifty-two households were dropped due to missing consumption data and seven households were dropped due to missing asset data.  One household is missing data on household size and is dropped because per capita measures of consumption and food security cannot be calculated.

8
# **_5.b Participation in government programs_**

As mentioned, the interaction of IKP and NREGS is complex. Did IKP affect the likelihood that a household benefit from NREGS? Our data point to a strong correlation between membership in IKP and benefiting from NREGS: across all poverty categories and lengths of exposure, IKP participants were significantly more likely to report benefiting from NREGS. Not only, but poorest and poor IKP participants were also significantly more likely to report benefiting from midday meals and student hostel schemes. Poor and not so poor participants were more likely than non-participants to benefit from housing schemes (Table 13). IKP clearly facilitated participation in these key government programs in a major way. The correlation with benefiting from ICDS, access to which was not stressed under IKP, is not so strong.

**Table 13: Additional probability of benefiting from NREGS and other programs for IKP** **<u>participants, 2008</u>**

||_NREGS_|_Midday_<br>_meal, hostels_|_Housing_|_ICDS_|
|---|---|---|---|---|
|_Poorest_|||||
|Early joiners|31***|21***|5|-2|
|Mid joiners|24***|13|12**|5|
|Latejoiners|15***|12**|-1|6|
|_Poor_|||||
|Early joiners|_22***_|9*|10***|-3|
|Mid joiners|17***|19***|21***|11***|
|Latejoiners|25***|15***|4|6|
|_Not so poor_|||||
|Early joiners|18***|0|11***|5**|
|Mid joiners|13*|8|11***|-3|
|Latejoiners|18***|10*|14***|7**|

Note: Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

# **_5.c Human development indicators_**

Next, we turn to education and health indicators. Education indicators are generally better in AP than in northern Indian states, and the data confirm that most children attended at least some school. Enrollment rates increased for all, but differences were not significant in either 2004 or 2008. Current school attendance increased slightly for children of IKP participants (all lengths of exposure taken together), and actually appears to have declined for children of nonparticipants (Table 14). The increase in current attendance is largest for children of longest IKP participants, including the poorest (Table A.5).

**Table 14: Percentage of school-age children who attended at least some school and who were** **<u>currently in school</u>**

||_All chil_|_dren who ever_|_attended_|_Female c_|_hildren who ev_|_er attended_|
|---|---|---|---|---|---|---|
||_IKP_<br>_particip_|_Non-partic_|_Difference_<br>_partic. vs._<br>_non-partic._|_IKP_<br>_particip_|_Non-partic_|_Difference_<br>_partic. vs._<br>_non-partic._|
|2003|81|82|-1|81|80|0|
|2008|84|85|-1|81|80|0|
|_Diff. over time_|3|3|0|0|0|0|

15
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and then shows results for the IV specification excluding one quarter (Column 2) and three quarters (Column 3) of post-commitment disbursements respectively. Both specifications yield an estimate of the effect of aid on haven deposits that is statistically significant (in the latter specification only at the 10% level) but somewhat smaller than the OLS baseline: an (instrumented) disbursement equivalent to one percent of GDP induces an increase in haven deposits of around 2.5-3%.

We conduct a number of additional robustness tests of the relationship between aid disbursements and haven deposits. First, we show how the estimates change when we exclude country-quarters characterized by wars (Column 4), coups (Column 5), natural disasters (Column 6), and financial crises (Column 7). Each of these restrictions reduces the sample size considerably, reflecting that the countries in our sample frequently suffer severe shocks. However, the coefficient on aid disbursements barely changes (except in the case of wars where it drops to around 2.75) and remains statistically significant in all cases. Second, we show results from the baseline model augmented with country-year fixed effects (Column 8). The estimated effect of aid on haven deposits remains almost unchanged when identified exclusively from variation in disbursements within the year although the precision of the estimate decreases. Third, we show that the baseline result is robust to controls for exchange rate movements<sup>14</sup> (Column 9) and resource rents<sup>15</sup> (Column 10). Finally, we show that the baseline result does not depend on the winsorization procedure employed to limit the effect of extreme observations: the coefficient on aid increases marginally when growth rates in haven deposits are not winsorized (Column 11).

# **4.3 Implied leakage rates**

We restate the baseline estimates so they express the leakage rate: the dollar increase in haven deposits associated with a 1 dollar aid disbursement. This step helps assess the likely scale of elite capture through offshore accounts. Since our empirical model does not deliver the leakage rate directly, we need to rely on simple transformations to obtain it.

> 14Changes in exchange rates can cause changes in our deposit measures because they aggregate different currencies into USD equivalents using contemporaneous exchange rates. The model in Column (9) controls for exchange rate movements by including a variable that expresses the mechanical change in deposits following from exchange rate changes. We construct this variable as the average percentage change in exchange rates (relative to USD) weighted by country-specific currency shares in deposits (obtained from the BIS Locational Banking Statistics).

> 15Andersen et al. (2017) show that rents from petroleum production are associated with money flows to havens in countries with poor democratic governance. We control for resource rents by including the interaction between the time dummies and an indicator for petroleum producing countries.

13