rafmacalaba/data-use-mentions-extended huggingface dataset:
The study appears well-balanced; Table A2 shows balance using the household data, including all TASAF members, and incorporating dummies for Early TASAF treatment and Training, as well as the UCT and UCT*Training interactions as above. Across all primary outcomes we find only 1 out of 40 comparisons significant at the 5% level, slightly less than what we would expect by random chance. Because the TASAF groups do not exist at baseline we do not test for balance on the group data.

# **4. OBSERVATIONAL EVOLUTION OF THE TASAF GROUPS OVER TIME**

A starting point in understanding what the TASAF investment did is a simple descriptive analysis of the evolution of enterprise outcomes. In 92% of cases the TASAF program created the group enterprises from nothing, and so business outcomes such as business assets and profits are typically zero prior to treatment (meaning the baseline for the Early group, and the baseline, the rapid resurvey (RR) at six months, and the midline at one year for the Late group).<sup>13</sup> Using our three post treatment surveys we can construct four different durations since treatment: 6 months (RR), 12 months (R2), and 36 months (R3) for the Early treatment group, and 24 months (R3) for the Late-treatment group. To avoid conflating the effects of the UCT with the normal operating of the TASAF groups, which are only relevant for R3 outcomes and presented later in this section, we only include the groups that received no UCTs.

Table 1 provides basic summary statistics of group outcomes (first six columns) and individual contributions to/earnings from groups (remaining columns) across the four rounds of the survey. It illustrates the way the pipeline design worked, with no activity in any group at baseline, the Early groups in full swing by the 6- month RR, and then despite some initial stirrings

> 13 Six of the 60 groups in the delayed treatment arm had positive total stock values at the
# **B Integrity of the Assignment Variable**

The identification strategy relies on the assumption that there was no manipulation of the assignment variable (land pledged as collateral at the time of loan origination), which would make selection to either side of the discontinuity non-random. This appendix presents several tests verifying the integrity of the assignement variable. As a first test, Figure B.1, Panel (a) plots the density of the forcing variable according to bank records for all surveyed households within a band of _±_ 1 hectares of the program eligibility threshold in bins of 0.1 hectares. The plots indicate that both for the entire sample frame and the sample of surveyed households, there is notable bunching at the cutoff but also, to a lesser extent, around 1.5 and 2.5 hectares. When the distribution is plotted separately for commercial and cooperative banks in Figure B.1, Panel (b), we see that this pattern is almost entirely due to the subsample of cooperative bank accounts. McCrary’s (2008) test for discontinuity in the forcing variable correspondingly fails to reject the presence of a discontinuity with p _<_ 0.01. It is worth noting that the McCrary test also fails to reject discontinuities at 4 and 6 acres, suggesting that bunching at whole numbers is likely to be an important part of the explanation.

In order to gauge the extent of potential manipulation, and to provide for the possibility of a robustness check using a manipulation-free sub-sample, we audited the official land records of surveyed households, taking advantage of the state of Gujarat’s electronic repository of land records. In the state of Gujarat, all official landholdings are recorded in the centralized electronic _e-Dhara_ system. Manipulation of _e-Dhara_ records is considered highly unlikely for several reasons: electronic land records are centrally administered by an authority separate from the institutions keeping land records at the village level. There are several additional checks against manipulation and any changes in landholding status have to be cleared and verified
Ecuador remains below the 2012 level. To this date more than 50% of the population considered to be in extreme poverty lacks coverage by the system and social integration of the poor population is still very limited.

> 10 _ENEMDU_ stands for _Encuesta Nacional de Empleo, Desempleo y Subempleo_ , that is, _National Survey on Employment, Unemployment and Underemployment_ . For details on the methodology used in the ENEMDU survey, see ENEMDU Methodology.

> 11We eliminate the 21 smallest _cantons_ in terms of population. Specifically, we drop cantons with fewer than 4,000 individual observations in the ENEMDU. As a result, our analysis is based on 200 of the 221 overall number of _cantons_ . The data for 2019 corresponds to the second quarter, which was the most recent one available at the time of conducting the analysis.

> 12In constructing these indicators we use individual observations for all (working-age) individuals, regardless of nationality or country of birth.

> 13Our definition of the rates of adequate employment and informality use the working-age population

8
,1996 Gorodets,1996<br>Sources: Vladimir and Gorodets Longitudinal Household Survey, December 1996<br>Fig. 2.3. Households' plans to improve their housing conditions in the near future<br>(% of households polled)<br>90 9. 84.6 85.4<br>so - 77.2 76.4 76.5<br>70 -<br>60<br>40<br>30 22.8 205 23.6 2 .<br>20 - 15.4 14.6<br>10 _<br>0<br>1993 1996 1994 1996 1994 1996<br>Moscow Vladimir Gorodets<br>I U have plans * have no plans<br><!-- End of picture text --> Sources: Vladimir and Gorodets Longitudinal Household Survey, December 1996 Sources: Household Survey in Seven Cities, Moscow, December 1993; The Moscow Longitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Surveys, Oct-1994 and Dec-1996.
and Haryana.<sup>29</sup> The baseline predictor attributes used to determine the synthetic control includes variables such as the share of urban population, the literacy rate, the number of males, females, number of workers, number of main and marginal workers, cultivators, agricultural laborers and percentage of Scheduled Caste and Scheduled Tribes, reflecting key structural features of Indian states. These variables are available from the 2001 Census. The predictor variables also include lagged values of the outcome variable for three years: 2014, 2009 and 2004.

# **4.2 State-level descriptive statistics**

Tables 10 in the Appendix summarizes the key outcome variables of the ASI state level panel data set while Table 11 summarizes the predictor variables.

Figure 1 presents trends in state-level nominal output and employment in the formal manufacturing sector during 2001 and 2016, comparing Rajasthan to the average of other states in donor pool. Rajasthan has a smaller formal manufacturing sector that the average state. Notably, Rajasthan’s output trend has differed from that of the average other state. Between 2001 and 2010, Rajasthan grew slower than other states, with the ratio of their outputs falling from approximately 0.6 to 0.5. This relative trend appears to have reversed later, with the ratio of output in Rajasthan and the average other state climbing back to 0.6 in 2015. This differential aggregate trend underscores the potential gains from a data driven approach to selecting the control group.

> 29Three new states – Uttarakhand, Jharkhand and Chattisgarh- were carved out of the states of Uttar Pradesh, Bihar and Madhya Pradesh, respectively, in 2001. The state of Telangana was carved out of Andhra Pradesh in 2014. Our state level data set features the undivided versions of these states to maintain consistency in state definitions.

15
0.00417**|(2.03)|0.00429**|(2.20)|
|Mother’s years of schooling|-0.00562**|(-2.11)|-0.00554**|(-2.09)|-0.00461|(-1.66)|-0.00456|(-1.63)|
|Mother’s age|0.000941|(1.01)|0.000892|(0.96)|0.00129|(1.11)|0.00128|(1.11)|
|Number of siblings|0.0114***|(3.97)|0.0114***|(3.94)|0.00970***|(2.72)|0.00956***|(2.65)|
|Poverty incidence rate|||0.00579**|(2.23)|||0.00395|(1.34)|
|Infrastructure index|||-0.0486|(-0.61)|||-0.0523|(-0.68)|
|Number of students per classroom in high<br>school|||-0.000256|(-0.57)|||-0.000963|(-1.62)|
|Other variables and school fixed effects|yes||yes||yes||yes||
|Percentage of predicted values out of [0,1]|6.3%||8.0%||7.6%||9.2%||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include
adult will fail to survive into old age and so not produce any output in that phase of life, but rather that he or she will not enjoy her claim on the common pot in old age, whose value is _ρ ·_ **p** _t_ +1 **x** _t_ +1. Young adults in period 1 face the risk of losing 5.270 in period 2, which in relation to the VSL implies a ratio of 3.66 to one. Their counterparts in period 2 face the possible loss of 8.535 in period 3, which implies a ratio of 9.67 to one. All in all, these ratios for period 1 do seem to be rather on the low side, which suggests that the benefits stemming from the posited reductions in mortality may be somewhat higher than those implicitly reported in Tables 2 and 3.

# **5 Conclusions**

Providing backward rural areas with all-weather roads promotes not only production and trade in what can be called the ‘commercial’ sphere of life, but also the formation of human capital and health in the ‘non-commercial’ one. The main object of this paper has been to develop a method to value their respective contributions to the total benefit generated by rural roads. For given that obtaining independent estimates of the benefits, defined as willingness to pay, in the commercial sphere of production and trade is relatively straightforward, the respective shares of the ‘commercial’ and ’non-commercial’ spheres in the total benefits, as derived in the unified OLG-framework employed here, then yield an estimate of the willingness to pay in the non-commercial sphere that is anchored to the independent estimate of that in the commercial sphere. Based on survey evidence from a backward region of Orissa in east India and Bangladesh, as well as elements of more usual forms of calibration, the model yields a ratio of commercial to non-commercial benefits of about two-to-one in the first generation, falling to three-to-four in the second. This is broadly consistent with the valuations
collected data across 9 topic areas: anthropometry, clinical and health, dietary, socioeconomic, food security, government nutrition and health program participation, maternal health and nutrition, infant and young child feeding and MDG health related outcomes. For the analyses in this report, 10 NNS modules made available from DOST-FNRI were used (Table 1), representing 42,310 households and 202,570 household members. Data on household income and consumption from the 2015 Family Income & Expenditure Survey (FIES) are also used, given that households in the 2015 FIES can be matched to the 2015 NNS, since both samples use the same sampling frame.

**Table 1**

|**Survey program**|**Module**|**Population**|**Observations**|
|---|---|---|---|
|2015 Family Income &<br>Expenditure Survey (FIES)<br>(1st semester)|--|Households|43,882|
|||Household members (all ages)|202,570|
||Form 1.1|Household members (fathers)|28,140|
|||Household members (mothers)|31,654|
||Form 1.2|Households|42,310|
||Form 1.3|Households|42,310|
||Form 1.5|Households|42,310|
|2015 National Nutrition|Form 1.6|Households|42,310|
|Survey||Anthropometry (children < 61 months)|18,629|
||Form 2.1|Anthropometry (fathers)|28,140|
|||Anthropometry (mothers)|31,654|
||Form 3.1|Mothers & pregnant women with children|8,568|
||Form 4.1|Children (0-71 months)|20,303|
||Form 4.2
Figure 1: Sample Selection and Experimental Design <!-- Start of picture text --> Enrollment Survey<br>N=6,258<br>Date: August 2020<br>Not Eligible Eligible<br>N=1,540 N=4,718<br>Complete Baseline Survey<br>N=3,103<br>Date: August-September 2020 Experiment Sample Endline survey<br>N=599 N=3,027<br>No Baseline Survey Date: October 2020 Date: December 2020-January 2021<br>N=1,615<br>Group 1: Group 2:<br>CATI WhatsApp<br>N=300 N=299<br><!-- End of picture text --> _Notes_ : This figure shows the sample selection and randomization procedure applied in this survey-mode experiment design (orange boxes). From the total of enrolled and eligible caregivers that participated in the impact evaluation in Amaral et al. (2021), we randomly selected a subsample of 599 caregivers. Half of this subsample were assigned to group 1 (CATI or phone-based survey) and the rest to group 2 (WhatsApp or self-completion survey).

35
To document more systematically the evidence of a middle-income trap for MENA relative to EAP, we use the non-parametric local-linear regression technique that give the mean and standard errors of the estimated growth rate of each region at each level of income:<sup>7</sup> estimated growth rate of each region at each level of income: <u>yi</u> i U = , f ~~�~~ U ∆log (yy) ,ii+10 −∆log (yy) ii,ii+10 ff yy ii ii�+ εεii where i and U are overlapping annualized decadal growth in GDP per capita (or TFP) of country ∆log (yy) ,ii+10 and of the U.S. between time ∆log (yy) ii,ii+10 and time , <u>yUi</u> is the country’s relative income per capita relative to the U.S. at time ii . We use GDP per capita derived from output-side real tt tt+ 10 yy ii GDP at chained PPPs and total factor productivity (TFP), both from Penn World Table 9.0. The tt regressions also include overlapping decade fixed-effect, f , to control for common global shocks. ii Note that we are agnostic about the form of the function <u>yi</u> , f . Unlike linear regression, a ~~�~~ U � <mark>nonparametric regression is agnostic about the functional form between the outcome and the explanatory</mark> ff yy ii ii <mark>variables and is therefore not subject to misspecification error. In our context, a non-parametric regression could capture sharp changes in growth rates as relative income rises, a key advantage for us to identify an income trap.</mark> For each region, the non-parametric regressions (with 100 bootstrap replications) help provide the average predicted values and confidence intervals of annualized decadal growth in GDP per capita at different levels of relative
*

||**Education Service Delivery**|**Health Service Delivery**|
|---|---|---|
|**Ov**<br>(pro|**erall Education Index**<br>vision + outcomes)|**Overall Health Index**<br>(provision + outcomes)|
|**Ed**|**ucation Provision Sub-Index**|**Health Provision Sub-Index**|
|•<br>•|Education Provision & Quality (BS)<br>Education System Quality (WEF)|•<br>Basic Health Services (AFR)<br>•<br>Public Health Campaigns (GI)|
|•<br>**Ed**|Ratio of Pupils to Teachers in Primary School<br>(UNESCO)<br>**ucation Outcomes Sub-Index**|•<br>Immunization (WB/WHO)<br>•<br>Immunization against Measles (WB)<br>•<br>Immunization against DPT (WB)<br>•<br>Immunization against Hepatitis B (WHO)|
|•<br>•<br>•<br>•|Primary School Completion (WB)<br>Secondary School Enrolment (UNESCO)<br>Tertiary Education Enrolment (UNESCO)<br>Literacy (UNESCO)|•<br>Antiretroviral Treatment (ART)<br>•<br>Provision (UNAIDS)<br>•<br>ART Provision (UNAIDS)<br>•<br>ART Provision for Pregnant Women (UNAIDS)<br>~~•~~<br>~~Access to Sanitation (WHO/UNICEF)~~<br>~~•~~<br>~~Access to Improved Sanitation (WHO/UNICEF)~~<br>**Health Outcomes Sub-Index**
4|
|**Panel(B): raw headcount of each dimension**|||
|AROP|22.1|58.8|
|low work intensity|6.1|26.4|
|material and social deprivation +|19.3|62.6|
|Education|32.9|94.4|
|Health|5.1|9.6|

Note: Percentage of individuals (using survey weights). AROPE + is an extension of AROPE. Material and social deprivation + extends the material and social deprivation included in the AROPE to incorporate if the person lives in an overcrowded house (that is if the average number of people per room available to the household is greater than two). A person is educationally deprived if at least one adult in the household has only attained primary education (or less). A person is deprived in health if at least one adult in the household has unmet needs for medical examination of treatment during the last twelve months.

Source: Own estimates based on 2021 Bulgaria SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

**Eight out of ten Roma who are considered AROPE + experience more than one deprivation, in comparison to nearly five out of ten at the national level.** The proportion of the Roma population who are AROPE + and are deprived in more than one dimension is considerably large (Table 12); 30.0 percent is simultaneously deprived in three dimensions (compared to 15.1 percent at the national level), 18.6 percent is deprived in four dimensions (compared to 5.9 percent), and 2.9 percent in all five dimensions (compared to 0.7 percent). This result suggests that multidimensional poverty among the Roma should integrate a wide spectrum of policies.

**Table 12** . Overlapping of AROPE + dimensions, national average, and Roma population (2021)

|*
how many days) as well as water sharing practices (whether households were giving/receiving piped water to/from other households). All but 23 households covered by the baseline survey provided quarterly information throughout the experiment.

**Experimental sample** Our final experimental sample consists of 1,462 water customers.<sup>8</sup> Of these, 114 or 8 percent had been unconnected as of July 2015, but subsequently received a (free) piped water connection through the project in time to be included in the baseline survey. As noted above, prior to our interventions, a larger fraction of commune households (16 percent) did not have connections, presumably because they were not willing to pay the fee. When we turn to optimal pricing in section 4.4, and specifically to the computation of social welfare for the commune, we augment the estimation sample by randomly drawing from the 114 households so that previously unconnected households (inclusive of the original 114) make up precisely 16 percent of the sample.

**Price discounts** The actual price experiment started in January 2016, 6 months into the price linearization; see Figure 1 for a timeline. All the households surveyed at baseline agreed to participate in the price discount scheme and were randomly divided into three groups. Each group would, according to staggered schedules, receive price discounts of 50 or 75 percent and thus be charged 50 or 25 percent of the July 2015 flat price, respectively (Figure 1).<sup>9</sup> Each price subsidy regime ran for 6 months with the water utility reimbursed for the difference between official and subsidized price. However, households were not informed of the overall duration of the price experiment or of any particular discount more than a month in advance.

The experimental design incorporated a six-month pause in all discounts beginning January 2017, one year into the price experiment. The purpose of the pause was to allow a reduced-form test of demand persistence, exploiting the fact the each
HIES 2016/17 sample might be prone to larger margins of error than estimated due to the small district-level sample sizes.

In this section, we use a simple test of robustness to assess how the relatively small district level sample sizes in the HIES 2010 might have affected accuracy statistics for the HIES 2016/17. First, we estimate different sets of division-level statistics for urban and rural areas using both a “bottom-up” and then a “top-down” approach. The bottom-up approach, the methodology we have used to produce the accuracy statistics reported in Table 12, constructs stratum-level statistics by aggregating “up” from district-level accuracy statistics. This approach produces aggregated statistics at the division level for urban and rural areas assuming that the sample is selected from each district independently, as is the case for the HIES 2016/17. The top-down approach instead estimates directly all division-level performance statistics for urban and rural areas, which is the lowest level at which the HIES 2010 sample produced accurate poverty statistics. Comparing the top-down approach to division-level aggregated estimates for urban and rural areas, gives a good test of the accuracy of our bottom-up approach as the latter are estimated based on a sample that has been designed to produce reliable estimates at this level.<sup>25</sup> Tables 14, 15, and 16 summarize the main results from this exercise. RSE estimates of mean nominal per capita and total household expenditure, and SE estimates of poverty headcount ratios derived using the bottom-up approach are lower than estimates produced using the topdown approach. Therefore, the bottom-up approach produces more precise estimates in our application than the top-down. Notably, however, even the top-down approach in most cases produces accuracy estimates within the desired levels.<sup>26</sup> These results imply that the performance of the HIES 2016/17 sample achieves the desired level of accuracy for poverty

> 25 For allocation of PSUs across sub-strata, however, we cannot use the bottom-up approach because we cannot fine tune the estimates of the key sampling parameters
|0.212|
|||_t_|1.096|1.195 1.192|1.550||**3.190**||||
|1998|MaxRateUS|est|0.044|−0.017 0.315|−0.468|1.860||30|0.399|0.303|
|||_t_|1.213|**1.775 1.731**|**2.946**|**3.527**|||||
|1998|MaxRateUS|est|−0.077|−0.016 0.307|−0.224||0.302|30|0.374|0.274|
|||_t_|1.345|1.648 **1.652**|**1.717**||**3.304**||||

Notes: Data Source: Hoekman, Ng, and Olarreaga (2004) In sum, the econometric estimates point to two fairly strong associations from US data from the 1990s across agricultural products, and a fairly surprising non-association. The first strong association is that between lobbying spending by agricultural PACs and measures of nontariff barriers. This association is seen to be robust across aggregate NTMs as well NTMs disaggregated by type. While there is no evidence of such an association with ad valorem tariffs, probably because they have been multilaterally lowered, lobbying is still associated with specific tariffs. The second strong association is that between the average US tariff (taken over the 1995-98 period) on those goods that also enjoy an export subsidy in the US and lobbying spending. The surprising non-association is between the agricultural protection, however it is defined, and trade measures such as import penetration and export-to-output ratio. If anything, the surprise is that it is not these variables, often the key issue variables in 53