rafmacalaba/datause-encoder-data huggingface dataset:
“use of public spaces” index (p-value<0.01). Columns 3-6 show that this effect is due to an increase in both of the components included in this index.

The impact on the willingness to use public spaces remains positive even when the urban design elements are complemented with social diversity, with an increase of 0.34 standard deviation on the index (p-value<0.01). Although the effect is slightly larger for T3, the F-test confirms that treatment arms T1 and T3 are not statistically different from each other. To help interpret the magnitude of the effect, it is helpful to note that 70 percent of the control group, on average, said they liked and wanted to come back to the park, whereas those exposed to T1 and T3 increased their likeness and willingness to come back by 9 to 13 percentage points. On the other hand, social diversity intervention has no statistically significant impact on the respondents’ willingness to use the public space, except for a small positive (although statistically insignificant) impact on the respondents’ reported attraction to the park.

Table 2. Treatment Effects on Willingness to Use Public Spaces

|VARIABLES|(1)<br>Index Pu<br>(z-s|(2)<br>blic Space<br>core)|(3)<br>Lik|(4)<br>e Park<br>(=1)|(5)<br>Ret|(6)<br>urn Park<br>(=1)|
|---|---|---|---|---|---|---|
|[T1] Urban Design|0.32***|0.32***|0.13***|0.13***|0.09***|0.09***|
||(0.059)|(0.056)|(0.027)|(0.026)|(0.027)|(0.027)|
|[T2] Diversity|0.03|0.04|0.04|0.04|0.02|0.02|
||(0.061)|(0.060)|(0.028)|(0.028)|(0.028)|(0.028)|
|[T3] Urban Design + Diversity|0.34***|0.34***|0.13***|0.13***|0.10***|0.10***|
||(0.057)|(0.055)|(0.026)|(0.026)|(0.027)|(0.026)|
|Observations|1,924|1,924|1,924|1,924|1,924|1,924|
|Control Mean|0.00|0.00|0.69|0.69|0.70|0.70|
|Control Mean (No z-score)|0.70|0.70|||||
|F-test T1=T2 (p-value)|0.00|0.00|0.00|0.00|0.01|0.00|
|F-test T1=T3 (p-value)|0.63|0.76|0.75|0.87|0.54|0.69|
|F-test T2=T3 (p-value)|0.00|0.00|0.00|0.00|0.00|0.00|
|Selected Controls||X||X||X|

Notes: Specifications in odd-numbered columns include experiment location fixed effects while even-numbered columns include all the covariates selected. Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

While the overall impacts of T1 and T3 are large and positive, there is heterogeneity in the effects of the intervention on different subgroups. The interventions resulted in a lower willingness to use public spaces among youth in vulnerable groups, namely, those with lower education, lower income, and nonUrdu speakers. There are, however, no statistically significant differences between males and females in terms of the interventions’ effect on the use of public spaces. Table 2.A reports the effect of the 14
poor’ have larger households and with a different demographic composition. On average, they have more children and more senior adults, but fewer prime-age males. Once again, this is consistent with these households spending fewer resources in FAFH and therefore moving downwards in their relative position in the expenditure distribution once FAFH is accounted for. In terms of household head characteristics, the head is more likely to be indigenous and more likely to have a lower education level. In contrast with extreme poverty, ownership status is also different: the ‘FAFH poor’ are more likely to be owners and less likely to be renters. They are also less likely to have access to different services such as water, sewage, or electricity. There are statistically significant differences in the geographic distribution of the poor population, and finally, marked differences in labor market outcomes. Consistent with increasing consumption of FAFH as resources increase, income per capita is significantly lower among the ‘FAFH poor’ relatively to the ‘at-home poor’, and those households have fewer members employed.

# **5. Conclusion**

Food consumption away from home is rapidly growing across the developing world, yet Household Consumption and Expenditure Surveys are failing to properly account for these changes in the consumption patterns. This is likely to have significant consequences for welfare analysis, since the measurement of food consumption is critical for poverty, food security, health, and nutrition analyses, among others.

In this paper we take advantage of the rich data on FAFH collected in the National Household Survey in Peru (ENAHO) to provide evidence on the potential impact that lack of accounting for FAFH consumption may have on poverty and consumption inequality. We show that conceptually, the direction of the bias on poverty and inequality cannot be established ex ante. Then, we provide evidence on the direction and magnitude of the bias in poverty and inequality indexes for the case of Peru in 2010 by simulating the scenario where no FAFH is accounted for. Peru is a relevant context for this analysis, as FAFH has been growing over the last decade, with the average household spending, by 2010, about 28 percent of the food budget on food away from home.

We find that the effect on poverty and inequality indices is substantial in magnitude and statistically significant. The extreme poverty rate _increases_ by 18 percent while the moderate poverty rate _falls_ by 16 percent once FAFH is accounted for. The impact on the poverty gap and severity of 24
<!-- Start of picture text --> Manufacturing employment in India<br>2<br>2 s<br>=<br>=<br>S<br>Tom's)<br>3<br>1990 1995 peat 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing employment in India<br>=<br>~~ ~~<br>_*>— a<br>2Oo aos “7 i)<br>Ee e--<br>£<br>3<br>a<br>i = a rn<br>Se<br>1990 1995 2000 2005 2010<br>ea<br>—*— Formalsector —-—#-—- Informal sector<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing employment in India<br>a<br>oR<br>Ss<br>x= = _-o- -_—7<br>= _le--------*<br>Eo en<br>E.|e--<br>ire) = _—--~> — — ——<br>Oo<br>1990 1995 2000 2005 2010<br>Year<br>-@--~ Formal tradables — ®— - Formal non-tradables<br>——@e-—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text -->

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then other outcomes measured at the individual level for the principal applicant. We then turn to measuring household level outcomes, first for the household that lives with the principal applicant, and then for the specific type of extended family defined by our partner household rules.

# _4.1 Impacts of Migration on Income of the Migrant_

Table 2 presents the estimated gain in weekly income (in current New Zealand dollars) under a variety of different specifications. Columns 1 to 3 show ITT impacts, and Columns 4 to 6 the LATE impacts. Our base specification is seen in Panel A, columns 1 and 3. This estimate the impact using the exchange rate to convert Tongan pa’anga into New Zealand dollars, and uses the weighting in Figure 1 to reflect the population proportions of ever migrating to New Zealand versus non-compliers. This results in a NZ$247/week gain from winning the ballot and NZ$340/week gain from migrating. The ballot losers average NZ$126 per week in income in Tonga, so the gain from migrating represents a 271 percent increase in weekly income.

Columns 2 and 4 use an alternative weighting scheme that uses cross-border passport scan data to weight differently the migrant ballot winners in New Zealand from those who have moved to other countries, while Columns 3 and 6 allow further for the possibility that onmovement among the ballot losers is higher than our sample suggests by giving twice the weight to the migrants among the ballot loser sample as to the non-migrants. We see that our measure of the income gain is relatively insensitive to these choices of weighting scheme: the LATE gain varies from NZ$328 to NZ$340 per week across the three specifications.

Panel B then examines robustness to adjustments for cost of living. We make two adjustments here. The first was to collect prices for a common basket of goods in Tonga and New Zealand to allow us to calculate a PPP exchange rate. When we did this in 2005 (McKenzie et al, 2010) we found the exchange rate and PPP exchange rate coincided at the 15
with agricultural census years of 1971 and 1981. Agricultural employment is the number of persons who were employed in farm activities for at least 183 man-days in one of the census years. A similar definition is used for nonfarm employment.

The data for the commercial banks and the central commercial banks are published by the Reserve Bank of India in Banking Statistics. NABARD has kindly provided unpublished data on the PACs and the land development banks, which were collected by sending questionnaires to the state headquarters of these institutions.9' Note that the central commercial banks primarily advance credit to agriculture by lending to the PACs and the land development banks. Thus, rural credit, in this paper, is defined as the amount of institutional credit advanced to the rural sector by the commercial banks and the credit advanced to agriculture by the PACs and the land development banks. Total rural credit thus reflects the agrigultural credit advanced by the PACs, land development banks, commercial banks, and nonagricultural rural credit advanced by the commercial banks. A pure agricultur_1 credit variable cannot be constructed because district-level data on agricultural credit advanced by commercial banks do not exist. However, we also report the effects of agricultural credit advanced by the cooperative sector (PACs and land development banks) to compare with those of total rural credit advanced by the whole banking system (including the commercial banks). The mean and standard deviation of the variables involved in this paper are presented in Table 3.

> fil **Thanks to Dr. Gadgil of NABARD who has kdndly opened the database and personally organized the assembly of the unpublished banking data. This paper would not have been feasible without his kind help** in **coUecting the banking data.**

**18**
industries as natural gas distribution, spatial dimensions depend on the number and density of consumers and the size of the geographic area.

Economies of scale are not infinite. If consumer density is too high, economies of scale will disappear as administrative costs rise. If economies of scale were never exhausted, the minimum pipeline delivery cost would be achieved by having a single distributor supply the whole market, regardless of the size of the geographic area.

_Distribution Costs._ The costs of natural gas distribution are such that:

- Connection costs can decrease as the urban network develops but may increase with network congestion.

- It is cheaper to provide the distribution service to industrial consumers than to residential consumers, because capacity utilization of industrial consumers is greater and more uniform over time.

- The unit cost of connecting a consumer to the network increases with greater distance from the network.

Therefore, the cost function of the distribution firm will be determined by input prices, volume throughput, the number of consumers and their geographic dispersion and consumption levels. Estimates of distribution costs for alternative partitions of the ZMCM were calculated using coefficients of a translog cost function<sup>3</sup> for the natural gas distribution market in the United States.

The U.S. natural gas market was chosen as a cost and demand benchmark because of its abundance of relevant and reliable data on natural gas distribution systems in the country. Since the Mexican market is part of the North American market,<sup>4</sup> the U.S. local distribution companies are a relevant target model for Mexican local distribution companies in network development, service standards, and cost efficiency. And because the U.S. gas market is more mature than Mexico’s, the behavior of local distribution companies in the United States may foreshadow the behavior of those in Mexico. Thus the unit cost analysis for the ZMCM was presumed to be valid up to 2010. In other words, the proportion among unit costs in distinct distribution franchise zones was assumed to remain constant for 12 years.

> 3 The translog functional form has been widely used in studies on determining cost functions. It does not impose _a priori_ restrictions on substitution possibilities among factors of production. It allows for variation in scale economies at different production levels, which is essential for the unit cost function to be U- shaped. And due to its generality it has been shown to be superior to other functional forms used in applied research. See Christensen, Jorgensen, and Lau (1973).

> 4 The price of natural gas in Mexico is determined through a regulatory formula based on the prices in south Texas (see Brito and Rosellón 1998). Moreover, the Mexican pipeline system is physically linked to the North American one also in south Texas.

3
gender equity via joint land ownership by spouses and inclusion of their pictures on certificates; (iii) using a participatory and highly decentralized process of field adjudication; and (iv) constraining needs for spatial information to a minimum to reduce costs. This could potentially provide a basis for a rapid and large-scale approach to avoid the shortcomings that reduced the sustainability and poverty impact of past land registration programs and help provide a response to the emerging challenges.

To assess whether Ethiopia’s experience holds lessons for others, we assess the impact of the program as implemented in the Amhara region on perceived tenure security, land-related investment, and land market participation. Limited capacity at the _woreda_ level led to a phase-in of the program that, together with a four-round panel household survey, allows us to implement a difference-in-difference strategy to identify early program impacts. Evidence points not only towards significant positive program effects on the outcomes of interest but also a very positive benefit-cost ratio. A number of steps to ensure sustainability of benefits or to increase them are suggested and study of differentiated impacts, as well as channels through which they materialize, will be desirable. At the same time, there are clear lessons that could help other countries in confronting some of the secular challenges they face in the area of land policy and administration.

The paper is structured as follows. Section two reviews evidence on impacts of land-related programs as well as characteristics of the Ethiopian effort that are used to formulate hypotheses on program impact. Section three presents descriptive data for the entire sample and for treatment and control groups and discusses the modalities of program implementation and their implications for the estimation strategy. Section four provides estimates of certification impacts on the key variables of interest and section five concludes with a number of implications for Ethiopia and other countries in the region.

# **2. Background and approach**

To set the stage, we discuss how and why measures to strengthen land rights or improve the way in which they can be enforced will affect owners’ incentives to make land-related investment, transfer land to more efficient uses through market transactions, and use it as collateral for credit. These predictions are contrasted with empirical evidence from different settings, especially in Africa. Key characteristics of the program and policy environment in Ethiopia are then used to review expected impacts, identify proxies to measure them empirically, and formulate hypotheses that can be subjected to statistical tests.

# **2.1 Evidence from the literature and implications for Africa**

The literature identifies three channels through which higher security and better enforcement of property rights can, in principle, affect economic outcomes. First, clearly defined property rights to land and the ability to draw on the state’s enforcement capacity will reduce the risk of eviction, increase incentives for 2
Table 6: Characteristics of retained recruits at endline

||Grading|Task|Dictator|Game|
|---|---|---|---|---|
|Experienced P4P||0.57||-0.04|
|||[0.63]||[0.06]|
|Observations||170||169|

Notes: For each estimated parameter, the table reports the point estimate and _p_ -value in brackets. Randomization inference is conducted on the associated _t_ statistic. In the first column, the outcome is the Grading Task score of the teacher at endline on a (raw) scale from 0 to 30; in the second column, it is the teacher’s share sent in the framed Dictator Game played at endline. All specifications include the outcome measured at baseline and controls for district and subject-of-qualification.

to the endline survey question: “What is your overall opinion about the idea of providing high-performing teachers with bonus payments on the basis of objective measures of student performance improvement?”<sup>38</sup> The proportion giving a favorable answer exceeds 75 percent in every study arm. In terms of Figure 1, group _a_ (recruits who both applied for and experienced FW) had the most negative view of pay-for-performance, while group _c_ (who applied for FW but experienced P4P) had the most positive view. Hence it seems that it was the idea, rather than the reality, of pay-for-performance that was unpopular with (a minority of) recruits.<sup>39</sup>

# **5 Discussion**

**Compositional margin** To recap from Section 4.1, we find no evidence of an advertised treatment impact on the measured quality of applicants for upper-primary teaching posts in study districts, but we do find evidence of an advertised treatment impact on the measured intrinsic motivation of individuals who are placed into study schools. We draw three conclusions from these results.

First, potential applicants were aware of, and responded to, the labor market intervention. The differences in distributions across advertised treatment arms in Figure 3b (Dictator Game share sent) and Figure 4b (teacher valued added in Year 2) show that the intervention changed behavior. Since these differences are for placed recruits not applicants, it could be that this behavior change was on the labor

> 38We follow the phrasing used in the surveys run by Muralidharan and Sundararaman (2011a).

> 39Consistent with our failure to find ‘surprise effects’ in student learning, there is no evidence that the re-randomization resulted in hostility toward pay-for-performance; if anything the reverse.

35
Using a standardized price index to combine both sorghum and maize prices, we find that treated households receive prices 0.15 standard deviation higher than control households. Figure 5 plots the distribution of this standardized price index separately by household-level treatment assignment. The distribution of prices for treated households is clearly shifted to the right of the corresponding distribution for control households. Using a Kolmogorov- Smirnov test, we found that we could reject the null that the empirical distributions come from the same underlying distribution (p-value: 0.08).

Figure 4: Impact of warrantage on sales seasonality of Sorghum and Maize <!-- Start of picture text --> Treated<br>Sep Oct<br>Nov Dec<br>Jan Feb<br>Mar Apr<br>May Jun<br>Jul Aug<br>.1<br>.05<br>0<br>-.05<br><!-- End of picture text --> <!-- Start of picture text --> Treated<br>Sep Oct<br>Nov Dec<br>Jan Feb<br>Mar Apr<br>May Jun<br>Jul Aug<br>.1<br>.05<br>0<br>-.05<br>-.1<br><!-- End of picture text --> <!-- Start of picture text --> a. Sorghum b. Maize<br><!-- End of picture text --> Note: The outcome variable is defined as equal to one if the household conducted any sales of a) Sorghum b) Maize in that particular month. The graph plots the estimated treatment coefficients from regression (1).

11 We do see an uptick in sales beginning in the month of April, prior to the redistribution activities. Households may be selling off more of the grains they have in home storage at this time; alternatively, this result may indicate recall error. We do not measure sales every month and instead rely on a retrospective sales history. Retrospective agricultural data have been shown to be heavily influenced by current agricultural experiences (see for example, Godlonton _et al._ , 2018); a similar issue with recall bias may influence these timing results.

23
our six categories of households. This parametric approximation is of course very good, given that the artificial samples that we generated were based on the log-normal distribution itself. More generally, a number of alternative statistical distributions-such as the beta distribution-can be specified in IMMPA to approximate the actual distribution of any given income group (see Agenor and Grimm (2003)).

These results produced an income-based headcount index of 61.8 % in the agricultural sector (86.1% in the nontraded sector and 37.2% in the traded sector), 52.8% for urban unskilled households (78.8% in the informal sector and 14.0% for unskilled workers in the formal sector), and 0% for skilled workers and capitalists. For the economy as a whole, the income-based poverty rate amounted to 53.2%. With the income-based poverty gap, the results obtained were 38.9 % for the agricultural sector (44.0% in the nontraded sector and 26.8% in the traded sector), 40.8% for urban unskilled households (43.8% in the informal sector and 15.7% for unskilled workers in the formal sector) and again 0% for skilled workers and capitalists.<sup>3 2</sup> The aggregate poverty gap index reached 38.7%. For the consumption-based headcount index, the results are 64.4 % in the agricultural sector (88.7% in the nontraded sector and 39.8% in the traded sector), 62.3% for urban unskilled households (91.8% in the informal sector and 18.4% for unskilled workers in the formal sector), and 0% for skilled workers and capitalists. For the economy as a whole, the consumption-based poverty rate amounted to 56.9%. With the consumption-based poverty gap, we obtained 40.6 % for the agricultural sector (47.0% in the nontraded sector and 26.1% in the traded sector), 46.3% for urban unskilled households (50.0% in the informal sector and 18.9% for unskilled workers in the formal sector) and again 0% for skilled workers and capitalists. The aggregate poverty gap index reached 41.4%. These numbers are broadly in line with the evidence for sub-Saharan Africa.

For each policy or exogenous shock, therefore, the user can assess the short-, medium-, and long-term effects on poverty and income distribution and thus examine possible trade-offs with other policy objectives. The main benefit of this approach is that it allows us to link IMMPA simulation results directly with _actual_ patterns of income and expenditure and to provide a

> 32For unemployed skilled workers (who do not receive wage income but are assumed to receive interest income on financial assets as well as government transfers and a share of distributed profits by firms in the private sector), the average income is close to the poverty line in the base period. For simplicity, we treat the group of skilled workers as a homogeneous one and generate a distribution in which no skilled worker is poor.

54