metadata: authors: 0: author: Gine,Xavier
1: author: Martinez Ruiz,Meritxell
2: author: Melo,Virginia
count: Dominican Republic
docna: 0: docna: Anticipation, Disappointment and Adaptation
            : Evidence from a Home Upgrading Project
docty: Policy Research Working Paper
theme: Labor Market Institutions,Participation and Civic Engagement,Human Development and Gender,Social Protection,Social Development and Protection,Private Sector Development,Labor Market Policy and Programs,Active Labor Market Programs,Urban and Rural Development,Jobs,Social Inclusion,Services and Housing for the Poor,Urban Development,Social protection delivery systems,Social Safety Nets
lndinstr: 0: lndinstr: Investment Project Financing
lndinstr_key: 1353855
abstracts: cdata!: This paper exploits unexpected delays in
            the implementation of a home upgrading government program
            for low-income households in the Dominican Republic to
            quantify the adverse impact of delays on subjective measures
            of well-being. The findings show that failure to account for
            delays in program implementation leads to serious
            underestimation of program impacts on well-being.
display_title: Anticipation, Disappointment and
            Adaptation : Evidence from a Home Upgrading Project
listing_relative_url: /research/2023/08/34150489/anticipation-disappointment-adaptation-evidence-home-upgrading-project
EnvironmentalCategory: C
topicv3: Social Protection and Labor m1327194 697
publishtoextweb_dt: 2023-08-25T00:00:00Z
dois: 10.1596/1813-9450-10554
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 34
author_keys: 0: author_key: 658884
1: author_key: 2065398
2: author_key: NA
model_extractions: Policy Research Working Paper 10554 Anticipation, Disappointment and Adaptation Evidence from a Home Upgrading Project _Xavier Giné Meritxell Martínez Virginia Melo_ Development Economics Development Research Group August 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10554

# **Abstract**

This paper exploits unexpected delays in the implementation of a home upgrading government program for low-income households in the Dominican Republic to quantify the adverse impact of delays on subjective measures of well-being. The findings show that failure to account for delays in program implementation leads to serious underestimation of program impacts on well-being.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at xgine@worldbank.org and mmartinezruiz@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Anticipation, Disappointment and Adaptation: Evidence from a Home Upgrading Project<sup>_∗_</sup>

Xavier Giné<sup>1</sup> , Meritxell Martínez<sup>2</sup> , and Virginia Melo<sup>3</sup>

> 3Gabinete de Coordinación de Políticas Sociales, Dominican Republic

Originally published in the Policy Research Working Paper Series on _August 2023._ This version is updated on _October 2023._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** anticipation effects, disappointment, subjective well-being. **JEL Codes:** D84, I31, I38.

> _∗_ Giné: `xgine@worldbank.org` . Martínez: `mmartinezruiz@worldbank.org` . Melo: `virginia.melo@ gabsocial.gob.do` . Funding from the World Bank and ProSoli is gratefully appreciated. We would like to thank Dan Silverman, Tavneet Suri and Daan Van Soest for very helpful comments and Miriam Montenegro and Alejandro De la Fuente for constant encouragement. MKT and a team of enumerators from SIUBEN contributed to baseline data collection. Views expressed in this paper are those of the authors, and do not necessarily reflect either the opinions of the World Bank, its executive directors, the countries they represent, or the views of the federal government of the Dominican Republic.
# **1 Introduction**

It has long been recognized that anticipation of a future event has an impact on immediate well-being (Bentham, 1970). Jevons (1905) was one of the first to apply the idea that anticipation is an important part of utility in the study of intertemporal trade-offs, denoting the anticipation of future events as ’anticipal pleasure’ when future consumption involved positive utility, e.g. a vacation, and as ’anticipal pain’ when it involved negative utility, e.g. a visit to the dentist for a root canal treatment.

For some positive events, e.g. the release of a blockbuster movie or receiving a government program (as in the case of this paper), the timing of the event is beyond the individual’s control, and because it may occur well after its anticipated date, individuals may feel disappointed while they wait (see e.g. Loomes and Sugden, 1986). We call this situation ’delay disappointment’.

Inaccurate expectations about the timing of future events may potentially impact subjective well-being (SWB) differently from life events, such as a birth, death, or illness in the family. Similarly, while delay disappointment may lower the _reference_ level of SWB, adaptation posits that some or all of the gains or losses in SWB after experiencing the event are offset over time (Frederick and Loewenstein, 1999; Di Tella, MacCulloch, and Oswald, 2003; Di Tella, Haisken-De New, and MacCulloch, 2010; Kimball, Nunny, and Silverman, 2015; Galiani, Gertler, and Undurraga, 2018).<sup>1</sup> Figure 1 considers the SWB of an individual before and after the announcement, at time _A_ , of a government program under three different implementation dates _T_ . In all three cases, SWB jumps after the announcement at _A_ , and then increases until _T_<sup>_e_</sup> . the expected program implementation date In Panel (a) of Figure 1, the program is implemented _before_ the expected date (i.e _T < T_<sup>_e_</sup> ) and so SWB jumps at _T_ by the sum of a permanent increase _βP_ and a transitory increase _βT_ . After _T_ , due to adaptation, SWB declines until the long-term level _βP_ is reached. In Panel (b), the program is implemented at the expected time (i.e. _T_ = _T_<sup>_e_</sup> ), and SWB first increases in anticipation of the program and then declines afterwards due to adaptation. In Panel (c) the program implementation is unexpectedly delayed (i.e. _T > T_<sup>_e_</sup> ). Due to delay disappointment, SWB declines after _T_<sup>_e_</sup> until _T_ , when SWB reaches a lower level than before the announcement at _A_ .

This paper uses a randomized control trial of a government program in the Dominican Republic and exploits differences in the timing of its implementation to assess the magnitude of delay disappointment and the degree of adaptation. The program upgrades the homes of low-income households by replacing dirt floors with

> 1See also Diener, Lucas, and Napa (2006) for a review of the evidence on the limits of adaptation.

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new cement floors and applying a fresh coat of paint to the windows and exterior walls of the house.

Small geographical clusters of 3 to 6 eligible study households were assigned to either receive the home improvements (treatment group) or to the control group. To assess potential spillovers, some households in treated clusters were not targeted for the program (similarly to households in control clusters), thus creating exogenous variation in the distance between targeted (i.e. offered treatment) and untargeted households. Untargeted households were not told about the program, although they could have felt frustrated if they learned about it, especially if they lived in treatment clusters close to neighbors that had been offered the program.

After an initial visit where the program was announced to targeted households, the program was launched in November 2019 but had to be paused in March 2020 due to COVID-19. By March 2020, about half of the targeted houses had been upgraded and importantly, the rest of the targeted households were never told about the plans to delay the implementation. Construction eventually resumed in July 2020 and concluded in April 2021. COVID-19 lockdown measures thus created exogenous variation in the time that elapsed between the announcement of the program and its eventual implementation. In addition, we use two rounds of follow-up data, collected after the program implementation, to measure within-household variation in exposure time to the program.

We use and overall of life as our main measures housing quality quality family of SWB. These evaluative measures have been used extensively in the literature, including in Cattaneo, Galiani, Gertler, Martinez, and Titiunik (2009) and Galiani et al. (2018).

We adapt the model of adaptation developed by Kimball et al. (2015) to account for delay disappointment and find that disappointment losses, while statistically significant, only account for less than 4% of subjective measures of well-being. Consistent with Galiani et al. (2018), we also find that permanent gains are indistinguishable from zero, suggesting that in the long-run, because disappointment losses are negligible, SWB reverts back to the level prior to the program.

More importantly, however, failure to account for delay disappointment results in estimated total gains that are only 36% (43%) of the actual gains when using satisfaction with overall house quality (satisfaction with quality of family life) as a proxy for SWB. Taken together, these results suggest that failure to account for delays in program implementation may lead to a large underestimation of program impacts.

This contributes to the literature on of paper ’anticipatory’ utility consumption (see, for instance, Loewenstein, 1987; Caplin and Leahy, 2001; Brunnermeier and 3
Parker, 2005; Koszegi, 2006; Koszegi and Rabin, 2009). While this literature is mostly theoretical, this paper takes a stylized model of anticipatory utility to actual data. In this sense, it also contributes to the literature that studies the impact over time of life events on SWB (see, e.g. Kimball et al., 2015; Galiani et al., 2018). These papers study the degree to which SWB eventually returns to the level prior to the event, often exploiting variation found in the data in the time elapsed since the event. In contrast, this paper studies adaptation by relying on multiple measurements after the event for the same household. More importantly, this paper explicitly addresses anticipation effects, and it is the first to highlight that unexpected delays can lead to losses in subjective measures of well-being.

# **2 Experimental design and data**

In collaboration with the cash transfer program ProSoli of the Government of the Dominican Republic, we designed and implemented a randomized controlled trial of a home upgrading program for ProSoli beneficiaries. The program upgraded dirt (and badly damaged cement) floors with new cement floors and painted the windows and exterior walls of the house. The program is based on Piso Firme in Mexico (Cattaneo et al., 2009), but unlike Piso Firme which mostly focused on peri-urban neighborhoods or slums, the program in the Dominican Republic also included rural areas.<sup>2</sup> Study households were grouped into 336 small geographical areas or clusters. Eighty percent of the clusters were assigned to receive the program (treatment group) while the 20% were to the control To assess remaining assigned group. potential spillovers, 20% of the study households in treatment clusters were not targeted to receive the program. As a result, the average distance between untargeted households in treatment and control clusters and the closest targeted household was 0.5 km and 1.4 km, respectively, (p-value of t-test of equality of means is 0.000).

Panel A of Figure A1 shows a map with the location of all study households while Panel B zooms in on a map of two study provinces with the location of study households in different colors depending on their treatment status.

Figure A2 describes the timeline of data collection and interventions. The baseline data was collected in-person between October 2019 and February 2020, with a total of 1,192 households. The preferred respondent was the head of household, and if unavailable, the primary holder of the Prosoli beneficiary card.<sup>3</sup>

> 2The program was implemented in the provinces of Santo Domingo, San Cristóbal, Santiago, Monte Plata, Barahona and Baoruco.

> 3The initial list used beneficiary data from 2018 and contained 2,172 households. By the time of the

4
The first round of follow-up data collection (post-construction) began in February 2020, but had to be stopped two weeks later due to COVID-19. It resumed in May 2020 via telephone and later in person, ending in October 2021. The team reached 1,036 households or 87% of those interviewed at baseline, and attrition rates were not different by treatment status.<sup>4</sup> The second (and last) round of follow-up data was only collected via phone between December 2021 and May 2022 using a reduced team of 4 enumerators. Although the team tried to get the cell phone numbers for all respondents during the first follow-up, many households did not have a cell phone and so data was only collected for 622 households. Again, attrition was not different by treatment status.

At the end of the baseline visit, targeted households were offered the home upgrading program and were told that they would soon be contacted by ProSoli staff and the construction crew to schedule the intervention. Targeted households that had accepted the program were visited by ProSoli staff a few days before the intervention and were asked to sign a letter accepting the program. They also agreed to remove all household items from the house floors before the arrival of the construction crew and to keep them overnight outside the house while the cement floors dried. During these visits, ProSoli staff were often accompanied by a member of the construction crew who informed the household about the exact dates of construction and lent to them a tarpaulin to cover the household items that had to be removed. The upgrading of the floor took half a day but an additional 24 hours were required for the cement to dry. For this reason, the paint job, which only took an average of two hours was done the following day after the cement was dry by different members of the crew. A few days after the construction, ProSoli staff visited beneficiary households to assess the quality of the work. Construction crews were then paid based on this quality assessment.

Construction began soon after baseline data collection in November 2019 but was paused due to COVID-19 in March 2020. By the time of the lockdown, half of the targeted houses had been upgraded. Construction eventually resumed in July 2020 and concluded in April 2021.

The COVID-19 lockdown measures created exogenous variation in the time elapsed from the announcement of the program during the baseline visit until the time of the actual intervention. We denote this time, expressed in months, by ∆ _T_ . In the data,

> visit, however, many households had already upgraded their floors with cement, while others could not be located or lived in a house that could not be upgraded due to its difficult access or state of general disrepair.

> 4We collected 463 interviews via phone from May 2020 to June 2021 and 534 households from midJuly until early October 2021. In-person visits were made to households that could not be reached via phone because they lacked cell phones or had connectivity issues.

5
∆ _T_ ranges from 1 to 17 months. While Figure 1 distinguishes between the date of the announcement _A_ and the expected intervention date _T_<sup>_e_</sup> , we assume here that both dates are in the same month and as a result, ∆ _T_ = _T − T_<sup>_e_</sup> _≈ T − A_ . Similarly, since two rounds of follow-up data were collected, there is variation in the time elapsed from the intervention until the time(s) the household was surveyed ranging from 0 to 30 months, which we denote by ∆ _S_ . Figure A3 plots ∆ _T_ in the X-axis and ∆ _S_ in the Y-axis for targeted and untargeted households in blue and red circles, respectively. The diameter of the circle indicates the number of observations with a given (∆ _T_ , ∆ _S_ ) combination. Note that households interviewed twice will each appear as two different observations in Figure A3, with the same ∆ _T_ but different ∆ _S_ depending on the interview date. Untargeted households in treatment clusters are assigned the average intervention date among targeted households in their cluster, while those in control clusters are assigned the average intervention date of their closest treatment cluster. By design, as shown in Figure A3, ∆ _T_ ’s and ∆ _S_ ’s of targeted and untargeted households are similar.

Appendix Table A2 reports household and dwelling characteristics for untargeted households in control and treatment clusters in columns 1 and 2, respectively, and targeted households in column 3. Appendix Table A1 reports the definitions of variables used in the analysis. According to column 4 of Appendix Table A2, we cannot reject that any differences in means across experimental arms in columns 1-3 is jointly different from zero.

To become a beneficiary of the home upgrading program, a household must have a quality of life index (ICV) of 1 on a scale 1-4, be a recipient of ProSoli and own a house with dirt (or badly damaged cement) floors. According to Appendix Table A2, virtually all households own the house they live in, 83% of the houses have dirt floors and 71% of households have ICV 1, while the remaining 29% have ICV 2.<sup>5</sup> Our primary outcomes of interest are subjective measures of housing quality and overall quality of family life based on self-reported Likert-scale measures similar to those used in Cattaneo et al. (2009) and Galiani et al. (2018) to evaluate a similar home upgrading program and advocated in Dolan and Metcalfe (2012) to measure the impact of social programs. These evaluative measures require that the respondent think about the question and have been shown to correlate more with income than hedonic measures of well-being, such as responses to questions like “Did you experience a lot of happiness yesterday?” (Kahneman and Deaton, 2010). The measures we use have been used extensively in the literature after research demonstrat-

> 5Households with ICV 2 were also included in the program because they met all the other eligibility requirements (notably they owned houses with dirt floors) and because ProSoli estimated a budget surplus if only eligible households with ICV 1 were included in the study.

6
ing that people have a common understanding of subjective well-being and that numerical measures are effective.

Following Cattaneo et al. (2009) and Galiani et al. (2018) we construct a simple dummy variable that equals 1 if the respondent is “Satisfied” or “Very satisfied” and 0 otherwise. This measure minimizes the potential problem with cardinality by ignoring interpersonal variation in satisfaction. In addition, to avoid any desirability bias among targeted households, we ask satisfaction questions about house characteristics that were both directly affected by the intervention (floor, color of walls) and not affected (material of walls and roof). If respondents were truthful, we would expect changes in satisfaction only for those characteristics directly affected by the program.

# **3 Empirical framework and results**

We estimate the following intention-to-treat effects regression where _Yict_ is the outcome variable for household _i_ living in cluster _c_ at time _t_ , **T** _ic_ is a dummy equal to 1 if household _i_ in cluster _c_ was targeted for the program and 0 otherwise, _Yic_ 0 is the value of the outcome variable at baseline and _Xic_ 0 is a vector of covariates that were unbalanced at baseline and municipality fixed effects, which capture the average unobservable regional differences in the outcome variable. Standard errors are clustered at the cluster level since the randomization was conducted at that level.

The specification includes ∆ _Tic_ and ∆ _Sict_ to control for time effects created by the COVID-19 pandemic and the lockdown measures. For example, time spent at home during the pandemic could affect measures of SWB, and household heads do report spending from 6.5 hours at home per day in October-December 2019 (pre-pandemic) to 8.2 hours in October-December 2020 (p-value of t-test is 0.000).<sup>6</sup> The parameters of interest are _γT_ , capturing the impact of the program when ∆ _Tic_ and ∆ _Sict_ are zero and _γTT_ and _γTS_ which measure the degree of delay disappointment and adaptation, respectively, when _Y_ are subjective measures of well-being.

The parameter _γT_ is identified because random assignment of the program guarantees treatment exogeneity for all values of ∆ _T_ and ∆ _S_ . Parameters _γTT_ and _γTS_ are

> 6Spouses and children (if any) also report spending more time at home during the pandemic, from 6.7 to 7.7 hours for spouses, and from 5 to 10.5 hours for children.

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identified if samples for different values of ∆ _T_ and ∆ _S_ are comparable. Although ∆ _T_ and ∆ _S_ were not randomly assigned, Appendix Tables A3 and A4 suggest that we cannot reject the null hypothesis of no differences in household and dwelling characteristics (including SWB measures) between targeted and untargeted households that have ∆ _T_ and ∆ _S_ below or above the median, respectively.<sup>7</sup> We thus conclude that the samples of targeted and untargeted households for different values of ∆ _T_ and ∆ _S_ are statistically comparable before the program, and we can interpret _γTT_ and _γTS_ as measures of delay disappointment and adaptation, respectively, when using subjective measures of well-being.

# **3.1 Reduced-form estimates**

Table 1 reports the results of estimating equation (1). According to Panel A, which focuses on variables related to the take-up of the program, about 72% of targeted households received the program, and as expected, a similar percentage reports recent upgrades to the floors and walls. The share of rooms with dirt floor owned by targeted households declined from 38% (among untargeted households) to less than 10%. In addition, delays in program implementation did not affect the probability that targeted households received the program, as coefficient _γTT_ on ∆ _Tx_ **T** is always indistinguishable from zero in Panel A. In contrast, Aibinu and Jagboro (2002) reports that delays led to lower completion rates which would affect SWB measures directly even in the absence of delay disappointment.<sup>8</sup> Panel B reports the subjective measures of housing quality that were likely affected by the program. While satisfaction with floor quality, wall color and overall house quality are substantially greater for targeted households, they are significantly lower for households that experienced delays in receiving the program. Households that waited 17 months (the maximum of ∆ _T_ ) reported 57% lower satisfaction with overall housing quality, compared to households that did not experienced a delay. Consistent with Kimball et al. (2015) and Galiani et al. (2018), we also find evidence of adaptation. After 12 months, about 44% of the increase in satisfaction with housing quality has dissipated.

As expected, Panel C reports no differences in the satisfaction with the quality

> 7In the case of ∆ _S_ , because most households were interviewed twice and thus appear both in the sample above and below the median, one would expect balance in Appendix Table A4 by design. Note that untargeted households in treatment and control clusters are grouped together because, as reported in the next subsection, we do find no evidence of spillover effects.

> 8During the intervention, other public and religious organizations (e.g. Instituto Nacional de la Vivienda or INVI) were also offering home upgrading programs. Consistent with this, the results reflect a small increase in the probability that the household received home improvements over time decreasing the share of rooms with dirt floor.

8
of house characteristics that were not affected by the program, such as walls or the roof. This lack of impact allays concerns about potential survey response biases.

Finally, Panel D reports satisfaction with quality of family life, trust and the monthly rent that households would charge for leasing out their house. While households experienced delay disappointment and adaptation with satisfaction with housing quality and to a lesser extent with quality of family life, this is not the case with selfreported monthly rent as it increases by 32% (from a base of 34.4 USD) irrespective of the delay in construction or exposure time to the improved house. This suggests that while SWB is negatively affected by delays and adapts over time, the rental price only responds to the upgrades made to the house (see Easterlin, 2001; Ferrer-i Carbonell, 2005; Deaton, 2008, for papers that study income as a determinant of wellbeing). As a result, these different proxies for well-being behave differently. Consistent with the literature, trust behaves like SWB (see, e.g. Helliwell, 2003; Helliwell and Putnam, 2005). Targeted households are more trusting of others immediately after receiving the program if there were no delays, but with a delay of 12 months, trust among targeted households is no longer statistically different from that of untargeted households. There is also adaptation in trust, but it is weaker and smaller in magnitude.

We note that our interpretation of the results would be invalid if outcomes for untargeted households also changed as a result of the program, due for example to frustration or envy that their neighbors received the program while they did not. Appendix Table A5 exploits our experimental design to address this concern directly by comparing untargeted households in treated and control clusters. We find no differences between untargeted households in either type of cluster and thus conclude that there are no spillover effects. We therefore pool all untargeted households together as done in Table 1.

In addition, the adaptation result could be confounded with housing wear and tear since lower perceived levels of housing quality over time could be the result of actual worsening of quality given the time elapsed since treatment. However, the coefficient ∆ _S_ reported in column 3 of Table 1 shows that at least among untargeted households, housing quality did not deteriorate over the time of exposure. More importantly, housing wear and tear cannot explain delay disappointment because keeping the time elapsed from baseline to follow-up fixed, a delay would shorten the time since the intervention and thus reduce wear and tear.

9
# **3.2 Structural estimation**

We adapt the adaptation model of Kimball et al. (2015) and Galiani et al. (2018) to incorporate delay disappointment where both adaptation and disappointment are modelled by exponential decay as in Figure 1. In particular, we estimate via Nonlinear Least Square (NLS) the following equation where the second and third terms in parentheses capture hedonic adaptation and delay disappointment, respectively. Equation (2) is valid for _t > T_ , consistent with our post-intervention follow-up data.<sup>9</sup> We focus on satisfaction with overall house quality and satisfaction with quality of family life as outcome measures of SWB.<sup>10</sup> Table 2 reports the estimated parameters of equation (2). We estimate the full version of the model which includes delay disappointment, and a restricted version that ignores it, essentially estimating the equation in Galiani et al. (2018). The delay effect _βD_ is the only statistically significant parameter when satisfaction with overall house quality is used as a proxy for SWB. Recall that SWB would eventually decline by _βD_ if the program were never implemented. In contrast, when satisfaction with quality of family life is used, only the transitory effect _βT_ is statistically significant.

Following Kimball et al. (2015) and Galiani et al. (2018), we now consider the cumulative impact of the program (i.e. “area under the curve”) and measure the specific contributions of the permanent, transitory and disappointment effects, respectively. In particular, for an individual with gross interest rate _R_ (which includes an annual mortality risk _d_ and an interest rate _r_ ), the cumulative impact of the pro-

> 9The analogous expression when _Te ≤ t ≤ T_ is

> With only post-intervention follow-up data, we cannot test if _βP_ and _βT_ changed due to delays. We thus estimate _βD_ assuming that _βP_ and _βT_ are fixed. With pre-intervention data, one could estimate _βD_ directly and allow for different values of _βP_ and _βT_ depending on whether households experienced delays or not.

> 10We do not include the 28 municipality fixed effects in equation 2 because the nonlinear routine in STATA did not converge for either SWB measure when included. Appendix Table A6 replicates Table 1 without the municipality fixed effects and finds were similar results.

10
gram can be calculated as The first term of _βcumm_ . is the permanent gain, the second is the transitory gain and the third is the loss due to delay disappointment. If there are no unexpected delays (∆ _T_ = 0) or if the program is never implemented ( _T →_ ∞), then, respectively, Table 3 reports the average cumulative impact of the program for the model with and without delay disappointment. Consistent with Galiani et al. (2018), we find that permanent gains are indistinguishable from zero using either proxy of SWB and version of the model. Unlike Galiani et al. (2018), transitory gains are also indistinguishable from zero, except in the version of the model with delay disappointment that uses quality of family life as a proxy for SWB (column 2). Average delay disappointment is statistically significant for both subjective measures of well-being, but it only accounts for 3.2% in column 1 and 1.7% in column 2 of the total gains in SWB, respectively. Since delay disappointment is a function of ∆ _T_ , Appendix Table A8 reports the transitory gains and delay disappointment losses for different percentiles of the ∆ _T_ distribution. While longer unexpected delays (larger ∆ _T_ values) are associated with larger disappointment losses, they are always small relative to transitory gains.

Total gains are all positive and statistically significant for both measures of SWB and versions of the model. But failure to account for delay disappointment, however, results in estimated total gains that are only 36% in column 1 and 43% in column 2 of the actual gains, depending on the measure of SWB. Similarly, the pooled linear regression coefficient of the treatment effect, which ignores both delay disappointment and hedonic adaptation is only 1.8% and 2.4% of the total gains that include delay disappointment.

Taken together, these results suggest that failure to account for delays in program implementation may lead to a severe underestimation of program impacts.

Given that delays were due to the COVID-19 pandemic, one could argue that would value the on time as were beneficiaries particularly receiving program they 11
forced to spend more time at home during the lockdowns. Put differently, individuals that experienced delays might be more upset while those who did not might be grateful that they could spend the pandemic at an upgraded home. In this case, our estimate of ∆ _D_ may overestimate the impact of delays.

To investigate this possibility, we re-estimate the nonlinear model in equation (2) allowing for different values of ∆ _D_ depending on whether households had children up to age 10 or not. Intuitively, a household with small children should value more the upgrades during a lockdown that one without. As expected, Appendix Table A9 reports that households with and without children are different (see column 7). The randomization, however, produced comparable targeted and untargeted households irrespective of whether they have children. When we test for differences in the delay effect parameter ∆ _D_ , we find small differences of _−_ .051 (0.007) when using satisfaction with the quality of the house (family life) as a proxy of SWB, indicating that ∆ _D_ among households with children is slightly lower (higher) than among those without, but neither difference is statistically significant (p-values are 0.482 and 0.972, for quality of the house and of family life, respectively).<sup>11</sup>

# **4 Conclusion**

The actual delivery of government programs often falls short of citizens’ expectations. In the extreme, this shortfall can lead to citizen protests (Archibong, Moerenhout, and Osabuohien, 2022) or worse.

In this we the one of the reasons citizens be discontent paper, study why may with government policy, namely implementation delays, and their impact on citizens’ self-reported well-being. Since the delays studied were due to the COVID-19 pandemic, one could argue that beneficiaries experiencing delays would recognize they were beyond the government’s control and thus justified. We still find, however, lower levels of satisfaction with the program and quality of family life among beneficiaries that experienced delays.

> 11We also allow for all three effects (permanent, transitory and delay effect or _δP_ , _δT_ and _δD_ , respectively) to be different depending on whether the household has children up to age 10 or not. In practice, we estimate the following version of equation (2):

> where **1** _icK_ is a dummy with value 1 if household _i_ in cluster _c_ has children up to age 10 and zero otherwise. As reported in Appendix Table A10, the presence of children does not matter for any of the effects does matter using either proxy of SBW. Finally, Appendix Table A11 reports that these differences in parameters do not translate into statistically significant differences in average gains and losses.

12
Given the recent interest in using self-reported measures of well-being for policymaking (see, e.g, Dolan and Metcalfe, 2012; Oishi and Diener, 2014), our findings suggest that citizens’ disappointment with delays, albeit small, should be considered when assessing the impact of government policy.

13
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15
|(8)<br>N. HHs<br>1,007<br>1,007<br>1,036<br>1,007<br>1,007<br>1,036<br>1,036<br>1,036<br>1,036<br>1,036<br>1,036<br>1,036<br>613<br> regression:<br>_t_,∆_Tic_ is the<br>r if they are<br> _i_in cluster<br>ccording to<br>cients with|
|---|
|(7)<br>N. Obs<br>1,629<br>1,629<br>1,658<br>1,629<br>1,629<br>1,658<br>1,658<br>1,658<br>1,658<br>1,658<br>1,658<br>1,658<br>965<br> following <br>r_c_at time <br>ent) cluste<br>household<br>balanced a<br>evel. Coeff|
|(6)<br>∆_Sx_**T**<br>Coef.<br>(s.e.)<br>-0.000<br>(0.002)<br>-0.002<br>(0.002)<br>0.006**<br>(0.003)<br>-0.003<br>(0.002)<br>0.000<br>(0.002)<br>-0.015***<br>(0.004)<br>-0.012***<br>(0.004)<br>-0.009**<br>(0.004)<br>-0.005<br>(0.004)<br>-0.004<br>(0.003)<br>-0.007*<br>(0.004)<br>-0.060*<br>(0.031)<br>-0.289<br>(0.281)<br>s from the <br>ng in cluste<br>osest treatm<br>es value 1 if<br>hat were un<br>the cluster l|
|(5)<br>∆_Tx_**T**<br>Coef.<br>(s.e.)<br>-0.005<br>(0.005)<br>-0.003<br>(0.005)<br>0.000<br>(0.005)<br>-0.003<br>(0.005)<br>-0.004<br>(0.005)<br>-0.014**<br>(0.007)<br>-0.009<br>(0.006)<br>-0.012**<br>(0.005)<br>0.001<br>(0.005)<br>-0.000<br>(0.005)<br>-0.002<br>(0.006)<br>-0.110**<br>(0.046)<br>-0.310<br>(0.559)<br> parenthesi<br>sehold_i_livi<br> in their (cl<br>my**T**_ic_ tak<br>e variables t<br>lustered at|
|(4)<br>**T**<br>Coef.<br>(s.e.)<br>0.719***<br>(0.051)<br>0.712***<br>(0.052)<br>-0.308***<br>(0.063)<br>0.686***<br>(0.054)<br>0.709***<br>(0.051)<br>0.420***<br>(0.097)<br>0.577***<br>(0.094)<br>0.408***<br>(0.083)<br>0.155*<br>(0.086)<br>0.112<br>(0.084)<br>0.218**<br>(0.091)<br>1.465**<br>(0.696)<br>11.080*<br>(6.534)<br>d errors in<br>ome of hou<br>average ∆_T_ <br>nd the dum<br>the baselin<br>theses are c|
|(3)<br>∆_S_<br>Coef.<br>(s.e.)<br>0.000<br>(0.001)<br>0.002*<br>(0.001)<br>-0.004*<br>(0.002)<br>0.002<br>(0.001)<br>0.000<br>(0.001)<br>0.005<br>(0.003)<br>0.003<br>(0.003)<br>-0.002<br>(0.003)<br>-0.001<br>(0.003)<br>-0.000<br>(0.003)<br>-0.000<br>(0.003)<br>0.104***<br>(0.025)<br>1.009***<br>(1.009)<br>nd standar<br> is the outc<br>signed the<br>view date a<br>riates with<br>ors in paren|
|(2)<br>ng<br>∆_T_<br>Hs<br>Coef.<br>(s.e.)<br>-0.016<br>(0.016)<br>-0.016<br>(0.016)<br>-0.004<br>(0.013)<br>-0.011<br>(0.017)<br>-0.017<br>(0.015)<br>0.013<br>(0.013)<br>0.010<br>(0.014)<br>-0.003<br>(0.011)<br>-0.011<br>(0.012)<br>-0.008<br>(0.013)<br>-0.012<br>(0.013)<br>0.199*<br>(0.107)<br>2.570*<br>(1.528)<br> coeffcients a<br> _ict_, where_Yict_ <br>eholds are as<br>ntil the inter<br>ector of cova<br> Standard err<br>ly. .|
|(1)<br>Dep.var. amo<br>untargeted H<br>Mean<br>_SD_<br>_Panel A. Take-up of Program_<br>Household received home improvements from ProSoli<br>0.000<br>_0.000_<br>Household received home improvements from any organization<br>0.023<br>_0.150_<br>Share of rooms with dirt foor<br>0.378<br>_0.326_<br>Walls recently painted (1=Yes)<br>0.019<br>_0.136_<br>Floor recently upgraded / repaired (1=Yes)<br>0.000<br>_0.000_<br>_Panel B. Satisfaction with housing quality affected by program_<br>Satisfed with overall house quality (1=Yes)<br>0.246<br>_0.431_<br>Satisfed with foor quality (1=Yes)<br>0.198<br>_0.399_<br>Satisfed with wall color (1=Yes)<br>0.159<br>_0.366_<br>_Panel C. Satisfaction with housing quality not affected by program_<br>Satisfed with wall quality (1=Yes)<br>0.169<br>_0.376_<br>Satisfed with roof quality (1=Yes)<br>0.184<br>_0.388_<br>_Panel D. Satisfaction and Trust_<br>Satisfed with quality of family life (1=Yes)<br>0.205<br>_0.404_<br>Trust in others (from 1 to 10)<br>6.541<br>_3.156_<br>Monthly rent if leased out (USD)<br>34.39<br>_24.98_<br>Notes: See Appendix Table A1 for variable defnitions.<br>Columns 2-6 report the <br>_Yict_ = _γT_0∆_Tic_+_γS_0∆_Sic_+_γT_**T**_ic_+_γTT_**T**_ic ×_∆_Tic_+_γTS_**T**_ic ×_∆_Sict_+_γ_0_Yic_0+_X′_<br>_ic_0_θ_+_ε_<br>time elapsed in months from the announcement to the intervention. Untargeted hous<br>located in a treatment (control) cluster. ∆_Sic_ is the time elapsed from the intervention u<br>_c_is targeted. When available,_Yic_0 is the value of the outcome at baseline and_Xic_0 is a v<br>Appendix Tables A4 and A3. The specifcation also includes municipality fxed effects. <br>*, ** and *** indicate that they are statistically signifcant at the 10, 5 and 1%, respective|

17
**Table 2:** Parameter Estimates

|||(1)<br>Satisfed with overall<br>house quality (1=Yes)|(2)<br>Satisfed with quality<br>of family life (1=Yes)|
|---|---|---|---|
|_Model with Disappointment_||||
|Permanent Effect|_βP_|-0.109|0.041|
|||(0.786)|(0.141)|
|Transitory Effect|_βT_|0.543<br>(0.757)|0.2982**<br>(0.131)|
|Hedonic adaptation rate|_δT_|0.026|0.054|
|||(0.056)|(0.057)|
|Delay Effect|_βD_|0.276***<br>(0.066)|0.51<br>(1.301)|
|Delay adaptation rate|_δD_|0.19<br>(0.132)|0.029<br>(0.097)|
|_Model without Disappointment_||||
|Permanent Effect|_βP_|0.0245<br>(0.131)|0.049<br>(0.117)|
|Transitory Effect|_βT_|0.172*|0.109|
|||(0.099)|(0.098)|
|Hedonic adaptation rate|_δT_|0.061<br>(0.104)|0.059<br>(0.136)|

Notes: The specifications include baseline values as covariates when available. The first five rows under the heading “Model with Disappointment” report the structural parameter estimates of the NLS regression: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βT e_<sup>_−δT_(</sup><sup>_t−Tic_)</sup> + _βD_ ( _e_<sup>_−δD_(</sup><sup>_Tic−Te_)</sup> _−_ 1)� + _Xic_<sup>_′θ_+</sup><sup>_εict_.The model without disappoint-</sup> ment losses estimates the NLS regression: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βT e_<sup>_−δT_(</sup><sup>_t−Tic_)�</sup> + _Xic_<sup>_′θ_+</sup><sup>_εict_.Coefficients</sup> with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

18
**Table 3:** Average Gains and Losses

|||(1)<br>Satisfed with<br>overall house<br>quality (1=Yes)|(2)<br>Satisfed with<br>quality of family<br>life (1=Yes)|
|---|---|---|---|
|_Model with Disappointment_||||
|Permanent Gains|_βP_<br>_R_|-1.797|0.678|
|(PG)||12.952|2.33|
|Transitory Gains|_βT_<br>�<br>_e_<sup>_−R_∆</sup><sup>_T_</sup><br>_δT_+_R_ <sup>+ 1</sup><sup>_−e−R_∆</sup><sup>_T_</sup><br>_R_<br>�|7.127|3.326|
|(TG)||12.71|2.188|
|Delay Losses|_βD_<br>_δD_<br>_δD_+_R_<br>�<br>1_−e_<sup>_−_(</sup><sup>_δD_+</sup><sup>_R_)∆</sup><sup>_T_</sup><br>�|0.165**|0.071*|
|(DL)||0.072|0.043|
|Total Gains||5.164***|3.932***|
|(PG+TG-DL)||1.129|0.929|
|_Model without Disappointment_||||
|Permanent Gains|_βP_<br>_R_|0.404|1.589|
|(PG)||2.166|0.427|
|Transitory Gains|_βT_<br>_δT_+_R_|1.415|0.9104|
|(TG)||1.845|1.712|
|Total Gains||1.819***|1.729***|
|(PG+TG)||0.548|0.492|
|_OLS estimate_||||
|Pooled coeffcient||0.095***|0.096***|
|(PG)||(0.026)|(0.026)|

Notes: Gains and losses are computed using the NL estimated coefficients in Table 2 and evaluated at the mean of ∆ _T_ of 6.8 months. Standard errors in parentheses are computed using the Delta Method. Following Galiani et al. (2018) the gross discount factor _R_ is the sum of the average mortality rate (by age and gender) in 2018 in the Dominican Republic reported by World Health Organization plus an interest rate of 5%. Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

19
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# **Table A1:**

|**Variable**|**Defnition**|
|---|---|
|_Household characteristics_||
|Household size|Number of family members living in the household as reported by respondent.|
|Household head is female (1=Yes)|Dummy equal to 1 if the gender of the household head is female.|
|Education of head of household (years)|Number of years of complete education by the household head.|
|Satisfed with ... (1=Yes)|Measures based on the response to the following question: “How satisfed are<br>you with... (i) the quality of your foor; (ii) the quality of your walls; (iii) the<br>quality of your roof; and (iv) the quality of your family life. Would you say you<br>are “Very Unsatisfed”, “Unsatisfed”, “Neither satisfed nor unsatisfed”, “Satis-<br>fed”, or “Very satisfed?" Each measure is a dummy variable that equals 1 if the<br>respondent reports that being “Satisfed” or “Very satisfed” and 0 otherwise.|
|Quality of Life Index (ICV)|Index computed by SIUBEN to assess the poverty status of a given household<br>based on the following characteristics: availability of electricity and water, waste<br>management, fuel used, hosueholds goods, type of house, household head edu-<br>cation, schooling of children, type of foor, walls and roof, type of toilet used and<br>household size divided by house size. The ICV ranges from 1 (lowest quality of<br>life) to 4 (highest).|
|Hours at home per day|<br>Number of hours that the head of the household reported staying at home on a<br>typical day.|
|Trust in others (from 1 to 10)|Based on the response to the following question: "Do you believe most people<br>would take advantage of you, if they could, or would they try to be fair to you?".<br>The answer is a scale from 1 to 10 where 1 reports a strong belief that others<br>would take advantage of the respondent, and 10 a strong disbelief.|
|_House characteristics_||
|House with dirt foor (1=Yes)|House has at least one room with dirt foor.|
|Number of rooms in the house|Number of rooms in the house excluding bathroom and kitchen.|
|Share of bedrooms with dirt foor|Number of bedrooms with dirt foor over total number of bedrooms in the house.|
|Walls made of wood or zinc (1=Yes)|Main material of external walls is either wood or zinc.|
|House is owned (1=Yes)|Dummy that takes value 1 if house is owned and fully paid, 0 otherwise.|
|Monthly rent if leased out (USD)|Based on the response to the following question: "If you were leasing your house,<br>how much would you ask for it monthly?". Respondents could answer using<br>ranges of monthly rents. The value in this variable uses the mid-point in each<br>|
||range.|
|Most important repair was not foor upgrade (1=Yes)|Based on the response to the following question: "What is the most important<br>upgrade or repair that you would do to the house?" The measure is a dummy<br>variable that equals 1 if the respondent did not answer foor upgrade.|
|Distance to closest targeted household (in km)|Km to closest targeted household computed using the GPS location obtained dur-<br>ing the baseline survey.|

22
**Table A2:** Orthogonality Check: Untargeted vs Targeted

||(1)<br>Untargete|(2)<br>d HHs in...|(3)|(4)|
|---|---|---|---|---|
||Control<br>Clusters|Treated<br>Clusters|Targeted<br>HHs|p-value<br>of F-test|
|_Household Characteristics_|||||
|Household size|3.263|3.354|3.482|0.230|
|Household head is female|0.449|0.466|0.455|0.802|
|Education of head of household (years)|3.122|3.024|2.812|0.590|
|Satisfed with overall house quality (1=Yes)|0.280|0.236|0.270|0.442|
|Satisfed with quality of the house foor (1=Yes)|0.119|0.129|0.085|0.221|
|Satisfed with wall quality (1=Yes)|0.169|0.163|0.177|0.871|
|<br>Satisfed with roof quality (1=Yes)|0.145|0.163|0.169|0.919|
|Satisfed with quality of family life (1=Yes)|0.178|0.209|0.186|0.719|
|Quality of Life Index (ICV)|1.333|1.260|1.307|0.275|
|Hours at home per day|6.576|6.315|6.445|0.927|
|_House Characteristics_|||||
|House with dirt foor (1=Yes)|0.822|0.826|0.847|0.704|
|Number of rooms|2.619|2.472|2.551|0.681|
|Share of bedrooms with dirt foor|0.713|0.715|0.754|0.598|
|<br>Walls made of wood or zinc (1=Yes)|0.559|0.551|0.536|0.887|
|House is owned (1=Yes)|0.932|0.944|0.959|0.687|
|Monthly rent if leased out (USD)|85.037|41.478|65.071|0.118|
|<br>Most important repair was not foor upgrade(1=Yes)|0.441|0.472|0.430|0.597|
|Number of households|118|178|740|1036|

Notes: See Appendix Table A1 for variable definitions. Columns 1-3 report means while column 4 reports the p-value of an F-test that means across groups in columns 1-3 are jointly different from zero. Municipality fixed effects are included. SE are clustered at the cluster level. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1).

23
|(7)<br>p-value<br>of t-test<br>(1&2)=(3&4)<br>0.887<br>0.377<br>0.483<br>0.690<br>0.888<br>0.483<br>0.482<br>0.748<br>0.575<br>0.314<br>0.246<br>0.530<br>0.103<br>0.708<br>0.021<br>0.647<br>0.402<br>0.903<br>1036<br>argeted households<br>) also report the p-<br>and 0 if untargeted<br> with value 1 if the|
|---|
|(6)<br>p-value<br>of t-test<br>(3)=(4)<br>0.272<br>0.843<br>0.123<br>0.733<br>0.248<br>0.343<br>0.686<br>0.314<br>0.761<br>0.942<br>0.799<br>0.683<br>0.458<br>0.400<br>0.782<br>0.330<br>0.372<br>0.206<br>487<br>eted and unt<br>f column 5 (6<br>) the median<br>ith a dummy|
|(5)<br>p-value<br>of t-test<br>(1)=(2)<br>0.502<br>0.934<br>0.617<br>0.669<br>0.111<br>0.522<br>0.877<br>0.509<br>0.700<br>0.856<br>0.190<br>0.192<br>0.504<br>0.695<br>0.317<br>0.996<br>0.140<br>0.540<br>549<br>between targ<br> The bottom o<br>below (above<br>e same test w<br>5, * p_<_0.1).|
|(3)<br>(4)<br>uncement until intervention (∆_T_)<br>Above Median<br>Untargeted<br>Targeted<br>HHs<br>HHs<br>3.292<br>3.500<br>0.526<br>0.514<br>3.307<br>2.829<br>0.321<br>0.369<br>0.168<br>0.134<br>0.197<br>0.249<br>0.205<br>0.228<br>0.292<br>0.254<br>1.307<br>1.366<br>6.613<br>6.766<br>0.803<br>0.803<br>2.599<br>2.483<br>0.642<br>0.682<br>0.650<br>0.603<br>0.949<br>0.960<br>28.160<br>34.636<br>0.511<br>0.557<br>137<br>350<br>the p-values of t-test of equality of means<br>households above and below the median. <br>value 1 if the household is targeted and<br>ble. In column 7 reports the p-value of th<br>d at the cluster level. (*** p_<_0.01, ** p_<_0.0|
|(2)<br>d from anno<br>Median<br>Targeted<br>HHs<br>3.467<br>0.403<br>2.710<br>0.182<br>0.041<br>0.110<br>0.123<br>0.190<br>1.200<br>6.156<br>0.887<br>2.613<br>0.818<br>0.477<br>0.959<br>53.509<br>0.315<br>390<br>n 5 (6) reports<br>ns between all<br>a dummy with<br>orted in the ta<br> SE are clustere|
|(1)<br>Time elapse<br>Below<br>Untargeted<br>HHs<br>_Household Characteristics_<br>Household size<br>3.340<br>Household head is female<br>0.403<br>Education of head of household (years)<br>2.623<br>Satisfed with overall house quality (1=Yes)<br>0.195<br>Satisfed with quality of the house foor (1=Yes)<br>0.088<br>Satisfed with wall quality (1=Yes)<br>0.138<br>Satisfed with roof quality (1=Yes)<br>0.126<br>Satisfed with quality of family life (1=Yes)<br>0.113<br>Quality of Life Index (ICV)<br>1.226<br>Hours at home per day<br>6.252<br>_House Characteristics_<br>House with dirt foor (1=Yes)<br>0.843<br>Number of rooms in the house<br>2.472<br>Share of bedrooms with dirt foor<br>0.777<br>Walls are made of wood or zinc (1=Yes)<br>0.472<br>House is owned (1=Yes)<br>0.931<br>Monthly rent if leased out (USD)<br>49.266<br>Most important repair was not foor upgrade (1=Yes)<br>0.415<br>F-test of joint signifcance (p-value)<br>Number of households<br>159<br>Notes: See Appendix Table A1 for variable defnitions. For each characteristic, colum<br>below (above) the median. Column 7 reports the p-values of t-test of equality of mea<br>value of the F-test that all variables are joint signifcant when running a regression of<br>and below (above) the median against all household and dwelling characteristics rep<br>household is below the median and 0 if above. Municipality fxed effects are included.|

24
|(7)<br>p-value<br>of t-test<br>(1&2)=(3&4)<br>0.641<br>0.471<br>0.883<br>0.078<br>0.003<br>0.098<br>0.021<br>0.566<br>0.340<br>0.402<br>0.807<br>0.385<br>0.327<br>0.415<br>0.454<br>0.445<br>0.582<br>0.231<br>1036<br>d households below<br>t the p-value of the<br>d below (above) the<br>s below the median|
|---|
|(6)<br>p-value<br>of t-test<br>(3)=(4)<br>0.238<br>0.892<br>0.951<br>0.515<br>0.069<br>0.214<br>0.522<br>0.568<br>0.614<br>0.878<br>0.188<br>0.409<br>0.358<br>0.141<br>0.791<br>0.977<br>0.556<br>0.824<br>500<br>d untargete<br>6) also repor<br>ntargeted an<br>household i|
|(5)<br>p-value<br>of t-test<br>(1)=(2)<br>0.140<br>0.753<br>0.421<br>0.180<br>0.427<br>0.141<br>0.406<br>0.983<br>0.635<br>0.718<br>0.742<br>0.914<br>0.667<br>0.026<br>0.922<br>0.206<br>0.946<br>0.287<br>536<br>n targeted an<br>of column 5 (<br>ian and 0 if u<br>value 1 if the|
|(3)<br>(4)<br>vention until follow-up interview (∆_S_)<br>Above Median<br>Untargeted<br>Targeted<br>HHs<br>HHs<br>3.279<br>3.442<br>0.415<br>0.428<br>2.755<br>2.742<br>0.218<br>0.201<br>0.102<br>0.045<br>0.170<br>0.127<br>0.136<br>0.130<br>0.156<br>0.142<br>1.211<br>1.249<br>6.116<br>6.241<br>0.823<br>0.890<br>2.544<br>2.620<br>0.749<br>0.809<br>0.442<br>0.490<br>0.932<br>0.955<br>54.949<br>54.859<br>0.408<br>0.357<br>147<br>353<br>the p-values of t-test of equality of means betwee<br>holds above and below the median. The bottom<br>household is targeted and below (above) the med<br>the p-value of the same test with a dummy with<br>1, ** p_<_0.05, * p_<_0.1).|
|(2)<br>ed from inter<br>Median<br><br>Targeted<br>HHs<br>3.519<br>0.481<br>2.788<br>0.333<br>0.121<br>0.220<br>0.204<br>0.225<br>1.305<br>6.630<br>0.809<br>2.488<br>0.703<br>0.579<br>0.964<br>35.209<br>0.496<br>387<br>mn 5 (6) reports<br>tween all house<br>th value 1 if the<br>olumn 7 reports<br>level. (*** p_<_0.0|
|(1)<br>Time elaps<br>Below<br>Untargeted<br>HHs<br>_Household Characteristics_<br>Household size<br>3.356<br>Household head is female<br>0.503<br>Education of head of household (years)<br>3.121<br>Satisfed with overall house quality (1=Yes)<br>0.289<br>Satisfed with quality of the house foor (1=Yes)<br>0.148<br>Satisfed with wall quality (1=Yes)<br>0.161<br>Satisfed with roof quality (1=Yes)<br>0.174<br>Satisfed with quality of family life (1=Yes)<br>0.235<br>Quality of Life Index (ICV)<br>1.315<br>Hours at home per day<br>6.718<br>_House Characteristics_<br>House with dirt foor (1=Yes)<br>0.826<br>Number of rooms in the house<br>2.517<br>Share of bedrooms with dirt foor<br>0.680<br>Walls are made of wood or zinc (1=Yes)<br>0.664<br>House is owned (1=Yes)<br>0.946<br>Monthly rent if leased out (USD)<br>24.253<br>Most important repair was not foor upgrade (1=Yes)<br>0.510<br>F-test of joint signifcance (p-value)<br>Number of households<br>149<br>Notes: See Appendix Table A1 for variable defnitions. For each characteristic, colu<br>(above) the median. Column 7 reports the p-values of t-test of equality of means be<br>F-test that all variables are joint signifcant when running a regression of a dummy wi<br>median against all household and dwelling characteristics reported in the table. In c<br>and 0 if above. Municipality fxed effects are included. SE are clustered at the cluster|

25
|(8)<br>N. HHs<br>290<br>294<br>290<br>294<br>294<br>294<br>294<br>294<br>294<br>294<br>167<br>s and stan-<br>outcome of<br>useholds in<br>gned inter-<br>outcome at<br>pality fxed<br>ectively.|
|---|
|(7)<br>N. Obs<br>480<br>484<br>480<br>484<br>484<br>484<br>484<br>484<br>484<br>484<br>279<br>coeffcient<br> _Yict_ is the<br>argeted ho<br>om the assi<br>alue of the<br>des munici<br>nd 1%, resp|
|(6)<br>∆_Sx_**TC**<br>Coef.<br>(s.e.)<br>-0.001<br>(0.002)<br>0.008<br>(0.005)<br>-0.001<br>(0.002)<br>-0.000<br>(0.007)<br>0.004<br>(0.006)<br>0.004<br>(0.005)<br>-0.000<br>(0.006)<br>-0.006<br>(0.006)<br>0.002<br>(0.007)<br>0.063<br>(0.052)<br>0.505<br>(0.443)<br>report the<br> _εict_, where<br>ntion. Unt<br>elapsed fr<br> _ic_0 is the v<br>n also inclu<br>the 10, 5 a|
|(5)<br> <br>∆_Tx_**TC**<br>Coef.<br>(s.e.)<br>-0.003<br>(0.002)<br>0.004<br>(0.008)<br>-0.003<br>(0.002)<br>0.007<br>(0.012)<br>0.017*<br>(0.009)<br>0.002<br>(0.009)<br>0.006<br>(0.009)<br>-0.012<br>(0.009)<br>0.001<br>(0.010)<br>-0.059<br>(0.070)<br>0.733<br>(0.992)<br> Columns 2-6<br>_ic_0+_X′_<br>_ic_0_θ_+<br>of the interve<br>_S_ is the time<br>n available,_Y_<br>specifcatio<br>signifcant at|
|(4)<br>Untargeted HH in<br>Treated Cluster<br>(1=Yes)<br>Coef.<br>(s.e.)<br>0.026<br>(0.037)<br>-0.103<br>(0.109)<br>0.033<br>(0.037)<br>-0.086<br>(0.179)<br>-0.209<br>(0.147)<br>-0.046<br>(0.135)<br>0.008<br>(0.147)<br>0.155<br>(0.144)<br>-0.034<br>(0.158)<br>-0.727<br>(1.196)<br>-13.182<br>(10.747)<br>riable defnitions. <br>**TC**_ic ×_∆_Sict_+_γ_0_Y_<br>the assigned date<br>t treated cluster. ∆<br>es is treated. Whe<br>les A4 and A3. The<br>ey are statistically|
|(3)<br>∆_S_<br>Coef.<br>(s.e.)<br>0.002<br>(0.001)<br>-0.007**<br>(0.003)<br>0.002<br>(0.001)<br>0.006<br>(0.006)<br>0.001<br>(0.004)<br>-0.003<br>(0.004)<br>-0.000<br>(0.004)<br>0.002<br>(0.004)<br>-0.002<br>(0.004)<br>0.072**<br>(0.034)<br>0.765***<br>(0.283)<br>ble A1 for va<br>_×_∆_Tic_+_γTS_<br>cement until<br> of the closes<br>ousehold_i_liv<br>ppendix Tab<br>dicate that th|
|(2)<br> <br>∆_T_<br>Coef.<br>(s.e.)<br>0.003<br>(0.006)<br>-0.019<br>(0.020)<br>-0.003<br>(0.004)<br>0.014<br>(0.022)<br>0.012<br>(0.028)<br>0.008<br>(0.026)<br>-0.010<br>(0.025)<br>-0.000<br>(0.025)<br>-0.012<br>(0.025)<br>0.392**<br>(0.163)<br>8.392***<br>(3.171)<br>Appendix Ta<br>_c_+_γTT_**TC**_ic _<br>f the announ<br>e average∆_T_<br>ntargeted h<br>cording to A<br>** and *** in|
|(1)<br>Dep.var. among<br>untargeted HHs in<br>control clusters<br>Mean<br>_SD_<br>_Panel A. Take-up of Program_<br>Household received home improvements from any organization<br>0.0283<br>_0.166_<br>Share of rooms with dirt foor<br>0.356<br>_0.324_<br>Walls recently painted (1=Yes)<br>0.0243<br>_0.154_<br>_Panel B. Satisfaction with housing quality affected by program_<br>Satisfed with overall house quality (1=Yes)<br>0.271<br>_0.445_<br>Satisfed with foor quality (1=Yes)<br>0.207<br>_0.406_<br>Satisfed with wall color (1=Yes)<br>0.159<br>_0.367_<br>_Panel C. Satisfaction with housing quality not affected by program_<br>Satisfed with wall quality (1=Yes)<br>0.147<br>_0.355_<br>Satisfed with roof quality (1=Yes)<br>0.187<br>_0.391_<br>_Panel D. Satisfaction and Trust_<br>Satisfed with quality of family life (1=Yes)<br>0.195<br>_0.397_<br>Trust in others (from 1 to 10)<br>6.526<br>_3.081_<br>Monthly rent if leased out (USD)<br>37.52<br>_24.85_<br>Notes: This table replicates Table 1 using only the sample of untargeted households. See<br>dard errors in parenthesis from the following regression: _Yict_ = _γT_0∆_Tic_+_γS_0∆_Sic_+_γT_**TC**_i_<br>household_i_in cluster_c_at time_t_,∆_Tic_is the time elapsed in months from the assigned date o<br>treated clusters are assigned the average∆_T_ in their cluster, otherwise, they are assigned th<br>vention date until the interview date and the dummy_TCic_ takes value 1 if cluster_c_where u<br>baseline and_Xic_0is a vector of covariates with the baseline variables that were unbalanced ac<br>effects. Standard errors in parentheses are clustered at the cluster level. Coeffcients with *,|

26
|(8)<br>N. HHs<br>1,007<br>1,007<br>1,036<br>1,007<br>1,036<br>1,036<br>1,036<br>1,036<br>1,036<br>1,036<br>1,036<br>603<br>dard errors<br>ousehold_i_<br>eir (closest<br>s value 1 if<br>ere unbal-<br>statistically|
|---|
|(7)<br>N. Obs<br>1,629<br>1,629<br>1,658<br>1,629<br>1,007<br>1,658<br>1,658<br>1,658<br>1,658<br>1,658<br>1,658<br>1,658<br>965<br>ts and stan<br>utcome of h<br>age∆_T_ in th<br>my**T**_ic_ take<br>ables that w<br>at they are|
|(6)<br>∆_Sx_**T**<br>Coef.<br>(s.e.)<br>0.000<br>(0.002)<br>-0.001<br>(0.002)<br>0.005*<br>(0.003)<br>-0.002<br>(0.002)<br>1,629<br>(0.002)<br>-0.014***<br>(0.004)<br>-0.012***<br>(0.004)<br>-0.009**<br>(0.004)<br>-0.004<br>(0.004)<br>-0.005<br>(0.003)<br>-0.007*<br>(0.004)<br>-0.064**<br>(0.031)<br>-0.191<br>(0.267)<br>e coeffcien<br>_Yict_ is the o<br>ed the aver<br>d the dum<br>aseline vari<br>indicate th|
|(5)<br>∆_Tx_**T**<br>Coef.<br>(s.e.)<br>-0.007<br>(0.004)<br>-0.006<br>(0.004)<br>0.003<br>(0.005)<br>-0.005<br>(0.004)<br>0.001<br>(0.004)<br>-0.014**<br>(0.007)<br>-0.010*<br>(0.006)<br>-0.012**<br>(0.005)<br>0.000<br>(0.005)<br>-0.001<br>(0.005)<br>-0.003<br>(0.006)<br>-0.092**<br>(0.043)<br>-0.395<br>(0.523)<br>2-6 report th<br> _εict_, where <br>s are assign<br>view date an<br>s with the b<br>*, ** and ***|
|(4)<br>**T**<br>Coef.<br>(s.e.)<br>0.742***<br>(0.048)<br>0.738***<br>(0.048)<br>-0.312***<br>(0.067)<br>0.705***<br>(0.049)<br> <br>-0.007<br>(0.048)<br>0.410***<br>(0.097)<br>0.600***<br>(0.092)<br>0.418***<br>(0.086)<br>0.156*<br>(0.086)<br>0.118<br>(0.081)<br>0.229**<br>(0.092)<br>1.410**<br>(0.699)<br>8.568<br>(6.104)<br>s. Columns<br>_Yic_0+_X′_<br>_ic_0_θ_+<br>d household<br>ntil the inter<br>of covariate<br>fcients with|
|(3)<br>∆_S_<br>Coef.<br>(s.e.)<br>0.000<br>(.)<br>0.002<br>(0.001)<br>-0.004<br>(0.002)<br>0.002*<br>(0.001)<br>0.000 0.733***<br>(.)<br>0.005*<br>(0.003)<br>0.004<br>(0.003)<br>-0.001<br>(0.003)<br>-0.000<br>(0.003)<br>0.000<br>(0.003)<br>0.000<br>(0.003)<br>0.107***<br>(0.025)<br>1.038***<br>(0.218)<br>able defnition<br>_ic ×_∆_Sict_+_γ_0<br>ion. Untargete<br>ntervention u<br>_Xic_0 is a vector<br>ster level.Coef|
|(2)<br><br>∆_T_<br>s<br>Coef.<br>(s.e.)<br>0.000<br>(.)<br>-0.001<br>(0.001)<br>0.001<br>(0.004)<br>-0.001<br>(0.001)<br>0.000<br>(.)<br>0.002<br>(0.006)<br>-0.007<br>(0.004)<br>-0.005<br>(0.004)<br>-0.007*<br>(0.004)<br>-0.010**<br>(0.004)<br>-0.008*<br>(0.005)<br>0.136***<br>(0.034)<br>2.112***<br>(0.391)<br>ble A1 for vari<br>_×_∆_Tic_+_γTS_**T**<br>the intervent<br>sed from the i<br>baseline and <br>ered at the clu|
|(1)<br>Dep.var. among<br>untargeted HH<br>Mean<br>_SD_<br>0.000<br>_0.000_<br>0.0229<br>_0.150_<br>0.378<br>_0.326_<br>0.0187<br>_0.136_<br>0.000<br>_0.000_<br>0.246<br>_0.431_<br>0.198<br>_0.399_<br>0.159<br>_0.366_<br>0.169<br>_0.376_<br>0.184<br>_0.388_<br>0.205<br>_0.404_<br>6.541<br>_3.156_<br>34.39<br>_24.98_<br>ee Appendix Ta<br>_γT_**T**_ic_+_γTT_**T**_ic _<br>nnouncement to<br> is the time elap<br>the outcome at<br>ntheses are clust|
|_Panel A. Take-up of Program_<br>Household received home improvements from PROSOLI<br>Household received home improvements from any organization<br>Share of rooms with dirt foor<br>Walls recently painted (1=Yes)<br>Floor recently upgraded / repaired (1=Yes)<br>_Panel B. Satisfaction with housing quality affected by program_<br>Satisfed with overall house quality (1=Yes)<br>Satisfed with foor quality (1=Yes)<br>Satisfed with wall color (1=Yes)<br>_Panel C. Satisfaction with housing quality not affected by program_<br>Satisfed with wall quality (1=Yes)<br>Satisfed with roof quality (1=Yes)<br>_Panel D. Satisfaction and Trust_<br>Satisfed with quality of family life (1=Yes)<br>Trust in others (from 1 to 10)<br>Monthly rent if leased out (USD)<br>Notes: This table replicates Table 1 without municipality fxed effects. S<br>in parenthesis from the following regression:_Yict_ = _γT_0∆_Tic_+_γS_0∆_Sic_+ <br>living in cluster_c_at time_t_,∆_Tic_ is the time elapsed in months from the a<br>treatment) cluster if they are located in a treatment (control) cluster. ∆_Sic_<br>household_i_ in cluster _c_ is targeted. When available,_Yic_0 is the value of<br>anced according to Appendix Tables A4 and A3. Standard errors in pare<br>signifcant at the 10, 5 and 1%, respectively.|

27
|(8)<br>N. HHs|622<br>622<br>622<br>622<br>622|622<br>622<br>622|622<br>622<br>622<br>622<br>359<br> defnitions.<br>_ic ×_∆_Sict_+<br>ntervention.<br>ed from the<br>at baseline<br>unicipality<br>he 10, 5 and|
|---|---|---|---|
|(7)<br>N. Obs|1,244<br>1,244<br>1,244<br>1,244<br>1,244|1,244<br>1,244<br>1,244|1,244<br>1,244<br>1,244<br>1,244<br>726<br>r variable <br>∆_Tic_+_γTS_**T**<br>nt to the i<br>time elaps<br>he outcome<br>includes m<br>ifcant at t|
|(6)<br>∆_Sx_**T**<br>Coef.<br>(s.e.)|0.001<br>(0.001)<br>-0.000<br>(0.002)<br>0.004<br>(0.003)<br>-0.000<br>(0.002)<br>0.001<br>(0.002)|-0.015***<br>(0.004)<br>-0.016***<br>(0.005)<br>-0.008**<br>(0.004)|-0.005<br>(0.004)<br>-0.005<br>(0.004)<br>-0.005<br>(0.004)<br>-0.058<br>(0.036)<br>-0.332<br>(0.279)<br> Table A1 fo<br>+_γTT_**T**_ic ×_ <br>nnounceme<br>r. ∆_Sic_ is the<br>e value of t<br>fcation also<br>stically sign|
|(5)<br>∆_Tx_**T**<br>Coef.<br>(s.e.)|-0.004<br>(0.005)<br>-0.002<br>(0.005)<br>-0.001<br>(0.005)<br>-0.001<br>(0.005)<br>-0.004<br>(0.005)|-0.015**<br>(0.008)<br>-0.014*<br>(0.008)<br>-0.016**<br>(0.006)|-0.005<br>(0.007)<br>-0.004<br>(0.006)<br>-0.003<br>(0.007)<br>-0.127**<br>(0.051)<br>-0.517<br>(0.673)<br> Appendix <br>_ic_+_γT_**T**_ic_ <br>s from the a<br>ntrol) cluste<br>le,_Yic_0 is th<br>. The speci<br>ey are stati|
|(4)<br>**T**<br>Coef.<br>(s.e.)|0.715***<br>(0.053)<br>0.709***<br>(0.053)<br>-0.281***<br>(0.070)<br>0.650***<br>(0.057)<br>0.715***<br>(0.053)|0.462***<br>(0.112)<br>0.699***<br>(0.114)<br>0.440***<br>(0.096)|0.224**<br>(0.110)<br>0.179*<br>(0.100)<br>0.205*<br>(0.106)<br>1.549*<br>(0.832)<br>15.566**<br>(7.826)<br>p data. See<br>_Tic_+_γS_0∆_S_<br>d in month<br>atment (co<br>hen availab<br>A4 and A3<br>cate that th|
|(3)<br>∆_S_<br>Coef.<br>(s.e.)|-0.000<br>(0.001)<br>0.001<br>(0.001)<br>-0.006**<br>(0.003)<br>0.001<br>(0.001)<br>-0.001<br>(0.001)|0.005<br>(0.004)<br>0.006<br>(0.004)<br>-0.002<br>(0.003)|-0.001<br>(0.003)<br>0.000<br>(0.003)<br>0.000<br>(0.003)<br>0.099***<br>(0.028)<br>1.018***<br>(0.239)<br>of follow-u<br>_Yict_ = _γT_0∆<br>time elapse<br>ated in a tre<br>argeted. W<br>ndix Tables<br>and *** indi|
|(2)<br><br>∆_T_<br>s<br>Coef.<br>(s.e.)|-0.014<br>(0.017)<br>-0.018<br>(0.017)<br>-0.006<br>(0.014)<br>-0.010<br>(0.018)<br>-0.016<br>(0.017)|0.022<br>(0.015)<br>0.030*<br>(0.016)<br>0.006<br>(0.012)|0.007<br>(0.013)<br>0.008<br>(0.014)<br>0.006<br>(0.015)<br>0.150<br>(0.137)<br>2.554<br>(1.615)<br>oth rounds <br>regression: <br> ,∆_Tic_ is the<br>they are loc<br>cluster_c_is t<br>ing to Appe<br>ts with *, **|
|(1)<br>Dep.var. among<br>untargeted HH<br>Mean<br>_SD_|_Panel A. Take-up of Program_<br>Household received home improvements from PROSOLI<br>0.000<br>_0.000_<br>Household received home improvements from any organization<br>0.0158<br>_0.125_<br>Share of rooms with dirt foor<br>0.379<br>_0.330_<br>Walls recently painted (1=Yes)<br>0.0158<br>_0.125_<br>Floor recently upgraded / repaired (1=Yes)<br>0.000<br>_0.000_|_Panel B. Satisfaction with housing quality affected by program_<br>Satisfed with overall house quality (1=Yes)<br>0.229<br>_0.421_<br>Satisfed with foor quality (1=Yes)<br>0.189<br>_0.392_<br>Satisfed with wall color (1=Yes)<br>0.153<br>_0.360_|_Panel C. Satisfaction with housing quality not affected by program_<br>Satisfed with wall quality (1=Yes)<br>0.150<br>_0.358_<br>Satisfed with roof quality (1=Yes)<br>0.166<br>_0.372_<br>_Panel D. Satisfaction and Trust_<br>Satisfed with quality of family life (1=Yes)<br>0.187<br>_0.390_<br>Trust in others (from 1 to 10)<br>6.661<br>_3.166_<br>Monthly rent if leased out (USD)<br>35.30<br>_24.86_<br>Notes: This table replicates Table 1 using the balanced sample of respondents with b<br>Columns 2-6 report the coeffcients and standard errors in parenthesis from the following<br>_γ_0_Yic_0+_X′_<br>_ic_0_θ_+_εict_, where_Yict_ is the outcome of household_i_living in cluster_c_at time_t_<br>Untargeted households are assigned the average∆_T_ in their (closest treatment) cluster if<br>intervention until the interview date and the dummy**T**_ic_ takes value 1 if household_i_in<br>and_Xic_0 is a vector of covariates with the baseline variables that were unbalanced accord<br>fxed effects. Standard errors in parentheses are clustered at the cluster level. Coeffcien<br>1%, respectively.|

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**Table A8:** Percentiles of Gains and Losses

||(1)<br>Satisfe<br>houseq|(2)<br>d with overall<br>uality (1=Yes)|(3)<br>Satisfed<br>of famil|(4)<br>with quality<br>ylife(1=Yes)|
|---|---|---|---|---|
||Transitory<br>Gains<br>(TG)|Disappointment<br>Losses<br>(DL)|Transitory<br>Gains<br>(TG)|Disappointment<br>Losses<br>(DL)|
|No Delay|6.282|0|2.587|0|
||(12.822)|NA|(2.255)|NA|
|10th pct of∆_T_|6.439|0.046|2.724|0.014|
||(12.801)|(0.03)|(2.24)|(0.011)|
|25th pct of∆_T_|6.726|0.110*|2.975|0.039|
||(12.763)|(0.061)|(2.216)|(0.028)|
|50th pct of∆_T_|6.98|0.149**|3.196|0.059|
||(12.729)|(0.07)|(2.197)|(0.039)|
|75th pct of∆_T_|7.307|0.180**|3.483|0.084*|
||(12.687)|(0.0707)|(2.178)|(0.047)|
|90th pct of∆_T_|7.998|0.206***|4.086*|0.129***|
||(12.599)|(0.061)|(2.155)|(0.046)|

Notes: Gains and losses are computed using the NLS estimated coefficients in Table 2 and are evaluated at different percentiles of ∆ _T_ (time elapsed from announcement until intervention, in months). Standard errors in parentheses are computed using the Delta Method. Following Galiani et al. (2018) the gross discount factor _R_ is the sum of the average mortality rate (by age and gender) in 2018 in the Dominican Republic reported by World Health Organization plus an interest rate of 5%. Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

29
|(7)<br>p-value<br>of t-test<br>(1&2)=(3&4)|0.000<br>0.000<br>0.000<br>0.415<br>0.076<br>0.102<br>0.451<br>0.350<br>0.246<br>0.774<br>0.891<br>0.000<br>0.847<br>0.181<br>0.046<br>0.270<br>0.279<br>0.000<br>1036<br>ed and untargeted<br>ldren. The bottom<br>targeted and with<br>the p-value of the<br>level. (*** p_<_0.01,|
|---|---|
|(6)<br>p-value<br>of t-test<br>(3)=(4)|0.105<br>0.599<br>0.176<br>0.592<br>0.123<br>0.434<br>0.776<br>0.755<br>0.615<br>0.972<br>0.248<br>0.315<br>0.306<br>0.557<br>0.964<br>0.791<br>0.354<br>0.633<br>663<br>etween target<br>or without chi<br>e household is<br>umn 7 reports<br>d at the cluster|
|(5)<br>p-value<br>of t-test<br>(1)=(2)|0.074<br>0.509<br>0.819<br>0.640<br>0.459<br>0.731<br>0.820<br>0.444<br>0.405<br>0.726<br>0.694<br>0.935<br>0.916<br>0.862<br>0.231<br>0.556<br>0.640<br>0.969<br>373<br>y of means b<br>eholds with<br>value 1 if th<br>he table. Col<br>are clustere|
|(4)<br>t children<br>Targeted<br>HHs|2.492<br>0.400<br>2.567<br>0.277<br>0.079<br>0.174<br>0.174<br>0.193<br>1.295<br>6.450<br>0.852<br>2.450<br>0.756<br>0.552<br>0.967<br>54.957<br>0.419<br>480<br>-test of equalit<br>tween all hous<br>a dummy with<br>s reported in t<br>e included. SE|
|(3)<br>HH withou<br>Untargeted<br>HHs|2.350<br>0.421<br>2.838<br>0.246<br>0.115<br>0.148<br>0.148<br>0.187<br>1.298<br>6.388<br>0.814<br>2.399<br>0.710<br>0.585<br>0.962<br>59.896<br>0.464<br>183<br>the p-values of t<br>ality of means be<br>ng a regression of<br>lling characteristic<br>lity fxed effects ar|
|(2)<br>children<br>Targeted<br>HHs|5.312<br>0.558<br>3.261<br>0.258<br>0.096<br>0.181<br>0.162<br>0.173<br>1.329<br>6.435<br>0.838<br>2.738<br>0.750<br>0.508<br>0.946<br>81.805<br>0.450<br>260<br>n 5 (6) reports<br>of t-test of equ<br>nt when runni<br>hold and dwe<br>ove. Municipa|
|(1)<br>HH with<br>Untargeted<br>HHs|_Household Characteristics_<br>Household size<br>4.885<br>Household head is female<br>0.522<br>Education of head of household (years)<br>3.414<br>Satisfed with overall house quality (1=Yes)<br>0.265<br>Satisfed with quality of the house foor (1=Yes)<br>0.142<br>Satisfed with wall quality (1=Yes)<br>0.195<br>Satisfed with roof quality (1=Yes)<br>0.168<br>Satisfed with quality of family life (1=Yes)<br>0.212<br>Quality of Life Index (ICV)<br>1.275<br>Hours at home per day<br>6.469<br>_House Characteristics_<br>House with dirt foor (1=Yes)<br>0.841<br>Number of rooms in the house<br>2.743<br>Share of bedrooms with dirt foor<br>0.721<br>Walls are made of wood or zinc (1=Yes)<br>0.504<br>House is owned (1=Yes)<br>0.903<br>Monthly rent if leased out (USD)<br>55.753<br>Most important repair was not foor upgrade (1=Yes)<br>0.451<br>F-test of joint signifcance (p-value)<br>Number of households<br>113<br>Notes: See Appendix Table A1 for variable defnitions. For each characteristic, colum<br>households with (without) children up to 10 years old. Column 7 reports the p-values<br>of column 5 (6) also reports the p-value of the F-test that all variables are joint signifca<br>(without) children and 0 if untargeted and below (above) the median against all house<br>same test with a dummy with value 1 if the household is below the median and 0 if ab<br>** p_<_0.05, * p_<_0.1).|

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**Table A10:** Parameter Estimates: Presence of Children

||Househ<br>with Ch|(1)<br>olds<br>ildren|Ho<br>witho|(2)<br>useholds<br>ut Children|p-value<br>(1) = (2)|
|---|---|---|---|---|---|
|_Satisfed with overall h_|_ouse quality (_|_1=Yes)_||||
|Permanent Effect|_βP_+_βPK_|0.115<br>(0.213)|_βP_|-0.235**<br>(0.091)|0.234|
|Transitory Effect|_βT_+_βTK_|0.258<br>(0.213)|_βT_|0.698***<br>(0.163)|0.170|
|Delay Effect|_βD_+_βDK_|0.248**<br>(0.105)|_βD_|0.283***<br>(0.089)|0.834|
|_Satisfed with quality o_|_f family life (1_|_=Yes)_||||
|Permanent Effect|_βP_+_βPK_|0.096<br>(0.072)|_βP_|0.006<br>(0.048)|0.280|
|Transitory Effect|_βT_+_βTK_|0.260*<br>(0.145)|_βT_|0.316***<br>(0.109)|0.886|
|Delay Effect|_βD_+_βDK_|0.585**<br>(0.257)|_βD_|0.455**<br>(0.203)|0.471|

Notes: Of the total of 1,658 panel observations, 614 correspond to households with children up to age 10. The specifications include baseline values as covariates when available. Column 1 (2) reports the structural parameter estimates of the permanent, transitory and delay effect of the following NLS regression for households with (without) children: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βPK_ **1** _icK_ + ( _βT_ + _βTK_ **1** _icK_ ) _e_<sup>_−δT_(</sup><sup>_t−Tic_)</sup> + ( _βD_ + _βDK_ **1** _icK_ )( _e_<sup>_−δD_(</sup><sup>_Tic−Te_)</sup> _−_ 1)� + _Xic_<sup>_′θ_+</sup> _εict_ . The last column reports the p-value of a test that the coefficient _βjK_ = 0, for _j_ = _P_ , _T_ or _D_ . Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

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**Table A11:** Average Gains and Losses by Presence of Children

||(1)<br>Household<br>with children|(2)<br>Household<br>without children|P-value<br>(1) = (2)|
|---|---|---|---|
|_Satisfed with overall house qua_|_lity (1=Yes)_|||
|Permanent Gains (PG)|1.895|1.589|0.233|
||(2.213)|(1.195)||
|Transitory Gains (TG)|3.385|2.912*|0.169|
||(2.796)|(1.629)||
|Delay Losses (DL)|0.209**|0.267**|0.834|
||(0.088)|(0.117)||
|Total Gains (PG+TG-DL)|5.071***|4.234***|0.972|
||(1.356)|(1.245)||
|_Satisfed with quality of family l_|_ife (1=Yes)_|||
|Permanent Gains (PG)|-3.889**|0.149|0.279|
||(1.500)|(0.801)||
|Transitory Gains (TG)|9.153***|3.534***|0.885|
||(2.149)|(1.228)||
|Delay Losses (DL)|0.170***|0.069*|0.471|
||(0.054)|(0.0415)||
|Total Gains (PG+TG-DL)|5.094***<br>(1.262)|3.575***<br>(1.008)|0.408|

Notes: Of the total of 1,658 panel observations, 614 correspond to households with children up to age 10. Gains and losses are computed using the NL estimated coefficients in Table A10 and evaluated at the mean of ∆ _T_ of 6.8 months. Standard errors in parentheses are computed using the Delta Method. Following Galiani et al. (2018) the gross discount factor _R_ is the sum of the average mortality rate (by age and gender) in 2018 in the Dominican Republic reported by World Health Organization plus an interest rate of 5%. Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

32