rafmacalaba/datause-encoder-data huggingface dataset:
While detailed firm-level studies do not exist for India, a combination of anecdotal evidence and data suggests three stages in India’s microeconomic transition leading to the recent growth surprise: in the first, India’s large business houses (“oligopolistic firms”) jockey for position in the new regime marked by the dismantling of the permit raj (elimination of industrial licensing) and lower import barriers after 1991 for machinery and inputs; tariffs on consumer goods have been brought down much more slowly. They do so by going on an investment binge. In the second stage, which more-or-less coincides with the Ninth Plan period, there is a slowdown after the private investment spree of the Eighth Plan period reinforced by the cuts in government capital expenditure: while private saving increased by 3 percentage points of GDP during the Ninth Plan period relative to the Eighth Plan period, private investment increased only by 0.6 percentage points of GDP.<sup>17</sup> The third stage coincides with the resurgence in growth in 2003/04. Figure 2 shows investment booming in recent years, with gross domestic investment increasing from 22.9% in 2001/02 to 33.8% in 2005/06, driven mainly by a pick up in private investment. Box 2 shows that TFP was positively affected by the post-1991 reforms, although it has taken time for a visible impact to emerge.<sup>18</sup> Some of the reasons for this are discussed in further detail below.

**Figure 2: Investment as a share of GDP, 1985–86 to 2005–06**

<!-- Start of picture text --> 30.0%<br>25.0%<br>20.0% Private Investment<br>Government Capital<br>15.0%<br>Spending<br>Gross Fixed Capital<br>10.0% Formation<br>5.0%<br>0.0%<br>1985- 8th 9th 2002- 2003- 2004- 2005-<br>86 to Plan Plan 03 04 05 06<br>1989-<br>90<br><!-- End of picture text --> Source: Economic Survey 2006-07.

17 This observation led to the argument that the growing fiscal deficit was swallowing up the increased private saving instead of letting it finance additional private investment; in other words, crowding out was taking place. See Table 4 and related discussion in Pinto and Zahir (2004b, pp. 1042-1043). 18 The growth impact of the post-1991 reforms has not been without controversy. Rodrik and Subramanian (2004) argue that the growth spurt took place during the 1980s. For an opposing view, see Panagariya (2004a, b). Most businessmen would probably side with Forbes (2002, p. 167) who writes: “The changes since 1991 have unleashed a new dynamic in Indian industry [..] that is forcing change in every sector as firms finally are forced by new firms and the availability of imported products to provide consumers with products, prices and service that are beginning to approach internationally competent levels.” 10
# **ACRONYMS**

**AMC** Ahmedabad Municipal Corporation

**AUDA** Ahmedabad Urban Development Authority

**BRTS** Bus Rapid Transit System

**CBD** Central Business District

**FSI** Floor Space Index allowable on given plot, Index is calculated as the ratio of built floor area divided by the total plot area.

**GIS** Geographic Information System

**HPEC** High Powered Expert Committee

**IDF** India Development Foundation

**TPS** Town Planning Scheme

3
# Figure 1

# Overall Environmental Performance vs.

# ICP Income Per Capita

<!-- Start of picture text --> 7.00<br>*.<br>I . *~~~~~~~~~~~~~.<br>9<br>6.50!<br>* 9<br>X 6.00 9<br>_- I 9 9<br>5.50<br>5.00 6.00 7.00 8.00 9.00 10.00 11.00<br>ln ICPGDP<br><!-- End of picture text -->

**15**
are well-balanced at baseline across the control and treatment arms.

# **3.1.2 Attrition**

Turning to attrition, Table 4 shows the share of study participants that were lost to follow-up by treatment arm for each round. Attrition in the control group was 13.9% at rapid resurvey. Columns 1-2 show that the level of attrition is not differential in the pooled treatment group at the rapid resurvey, our preferred specification for this round. Columns 3-4 show that the — level of attrition was 3.5 percentage points (pp) lower in the IPT-G+ group compared to control and to IPT-G.<sup>17</sup> Columns 5-8 in Table 4 report attrition at midline and endline - approximately 12 and 24 months after baseline, respectively. We note that the share of study participants in the control group lost to follow-up increased to 20.5% by midline and 26.1% by endline. These levels of attrition are on par with other longitudinal studies with adolescents in SubSaharan Africa (e.g., Bandiera et al. 2020). The level of attrition in the IPT-G arm is always indistinguishable from that in the control group. However, attrition is lower in the combined treatment arm (IPT-G+) by 8 to 10 pp. It is possible that cash transfers induced some reciprocity in this group, which resulted in higher rates of participation in follow-up surveys.

F-tests for the joint significance of covariates and their interactions with each treatment arm, reported at the bottom of Table 4, indicate that these covariates are not prognostic of attrition and that attrition is not differential by baseline characteristics across treatment arms.<sup>18</sup> We report ITT effects that are **not** adjusted for attrition in the main tables. Ap– pendix Tables A11 A12 report upper and lower bounds for the impact estimates in the IPT-G+ arm, where attrition levels are significantly lower than the other two study arms.

# **3.1.3 Compliance**

In terms of compliance, Column 1 in Table 5 shows that no one in the control group had access to the therapy intervention. Participation rates in IPT-G in the two treatment arms – were good for a community-based program targeting adolescents 56% in the IPT-G group and 52% in the IPT-G+ group attended at least one session (this compares to 21% in Bandiera et al. (2020), for example). Conditional on attending at least one session, the average number of sessions attended was high, approximately 11 out of 14 sessions in both the IPT-G and IPT-G+ groups.<sup>19</sup> Figure 2 shows the cumulative distribution of attendance

> 17We remind the reader that this round of data collection took place before the announcement and disbursement of cash transfers in the combined treatment arm (IPT-G+).

> 18These coefficients, which are not presented in this table, are reported in full in Appendix Table A1.

> 19The share of participants that attended a high share of sessions is lower, however, than that reported in Bolton et al. (2003) among adults in rural Uganda. In that study, 54% of the participants attended at

13
In the analysis, distress is defined as early termination of a project. The PPI database labels any project from which the private party has exited by selling or turning over its shares back to government, or by ceasing operations as canceled. The database labels any project that is in international arbitration or for which contract termination has been requested by either the government or the private party as distressed. Using news articles and other public online sources, the current status of the distressed projects was determined and the projects were relabeled as canceled, concluded, or active. When no definitive information about the resolution of the distress could be found, the project was dropped from the sample.

The contract periods of some projects in the PPI database have been completed although they are labeled as still active. Potential reasons are that successfully concluded projects have gone unnoticed because they are not covered in the news, or that project companies have obtained contract extensions after the fulfillment of their initial contract terms. The projects with completed contract periods but still labeled as active are kept in the analysis and relabeled as concluded.

Figure 5 shows the distribution of the percentage of contract period elapsed within the estimation sample. It includes 3,977 projects, out of which 167 are canceled. One stark observation is that an overwhelming majority of the included projects have not passed half of their contract periods. One reason is that PPPs are a relatively new phenomenon compared to the median contract period of a PPP in the sample, which is 25 years. Another reason is that some of the older projects in the PPI database are missing crucial information, such as the contract period and level of contracting government, hence they are dropped from the sample used for the econometric estimation.

**Figure 5 Distribution of percentage of contract period elapsed, 1990–2018**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>% of Contract Period<br>Frequency<br><!-- End of picture text --> _Source_ : PPI database.

# **_4.1.3. Estimation results_**

Non-parametric estimates show that PPP projects are more likely to fail during the early portion of their contract periods, and risks accumulated during their contract periods are not trivial. Specifically, the smoothed hazard rate estimate using Gaussian kernel and optimal bandwidth 13
27 linear combination of a long list of variables capturing a household’s ownership of household durables, the materials of its house, etc. We use Principal Components Analysis (PCA) to derive the weights: using the first principal component, we obtain a z- score with zero mean and unit variance that is increasing in household wealth, and from this we form quintiles of wealth. To allow for increasing wealth between 1997 and 2002, we conducted the PCA on the pooled 1997 and 2002 data, and the weights from this exercise were then used to construct the wealth score in each year. The resultant wealth index has a zero mean in the pooled sample, but a higher mean in 2002 than in 1997 (the 2002 mean was positive and the 1997 mean negative).

> Among the _Z_ -variables in the selection equation, we include the province’s GDP per capita, and the fraction of a province’s government expenditures (on all sectors, not just health) covered through its own revenues. Given that the projects targeted poorer provinces, we would expect GDP per capita to enter in the program placement equation with a negative sign, and given provinces with institutional capacity were favored, we might expect the fraction of government expenditures financed locally to enter with a positive sign. Both coefficients, in the event, have the expected sign. It might be argued that the first of these variables could belong in the outcome equation; after all, studies have shown that, controlling for government health spending, GDP continues to exert an influence on infant mortality (cf. e.g. Filmer and Pritchett 1999; Bokhari _et al._ 2007). These are, however, aggregate-level studies; here, by contrast, we are exploring the impact of government health spending on child-level mortality data, holding constant the wealth of the child’s household. One would have to believe that GDP per capita in a
||||(0.083)|(0.072)|
|---|---|---|---|---|
|Constant|0.587***|0.489***|0.877***|0.580***|
||(0.024)|(0.091)|(0.099)|(0.167)|
|Observations|60|51|60|51|
|Adjusted R-squared|0.012|0.074|0.075|0.189|
|_Robust standard errors are in_|_parentheses. *** p<.0_|_1, ** p<.05, * p<.1_|||

Table 7 presents the results for specification [5]. We find that CO2 emissions in the year preceding the issue are significantly and positively related to later target dates. This holds across all models, suggesting that high polluters are more likely to set target dates later in a bond’s life, possibly to reduce penalty payouts for unmet targets. As before, we find that investment grade SLBs are likely to have later target dates.

Table 8 reports estimates from specification [6]. We fail to find a robust significant relationship between CO2 emissions and inclusion of a penalty-minimizing call option. As before, speculative grade bonds are more likely to include such call options. A closer inspection of the underlying data suggests that issuers of speculative SLBs tend to be significantly higher polluters on average. The average CO2 emissions in year preceding SLB issue for speculative SLB issuers in our sample is 15.1 million tons, compared to only 3.6 million tones for investment grade SLB issuers.

**<u>Table 8: Bond-Level Regression Analysis on Callability with CO2 Emissions</u>**

||(1)<br>Model 1|(2)<br>Model 2|(3)<br>Model 3|(4)<br>Model 4|
|---|---|---|---|---|
|CO2Emissions (tens of millions<br>tonnes)|-0.0002|-0.0004|0.010*|0.002|
||(0.004)|(0.002)|(0.005)|(0.006)|
|Investment grade dummy||-0.179*||-0.150**|
|||(0.099)||(0.063)|
|Amount issued (US$ billions)||0.115*||0.122*|
|||(0.061)||(0.065)|
|Tenor (years)||0.005||0.005|
|||(0.008)||(0.006)|
|Total assets (US$ billions)|||-0.006***|-0.001|
||||(0.002)|(0.003)|
|Leverage (%)|||0.192|0.047|
||||(0.200)|(0.136)|
|Public organization dummy|||-0.016|0.008|
||||(0.098)|(0.054)|
|Observations|88|67|88|67|
|Pseudo R<sup>2</sup>|0|0.398|0.084|0.408|

_Robust standard errors are in parentheses *** p<.01, ** p<.05, * p<.1_

# **6. Conclusion**

Our paper, motivated by rising practitioner concern about the effectiveness of SLBs in promoting sustainability, investigates the link between financial incentives and sustainability outcomes in these bonds. Using data from Thomson Reuters (Refinitiv) and BloombergNEF, 21
questions are the most feasible option in development settings, where the cognitive requirements involved may be difficult to cope with for less-educated enumerators and respondents (see discussion below).

# **2.2. TIME DIARIES**

Rather than focusing on a single activity as is conventionally followed with stylized questions, _time diaries_ collect information about all of a respondent’s activities during a specific reference period, typically the previous 24 hours. They can be self-reported or completed with the assistance of an enumerator.

With self-reported time diaries, respondents record all of their activities during the reference period, typically in 15 or 30 minute increments. Self-reporting can be done contemporaneously using a paper diary, or more recently an electronic device (e.g., tablet, smartphone), supplied to them by the research team. It can also occur retroactively via phone- or internet-based surveys, depending on the availability of technology. Self-reported time diaries require a high level of literacy, numeracy, and (in some cases) technological savvy, and as a result, are relatively untested in developing countries, though a few exceptions exist (e.g., Floro and Pichetpongsa 2010; Masuda et al. 2014).

Survey-based time diaries—by far the most common type used in developing countries—are completed retroactively with the help of an enumerator during a standard household survey interview based on respondents’ recollection of their activities during the reference period (again, usually the previous 24 hours). The actual structure of the interview can vary depending on the expertise of the survey team. Most commonly, respondents are asked to describe the sequence and duration of their activities over the reference period in chronological order, e.g., beginning with the time they woke up and ending when they went to sleep. See Box 2 for an example of a survey-based time diary from the WEAI (Alkire et al. 2013).

# **Box 2. Time diary in the Women’s Empowerment in Agriculture Index survey**

5
effectively rules out the possibility that changes in macroeconomic expectations do not affect consumption because individuals are unable to follow through on their consumption plans.

# **7 Conclusion**

How do macroeconomic expectations affect individual consumption decisions? To explore this question, we conducted a field experiment with 2,872 credit card customers of a large commercial bank. We then created exogenous variation in macroeconomic expectations through an information-provision experiment in which participants were provided with expert forecasts of inflation and the exchange rate. We measure the effects of these information shocks on consumers’ subsequent macroeconomic expectations (based on survey data), their self-reported consumption plans (measured in survey data), and their _actual_ spending (measured in administrative data). We test several predictions from standard macroeconomic models, such as whether an increase in inflation expectations increases spending on durables. We find that information provision shifts beliefs but does not change consumers’ actual spending behavior.

Our preferred interpretation of the findings is that consumers fail to incorporate revised macroeconomic expectations into their decision-making process, thus not re-optimizing their consumption plans when new information becomes available. Several recent findings support this interpretation. First, related work has found that agents fail to optimize in much simpler economic environments (see Campbell et al., 2011; Beshears et al., 2018). Specific to the context of credit card spending, Ponce, Seira, and Zamarripa (2017) and Gathergood et al. (2019) find that consumers do not borrow using the lowest interest rate card nor prioritize payment of the card with the highest interest rate. Chetty et al. (2020) shows that credit card spending does not react to anticipated income shocks. Second, and in contrast with the competing hypothesis that information provision is not meaningful (for example, because individuals mistrust the source or because it is not salient), growing evidence in similar settings indicates that participants retain information provided in the context of an information treatment for months after the experiment (see Cavallo, Cruces, and Perez-Truglia, 2017; Bottan and Perez-Truglia, 2020a) or even a year later. This suggests that it is unlikely that participants in our experiment simply discard the acquired information when making consumption decisions. Lastly, providing information with similar methods has been shown to affect behavior significantly in other contexts. For instance, individuals change their effort after updating beliefs about their future earnings potential (Cullen and Perez-Truglia, 2018) and delay home sales when updating beliefs about future home prices in the area (Bottan and Perez-Truglia, 2020b).

Our results have direct implications for the transmission of macroeconomic policy. Many macroeconomic policies are explicitly based on the premise that changes in economic expectations will affect households’ consumption choices. For example, central banks may try to en28
# **Table S20. Concerns about health and finance across consumption quintile**

||Concerned about<br>household health|Concerned about<br>household finances|
|---|---|---|
|Ethiopia|-0.219***<br>(0.019)|-0.150***<br>(0.016)|
|Nigeria|-0.141***|-0.032***|
||(0.017)|(0.012)|
|Uganda|-0.217***|-0.133***|
||(0.017)|(0.014)|
|Quintile 2|-0.004|-0.019|
||(0.031)|(0.027)|
|Quintile 3|-0.064*|-0.012|
||(0.032)|(0.026)|
|Quintile 4|-0.059**|0.004|
||(0.030)|(0.025)|
|Quintile 5|-0.094***|-0.006|
||(0.028)|(0.023)|
|Ethiopia-Nigeria|0.000|0.000|
|Ethiopia-Uganda|0.929|0.351|
|Nigeria-Uganda|0.000|0.000|
|Observations|8,846|8,847|
|R-squared|0.024|0.032|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent answered “yes” to the question of whether they were concerned that a household member may fall ill with COVID-19 or that the household would experience financial distress due to COVID-19. The binary variable is regressed on indicators for each country, with Malawi as the base case, and each pre-COVID-19 household annual per capita consumption quintile, with the lowest consumption quintile as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country or that the coefficient on one quintile indicator equals the coefficient on an indicator for a different quintile. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi and Uganda, and round 1 in Nigeria. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

47
nascent entrepreneurs should plan a human capital investment strategy which is wellbalanced across different competences and fields of expertise. Using cross section analyses, both Lazear (2005) and Wagner (2003) found that students who ended up as entrepreneurs had studied a much more varied curriculum than those who ended up working for others. Overall, these researchers conclude that accumulation of a balanced skill-mix ( _i.e._ general human capital) causally involves entrepreneurship and aboveaverage post-entry performance (in contrast with the positive role of specific human capital discussed above)<sup>23</sup> .

Turning our attention to DCs and taking into account that in this context entrepreneurship and self-employment are often carried out within the informal sector of the economy, the impact of education turns out to be controversial. In fact, higher education, either general or specific, augments the managerial capabilities which are necessary to run a business enterprise, but also increase the outside option for salaried employment in the formal sector of the economy. This is probably the reason why Van der Sluis, Van Praag and Vijverberg (2005), in their comprehensive survey of the previous literature focusing on DCs, found that in the majority of cases education lowers the likelihood of entering self-employment as contrasted with wage-earning employment. In contrast, Goedhuys and Sleuwaegen (2000), running logit estimations on data concerning the owners of 141 manufacturing firms in Côte d’Ivoire, found that the probability of being an entrepreneur is strongly stimulated by both apprenticeship and formal education, with the positive effect of education steadily increasing going from lower to higher levels of education. Similarly, Ghani, Kerr, and O’Connell (2011b), using cross-sectional establishment-level surveys of manufacturing and services companies in Indian districts, conclude that higher education in a local area significantly increases the supply of entrepreneurs<sup>24</sup> . However, this relationship becomes non-significant when the informal manufacturing sector is taken into account. This is an interesting outcome and confirms the fact that education may render the

> 23 Some recent papers cast doubt on this conclusion. In fact, individual unobservable characteristics may indeed simultaneously affect both skill accumulation and occupational choice, _i.e_ . individuals innately wellversed in a variety of fields would have the incentive both to accumulate more balanced skills and to become entrepreneurs. If such is the case, no causal relationship would be detectable between the spread of knowledge across different fields and the choice to become an entrepreneur (see Åstebro, 2005; Silva 2007; Åstebro and Thompson, 2011).

> 24 This outcome is consistent with similar evidence recently found for the developed countries, see Doms, Lewis and Robb, 2010; Glaeser, Kerr and Ponzetto, 2010.

22