rafmacalaba/datause-encoder-data huggingface dataset:
||(0.0196)|(0.0174)|(0.0186)|
|---|---|---|---|
|_BITall ij_|0.161***|0.145***||
||(0.0518)|(0.0445)||
|_PTAall ij_|-0.0628||0.189*|
||(0.103)||(0.102)|
|Constant|0.304|0.400**|0.568*|
||(0.267)|(0.193)|(0.298)|
|Shea partial R<sup>2</sup>(first stage)||||
|BIT|0.21|0.31||
|PTA|0.07||0.11|
|Hansen J statistic(p-value)|0.7244|0.7353|0.0541|
|Country FE|Y|Y|Y|
|Sector FE|Y|Y|Y|
|Observations|2,508|2,508|2,508|
|R-squared|0.159|0.160|0.152|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

# _ii. Interacted models_

Our results thus confirm that the presence of BITs and PTAs influences emerging market firms’ investment decisions. In this section, we explore the extent to which the impact of these agreements on firms’ decisions depends on cultural and geographic distance. We hypothesize that BITs, by providing institutional guarantees, mitigate the costs and uncertainty associated with investing in faraway, unfamiliar locations. We expect the relationship between distance and the effect of PTAs, however, to vary according to the motivations of investors, and to matter less for market-seeking investors as distance increases and the costs of trading grow beyond the expected benefits of these agreements.

To test these hypotheses, in Model 8, we add interaction terms between IEAs and distance. The estimated coefficient for the interaction term _BIT*Dist_ is positive and statistically significant in the logistic specification, suggesting that the further away two countries are, the greater the effect of the BIT on the likelihood that the country of origin will invest in the destination. In other words, the greater the distance between two countries, the stronger the investment-attraction effect of the investment treaty. Using the coefficients from the first-stage logistic regression, the odds of an investment increase by 1.0003 for every kilometer of distance between two countries with a BIT.

At the same time, this result suggests that the presence of a BIT acts to offset at least partially the transaction costs associated with geographic and cultural distance. BITs are thus (imperfect) substitutes for proximity. More specifically, the estimated coefficients suggest that the existence of a BIT is equivalent to an almost 80% reduction in the geographical distance between two markets. Thus, for example, if Brazil and India had signed and ratified a BIT, its effect would be equivalent to bringing India closer to Brazil – from 17,000 to less than 4,000 kilometers.

The estimated coefficient for the interaction term _PTA*Dist_ , on the other hand, is significant and negative only in the count model (negative binomial specification). This finding suggests that, in contrast to BITs, which have a greater effect as magnets for FDI the further away a potential host market, the effectiveness of PTAs in attracting FDI into a potential market _fades_ with physical distance. Unlike BITs, PTAs thus fail to 16
<!-- Start of picture text --> 0.3 Required PB to keep<br>debt stable |!<br>0.2 l<br>l<br>l<br>0.2 I<br>l<br>0.1 l<br>l<br>0.1 I<br>l<br>Historical PBs I<br>0.0<br>-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0<br><!-- End of picture text -->
Table 4 provides summary statistics for selected other indicators that have been identified in past work as relevant to nutritional outcomes, specifically GDP per capita, the national poverty rate, the female literacy rate, and access to improved water and sanitation facilities. Table 5 gives the correlation matrix for the three nutritional indicators from Table 2 and the five country-level indicators from Table 4. GDP and FLR are both negatively correlated with the nutritional indicators, as is access to water and sanitation. For GDP, the correlation is only statistically significant for stunting (r= -0.54). The FLR has a large and significant negative correlation with the wasting rate (r= -0.73), but the correlations are not statistically significant for underweight women or stunted children. The poverty rate is strongly correlated with stunting (r= 0.71). Water access is correlated with stunting (r= -0.33), while sanitation access is correlated with wasting (r= -0.31). Of course, these are only simple (pair-wise) correlations and may be deceptive. For example, if one regresses the stunting rates in Table 3 on both GDP and the poverty rate, only the latter is statistically significant.<sup>26</sup> In other words, the negative correlation between stunting incidence and GDP is due to an omitted variable bias, given that GDP is (negatively) correlated with poverty incidence, which is a strong covariate of the incidence of stunting.

For a subset of countries, the DHS also collected data on adult male anthropometrics which provide an insight into the extent of intra-household inequality. Table 6 provides summary statistics on the incidence of undernutrition for women and children stratified according to whether the male head of household is underweight or not. We see that the incidence of undernutrition among women and children is lower when the male head is adequately nourished. However, substantial inequality in nutritional status is also evident, and the gender inequality goes in both directions. The majority of women in households where the male head is underweight are not undernourished, and there is a high incidence of undernutrition among women and children in households where the male head is not underweight. Table 6 also gives (in parentheses) the proportions of undernourished women and children found in the two groups of households, identified by whether the male head is underweight or not. (Note that the proportions sum to unity horizontally.) We see that the bulk of underweight women (74%) are found in households where the male head is not underweight and similarly for stunted (80%) and wasted (53%) children.

**_Wealth and other covariates:_** When we say that a household is “wealth-poor” we are referring to the DHS wealth index within a given country. The wealth index is constructed by taking variables

> 26 Using our estimates from Tables 2 and 4 to regress the log of the stunting rate on the log of GDP plus the log of the poverty rate, it is readily verified that the regression coefficients are -0.09 (s.e.= 0.06) and 0.42 (s.e.= 0.12) respectively.

13
2

# **1. Introduction**

The global financial crisis has highlighted the crucial role that incentives play in finance. Misalignments between public interest and private incentives can become a problem in any economic sector. But they are particularly important in the financial sector, not only because of that sector’s critical role in development, but also because any incentive misalignments are compounded by other issues specific to finance, in particular asymmetric information. If economic agents in the financial system face a bad structure of incentives, this tends to lead to a build-up of systemic risk. These incentives include bank managers’ incentives to boost short-term profits and create banks that are “too big to fail”, credit rating agencies’ incentives to keep issuing high ratings for subprime assets, regulators’ incentives to forebear and withhold information from other regulators in stressful times, and policymakers’ incentives to keep bailing out weak financial institutions rather than allowing timely exit from the financial sector. These points are well documented in the existing literature. For example, Caprio, Demirgüç-Kunt, and Kane (2008) examine incentive conflicts as an explanation of how securitization went wrong and why credit ratings proved so inaccurate, and Levine (2009) discusses the role of incentive failures in regulatory authorities.

The crisis has also re-opened important policy debates on financial regulation. Among other things, at the global level, the Basel Committee has started working on new capital and liquidity requirements (e.g., Basel Committee 2010), and the Financial Stability Board (FSB) has developed an impressive agenda of reform (e.g., FSB 2010). At the country level, various national authorities have started reviewing their bank resolution regimes, macroprudential policy, consumer protection, and other aspects of their regulatory framework. (For a comprehensive review, see the World Bank’s Bank Regulation and Supervision Survey, presented in Čihák, Demirgüç-Kunt, Martínez Pería, and MohseniCheraghlou 2012.) How far have these reforms gone in addressing the underlying incentive failures in the financial sector? Outside reviews (e.g., Geneva Report 2009; the LSE Report on the Future of Finance 2010; the Squam Lake Working Group 2010; and the CEPR Future of Banking report 2010) suggest that much more is needed at the global level to address the underlying incentive breakdowns that led to the global financial crisis. Empirical analysis of the regulatory steps taken at the national level in a large sample of countries since the onset of the crisis also makes it clear that there is still a major scope for improvement to address the incentive problems in the financial sector (Čihák, Demirgüç-Kunt, Martínez Pería, and Mohseni-Cheraghlou 2012).

The motivation for this paper is the need for incentive issues to be more fully reflected in the design of regulatory systems. Reflecting the importance of incentives in the financial sector, several authors have argued for the need to make incentives more central to regulatory frameworks, or to embark on regulatory reforms that are incentive-robust
65

- emission test equipment;

- audit vehicles; and

- program management (data processing, quality assurance, public information, training, supplies, etc.).

Historical repair cost data are available from several functioning I/M programs in the United States and these results are summarized in Table 35. A major difficulty in obtaining reliable and accurate repair cost data is related to non-uniform reporting methods and the inability to segregate the costs of I/M related repairs. Finally, an important factor which can substantially distort repair costs is the warranty coverage provisions which can require the vehicle manufacturer to pay for all repairs necessary to pass the I/M test. The warranty coverage remains in effect, however, under the condition that the vehicle has not been tampered with or misused, and that the vehicle owner follows the maintenance practices prescribed by the manufacturer.

Table 35. **Average cost of repairs associated with l/M programs in the United States**

|I/M Program<br>Period|Covered<br>A|verage Repair Cost<br>(US$)|
|---|---|---|
|New Jersey|1979|$28.00|
||1980|18.71|
|Arizona|1979|30.00|
||1980|29.74|
|Portland|1980|17.00|
||1986|50.68|
|California|1979|32.00|
||1980 (July-Sept.)|29.09|
||1980 (Oct.-Dec.)|28.82|
|Washington, D.C.|1985|95.00|
|EPA|1986||
|Pre-1981 Vehicles||37.50|
|1981+ under 50,000 miles||47.50|
|1981 + over 50,000 miles||55.00|
**Table 3:** Physical stature and body mass

|||I<br>|||II<br>||
|---|---|---|---|---|---|---|
||_α_|_β_|_σ_|_α_|_β_|_σ_|
|height|0.044*|-0.060***|-0.030***|0.071***|-0.068***|-0.032***|
||(0.025)|(0.012)|(0.004)|(0.025)|(0.015)|(0.006)|
|BMI||||0.023***|-0.007|-0.002|
|||||(0.007)|(0.009)|(0.003)|
|literate|0.028|0.057|-0.010|0.024|0.061|-0.009|
||(0.035)|(0.044)|(0.014)|(0.035)|(0.044)|(0.014)|
|middle school|0.038|0.132*|-0.007|0.032|0.139*|-0.005|
||(0.062)|(0.071)|(0.020)|(0.062)|(0.071)|(0.020)|
|business|0.025|0.071|-0.061|0.024|0.053|-0.060|
||(0.095)|(0.168)|(0.053)|(0.094)|(0.183)|(0.057)|
|female|-0.057|0.193|-0.023|-0.021|0.174|-0.024|
||(0.076)|(0.118)|(0.030)|(0.075)|(0.121)|(0.030)|
|age|-0.012|0.042*|-0.005|-0.012|0.043*|-0.006|
||(0.016)|(0.023)|(0.006)|(0.016)|(0.023)|(0.006)|
|unmarried|0.010|-0.199*|0.020|0.021|-0.192*|0.024|
||(0.083)|(0.111)|(0.028)|(0.083)|(0.111)|(0.030)|
|Region fxed efects|✓|✓|✓|✓|✓|✓|
|constant|0.702***|0.716***|0.170***|0.254*|0.860***|0.202***|
||(0.038)|(0.037)|(0.015)|(0.148)|(0.181)|(0.069)|
|Subjects|496|496|496|496|496|496|
|_LL_|_−_12_,_516_._99|_−_12_,_516_._99|_−_12_,_516_._99|_−_12_,_507_._92|_−_12_,_507_._92|_−_12_,_507_._92|

Standard errors in parentheses; *p<0.1, **p<0.05, ***p<0.01 Continuous independent variables are entered as z-scores the sensitivity parameter now being highly significant. A higher BMI is further associated with more probabilistic sensitivity, although it has no significant effect on average risk tolerance. One interpretation of the latter is that it may be connected to the family’s wealth and ability to cushion shocks, which in Ethiopia may easily lead to starvation. Indeed, about 12% of our sample exhibits a BMI smaller than 18.5, which is considered a cutoff for being underweight (World Health Organization).

# **4.2 Stability analysis**

In this section, we replicate the main findings from above using non-parametric data and a one-parameter utility function. While non-parametric analysis will likely result in attenuation bias due to the noise incorporated in the measures, this is nevertheless useful in order to establish the stability of our main findings. Table 4 shows the four regressions from the two tables in the previous section, using OLS with robust standard errors. The dependent variable is now simply 20
# **Section 4: Dyadic regressions modeling firm performance and growth**

||||Ethiopia|||||Sudan|||
|---|---|---|---|---|---|---|---|---|---|---|
||[1]<br>Log( value-<br>added per|[2]<br>Labor<br>growth last 3|[3]<br> <br>Revenue<br>growth last|[4]<br>Revenue<br>growth last 3|[5]<br> <br>First<br>common|[6]<br>Log( value-<br>added per|[7]<br>Labor<br>growth last 3|[8]<br> <br>Revenue<br>growth last|[9]<br>Revenue<br>growth last 3|[10]<br> <br>First<br>common|
||employee)<br>|yi-yj||years<br>|yi-yj||year<br>|yi-yj||years<br>|yi-yj||factor<br>|yi-yj||employee)<br>|yi-yj||years<br>|yi-yj||year<br>|yi-yj||years<br>|yi-yj||factor<br>|yi-yj||
|i & j trade with each other|-0.0758|-0.0850|-0.0437|0.338|0.165||-0.534**|-0.413|1.652||
||(0.284)|(0.130)|(0.121)|(0.657)|(0.424)||(0.221)|(0.676)|(2.483)||
|i & j have common supplier|-0.106|0.0152|0.246**|0.00522|0.109|-1.651***|-0.277**|-0.904***|-0.711**|-0.480**|
||(0.111)|(0.0668)|(0.113)|(0.129)|(0.142)|(0.453)|(0.122)|(0.337)|(0.350)|(0.203)|
|i & j have common client|-0.182|-0.0136|0.195|-0.0119|0.113|-0.986|0.0900|-0.586***|0.307|-0.00893|
||(0.136)|(0.0929)|(0.151)|(0.202)|(0.195)|(0.660)|(0.139)|(0.203)|(0.443)|(0.216)|
|log Distance btw i & j|-0.0247**|0.0139|0.0179*|0.0193|0.0235|-0.0612|-0.0173|-0.0515**|-0.0515|-0.0103|
||(0.0122)|(0.0106)|(0.0108)|(0.0168)|(0.0149)|(0.0386)|(0.0111)|(0.0240)|(0.0346)|(0.0187)|
|i & j belong to same sector|-0.0819*|0.00508|-0.00350|-0.0243|-0.0130|-0.135|-0.0555|-0.0465|-0.0624|-0.0756|
||(0.0447)|(0.0247)|(0.0185)|(0.0515)|(0.0349)|(0.156)|(0.0394)|(0.0680)|(0.133)|(0.0869)|
|Abs diff firm age|-0.00314|0.00267|-0.00260***|-0.00564***|-0.00397***|<br>-0.00493|0.00112|0.00862|0.00458|0.00602|
||(0.00200)|(0.00347)|(0.000730)|(0.00161)|(0.00146)|(0.00781)|(0.00216)|(0.00651)|(0.00746)|(0.00459)|
|Abs diff managers’ education|-0.00722|0.00380|-0.00917|0.00885|-0.0164|0.0460|-0.00466|-0.0333|-0.0201|0.0114|
||(0.0280)|(0.0117)|(0.0116)|(0.0310)|(0.0174)|(0.0472)|(0.0116)|(0.0228)|(0.0268)|(0.0251)|
|Abs diff managers’ experience|0.000671|7.15e-06|-0.00272|-0.00171|-0.00410|-0.00732|0.000766|0.00303|-0.00385|-0.00186|
||(0.00321)|(0.00226)|(0.00204)|(0.00453)|(0.00284)|(0.00578)|(0.00132)|(0.00695)|(0.00580)|(0.00313)|
|Owners’ gender differ|0.121|0.103|-0.0813**|-0.220***|-0.110*|0.375|-0.00723|-0.161|0.0558|-0.138|
||(0.0932)|(0.0669)|(0.0389)|(0.0686)|(0.0582)|(0.582)|(0.0614)|(0.134)|(0.275)|(0.0960)|
|Abs diff log employment|0.0786**|0.00792|-0.0257**|-0.0375|-0.0235|0.00718|0.0654**|0.0906|0.102|0.0611|
||(0.0311)|(0.0242)|(0.0117)|(0.0360)|(0.0223)|(0.110)|(0.0258)|(0.0709)|(0.114)|(0.0600)|

_Note_ : The table shows OLS results. A constant is included in all specifications. The numbers in ( ) are bootstrapped standard errors that are robust to heteroskedasticity and cross-observation correlation in the error terms involving the same firms. Statistical significance at the 10%, 5% and 1% level is indicated by *,** and ***, respectively.
**Figure 5: Fiscal Position-Dependent Multipliers during Recessions**

<!-- Start of picture text --> A. On Impact B. 1 Year<br>0.7 1.6<br>0.6 1.4<br>1.2<br>0.5<br>1.0<br>0.4<br>0.8<br>0.3<br>0.6<br>0.2<br>0.4<br>0.1 0.2<br>0.0 0.0<br>12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4 12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4<br>C. 2 Years D. Long Run<br>2.5 2.5<br>2.0<br>2.0<br>1.5<br>1.5 1.0<br>0.5<br>1.0<br>0.0<br>0.5 -0.5<br>-1.0<br>0.0<br>-1.5<br>-0.5 -2.0<br>12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4 12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4<br><!-- End of picture text --> _Note:_ The graphs show the conditional fiscal multipliers during recessions for different levels of fiscal position at select horizons. These are based on estimates from the IPVAR model, where model coefficients are jointly conditioned on fiscal position and the phase of the business cycle. Government debt as a percentage of GDP is the measure of fiscal position and the values shown on the x-axis correspond to the 5<sup>th</sup> to 95<sup>th</sup> percentiles from the sample. Recessions are determined by the Harding-Pagan (2002) business cycle dating algorithm. Fiscal position is strong (weak) when government debt is low (high). Solid lines represent the median, and dotted bands are the 16-84 percent confidence bands.

21
# **D Robustness to Attrition**

For ethical as well as logistical reasons, participants were allowed to drop out of our experiment at any point. Participation in each ride opportunity was voluntary and compensation separately. Table A1 presents descriptive statistics on participation patterns of dropouts throughout the sequence of rides. Of the 546 initial participants who tried out at least one zero cent opportunity cost ride, 66.5% continued to rides with positive opportunity cost for riding the reserved space and 48.3% continued to the randomized assignment of reserved or public space. Much of this early attrition is driven by casual participants who experimented briefly with the app at the beginning and then did not continue: conditional on completing five or more rides with zero cent opportunity cost, 84% continue to the opportunity cost rides and 61% to the randomized assignment of space.

We first account for attrition bias originating from early attriters who only completed a few rides in the zero-opportunity cost phase of the experiment. First, we verify that observable characteristics do not jointly explain participation in the different phases of the experiment (Table D1). Second, we estimate a conservative bound our results on willingness to pay by assuming that all participants who tested the app and dropped out would always choose the public space. This is a conservative assumption since attriters are in fact somewhat more likely to choose the reserved space at when there is no opportunity cost. Figure D1 shows the results: over 20% of participants still demonstrate positive willingness to pay for the reserved space, even assuming that none of the attriters do so. We also note that the results presented in the event study graph in Figure A7 provide reassurance, as the treatment effects are remarkably stable even as density of observations decline within each phase of the experiment.

Second, we account for potential attrition bias originating from riders systematically responding to assignment to a given space. First, we simply regress rider’s take up of a task on treatment assignment (Table D2). Results confirm that participants did not selectively drop out in response to treatment assignment on any given day. Second, we show that our
|P143492|2015|2015|6/26/2015|1 Active|Brazil|
|---|---|---|---|---|---|
|P143492|2015|2016|12/18/2015|2 Active|Brazil|
|P143492|2015|2016|6/15/2016|3 Active|Brazil|
|P143492|2015|2017|12/1/2016|4 Active|Brazil|
|P143492|2015|2017|6/18/2017|5 Active|Brazil|
|P143492|2015|2018|12/22/2017|6 Active|Brazil|
|P143492|2015|2018|6/26/2018|7 Active|Brazil|
|P143492|2015|2019|12/13/2018|8 Active|Brazil|
|P143492|2015|2019|6/19/2019|9 Active|Brazil|
|P143492|2015|2020|12/18/2019|10 Active|Brazil|
|P143492|2015|2020|6/25/2020|11 Active|Brazil|
|P143495|2017|2017|4/21/2017|1 Closed|Guatemala|
|P143495|2017|2018|10/26/2017|2 Closed|Guatemala|
|P143495|2017|2018|4/18/2018|3 Closed|Guatemala|
|P143495|2017|2019|10/24/2018|4 Closed|Guatemala|
|P143519|2013|2019|5/9/2019|1 Closed|Vietnam|
|P143536|2013|2019|5/9/2019|1 Closed|Philippines|
|P143537|2013|2014|2/22/2014|1 Closed|China|
|P143537|2013|2019|5/7/2019|2 Closed|China|
|P143546|2015|2016|12/17/2015|1 Closed|Southern Africa|
|P143546|2015|2017|6/22/2017|2 Closed|Southern Africa|
|P143546|2015|2019|4/7/2019|3 Closed|Southern Africa|
|P143569|2013|2014|6/6/2014|1 Closed|Egypt,Arab Republic o|
|P143580|2014|2015|7/7/2014|1 Active|Bosnia and Herzegovin|
|P143580|2014|2015|3/11/2015|2 Active|Bosnia and Herzegovin|
|P143580|2014|2016|9/14/2015|3 Active|Bosnia and Herzegovin|
|P143580|2014|2016|4/1/2016|4 Active|Bosnia and Herzegovin|
|P143580|2014|2017|10/5/2016|5 Active|Bosnia and Herzegovin|
|P143580|2014|2017|4/27/2017|6 Active|Bosnia and Herzegovin|
|P143580|2014|2018|11/6/2017|7 Active|Bosnia and Herzegovin|
|P143580|2014|2018|6/19/2018|8 Active|Bosnia and Herzegovin|
|P143580|2014|2019|12/21/2018|9 Active|Bosnia and Herzegovin|
|P143580|2014|2019|6/27/2019|10 Active|Bosnia and Herzegovin|
|P143580|2014|2020|12/17/2019|11 Active|Bosnia and Herzegovin|
|P143580|2014|2020|6/5/2020|12 Active|Bosnia and Herzegovin|
|P143580|2014|2020|6/29/2020|13 Active|Bosnia and Herzegovin|
|P143588|2014|2015|10/8/2014|1 Active|Sierra Leone|
|P143588|2014|2015|4/17/2015|2 Active|Sierra Leone|
|P143588|2014|2016|10/28/2015|3 Active|Sierra Leone|
|P143588|2014|2016|4/18/2016|4 Active|Sierra Leone|
|P143588|2014|2017|11/21/2016|5 Active|Sierra Leone|
|P143588|2014|2017|6/16/2017|6 Active|Sierra Leone|
|P143588|2014|2018|1/17/2018|7 Active|Sierra Leone|
|P143588|2014|2019|9/6/2018|8 Active|Sierra Leone|
|P143588|2014|2019|5/23/2019|9 Active|Sierra Leone|
|P143588|2014|2020|12/17/2019|10 Active|Sierra Leone|
|P143588|2014|2020|6/18/2020|11 Active|Sierra Leone|