rafmacalaba/datause-encoder-data huggingface dataset:
bootstrapping.

As a robustness test we also present results _controlling_ for the total reports of crimes in that city and period. The idea behind including this control is that we want to estimate the effects of the program on the _crime report by migrants_ , keeping constant the _overall number of crime reports_ . Reassuringly our results remain robust with and without this control.

# **IV. A Addressing potential dynamic endogeneity**

The validity of the main estimates described in equation 1 rests on the parallel dynamic behavior on the outcomes we examine for municipalities that had higher and lower PEP program take-up. Although evidence on the validity of this assumption will be illustrated in the next section, we also estimate a specification that was robust to dynamic individual responses to the program as a robustness test.

For this purpose, we follow our approach in Bahar et al. (2021). In particular, we employ an instrumental variable approach and estimate specification (1) through two-stages least squares (2SLS).<sup>14</sup> Our instrument is the average number of days undocumented Venezuelan migrants had to register for the PEP program in the last quarter of 2018. The number of days was defined randomly at the individual level by the form number of the registration in the RAMV survey. According to the government officials who oversaw this process, the local registration point gave this number to migrants in the order they arrived and registered. More importantly for us, the numbers did not correspond to any particular geographic or socioeconomic characteristics of the municipalities. The officials said they took this approach to scatter the applications to manage the administrative load and distribute it more evenly across locations. Based on the individual numbers for all PEP holders at the time of registration in a given municipality, we estimate the average registration days per municipality as: Reg. Days _c_ = � RAMV registrants assigned to time window j _<u>c</u> ×_ [Days in time window j] (2) _jεK_ Total RAMV registrants _c_

> 14It is common in the economics literature to apply instrumental variables in a difference-in-differences setting. As a pioneering example of combining these methods, see the seminal study by Duflo (2001).

18
27

# V. FACTORS INFLUENCING AIR POLLUTION FROM LAND TRANSPORT SOURCES

The major factors affecting air pollution from transport sources include:

- the amount of energy consumption by the transport sector;

- the magnitude and density of population;

- the degree of urbanization;

- transport modes and their share of travel;

- fuel consumption and emission characteristics of transport modes; and

- the aggregate emission levels associated with transport sources.

# **Energy Consumption**

Air pollution is directly related to energy consumption. Statistically significant and positive relationships were obtained when data on energy consumption were regressed with data on air pollution for a sample of OECD countries (Table 6). Emissions of CO, NOx, and HC were highly correlated with energy consumption.

**Table 6. Relation between energy consumption and air pollution for OECD countries**

|Pollutant|Regression Equation|df|F|R<sup>2</sup>|
|---|---|---|---|---|
|CO|Y = -780.316 + 0.06548*X|8|1375|0.99|
|NOx|Y = -252.522 + 0.015306*X|14|1150|0.97|
|HC|Y = -593.935 + 0.018368*X|9|419|0.98|

Note: 1. X is energy in 1000 tonnes of oil equivalent 2. Y is pollutant emission in 1000 tonnes 3. Source of data [OECD 1987] 4. 1 ton of oil equivalent is equal to 426.216 joules of energy
- **Ito, Koichiro, Takanori Ida, and Makoto Tanaka** , “The Persistence of Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand,” _Working Paper_ , 2015.

- **Karlan, Dean, Margaret McConnel, and Jonathan Zinman** , “Getting to the Top of Mind: How Reminders Increase Savings,” _Forthcoming, Management Science_ , 2014.

- **, Melanie Morton, and Jonathan Zinman** , “A Personal Touch: Text Messaging for Loan Repayment,” _Working Paper_ , 2015.

- **K ̋oszegi, Botond and Adam Szeidl** , “A Model of Focusing in Economic Choice,” _Quarterly Journal of Economics_ , 2013, _128_ (1).

- **Laurin, Kristin, Azim F. Shariff, Joseph Henrich, and Aaron C. Kay** , “Outsourcing punishment to God: beliefs in divine control reduce earthly punishment,” _Proceedings of the Royal Society of London B: Biological Sciences_ , 2012, _279_ (1741), 3272–3281.

- **Liberman, Andres** , “The Value of a Good Credit Reputation: Evidence from Credit Card Renegotiations,” _Working Paper_ , 2015.

- **Madrian, Brigitte C. and Dennis F. Shea** , “The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior,” _Quarterly Journal of Economics_ , 2001, _116_ (4), 1149–1187.

- **Mazar, Nina, On Amir, and Dan Ariely** , “The Dishonesty of Honest People: A Theory of Self-Concept Maintenance,” _Journal of Marketing Research_ , 2008, _45_ (6), 633–644.

- **McKay, Ryan, Charles Efferson, Harvey Whitehouse, and Ernst Fehr** , “Wrath of God: Religious primes and punishment,” _Proceedings of the Royal Society B: Biological sciences_ , 2011, _278_ (1713), 1858–1863.

- **Pichon, Isabelle, Giulio Boccato, and Vassilis Saroglou** , “Nonconscious influences of religion on prosociality: a priming study,” _European Journal of Social Psychology_ , 2007, _37_ (5), 1032– 1045.

- **Randolph-Seng, Brandon and Michael E. Nielsen** , “Honesty: One Effect of Primed Religious Representations,” _The International Journal for the Psychology of Religion_ , 2007, _17_ (4), 303– 315.

- **Shariff, Azim F. and Ara Norenzayan** , “God Is Watching You: Priming God Concepts Increases Prosocial Behavior in an Anonymous Economic Game,” _Psychological Science_ , 2007, _18_ (9), 803–809.

- **Shu, Lisa L, Nina Mazar, Francesca Gino, Dan Ariely, and Max H Bazerman** , “Signing at the Beginning Makes Ethics Salient and Decreases Dishonest Self-Reports in Comparison to Signing at the End,” _Proceedings of the National Academy of Sciences_ , 2012, _109_ (38), 15197– 15200.

- **Wilkinson-Ryan, Tess** , “Breaching the Mortgage Contract,” _Vanderbilt Law Review_ , 2011, _64_ (5).

- **World Bank** , “Global Financial Development Report: Financial Inclusion,” 2014.

22
11 deposits in 1993, on average, to 21.4 percent of deposits in 2000 (Table 2). In eight of the 25 countries, government deposits represented over 30 percent of deposits in 2000, compared to four countries in 1990 and five in 1993. The ratio of government debt to deposits increased in 17 countries over the decade.

Table 2. Net Credit to Government/ Deposits

||2000(percent)||
|---|---|---|
|Korea|-3.6 Philippines|22.7|
|Chile|-3.1 Hungary|23.0|
|Thailand|1.4 Pakistan|24.6|
|Malaysia|2.3 Morocco|26.5|
|Peru|3.2 Argentina|30.8|
|S. Africa|4.9 India|34.6|
|Czech Rep.|4.9 Russia|35.3|
|China|6.0 Brazil|43.3|
|Egypt|7.8 Mexico|48.8|
|Venezuela|8.2 Algeria|50.6|
|Bangladesh|10.9 Indonesia|56.3|
|Poland|14.8 Turkey|64.7|
|Colombia|16.4 **Average**|**21.4**|

The individual country data display three patterns over the period 1993-2000, when information is available for all 25 countries. In eight of the countries the ratio of government debt to deposits rose between 10 and 64 percentage points between 1993 and 2000.<sup>20</sup> In five countries, government debt declined by 10 or more percentage points of deposits.<sup>21</sup> Finally, in the other 12 countries, the ratio of government debt to deposits was roughly constant, rising less than 6 percentage points (and in a few cases falling).

The rise in the average amount of government debt in the banks largely reflects the large injections of government recapitalization debt as part of bank restructurings.<sup>22</sup> Indonesia is the most obvious example — its bank recapitalization led to an enormous rise in net bank holdings of government debt, from a negative figure in 1990 to 29 percent of GDP in 2000.<sup>23</sup> Government debt also rose sharply in Brazil and Mexico

> 20 Argentina, up 12 percentage points (though down from 1990 to 2000); Brazil, 32 percentage points; Colombia, 18 percent points; Indonesia, 64 percentage points; Mexico, 45 percentage points; Philippines, 11 percentage points; Russia, 40 percentage points; and Turkey, 57 percentage points.

> 21 Egypt, down 20 percentage points; Hungary, 15 percentage points; Morocco, 13 percentage points; Pakistan 17 percentage points; Poland, 21 percentage points.

> 22 Future tax credits were also used in some cases, for example in Brazil.

> 23  Recapitalization involved banks’ turning over bad loans to the Indonesian Bank Reconstruction Agency and receiving initially enough government bonds to raise capital to 4 percent of risk weighted assets.
**Table 3**

**Other Explanatory Variables Influencing Power Sector Performance** **<mark>(</mark> Ex** **<mark>p</mark> ected Relationshi** **<mark>p)</mark>**

|||**Expec**<br>|**ted Sign **<br>|
|---|---|---|---|
|**Variables**|**Definition**|**Performance**|**Environmental**|
|||**indicators**|**indicators**|
|**i**|**BASIC CONTROLS**|||
|**GDP per capta**|= GDP per capita, PPP (constant 2005 international $)|**+**|**+**|
|**Installed capacity**<br>**(MW)**|= Total installed capacity in the power system (MW)|**+**|**-**|
||**POWER SYSTEM SIZE GROUP CONTROLS**|||
|**Group A**|= 1 if above the threshold GDP per capita and installed<br>capacity (determined by the lowest 25 percent of capacity and<br>income of the sample)<br>= 0 otherwise|**+**|**+**|
|**Group D**|= 1 if below the threshold GDP per capita and installed<br>capacity (determined by the lowest 25 percent of capacity<br>and income of the sample)<br>= 0 otherwise|**-**|**-**|
|**Group A*sectoral reforms**|<sup>= Interacted variable between Group A and each of the</sup><br>sectoral reforms(competition, privatization and regulation)|**+**|**-**|
||**OTHER CONTROLS**<br>|||
|**Financial Crisis**|= 1 for the 5 years after 2001<br>= 0 otherwise|**-**||
|**Share of fossil fuels**<br>**(% installed capacity)**|= Percentage of the total installed capacity in fossil fuels (oil,<br>coal and naturalgas|**?**||
|**Investment**<br>**(US$/MWh)**|= Capital expenditure per unit of energy generated (MWh)|**+**||
|<br>**Region**<br>_d_region1, 2, 3,4,5,6_|**REGIONALCONTROL**<br>Dummies , 1=EAP, 2=ECA, 3=LAC, 4=MNA, 5=SAR, 6=SSA|||

# **3.2 Theoretical hypotheses to be tested**

**Hypothesis 1 Sectoral Reforms and Performance and traditional and environmental power sector performance**

**_The implementation of key sectoral reforms, including vertical and horizontal unbundling, privatization and regulation, is expected to be significantly associated with higher access and better operational and financial performance of the power sector. Such reforms may however be associated with environmentally non sustainable outcomes._**

Some reforms (e.g. unbundling, wholesale competition) while expected to improve all indicators of technical and financial performance, may under some circumstances tend to discourage low carbon options. The arguments put forward are that integrated firms can coordinate the development of the network to accommodate renewable generations and a "single buyer" model allows policy makers to choose a mix of low carbon generators in a 10
15 of countries, study growth over a 30-year horizon, and systematically control for many possible determinants of economic growth such as initial income, educational attainment, inflation, black market exchange rate premia, government spending, openness to trade, and political instability. Furthermore, they examine whether financial development is associated with productivity growth and capital accumulation, which are two channels through which finance may influence economic growth.

King and Levine (1993b, henceforth KL) study 77 countries over the period 1960-1989. To measure financial development, KL focus on DEPTH, which equals the size of the financial intermediary sector. It equals the liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP. An important weakness with this measure of financial development is that DEPTH measures the size of the financial intermediary sector. It may not, however, represent an accurate proxy for the functioning of the financial system. It may not proxy for how well bank research firms, exert corporate control, or provide risk management services to clients. KL experiment with alternative measures of financial development that are designed to gauge who is conducting credit allocation, i.e., whether it is banks or the government, and to where the credit is flowing, i.e., to the private sector or to the government and state-owned enterprises produce. They obtain similar results with these alternative indicators of financial development. (Also, see La Porta et al. 2001.) KL assess the strength of the empirical relationship between DEPTH averaged over the 1960-1989 period and three growth indicators also averaged over the 1960-1989 period, _G._ The three growth indicators are as follows: (1) the average rate of real per capita GDP growth, (2) the average rate of growth in the capital stock per person, and (3) total productivity growth, which is
**Table 3: Effects of Household Income on the Propensity to Pay Bribe: IV estimates**

|||**Propensit**|**y topay**|||
|---|---|---|---|---|---|
||Main|Conditional|Additional|Alt. Income|Interaction|
||Specification|MLE|Controls|Variable|Instrument|
||(1)|(2)|(3)|(4)|(5)|
|**Per Capita Income**|**-0.153**|**-0.168**|**-0.158**||**-0.175**|
||**(-2.34)****|**(-2.30)****|**(-2.09)****||**(-2.85)****|
|**log(income)**||||**-0.498**||
|||||**(-2.28)****||
|Household size|-0.015|-0.017|-0.017|0.054|-0.015|
||(-2.51)**|(-2.70)***|(-2.06)**|(1.95)*|(-2.47)*|
|No. of School age children|0.016|0.018|0.019|0.011|0.006|
||(0.67)|(0.66)|(1.05)|(0.41)|(0.24)|
|Age of Head|-0.002|-0.002|-0.002|-0.000|-0.015|
||(1.95)*|(1.97)**|(2.60)***|(0.37)|(-0.98)|
|Head Female|0.027|0.028|0.025|-0.035|0.035|
||(0.86)|(0.91)|(0.83)|(1.03)|(1.07)|
|Head Muslim|0.065|0.068|0.067|0.080|0.074|
||(1.55)|(1.56)|(1.49)|(1.62)|(1.85)*|
|First Stage Residual||0.123||||
|||(1.65)*||||
|Head's education secondary or above|||0.047|||
||||(0.44)|||
|Head's occupation (professional=1)|||0.029<br>(0.38)|||
|Head's Age Squared|||||0.000|
||||||(-1.01)|
|Head's Age Cube|||||0.000|
||||||(-0.92)|
|**Sign and Significance of the Instrum**|**ent in First stage**|||||
|Heavy Rainfall Dummy|-0.642|-0.642|-0.555|-0.198||
||(3.12)***|(3.12)***|(3.58)***|(2.90)***||
|Interaction Based Instrument|||||2.258|
||||||(3.28)***|
|Angist-Pischke F Statistic|9.72|9.72|12.79|8.38|10.77|

Notes: (1) All regressions include regional dummmies. Standard errors are clustered at the Primary Sampling Unit (PSU) level. (2) 2SLS estimates are reported except for column 2.

(3) Robust z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% (4) Instrument for the last column is the predicted income based on houeshold's age and religion
The evaluation of the voucher program’s efficacy adopted a retrospective quasi-experimental design using the second of two rounds of data collection, which included respondents drawn from a group exposed to the voucher and a comparison group. The design was informed by the fact that the intervention sites were not randomly assigned. The original 2008 design incorporated a prospective cluster randomized study that called for the systematic placement of voucher distributors—with a random start at one cluster—in half of the 22 clusters of local administrative units (parishes) to ensure an even distribution of voucher- and nonvoucher-exposed communities. Each of the 22 clusters centered on a voucher-contracted facility. The target population, drawn from Bushenyi, Ibanda, Isingiro, Kiruhura, Kamwenge, and Mbarara districts, consisted of women who were between 15 and 49 years of age and had had a pregnancy during the previous 12 months. Respondents were selected from 22 clusters within five to ten kilometers of contracted facilities and three kilometers from a main road. It was assumed that individuals located within those ranges had similar levels of access to the contracted facilities. A two-stage design was used to identify potential study participants. The first stage was a simple random sample of villages within each cluster, and the second stage was a census of households with recent pregnancies within selected villages. The sampling frame used maps and population figures from the Ugandan Bureau of Statistics (UBOS) 2002 census.

A sample of 2,627 women was to be selected from 84 villages in the 22 clusters. Using the national rate of 5.2 deliveries per 100 total populations per year, the average number of pregnant and recently delivered women in all villages within each cluster was estimated to be 28 women per village. Assuming a baseline proportion of 30 percent facility deliveries, it was estimated that 120 pregnant and recently delivered women would be needed in each cluster to detect a change of 2 percent in the proportion of facility-based births between the baseline and a one-year follow-up among the treatment groups for a two-sided T-test at 5 percent significance and 90 percent power (120 deliveries * 22 clusters = 2640 female respondents).

Based on the target of 120 deliveries in each cluster and using the UBOS census figures to estimate village populations, the necessary number of villages was selected from a list of villages in each cluster, limiting the selection to villages with 20 or more estimated deliveries a year. The additional 467 women included in the sample size were intended to account for nonresponses.

However, a deviation from the original plan occurred immediately after the baseline survey was carried out because of insufficient communication between the researchers and the program operations team. The program operations team had recruited voucher distributors in villages 12
to offer are actually more likely to stop buying from Agruppa after having tried it out, and less likely to be customers after one year. This is surprising and goes against the idea that customers will be more likely to stick around if given wider variety. While we are controlling for household socioeconomic status, owner characteristics, distance to Corabastos, and a linear time trend in time since Agruppa started in the first block, this may still reflect unobserved characteristics of the blocks phased in later rather than earlier.<sup>12</sup> Our six-month follow-up survey (described below) asked current users the most important reasons for using Agruppa, and those who had stopped using the service the most important reasons for stopping. The main reasons for continued use were reducing travel time (62%), lower prices (15%), and transporting heavy items (13%). The most important reasons given for stopping use were not not enough variety (28%), not satisfied with customer service (28%), and prices not being competitive (22%). Only 11% said the reason was product quality. Qualitative work by Agruppa led them to conclude that their most loyal customers were owners of smaller shops, who had strong practical reasons for not wanting to have to travel to the market, such as safety or caring for children or elderly family members; while other clients were seen as using Agruppa mainly for sporadic needs, stocking up between visits to the market. They also noted that for many shopkeepers, going to Corabastos remained “central to their identity as shopkeepers, to their social life, and even to their ego, while appealing to their primordial need to have selected and negotiated (or in good Colombian "bargained") the product themselves”. This suggests it may have not just been an economic consideration as to whether to use and continue using the service or not.

# **3.4 Data for Measuring Impacts**

IPA Colombia conducted five rounds of high-frequency short-term follow-up surveys at 2, 4, 6, 10, and 14 weeks after the launch of Agruppa in a block. We would survey a treatment block and its corresponding control block in the same week, staggering the timing to match the staggered timing of the baseline surveys and introduction of Agruppa (see timeline in Appendix 2). These high-frequency surveys were short in length and intended to capture the immediate responses in terms of the key mechanisms through which the service was designed to affect firms: travel to Corabastos, purchase and sales prices for the core Agruppa products, and sales and profits for the

> 12 Agruppa staff also note that despite their best efforts, service quality may not have remained stable over time, so that this increase in product variety may have been accompanied by offsetting service issues.

13
between ivory prices, both in Africa and China, and gold prices, suggesting strong first stages. Quantitatively, the first stage results are displayed in the lower panel of Table 1 columns 5-8 and consistently indicate that the instrument is particularly strong with a partial F-statistic always above 50. The upper panel reports the second stage estimates in columns 5-8.

<!-- Start of picture text --> .5 1 1.5 2<br>Ln(gold price)<br>Number of poached elephants in each year Fitted values<br>1000<br>500<br>0<br><!-- End of picture text --> Figure 3: Gold prices and elephant poaching In column 5, the 2SLS estimate suggests a PIKE elasticity with respect to price of around 0.42. The estimate is statistically significant at the 1 percent level. The 2SLS coefficient is markedly higher than the OLS one, which might be due to measurement error in price data (either due to the inherent noise in the data or to the model errors following partialling out of the data) but also OLS being potentially biased downward as discussed above. In column 6, we control for the logarithm of the total number of carcasses found at site _s_ in year _t_ , i.e. the denominator of PIKE. As discussed earlier, the objective is twofold: first, to account more flexibly for patrol effort, and second, the coefficient of the price variable can now be interpreted as the elasticity of illegal ivory supply with respect to prices. The coefficient in column 6 is almost identical to the one in column 5 at 0.43, alleviating concerns that the variable “Total Carcass Count” itself is endogenous. Controlling for the number of conflicts and GDP growth rate in column 7 and ivory seizures in column 8 does not change the qualitative result much: the elasticity is always statistically significantly below one.

A 0.42 _−_ 0.43 elasticity is evidence of an inelastic supply of ivory: a 10 percent decrease in the price of ivory in a given range state in Africa implies a 4.2 _−_ 4.3 percent decrease in 18