rafmacalaba/datause-encoder-data huggingface dataset:
||`(0.16)`|`(0.08)`|`(1.32)`|`(1.44)`|
|---|---|---|---|---|
|`South Sulawesi`|`-0.563`|`-0.560`|`-0.368`|`-0.371`|
||`(6.61)**`|`(6.55)**`|`(3.59)**`|`(3.62)**`|
|`Non-farm dummy`|`0.562`||`0.264`||
||`(12.84)**`||`(5.78)**`||
|`Mining_Quarry`||`0.830`||`0.535`|
|||`(7.49)**`||`(3.64)**`|
|`Manufacturing`||`0.416`||`0.104`|
|||`(7.03)**`||`(1.59)`|
|`Electricity`||`0.592`||`0.345`|
|||`(4.80)**`||`(1.26)`|
|`Construction`||`0.524`||`0.247`|
|||`(7.77)**`||`(3.98)**`|
|`Wholesale`||`0.600`||`0.269`|
|||`(11.06)**`||`(4.93)**`|
|`Transport`||`0.545`||`0.109`|
|||`(8.57)**`||`(1.54)`|
|`Finance`||`0.813`||`0.607`|
|||`(5.63)**`||`(4.58)**`|
|`Social_services`||`0.704`<br>`(11.80)**`||`0.417`<br>`(7.23)**`|
|`Constant`|`8.071`|`8.178`|`8.096`|`8.235`|
||`(25.51)**`|`(25.46)**`|`(20.20)**`|`(20.43)**`|
|`Observations`|`5059`|`5059`|`4521`|`4521`|
|`R-squared`|`0.39`|`0.40`|`0.32`|`0.32`|

``` Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
``` 30
17

# **Table 4:  Treatment effects on effort in pro-social organizations, pre-selection (Unmotivated vs. motivated subjects)**

|Dependent Varia|ble: Effort for Charity Relative<br>Unmotivated subjects<br>(Dictator <= 500)<br>I|to Ability<br>Motivated subjects<br>(Dictator > 500)<br>II|
|---|---|---|
|Pay-for-Ability (D)|0.027<br>(0.04)|0.016<br>(0.03)|
|Pay-for-Performance (D)|0.153**<br>(0.06)|0.022<br>(0.04)|
|Constant|0.625<br>(0.39)|1.234***<br>(0.24)|
|Controls|Yes|Yes|
|R-squared|0.057|0.062|
|P-Value|0.136|0.404|
|Observations|259|171|

Note: * p<0.1, ** p<0.05, *** p<0.01. Dependent variable is ability-adjusted effort in the Pro-social task (task 2). OLS specification, standard errors in parentheses. The dictator game variable is divided by 1000 for presentational convenience. Please see table A2 for the same table but with a full set of controls.

Among all subjects (and, importantly, unmotivated ones), pay for performance elicits greater effort than all other pay systems. However, in the long run, pay systems affect the types of individuals who select into the mission sector. The earlier analysis indicates that flat pay systems, in particular, encourage low ability individuals to enter the sector, suggesting that the choice of pay scheme may have long run effects on effort, even when no effects emerge among incumbent workers in the short run. The final piece of analysis in this section therefore examines effort exerted by subjects who choose to work in the mission sector under the respective pay schemes.

Prior to selection (i.e., among incumbent mission sector workers) only pay for performance elicits greater ability-adjusted effort than the flat pay scheme. However, motivated workers are more likely to select into the mission sector. This raises the possibility that there may be no differences in effort after selection has occurred. To test this, we run the same specifications as in table 3, except with ability-adjusted effort with those subjects that have chosen the Mission sector. Table 5 presents these results.

From our original sample of 430 subjects, 219 elected to join the mission sector (51 percent). The results in table 5 are treatment effects on ability-adjusted effort of just these subjects who have selected into the mission sector, in contrast to table 3, which looks at all subjects prior to selection. The first row indicates that subjects who selected into the pay-forability mission sector exert significantly higher effort than those who select the flat pay mission sector (p<0.10). Effort effects for the pay-for-performance treatment are of the same magnitude as pay for ability, but are not significantly different (p=0.15), reflecting the lower sample size of the pay-for-performance treatment. Importantly, however, effort between the pay-for-ability and pay-for-performance are not different from each other (p=0.97). Using a log-linear specification
bias measurements of economic effects that do not control for this underlying potential. However, there are many cases where infrastructure investments are made to try to turn around and preserve struggling regions. They may also be directed through the political process towards non-optimal locations (i.e., “bridges to nowhere”). These latter scenarios would downward bias results.

The most prominent identification technique in this work is the use of historical transportation networks or straight lines between nodal cities to predict whether or not a major transportation route exists.<sup>3</sup> This approach helps us understand the importance of transportation networks in terms of their existence, but it does not help policy makers evaluate the likely impact from investments into improving existing networks. The sums of this latter type of investment are very large and growing. Through 2006 and inclusive of the GQ upgrades, India invested US$71 billion for the National Highways Development Program to upgrade, rehabilitate, and widen India’s major highways to international standards. A recent Committee on Estimates report for the Ministry of Roads, Transport and Highways suggests an ongoing investment need for Indian highways of about US$15 billion annually for the next 15 to 20 years ( _The Economic Times_ , April 29, 2012). Our panel analysis allows us to shed new light on these important questions, with the dynamic models and the comparison to the NS-EW highway preserving counterfactual scenarios as best possible.

The remainder of this paper is as follows: Section 2 gives a synopsis of highways in India and the GQ project. Section 3 describes the data used for this paper and its development. Section 4 presents the empirical work of the paper, determining the impact of highway improvements on economic activity. Section 5 concludes.

# **Section 2: India’s Highways and the Golden Quadrilateral Project**

Road transport is the principal mode of movement of goods and people in India, accounting for 65% of freight movement and 80% of passenger traffic. The road network in India has three categories: (i) national highways that serve interstate long-distance traffic; (ii) state highways and major district roads that carry mainly intrastate traffic; and (iii) district and rural roads that carry mainly intra-district traffic. As of January 2012, India possessed 71,972 km of national highways and expressways and 3.25 million

> 3 Donaldson (2010) rules out spurious effects in estimating the impact of railroad construction in India by evaluating the hypothetical effects of four railroad lines that were planned but never actually built.

6
<!-- Start of picture text --> Reduction of greenhouse gas (GHG)<br>emissions (G4‐EN19)<br>SME 28.8%<br>Large 55.3%<br>MNE 61.7%<br>All 54.4%<br><!-- End of picture text -->

**Table 16: Energy‐related environmental indicators, regressions, Probit model**

|**GRI G4**|**GRI Indicator**|**_MNE_**|**p‐value**|**_Large_**|**p‐value**|
|---|---|---|---|---|---|
|**Metric**||||||
|G4‐EN3|Energy consumption within the organization|‐0.125|0.304|**0.514*****|**0.003**|
|G4‐EN5|Energy intensity|‐0.114|0.297|**0.680*****|**0.000**|
|G4‐EN6|Reduction of energy consumption|‐0.044|0.687|**0.371****|**0.018**|
|**G4‐EN7**|**Reductions in energy requirements of products and services**|**‐0.208***|**0.065**|**0.597*****|**0.001**|
|G4‐EN15|Direct greenhouse gas (GHG) emissions (Scope 1)|0.077|0.532|**0.917*****|**0.000**|
|G4‐EN16|Energy indirect greenhouse gas (GHG) emissions (Scope 2)|0.040|0.739|**1.007*****|**0.000**|
|G4‐EN17|Other indirect greenhouse gas (GHG) emissions (Scope 3)|0.059|0.596|**0.823*****|**0.000**|
|G4‐EN18|Greenhouse gas (GHG) emissions intensity|‐0.101|0.360|**0.969*****|**0.000**|
|G4‐EN19|Reduction ofgreenhousegas(GHG)emissions|0.110|0.299|**0.660*****|**0.000**|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01. Note: Variables refer to 2015 GRI data (see section 2.2). All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

Figure 8 focuses on the average disclosure on water‐related environmental indicators. The analysis indicates that although more MNEs disclose information on the total water withdrawal by source (EN22) (46%), indicators related to water recycling and impacts of water usage show a much lower disclosure share. The share of MNEs disclosing their water sources significantly affected by withdrawal of water (EN9) is only 28%, that revealing the percentage and total volume of water recycled and reused (EN10) is 36%, and that reporting the identity, size, protected status, and biodiversity value of water bodies and related habitats significantly affected by the organization’s discharges of water and runoff (EN26) is only 18%. MNEs seem to disclose less information on all these indicators than large firms.

Interestingly, the regression analysis suggests that multinationals show a significantly lower probability of revealing information related to water recycling and impacts of water usage on habitats and water sources than non‐MNEs (Table 17). In particular, MNEs are less likely to disclose information on the percentage and total volume of water recycled and reused (EN10) as well as the identity, size, protected status, and biodiversity value of water bodies and related habitats significantly affected by the organization’s discharges of water and runoff (EN26).

25
# **Table 20: DID Regression (6)**

||Car for po|pulated area|||Car for mu<br>capital|nicipal|||
|---|---|---|---|---|---|---|---|---|
||Linearpr|obability|Probit||Linearpro|bability|Probit||
|D|0.002|(0.012)|0.092|(0.142)|0.013|(0.015)|0.100|(0.128)|
|T|-0.043|(0.014) ***|-0.524|(0.198) ***|-0.024|(0.018)|-0.158|(0.140)|
|D*T|0.050|(0.017) ***|0.527|(0.227) **|0.059|(0.023) **|0.363|(0.172) **|
|lnHH size|-0.007|(0.012)|-0.123|(0.153)|0.014|(0.016)|0.100|(0.112)|
|Adult share|0.055|(0.027) **|0.645|(0.337) *|0.110|(0.034) ***|0.804|(0.234) ***|
|HH head sex: Male|-0.012|(0.012)|-0.150|(0.173)|0.004|(0.020)|0.057|(0.133)|
|HH head education: Elementary|0.025|(0.009) ***|0.348|(0.139) **|-0.002|(0.014)|-0.049|(0.098)|
|HH head education: Secondary|0.044|(0.021) **|0.533|(0.216) **|0.053|(0.031) *|0.293|(0.160) *|
|HH head education: University|0.150|(0.072) **|1.028|(0.331) ***|0.117|(0.077)|0.489|(0.287) *|
|Appliance ownership: Refrigerator|0.010|(0.011)|0.113|(0.154)|0.003|(0.016)|0.015|(0.125)|
|Appliance ownership: Color TV|0.004|(0.012)|0.044|(0.142)|0.025|(0.016)|0.185|(0.120)|
|Appliance ownership: Radio|0.019|(0.009) **|0.236|(0.119) **|0.024|(0.012) **|0.181|(0.091) **|
|Appliance ownership: Gas stove|0.016|(0.009) *|0.303|(0.174) *|0.025|(0.014) *|0.247|(0.136) *|
|Appliance ownership: Washing|||||||||
|machine|0.022|(0.015)|0.211|(0.132) *|0.042|(0.020) **|0.228|(0.104) **|
|Gas use for cooking|0.007|(0.012)|0.070|(0.122)|0.018|(0.017)|0.108|(0.100)|
|Power use for lighting|-0.009|(0.011)|-0.101|(0.143)|0.001|(0.015)|0.029|(0.124)|
|Tap water for cooking|0.026|(0.010) ***|0.305|(0.108) ***|-0.005|(0.013)|-0.051|(0.087)|
|Constant|-0.035|(0.040)|-2.852|(0.544)***|-0.094|(0.053)*|-2.789|(0.385)***|
|Obs.|2,064||2,064||2,064||2,064||
|R-squared|0.037||||0.035||||
|F-stat|2.83||||3.88||||
|Pseudo-R2|||0.100||||0.068||
|Wald-stat|||65.20||||79.30||
|Z-statfor interactioneffect (mean& s|td.dev.)||1.451|(0.230)|||1.978|(0.164)|
|Note: Standard errors are sho|wn in parenth|eses. *, **, ***|indicate th|e statistical sig|nificance a|t 10%, 5% an|d 1%, resp|ectively.|

39
4 ture is supported by several studies in the United States and Europe which have identified an upward trend in risky sexual behaviors since the introduction of ART in 1996 (e.g. Gremy and Beltzer, 2004; Lakdawalla et al., 2006). More specifically, an association has been identified between decreased concern about HIV due to ART availability and unprotected sex, and in particular among men who have sex with men (e.g. Dukers et al., 2001; Kalichman, 1998; Lakdawalla et al., 2006; Mechoulan, 2007).

Investigations of disinhibition behaviors associated with increased access to ART in SubSaharan Africa are, however, limited. One of the earliest studies looked at change in the use of condoms by sex workers in Nairobi, Kenya (Jha et al., 2001). The findings are summarized in figure 1. There is at least some suggestive evidence that condom use by sex workers decreased when “fake” cures for AIDS were announced. Such a pattern is consistent with disinhibition behaviors, although the result may not be generalizable to the general population since it uses a very selected segment of the population. Cohen et al. (2009) is one of the few studies that use population based surveys to test risk compensation behavior in a Sub-Saharan African context. The authors found that in Kisimu (Kenya), ART-related risk compensation<sup>5</sup> , and the belief that ART cures HIV, were associated with an increased HIV seroprevalence in men but not women.

We differ from Cohen et al. (2009) in two ways. First, we focus on sexual behavior instead of HIV seroprevalence. Changes in sexual behaviors resulting from the introduction of ART can be viewed as a first order effect, while changes in sero-prevalence (as long as these changes are taking place through changes in sexual behaviors as theorized by Cohen et al. (2009)) are second order effects. Second, the panel structure of our data allows us to control for unobserved individual characteristics that can influence both sexual behavior (or HIV

> 5The authors define risk compensation by increased sexual risk taking now that ART is available.
24

# Table 6: Prospective effects of GM cotton adoption by non-adopters as of 2001 on national economic welfare, without and with Sub-Saharan Africa participating (equivalent variation in income, 2001 US$m)

## (a) _Without_ Sub-Saharan Africa adopting

||Welfare c|hanges due to|effects of:|Totalwelf|are change|
|---|---|---|---|---|---|
|**_First adopters as of 2001:_**|resource<br>re-<br>allocation|new<br>technology|terms of<br>trade<br>change|inUS$m|as % of<br>GDP|
|United States|106|0|-45|61|0.001|
|China|-13|204|-78|113|0.010|
|Australia|1|0|-15|-14|-0.004|
|South Africa|1|0|4|5|0.004|
|**_New and prospective adopters:_**||||||
|Other high-income countries|54|93|124|271|0.002|
|Eastern Europe and Central Asia|3|323|-1|325|0.049|
|<br>Southeast Asia|-1|26|6|31|0.008|
|South Asia|75|880|9|964|0.157|
|Middle East and North Africa|10|133|14|157|0.018|
|Sub-Saharan Africa (ex S. Africa)|-4|0|-14|-18|-0.009|
|Latin America and Carib.|12|116|-4|124|0.006|
|**World**|**244**|**1775**|**0**|**2018**|**0.006**|

## (b) _With_ Sub-Saharan Africa adopting

||Welfare ch|anges due to|effects of:|Totalwel|fare change|
|---|---|---|---|---|---|
|**_First adopters as of 2001:_**|resource<br>re-<br>allocation|new<br>technology|terms of<br>trade<br>change|in US$m|as % of<br>GDP|
|United States|139|0|-83|57|0.001|
|China|-14|204|-90|100|0.009|
|Australia|0|0|-28|-28|-0.008|
|South Africa|1|0|11|12|0.010|
|**_New and prospective adopters:_**||||||
|Other high-income countries|82|91|165|337|0.003|
|<br>Eastern Europe and Central Asia|0|321|-5|317|0.048|
|<br>Southeast Asia|-11|25|49|63|0.009|
|South Asia|80|877|13|970|0.158|
|Middle East and North Africa|14|132|28|175|0.020|
|Sub-Saharan Africa (ex S. Africa)|36|221|-69|187|0.091|
|Latin America and Carib.|12|115|9|135|0.007|
|**World**|**338**|**1985**|**0**|**2323**|**0.007**|

Source: Authors’ GTAP model simulation results
Finally, Section 7 concludes.

# **2 Background**

## **Contraceptive Coverage and Provision**

Burundi has one of the lowest contraceptive coverage rates globally (United Nations, 2022). In 2017, only 21.5% of women aged 15 to 49 used reversible modern contraceptive methods such as pills, condoms, injectables, implants, and IUDs, while 6.3% used traditional methods (Burundi Institute of Statistics and Economic Studies, 2017).<sup>4</sup> Even though birth control tools are free of charge, there is a significant unmet need for family planning in the country, with three out of every ten women of childbearing age not using any contraceptive method despite wanting to space their next birth or stop childbearing altogether. The average number of children per woman in a union is 5.5, which exceeds their desired number of children by 1.9 (Burundi Institute of Statistics and Economic Studies, 2017).

In Burundi, contraceptive services are primarily provided by the government, especially for modern methods. Approximately 85% of modern contraceptives dispensed in the country between 2016 and 2017 were supplied by the public sector (Burundi Institute of Statistics and Economic Studies, 2017).<sup>5</sup> This percentage is expected to be significantly higher in rural areas, where private facilities are scarce. Within this framework, health centers play a crucial role by delivering health planning services to defined catchment areas. Each health center is staffed with skilled personnel, supported by affiliated CHWs ( _Agents de Santé Communautaires_ ) and, occasionally, an associated health promotion officer (HPO, _Technicien de Promotion de la Santé_ ). Although less common, HPOs are trained healthcare professionals who possess more advanced skills compared to CHWs. HPOs and CHWs primarily operate within the community, with their affiliated health centers monitoring their activities and supplying them with the necessary resources for their home visits during monthly coordination meetings. CHWs receive performance incentives based on the delivery of a comprehensive package of community activities, with a key focus on family planning. Their tasks typically include distributing

> 4 These rates decrease to 21.1% and 6.1% in rural areas, where approximately 90% of the population reside.

> 5 Pharmacies are not common providers of modern contraceptive methods; for instance, only 0.2% of administered injectables are dispensed by pharmacies (Burundi Institute of Statistics and Economic Studies, 2017).

6
16-A

# **Figure** 5

Tanzania: Official Exchange Rate and Parallel Premium Quarterly, 1978:1 - 1990:4 <!-- Start of picture text --> 1000<br><!-- End of picture text --> <!-- Start of picture text --> 6 1000<br>800<br>700<br>4.5<br>600<br>4-<br>3i 3<br>-400<br>3-<br>300<br>Z.5 - - 2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~00<br>.5 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0<br>ItM 1W1 111 l#t gm lStm I St US i1 Us iN? 111S1<br><!-- End of picture text --> <!-- Start of picture text -->
-p-  Log of Official Exchange Rate (TShs/$)  ..-  Parallel Prernium (%)<br>So4u=: :S.  World Curamncy Yearbook.  amd  Kauzmam  and  O'Connell (1991)<br><!-- End of picture text -->
sumption caused by the program (Ib ́a ̃nez et al., 2024) might have reduced crimes by improving economic opportunities. Together, these findings highlight the critical role of regularization programs like PEP in enhancing legal protections, reducing social tensions, and improving safety for both migrants and host communities. Our results are in line with declines in the crime rates of newly documented migrants in Italy, as shown by Pinotti (2017a).

Our results suggest that facilitating forced migrant’s economic integration has powerful impacts that go beyond labor markets. For one, they affect migrant’s willingness to cooperate with governments and make their basic human rights be respected. Such policies seem to be particularly relevant for vulnerable groups such as women who may be facing disproportionate domestic and sexual violence by staying undocumented. Moreover, ensuring proper documentations facilitates enforcement and increases income resulting in lower crime rates.

This study contributes to work examining the relationship between immigration and crime (Butcher and Piehl, 1998b; Borjas et al., 2010; Bianchi et al., 2012; Alonso-Borrego et al., 2012; Bell et al., 2013; Spenkuch, 2014b; Baker, 2017; Pinotti, 2017b; Freedman et al., 2018; Fasani, 2018; Fasani et al., 2019; Ajzenman et al., 2020; Knight and Tribin, 2020). Specifically, for Colombia, Knight and Tribin, 2020 investigate the connection between violent crime and immigration, using data from Colombian municipalities during the recent wave of immigration from Venezuela. The authors find that after the ColombianVenezuelan border was closed and later re-opened in 2016—triggering a large influx of migrants—homicides in Colombia increased in areas near the Venezuelan border. This rise, however, was primarily driven by homicides involving Venezuelan victims, with no statistically significant increase in homicides targeting Colombians. While our study examines a related outcome, it departs from this body of work by focusing on the effects of a policy that improved the rights of undocumented migrants already residing in Colombia.

5
Figure 1: Employment Size, Store Types & Formality

- (a) # Employees on Formality in Retail Censuses

<!-- Start of picture text --> Rwanda<br>Cameroon<br>Peru<br>Mexico<br>0 1 2 3 4<br>Log Employment<br>1<br>.8<br>.6<br>Share Formal .4<br>.2<br>0<br><!-- End of picture text -->

- (b) # Employees by Store in Mexico

- (c) % Paying VAT by Store in Mexico

<!-- Start of picture text --> (1) Non-Market N.A. (1) Non-Market N.A.<br>(2) Non-Brick & Mortar (2) Non-Brick & Mortar<br>(3) Convenience Stores (3) Convenience Stores<br>(4) Specialized Stores (4) Specialized Stores<br>(5) Large Stores (5) Large Stores<br>0 1 2 3 4 5 0 .2 .4 .6 .8 1<br>Log of Median Number of Employees Share of Firms Reporting VAT Payments<br><!-- End of picture text --> These panels shows the association between formality status, employment and firm type. Panel A shows the share of formal firms as a function of log employment, using retail censuses of four core sample countries (Cameroon, Mexico, Peru and Rwanda). Formality is defined as ’being registered with the tax authority’ in Cameroon and Rwanda, and ’paying Value-Added-Taxes on sales’ in Mexico and Peru. Panels B and C use the 2013 Mexican retail census, which classifies retailers in similar categories as our broad place of purchase taxonomy. The figures show the log median number of employees by place of purchase (Panel C) and the share of firms paying Value-Added-Taxes on sales (Panel C). The data comes from the following firm censuses, keeping only firms which operate in the retail sector: Recensement G ́en ́eral des entreprises 2016 (Cameroun), Censo Econ ́omico 2014 (Mexico), Censo Nacional Econ ́omico 2008 (Peru), Establishment Census 2011 (Rwanda).

38