rafmacalaba/datause-encoder-data huggingface dataset:
**Table S5.E: Results for an alternative specification in which a single dummy variable for connectedness (** **_anylink_ ) replaces the three trading relationship dummies in the baseline specification**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|Anylink|0.0179|-0.0111|0.0406|-0.0210|-0.0428|0.00868|0.0502|0.0445|
||(0.0346)|(0.0404)|(0.0369)|(0.0506)|(0.0618)|(0.0132)|(0.0425)|(0.0383)|
|same_sec|-0.0327*|-0.0561**|-0.0216|-0.0573**|-0.1000***|-0.00228|-0.0174|-0.0306*|
||(0.0171)|(0.0260)|(0.0141)|(0.0280)|(0.0346)|(0.00493)|(0.0123)|(0.0178)|
|Ldist|-0.00477**|-0.00128|-0.0105*|-0.0172**|-0.0154*|-0.00194|-0.0168***|-0.0153***|
||(0.00238)|(0.00175)|(0.00543)|(0.00694)|(0.00833)|(0.00159)|(0.00298)|(0.00292)|
|ad_fmage|-0.000607|-0.000786***|-0.00100|-0.00186|-0.00322*|-0.000149|-0.000208|0.00181|
||(0.000574)|(0.000274)|(0.00105)|(0.00131)|(0.00173)|(0.000335)|(0.000718)|(0.00114)|
|ad_meduc|<br>0.00859|<br>0.0142|<br>-0.00998|<br>0.0644**|<br>0.0474**|<br>-0.00392*|<br>0.0112|<br>0.00643|
||(0.00905)|(0.00956)|(0.00833)|(0.0286)|(0.0226)|(0.00237)|(0.0184)|(0.0160)|
|ad_mexper|-0.000768|0.000113|-0.000576|-0.000886|-0.00313*|0.000211|-0.00167**|-0.000977|
||(0.000897)|(0.000387)|(0.00132)|(0.00151)|(0.00170)|(0.000477)|(0.000804)|(0.000837)|
|ad_mfemale|-0.00150|0.000617|-0.00558|0.0904**|0.00906|0.00764|0.0181|-0.0121|
||(0.0186)|(0.00597)|(0.0285)|(0.0454)|(0.0482)|(0.0111)|(0.0258)|(0.0177)|
|ad_lemp|0.00628|0.0219**|0.0304**|0.189***|0.121***|0.0110**|0.0876***|0.0987***|
||(0.00943)|(0.00902)|(0.0137)|(0.0264)|(0.0298)|(0.00490)|(0.0155)|(0.0143)|
|Constant|0.483***|0.475***|0.264***|0.431***|1.005***|0.172***|0.323***|0.275***|
||(0.0191)|(0.0216)|(0.0421)|(0.0598)|(0.0714)|(0.0132)|(0.0363)|(0.0363)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 19
persons use an “improved” toilet<sup>1</sup> at all times. A community-led total sanitation (CLTS) approach was adopted by the DWSS to motivate communities to attain ODF status. The central pillar of this approach is a behavior change communication (BCC) campaign that focuses on raising awareness and demand for health, hygiene, and sanitation; mobilizing collective action toward behavioral change; and generating peer pressure through the creation of community-based committees that monitor neighborhoods and encourage sanitary practices. The project also supported the construction of toilets in households across the state. As an incentive, Rs. 15,000 was offered to eligible beneficiaries to cover the full cost of constructing twin leach pit latrines.

It is expected that project interventions will have positive effects on people’s awareness and practice of household hygiene and sanitation, resulting in improved health and nutritional outcomes. The project promotes handwashing with soap and safe disposal of child feces, and encourages households to construct and use toilets. The use of toilets will lead to a reduction in the fecal contamination of soil and groundwater, thus improving water quality at the point of extraction and reducing intestinal worm infections. While project impacts on hygienic and sanitary practices, including toilet construction and use, are expected to be observable in the short run, positive health and nutritional outcomes will take longer to materialize.

A multiple-arm, cluster-randomized impact evaluation was designed to measure the impact of the SBMG in Punjab. This reports the results from a midline survey, focusing on the project’s short-term impacts on hygiene awareness and related practices and toilet construction and use. An end-line survey, planned for October 2020, will capture the project’s impacts on children’s health and nutritional outcomes.

This study provides evidence of the project’s success in triggering behavior change and promoting toilet construction and use. Specifically, the impact evaluation sought answers to the following research questions:

- i) Did the various project interventions influence households’ decisions to construct toilets?

- ii) Did the project interventions lead to a reduction in open defecation rates in beneficiary communities?

iii) How did the BCC campaign affect hygiene awareness and related practices?

- iv) Did school programs focused on raising students’ awareness of hygienic practices effectively boost such practices, toilet construction, and toilet use?

> 1 An improved toilet is designed to hygienically separate excreta from human contact.

3
(Auerbach and Hernandez 2012). Turning to recent research in economics, Tanaka (2015) finds that regulations to curb pollution from coal-based power plants in China led to 3.29 fewer infant deaths per 1000 live births, amounting to a 20 percent reduction in infant mortality rate.<sup>3</sup> Ghosh and Mukherji (2014) examine the effect of ambient air quality on children’s respiratory health in urban India and find that a rise in particulate matter significantly increased the risk of respiratory ailments. Arceo-Gomez et al. (2012) provide evidence from Mexico City that a 1 percent increase in particulate matter ( _PM_ 10 ) over a year led to a 0.42 percent increase in infant mortality, and the corresponding figure for _CO_ is 0.23 percent. Currie and Walker (2011) find that exposure to vehicular emissions around toll plazas in the northeastern United States increased the likelihood of premature births and also resulted in low birth weight. Some other studies that document the adverse health consequences of air pollution for the United States include Moretti and Neidell (2011); Lleras-Muney (2010); and Currie, Neidell, and Schmieder (2009).

The second compelling reason for this study is the extent of air pollution in Delhi. According to the World Health Organization’s (WHO) database, Ambient Air Pollution 2014, Delhi is the most polluted city in the world in terms of _PM_ 2.5 levels. In 2013, the annual mean concentration of _PM_ 2.5 in Delhi was almost twenty times the guideline value prescribed by the WHO.<sup>4</sup> The Central Pollution Control Board (CPCB), the national authority responsible for monitoring and managing air

> 3. Greenstone and Hanna (2014) find that environmental regulations in India have been effective in reducing air pollution. However, in contrast to Tanaka (2009), they find an insignificant impact of reduced air pollution on infant mortality. For reasons discussed in their paper, they advise readers to be cautious when interpreting their result on infant mortality.

> 4. The WHO guideline for annual mean concentration of _PM_ 2.5 is 10  _g m_ / 3 , and in 2013, the annual mean level

> of _PM_ 2.5 in Delhi was198  _g m_ / 3 . Notably, Delhi's _PM_ 2.5 level far exceeded that of Beijing which was at 56  _g m_ / 3 (Ambient Air Pollution Database 2014, WHO).

3
the average real producer price for 1986–8 is 1.55 USD per kg in the former country compared with 0.65 in the latter. In contrast, in the case of coffee, a large BD in output is observed because this crop is not produced in Ghana due to historically low administered producer prices in this country. Among rural villages, we therefore find a large BD in cocoa and coffee income of +184 USD per capita (table 5, col. 1b). Additional disaggregation (not shown) shows that cocoa accounts for two-thirds and coffee for the remaining one-third. Before 1990, the cocoa producer price differential generated a strong incentive to smuggle cocoa beans across the Cˆote d’Ivoire border (Bul ́ır 1998). However, our BD result suggests that smuggling was not widespread enough to equalize incomes across the border. When the cash crop income variable is added as a control in the BD estimates, the BD in cash expenditures that prevails among rural villages is strikingly erased, decreasing from +120 USD to a negligible -2 USD. Recall that expenditures and income figures are drawn from two independent parts of the surveys’ questionnaires.<sup>16</sup> When including cities and peri-urban villages (col. 1a), the cash expenditure discontinuity is only reduced from +177 to +135 USD, although it becomes statistically insignificant. However, if we add formal wage income earned by household members to cash crop income, then controlling for this new income variable successfully reduces the cash expenditures BD to a low and insignificant +21 USD.<sup>17</sup> At the Mali border, we detect a very large BD in cotton output per capita of +664 kg per capita among rural villages (col.2b). In contrast with the Ghana

> 16This procedure involves assuming that (i) the same saving rate applies to cash crop income on both border sides and (ii) this saving rate is consistently estimated by OLS.

> 17In 1986–8, the survey figures for the average annual wage of civil servants and public firms workers reached 5,223 USD in Cˆote d’Ivoire compared with 663 USD in Ghana. For private firm workers, they were 3,498 USD and 540 USD, respectively. The minimum wage in Cˆote d’Ivoire (1,400 USD) was also six times higher than in Ghana (240 USD).

32
# **I. Introduction**

By the standards of a very austere international poverty line, such as the World Bank’s $1.25 per person per day in 2005 Purchasing Power Parity (PPP) dollars, global poverty has fallen rapidly in recent decades (World Bank, 2015a). In many countries of the world, absolute poverty defined in these terms no longer affects significant segments of the population. This is a remarkable achievement that is rightly celebrated – even though it is clear that in certain countries and certain parts of the world, extreme poverty by this standard remains both widespread and stubbornly resistant to change. But poverty is not only thought of in absolute terms, and on the basis of an international standard. Most countries of the world assess poverty in their societies on the basis of national poverty lines that are largely anchored to the standards, expectations, and aspirations of their own societies. With social progress and economic growth, these standards typically evolve and consequently the poverty thresholds underpinning national poverty analysis also tend to rise (Ravallion and Chen, 2011). In this context, attention in many countries is shifting away from merely a focus on the rate of income growth among the poorer population groups towards also an assessment of the quality of this growth. In particular, a key question that resonates in many countries is whether the poor are able to participate to the same degree and extent as the non-poor in a given country’s growth process; whether they are sharing equally in the country’s rising prosperity. In an attempt to provide a quantifiable measure that engages with these concerns, the World Bank has recently proposed a definition of “shared prosperity” as growth in the income of the bottom 40 percent of the income distribution over time (e.g., Basu, 2013; Jolliffe et al., 2015).<sup>1</sup> This measure has many attractive features, most notably that it is easy to understand, can be straightforwardly estimated from household survey data, and has a historical pedigree within

> 1 In a slight abuse of notation, we use the terms income and consumption interchangeably in this note.

2
# Figure A.5.1 Inflation targeting in Brazil

## A. Actual inflation and inflation expectations

<!-- Start of picture text --> Percent, year-on-year Target upper and lower limit<br>15 5-year-ahead expectations<br>3-year-ahead expectations<br>Actual inflation<br>10<br>5<br>0<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2017<br><!-- End of picture text --> C. General government primary balance and gross debt <!-- Start of picture text --> Percent of GDP Percent of GDP<br>12 Primary balance Gross debt (RHS) 90<br>8<br>70<br>4<br>50<br>0<br>-4 30<br>2000 2002 2004 2006 2008 2010 2012 2014 2017<br><!-- End of picture text -->

## B. Central bank transparency

<!-- Start of picture text --> Index (15=best)<br>12 Brazil Median of EMDEs<br>9<br>6<br>3<br>0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> D. Sensitivity of inflation expectations to shocks <!-- Start of picture text --> Percentage points<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: Consensus Economics; Dincer and Eichengreen 2014; Haver Analytics; International Monetary Fund; World Bank.

Note: EMDEs = emerging market and developing economies; GDP = gross domestic product.

A.B.D. The start of the inflation targeting regime is shaded in gray.

B. Transparency is based on information from the Central Bank of Brazil’s website, statutes, annual reports, and other published documents, as calculated by Dincer and Eichengreen (2014).

C. The primary balance is net government lending and borrowing, excluding net interest payments.

D. Time-varying sensitivity is estimated by regressing long-term inflation forecast revisions on inflation shocks. Dotted lines denote the 68 percent confidence interval. Appendix 3. provides details on the methodology.

50
||(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|(0.000)|
|---|---|---|---|---|---|---|
|Married|0.090<sup>***</sup>|0.022|0.131<sup>***</sup>|0.011|0.007|0.025|
||(0.019)|(0.023)|(0.036)|(0.017)|(0.036)|(0.022)|
|No. of children below age 6|-0.017<sup>**</sup>|-0.020<sup>**</sup>|-0.018|0.003|-0.033|0.013<sup>*</sup>|
||(0.007)|(0.010)|(0.012)|(0.008)|(0.020)|(0.007)|
|No. of female adults|0.037<sup>***</sup>|0.027<sup>**</sup>|0.022|0.047<sup>***</sup>|0.081<sup>***</sup>|0.014|
||(0.010)|(0.012)|(0.019)|(0.014)|(0.030)|(0.013)|
|No. of male adults|0.019<sup>***</sup>|0.079<sup>***</sup>|-0.001|0.033<sup>***</sup>|0.065<sup>**</sup>|0.026<sup>**</sup>|
||(0.006)|(0.012)|(0.008)|(0.011)|(0.027)|(0.011)|
|Head Education (reference:|||||||
|no education)|||||||
|Primary|0.079<sup>***</sup>|0.068<sup>***</sup>|0.096<sup>***</sup>|0.018|0.060<sup>*</sup>|0.006|
||(0.019)|(0.026)|(0.029)|(0.017)|(0.035)|(0.017)|
|Secondary|0.152<sup>***</sup>|0.184<sup>**</sup>|0.152<sup>***</sup>|0.026|0.019|0.023|
||(0.033)|(0.073)|(0.038)|(0.021)|(0.061)|(0.018)|
|Tertiary|0.288<sup>***</sup>|0.391<sup>***</sup>|0.270<sup>***</sup>|0.054<sup>***</sup>|0.000|0.022|
||(0.058)|(0.146)|(0.062)|(0.019)|(.)|(0.020)|
|Remittance|-0.072<sup>**</sup>|-0.097<sup>**</sup>|-0.049|-0.129<sup>***</sup>|-0.144<sup>***</sup>|-0.132<sup>***</sup>|
||(0.028)|(0.043)|(0.039)|(0.021)|(0.041)|(0.026)|
|Access to land|0.000|-0.029|0.039|0.057<sup>***</sup>|0.160<sup>***</sup>|0.016|
||(0.027)|(0.037)|(0.038)|(0.017)|(0.037)|(0.017)|
|Livestock|0.156<sup>***</sup>|0.120<sup>***</sup>|0.178<sup>***</sup>|-0.000|-0.001|0.008|
||(0.017)|(0.024)|(0.025)|(0.015)|(0.034)|(0.015)|
|Refugee hosting regions|||||||
|Gambella|0.016<br>(0.029)|0.031<br>(0.035)|-0.003<br>(0.074)||||
|Somalia|0.114<sup>***</sup>|0.091<sup>***</sup>|0.116<sup>***</sup>|-0.011|-0.031|0.003|
||(0.024)|(0.034)|(0.035)|(0.018)|(0.043)|(0.017)|
|Benishangul-Gumuz|-0.133<sup>***</sup> <br>(0.022)|-0.096<sup>***</sup> <br>(0.032)|-0.192<sup>***</sup><br>(0.031)|0.060<sup>***</sup><br>(0.016)|0.105<sup>***</sup><br>(0.040)|0.045<sup>***</sup><br>(0.015)|
|Number of Obs.|3546|1828|1718|1676|512|1138|

Notes: The dependent variables are participation in employment. Robust standard errors are in parentheses. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

Marital status has no impact on employment for female-headed refugee households, but a strong positive effect of marital status on male-headed refugee households.

We would expect high dependency ratios to constrain women’s economic opportunities, especially in the absence of affordable childcare and uneven distribution of care responsibilities at home. In this case, we find that the number of young children has significantly reduces female - but not male - employment among refugees. And we find that children have a positive significant effect on male employment among hosts. This suggests that gendered divisions of unpaid labor adversely affect women’s employment opportunities.

16
population, poverty rate(proxy indicators)<sup>10</sup> , and village remoteness indicators such as distance to nearest district headquarters. Other variables relate to socio-demographic characteristics such as age, caste, dependency ratio, completed years of education, and characteristics of the household head. Data for the selection model were drawn from primary household and village surveys as well from Census 2011 and SECC 2013-14. In line with Khanna, Kochhar, and Palaniswamy (2015) we also use retrospective indicators of households such as ownership of amenities, income, land and dwelling characteristics. Village variables include population density, percentage of female working population, infrastructure facilities (such as road, primary school, primary health centers and agriculture marketing societies), percentage of household with amenities (electricity and bicycles), and village size.

The two primary assumptions of PSM: (a) Conditional Independence (CI); and (b) common support (CS) are then applied. CI implies that NRLM outcomes are independent of the treatment status of households in the absence of treatment, conditioned on observables that include household and village level covariates. The conditional independence condition is expressed as: where Y<sup>D</sup> and Y<sup>C</sup> represent outcomes for participants and non-participants, Di is the treatment status and Xi are the observables (Khandker 2010). Applying this, we calculate the propensity scores as in Table 2.

**Table 2: Household and Village Level Balance in the Surveyed Sample**

|**Variable**|**Me**|**an**|||
|---|---|---|---|---|
||**Treated**|**Control**|**Percent**<br>**Bias**|**P-Value**|
|Age of Head of Household|45.934|46.046|-0.9|0.759|
|SC or ST Household(Y/N)|0.791|0.784|1.6|0.574|
|DependencyRatio|0.649|0.644|0.7|0.81|
|Status of Primary School in Village in 2012|0.967|0.966|0.5|0.849|
|Education Level of Household Head|3.824|3.916|-2.2|0.476|
|Household Type Kutcha(Y/N)- SECC 2013|0.567|0.549|3.7|0.22|
|Household Owns Telephone/Mobile Phone -<br>SECC 2013|0.402|0.404|-0.6|0.854|
|Any Member of Household has a Salaried<br>Job-SECC 2013|0.053|0.061|-3.9|0.298|
|Household Owns Any Land-SECC 2013|0.459|0.467|-1.7|0.58|
|Percentage of Irrigated Land from Total<br>Land in the Household-SECC 2013|5.894|6.502|-3.2|0.235|
|Household Income Main Source: Cultivation<br>-SECC 2013|0.398|0.399|-0.3|0.931|
|Household Income Main Source: Non-<br>Agricultural Own Account Enterprise - SECC<br>2013|0.006|0.007|-2|0.567|

> 10 In the absence of poverty rates data at the village level, proxy indicators such as electricity, access to irrigation, income sources etc. were used.

8
**Figure 7. The decline in poverty was strong in both urban and rural areas**

Poverty trends in Moldova (regional poverty line of $2.5/day), 1999 to 2004 and 2006 to 2011 <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1999 2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2011<br>Urban Rural<br><!-- End of picture text --> Source: Calculations based on HBS data.

**Overall, mean consumption growth alone drove most of the poverty changes observed in the period of fastest poverty reduction.** Decomposition analysis proposed by Datt and Ravallion (1992) allows isolating the effects of consumption growth from those of distributional changes (i.e., shifts in the consumption distribution without mean consumption growth). Results show that of the total reduction in poverty of 31 percentage points from 1999 to 2004 (going from 78 percent to 47 percent), over threequarters are attributed to consumption growth alone (Figure 8). Changes in inequality during this period, although with significantly more modest effects, also contributed to less poverty. For the second period, however, growth and falling inequality played an equal and substantial role in achieving the 14 percentage point reduction in poverty. As depicted next in the growth incidence curves, this likely reflects lower mean growth rates in the second period, but still relatively higher rates of consumption growth for the poor.

8
the average maturity of the debt held by PFAs is only five years.<sup>15</sup> Again, this type of information suggests that PFAs have not been constrained to expand their holdings of long-term bonds.<sup>16</sup> Although the previous evidence helps us explore whether any type of institutional investor is exhausting the supply of long-term instruments, borrower decisions to issue securities likely depend on the demand for different maturities. To shed more light on the underlying demand of different institutional investors for securities with different maturities, we use unique data on auctions of government paper. The data set consists of detailed information on biddings for government bonds (in pesos, inflation-indexed pesos, and US dollars of maturity one year or longer) issued by the Central Bank of Chile and the Treasury between 2002 and 2009. The data come from the central bank, which organizes these auctions. The data set has information on biddings made by banks, insurance companies, and pension funds. Banks are likely to bid both for themselves and other institutions, notably mutual funds and small insurance companies that do not bid directly. This means that we cannot separately identify the bidding behavior of mutual funds. For this reason, and in light of the similarities in maturity structure between pension and mutual funds, we focus our analysis on the bids of pension funds. Although we do not explicitly analyze the bank bids, they are included in the sample as a control group. In total, the data set contains 1,185 auctions and 20,937 bids during 2002-2009.

> 15 While there is no information on the amount of corporate debt issuances purchased by PFAs, we use data on the corporate debt holdings of PFAs as a proportion of the outstanding corporate debt (from Braun and Briones, 2008) and information on the average maturities of PFA corporate debt holdings compared to the average maturity of outstanding corporate debt (from the Chilean Superintendency of Pensions and the Superintendency of Securities and Insurance).

> 16 With respect to the banking system, the proportion of certificate deposits held by PFAs has been very stable, oscillating between 25% and 30%. But banking sector information is less relevant to assess the extent to which PFAs might be constrained by instrument availability because banks can accept any amount of deposits.

19