rafmacalaba/datause-encoder-data huggingface dataset:
We conducted a randomized control trail (RCT) in Tbilisi, Georgia, to answer each of these questions. The experiment compares the efficiency of three types of information treatments: (1) receiving a pamphlet with information on pollution harms and avoidance behavior, (2) receiving a pamphlet and daily text messages with information on outdoor air pollution in one’s neighborhood, and (3) receiving a pamphlet, daily text messages on indoor and outdoor air pollution levels, and an indoor air pollution monitor. We surveyed households twice before and approximately two months after the treatment. The questionnaire contained questions about socioeconomic status, knowledge about air pollution, avoidance behavior, a cognition exercise, self-reported health, and willingness to pay for protective devices. To ensure that information on air pollution is as accurate as possible, we installed outdoor pollution monitors throughout Tbilisi, multiplying the number of outdoor monitors within the city by 10 for the duration of the intervention. Indoor air pollution monitors were also distributed to nearly 150 randomly selected households. Data from these indoor monitors provides insight into the extent of exposure of households that are representative of Tbilisi inhabitants.

We find that providing information in a pamphlet has no significant impact on our measured outcomes. Receiving daily text messages impacts knowledge of air pollution and the adoption of avoidance behavior. However, only the most intensive information treatment - receiving information about both indoor and outdoor pollution levels - leads to changes in avoidance behavior that result in health improvements during the intervention. These improvements appear to be driven by the fact that households who receive information about indoor air pollution levels reduce their level of indoor cigarette smoking. Consistent with some existing work (Greenstone et al., 2021), but contrary to others (Greenstone et al., 2023; Ahmad et al., 2023), we find that providing information and access to indoor air quality monitors (IAQMs) does not change households’ willingness-to-pay for either IAQMs or indoor air purifiers.

To examine the extent of infiltration in Tbilisi, we combine the data from the indoor air pollution monitors with the nearest outdoor monitors and estimate the rates of infiltration of outdoor pollution indoors. Results show that infiltration rates in Tbilisi exceed estimates from the U.S. by approximately 2-4 times (Lunderberg et al., 2023). Compared to the U.S very high infiltration is more common, which may be due to a lower average quality of housing. However, variations in infiltration rates seem to be mainly driven by short-term behavioral differences (e.g. length of time 3
10 Table 1. Selected ambient air quality standards (continued)

|Pollutant|Country|Averaging<br>Time|Standard||Remarks||
|---|---|---|---|---|---|---|
|Nitrogen<br>oxides|USA a)<br>Indonesia a)<br>China a)<br>Mexico c)<br>WHO Guidelines b)|AAM i)<br>24 hr<br>Daily Mean f)<br>Daily Moan f)<br>Daily Mean f)<br>Not Once g)<br>Not Once g)<br>Not Once g)<br>Hourly**Max.**<br>1 hr<br>24 hr|100 ug/cu.m(0.06<br>92.6 ug/cu.m(0.0<br> <br>60 ug/cu.m<br> <br>100 ug/cu.m<br> <br>160 ug/cu.m<br>100 ug/cu.m<br>160 ug/cu.m<br>300 ug/cu. .<br>0.21 ppm<br>400 ug/cu.m<br>150 ug/cu. m|ppm)<br>5 ppm)|Primary n)<br>First Class<br>Second Class<br>Third Class<br>First Class<br>Second Class<br>Third Class|p)<br>q)<br>r)<br>p)<br>q)<br>r)|
|Hydrocarbon<br>(nonmethan*|USA a)<br>)|3 hr (8 a.m.<br>to 9**a.m.)**|180 ug/cu. m<br>(0.24 ppm)|Prima|ryASecondar|y n) o)|
||Mexico c)|Hourly Max.|0.24 ppm||||
|Lead|USA<br>a)<br>Indonesia a)<br>WHO Guidelines b)|Quarterly Avg.<br>24 hr<br>Annual mean|1.6 ug/cu.m<br>60 ug/cu. r<br>0.6-1.0 ug/cu.m|Prima|ryASecondar|y n) o)|
|Photochemic<br>Oxidants|al<br>USA a)|1 hr d)|235 ug/cu.m<br>(0.12 ppm)|Prima|ryASecondar|y n) o)|
|(Ozone)|Brazil a)<br>Indonesia a)|1 hr<br>1 hr|<br>180 ug/cu.m<br>200 ug/cu.m(0.10|ppm)|||
||China a)|Hourly mean|<br>120 ug/cu. a||First Class|p)|
|||Hourly mean|180 ug/cu.m||Second Class|q)|
|||Hourly mean|200 ug/cu.m||Third Class|r)|
||Mexico c)<br>WHO Guidelines b)|<br>Hourly max.<br>1 hr<br>8 hr|<br>0.11ppm<br>150-200 ug/cu. m<br>100-120 ug/cu.m||||
|al) Source:<br>a) Source: [|[JAPCA 1984)<br>Sinha et al. 1989]||k) standard for dust||||
|b) Source: [<br>c) Source: [|WHO 1987, UNEP & WH<br>Walsh 1989b]|O 1988)|l) standard for fly d<br>m) standard for blac|ust<br>k smok|e||

- d) Annual Geometric Mean n) standard designed to protect human health e) Not to be exceeded more than once a year o) standard designed to protect general public f) the mean conc-ntration limit not to be **welfare** such as effects on soil, water, crops, exceeded by any day mean vegetation, man-made materials, etc.

- g) the concentration limit not to be exceeded p) **at** natural conservation areas, secnic spots, by any once of sampling and determination historical sits, and convalescent places

- h) 98% of the daily averages must fall below q) at residential areas, mixed areas of business this concentration cultural **areas**

- i) Annual Arithmetic Mean r) cities, towns and industrial areas having j) Annual Daily Mean serious air pollution problems

jl) This standard applies to PM10 (particles that are 10 micrometers or smaller) and is used **as** a primary health standard. j2) Applies to total suspended particles (TSP) Note: 1. ug = lxlO<sup>8</sup> gm; mg = 1 x 10-<sup>3</sup> gm; ppm = parts per million; cu.m = cubic metor. 2. mg/cu.m = ppm * MW * 40.91, where MW = molecular weight; (H=1, C=12, N=14, 0=16, S=32)
**<u>Table 5: Cross Section Regression: Savings and Growth, 2000-07</u>**

|_Average GDPgrowth_|2000-07|
|---|---|
|Saving (as ratio of GDP)|0.083***|
|Initial GDP per capita|-0.0001***|
|Financial development|-0.012*|
|Openness|0.01**|
|Life expectancy|0.017|
|Control for size|Yes|
|Observations|157|

Source: World Development Indicators, 2008. * represents significance at 10%,** represents significance at 5%,*** represents significance at 1%, Country size is measured by population.

The results suggest that domestic savings are positively and significantly related to growth controlling for various factors. South Asian countries have a large and significant positive savings rate compared to other developing countries, thus they are likely to recover quickly from the crisis.

# **_How vulnerable is South Asia to declining capital flows?_**

South Asia has been peculiar in attracting capital flows. Even though different kinds of flows in the region have been increasing, remittances are the most important capital flows into the region (dwarfing the other forms of flows).

**Figure 12: Capital Inflows into South Asia**

<!-- Start of picture text --> 50 US$ billion Recorded<br>Remittance<br>40<br>30<br>Private debt and<br>portfolio equity<br>20<br>FDI<br>10<br>ODA<br>0<br>199019911992199319941995199619971998199920002001200220032004200520062007e<br><!-- End of picture text --> _Source_ : World Development Indicators, 2008, World Bank.

25
**Table 6:  Shared Prejudices in Expert Assessments**

|**_Correlations Among_**|**_Experts_**|**_(R*) and C_**<br>|**_orrelation_**<br>|**_s with Surv_**<br>|**_ey (R)_**<br>|
|---|---|---|---|---|---|
|||**2002**|**2003**|**2004**|**2005**|
|Voice and Accountability|R*|0.85|0.85|0.86|0.84|
||R|0.66|0.65|0.61|0.74|
|Political Stability|R*|0.74|0.78|0.79|0.76|
||R|0.73|0.76|0.78|0.80|
|Government Effectiveness|R*|0.74|0.78|0.79|0.76|
||R|0.73|0.76|0.78|0.80|
|Regulatory Quality|R*|0.68|0.72|0.73|0.72|
||R|0.52|0.61|0.64|0.60|
|Rule of Law|R*|0.75|0.78|0.80|0.78|
||R|0.79|0.78|0.80|0.81|
|Control of Corruption|R*|0.77|0.80|0.82|0.80|
||R|0.79|0.83|0.83|0.82|
|Press Freedom|R*|0.79|0.83|0.82|0.83|
||R|0.80|0.79|0.79|0.77|
||**_Implie_**|**_d Estimate_**|**_s of_**λ|||
|||**2002**|**2003**|**2004**|**2005**|
|Voice and Accountability||0.57|0.56|0.64|0.40|
|Political Stability||0.03|0.09|0.05|..|
|Government Effectiveness||0.03|0.09|0.05|..|
|Regulatory Quality||0.32|0.28|0.26|0.30|
|Rule of Law||..|..|..|..|
|Control of Corruption||..|..|..|..|
|Press Freedom||..|0.17|0.16|0.28|

Note: ".." indicates that estimates of R* and R are not consistent with a positive value of λ 41
59% of businesses are operated by a single person. Single-person businesses are also very common in Zambia and Zimbabwe (around 45% in both countries) whereas, in Lao PDR, single-person and two-person businesses have similar frequencies (both around 40%). This data suggests an informal economy composed of many simple one person-operated enterprises.

Table 3 reports descriptive statistics across multiple business characteristics. Here we only note two aspects: informal businesses are very young and predominantly in the retail sector. Informal businesses have been in operation for about five and a half years on average across the seven countries in the sample, with 28% having been in business for a year or less. Formally registered micro-enterprises are, on average, two and a half years older than informal businesses, a difference of over 45% (7.95 vs. 5.52 years). A majority of informal businesses (68% across the seven countries) are in the retail sector, including hawkers, those in markets, and small shops, 2 percentage points higher than formal micro-enterprises. Only 16% are in manufacturing and related sectors, mostly food processing, woodworking, and furniture production. The remaining 16% are in non-retail services such as hairdressing or cleaning. The relative prevalence of each sector tends to be similar both across and within countries. In all cities, a large share of informal businesses are concentrated in the retail sector (from 48% in Sehore, India to 86% in Kitwe, Zambia), with manufacturing and other services alternating in a distant second or third place.

# **Finance**

Access to formal finance plays a pivotal role in firm survival and growth <mark>(Burgess and Pande, 2005; Kanbur, 2017</mark> ). However, by operating in the shadow economy, informal businesses are likely cut off from the formal financial system as they lack the legal basis that may be necessary to access it directly. Consequently, informal businesses likely have to rely on informal sources of credit, or access finance through credits to individuals or households. Indeed, one of the oftentouted benefits of formalization is that it improves access to formal credit, thus enabling businesses to seize growth opportunities. The <mark>Informal Sector Enterprise Surveys collect data on a variety of variables related to the informal sector’s access to financial products. Overall, we find that, as expected, informal businesses are poorly integrated into the formal financial system, reliant mostly on the owner’s personal funds and on informal sources of credit. Most businesses were started with the owner’s own funds: over 95% in Lao PDR, 81% in India, 84% in Iraq, and close to threequarters of businesses in Somalia, Mozambique, and Zambia. The next most common source of</mark> 15
Srinivasan, T.N., ed., 1993, with contributions by Justin Yifu, and Yun-Wing Sung. _Agriculture and Trade in China and India: Policies and Performance Since 1950._ San Francisco: Intemational Center for Economic Growth.

Srinivasan, T.N., and John Whalley. 1986. _General Equilibrium Trade Modelling._ Cambridge, Mass.: MIT Press.

Taylor, Alan. 1997. "International Capital Mobility in History: The Saving Investment Relation." NBER Working Paper 5743.

Taylor, Lance, and Edmar Bacha. 1976. "The Unequalising Spiral: A First Growth Model for Belindia." _Quafrerdy Journal of Economics_ 90:197-218.

Timberg, Thomas A. 1997. "Big and Free is Beautiful: China and India, the Past 40 years and the Next." _Economic Development and Cultural Change_ 45(2):435-42.

Townsend. 1994. "Risk and Insurance in a Village in India." _Econometrica_ 62(3):539-92.

Vogel, Ezra. 1989. _One Step Ahead in China: Guandong UnderReform._ Cambridge, Mass.: Harvard University Press.

Williamson, J. 1990. _The Progress of Policy Reform in Latin America._ Institute for Intemational Economics. Washington, D.C.

Wilson, Edward 0. 1975. _Socio-Biology, The New Synthesis._ Cambridge, Mass.: The Bellknap Press and Harvard University Press.

Young, Alwyn. 1994. "Lessons from the East Asian NICs: A Contrarian View." _European Economic Review_ 38(3/4):964-74.

49
> - 48-

- and the relative costs of different types of new generating plants.

- (iv) The precise shape of a demand curve is dictated both by the critical prices of gas which are important in determining the optimal plant mix (the lnvestment decLsLon) and by changes whlch alter the merlt ozder ranklng of plant. It li thus system speciflc. Where the latter is lmportant the demand curve usually assumes the conventional shape of having a negative slope with respect to the prlce axis."

The general shape of the demand curve is of considerable interest, given that econometric studies are an attempt to approximate it. Whereas the econometrlc model shows a continuous and decllnlng rate of substitution between two fuels, leading to a downward and smooth (partial) demand curve for a single fuel, the engLneering approach shows a downward curve made up of a serles of steps - each step corresponds to a switch to a different technology and its associated fuel. The width of the steps depends on the system characteriatics. Although there are a number of studies which analyze fuel choice in power generation from an engineering/least cost approach there ia not the same intereat in industrial processes. Of course individual flrms carry out such analysLs but little is said in general terms. This is because industries are so product specific that the nature of the energy requirements tends to be very specifLc. However there are virtually no problems of system optimization and the choice is able to be made on the characterLstics of individual energy using plants. The same principles apply as ln the first example for power - it is crucial to know the output required, the capital costs of competlng plants, the costs of competing fuels at the burner tip, the efficiency of various technologies in the specific use, the pollution characteristlce of the varLous choices etc.

A study by Fog and Nadkarni [19833 analyses possibilities of fuel substitution for the cement industry. In particular they consLder the economics of switchLng from the use of fuel oil to coal. Not only are the direct fuel costs compared but also the capital costs associated wlth the conversLon. Not only does the coal have to be specially prepared (ground and dried) prior to loading into the kiln but facilities for homogenLizng the calorific value of the
the interaction terms remain positively and significantly correlated with financial inclusion in all regressions. Hence, our findings are unlikely to be confounded by other cultural factors.

# **5.2. Alternative dependent variable**

Beyond the simple ownership of bank accounts, Demirgüç-Kunt and Klapper (2013) highlight the importance of considering their usage. The authors explain that 10 percent of account owners in developing economies maintained an inactive account, i.e., “they make neither withdrawals from nor deposits into their account”. Moreover, Aterido et al. (2011) show that the use of financial services also varies significantly between men and women by considering nine Sub-Saharan countries. As suggested by Duflo (2012), women are relatively less inclined than men to use the account if it was easy for their husbands to get the money out. Thus, one may argue that having an account does allow for women’s empowerment, whereas using the account to achieve development goals may be more relevant for accessing economic opportunities. This is particularly relevant in India, where despite the massive growth in ownership, 43 percent of accounts remain inactive, the highest rate in the world according to the 2021 Global Findex report.

Thus, we consider an alternative measure of financial inclusion to capture the frequency of account use. We focus on withdrawals as suggested by Allen et al. (2016), because such operations are actively initiated by account owners while deposits might be initiated by others (for example, employers or governments). Account holders answer the following question: “In a typical month, about how many times is money taken out of your personal account(s)?” Respondents are asked (categorically) if they conducted: ( _i_ ) 0 withdrawals, ( _ii_ ) 1–2 withdrawals, ( _iii_ ) 3–5 withdrawals, or ( _iv_ ) 6 or more withdrawals on average per month. In line with Allen et al. (2016), we qualified the frequent use of an account using a dummy equal 1 if funds are withdrawn at least three times during a month, and 0 otherwise.

The estimations are reported in **Table 7** . We find that the coefficient of the interaction terms of interest are positive and significant in all estimations when explaining account use. Thus, beyond enhancing the simple ownership of a bank account, higher legal equality may empower women through greater management of their money.

# **5.3. Addressing endogeneity concerns**

In the main estimations, we assumed that gendered laws are conditionally meanindependent, given the controls included in the initial specification. Nonetheless, even if we 16
decision. In such a design, “doing nothing” leads to participation in the plan and investment of assets in a long-term retirement portfolio.

Madrian (2013) suggests that automatic enrollment is by far the most effective method of increasing participation in defined contribution savings schemes. The research on participation in employersponsored savings plans in the United States shows that participation rates are substantially higher when the default is enrollment in the savings plan (i.e. individuals must opt out if they prefer not to save) than it is when individuals must take action to participate in the savings plan (that is must opt in). According to Vanguard (2014) the participation of individuals in Vanguard plans where the contributor must elect to join (opt-in) compared with plans where he or she is automatically enrolled (opt-out) is 65 and 82 percent respectively.<sup>7</sup> As shown in Table 1, for individuals earning less than $30,000 per year voluntary participation versus participation through automatic enrollment is 34 and 78 percent respectively. These figures are 29 and 68 percent for individuals under the age of 25. This data confirms that all groups benefit from auto-enrollment, but the most vulnerable groups of society (low income, less educated) are the groups that would benefit the most.

**Table 1. Participation in opt out versus opt in pension funds in the United States,**<sup>**£**</sup> **2013**

||VoluntaryEnrollment|AutomaticEnrollment|
|---|---|---|
|**All**|**65%**|**82%**|
|Income|||
|< 30,000|34%|78%|
|$30,000-|||
|$49,999|57%|86%|
|$50,000-|||
|$74,999|64%|91%|
|$75,000-|||
|$99,999|74%|94%|
|$100,000 +|88%|96%|
|Age|||
|<25|29%|68%|
|25-34|56%|83%|
|35-44|65%|84%|
|45-54|71%|85%|
|55-64|73%|85%|
|65+|68%|77%|
|Gender|||
|Male|63%|83%|
|Females|70%|84%|

> 7 These ratios vary significantly by income and age.

10
# **2.  Regional Allocation Using Poverty Indicators**

Among priority indicators, the two most common are probably poverty incidence (poor people / total population) and the poverty count (total poor people). Some program strategies limit activities to regions whose indicator values exceed arbitrarily-specified thresholds. More generally, budgetary allocations are guided by relative indicator values. Following the latter approach for Lao PDR, we compute regional budget shares that are proportional to the values of poverty incidence and the poverty count. Figures 2.1 - 2.3 display the results, and Table 2.1 indicates the associated priority rankings. Figure 2.1 and Table 2.1 present provincial shares on an approximate north-south axis, while Figures 2.2 and 2.3 display relative shares on a provincial map. The figures show that changing the indicator has a significant impact on provincial shares and priority rankings. Among provinces with top-five shares for poverty incidence, only two in the north (Huaphanh, Oudomxay) are in the top-five group for the poverty count. Changing the indicator from incidence to count shifts two provinces (Xaysomboon, Sekong) from the highest to the lowest group. Conversely, Savannakhet moves from the lowest to the highest group. Overall, the correlation result in Table 2.2 (ρ = .006) suggests that the two sets of provincial shares have effectively zero association: Allocation by one indicator creates a random result for the other.

Our conclusions are not changed when we include two measures related to the poverty gap.<sup>3</sup> First, we estimate a province's _total poverty gap_ by summing across poverty gaps for all individuals who are estimated to fall below the poverty line. Second, we estimate provinces' _poverty severity_ by squaring individual gaps before summing them. Poverty severity draws

> 3 The individual poverty gap is the difference between the poverty line and the actual income of a poor individual.

5
# **4.3 State household IT-use**

As mentioned before, in order to construct our instrumental variable, we combine IT sectoral intensity for the US obtained from Bloom et al. (2012b) with household IT use.

Information regarding household IT use at the state level is from the National Population and Housing Censuses 2000 and 2010. We also obtained information from the Module on Information Technology Availability and Use in Households (MODUTIH 2010 and 2013) in order to compare it to the census data.

Considering the great differences between the rural and urban sectors in some states, alternative household IT use variables were constructed restricting the sample to localities with more than 15,000 inhabitants (urban IT use).

# **4.4 Descriptive Statistics**

As mentioned before, the random sample design of the ENTIC surveys, made it possible to match only 701 firms and around 88% of them are big firms, as they are the ones that must be in both samples in order to achieve statistical representativeness. This fact has important implications regarding the interpretation of our results given that previous studies suggest that small firms are the ones more likely to exit and be negatively affected by the increasing competition (Iacovone et al., 2013). In this case, due to these data limitations we are only able to analyze the extensive margin.

Consistent with the size of the firms in our sample, approximately 50% of the firms have a share of FDI higher than 80%. Accordingly, the mean of exports/total sales is much higher than the one observed for the whole ENTIC. Table 1 shows descriptive statistics for the main variables used in this analysis for our sample and the whole ENTIC sample.

Analyzing IT use at the sectoral level, as Figure 1 shows the sectors that make more intensive use of IT measured as computers-per-worker are chemical products followed by computer equipment and electronics and beverages and tobacco. The second sector is not only an IT-use sector but an IT-producing sector. It is important to note that once we use the share of labor with Internet, the same sectors remain as the ones with most intensive IT use, but machinery also appears as an intensive user in terms of the share of labor with Internet.

8