rafmacalaba/datause-encoder-data huggingface dataset:
# C. **<u>Estimation</u>**

We ran ordinary-least squares (OLS) regressions estimating the impact of the interventions on four main outcome variables: 1) taking both hypertension and diabetes screening, 2) taking diabetes screening, 3) taking hypertension screening, and 4) taking at least one of the two screenings. As robustness checks, we further specified four regressions including an increasing number of control variables: only controls for facility and provincial fixed effects, further controls for the level of health facility, additional controls for age and gender of study participants and a final set of controls for a broader set of socio-economic variables collected during the baseline survey.

# **_Implementation Fidelity_**

This study protocol was approved by the Institutional Review Board of the Center of Medical Genetics and Primary Health Care in Armenia (#02570094). A process evaluation was conducted, as part of the qualitative research, to ascertain that the intervention did not deviate from its predefined design. The study was implemented consistent with the approved protocol. All the invitations were delivered to the 1,600 study participants in the intervention groups. In addition, 100 percent of the study participants who were in intervention group 3 were offered the pharmacy vouchers with the invitation letters, and 100 percent of the study participants who received Intervention 4 that reported attending screenings received pharmacy vouchers. There were no reported challenges in redeeming the vouchers in the preidentified pharmacies.

# **4. Results**

# **_A._** **_<u>Baseline characteristics and balance</u>_**

Table 4 presents the means and standard deviations for a range of baseline characteristics for the sample mean, 4 intervention groups and the control group. Appendix Table 1 reports the p-values of t-tests of whether the differences observed in the five groups are statistically significant at conventional levels indicated by the stars next to the p-value.

The mean age of our sample is 51 years old. Only 1 percent of the study participants do not have any education. More than 50 percent of our sample is unemployed, and most are married (86 percent). The average household size is 5 people. Our subjective measures on income revealed that 50 percent of participants responded that their income is sufficient for basic family needs, such as food, clothing, and utilities, but not enough for big purchases, such as equipment or a car, while 35 percent responded that their income is sufficient for everyday food but not for clothes and other basic needs. The majority of individuals are mapped to large-sized primary health care facilities (74 percent).

The balance tests for our set of variables indicate that the distribution of widowed, self-employed, and those who responded to our subjective welfare measures questions are not always similar across the different treatment arms and the control group. However, we find no statistically significant differences across 16
scheduled that feed subways. On the other hand, scale economies may be counteracted by a diseconomy to the extent that greater transit vehicle occupancy imposes crowding costs on passengers though longer delays while passengers board and alight, greater likelihood of standing, discomfort, etc. (Kraus 1991). However, we do not have the data to credibly estimate the net impact, let alone the magnitude, of these scale economies and diseconomies, and therefore they are excluded from our analysis.

Prior literature on environmental and transportation taxes emphasizes the potential importance of considering how these policies interact with factor market distortions created by the broader tax system, particularly tax distortions in the labor market. Specifically, the optimal (revenue-neutral) tax on a fuel or travel mode will exceed the level warranted on externality grounds if the fuel or travel mode is a relatively weak substitute for non-work time (see e.g., Bovenberg and Goulder 2002). However, the needed empirical information evidence to address this potential adjustment for Mexico City --particularly the substitution between (taxed) formal work effort and (untaxed) informal work in response to changing transportation prices and income taxes -- is not available. Therefore, broader fiscal interactions are also excluded from our analysis. Moreover, in the context of Mexico City, both labor market imperfections generally and the possibility of lower-income workers being priced out of access to formal sector employment with higher transit costs could affect optimal externality pricing. These factors also are excluded from our analysis.

A final caveat is that we focus only on economic efficiency and not the distributional incidence of tax reforms. A distributional analysis would require data on car ownership, fuel use, and transit patronage by income class as well as some measure of lifetime income for each class (which is thought to be a better measure of household wellbeing than annual income). Incidence studies for the United States typically find that fuel taxes are mildly regressive (e.g., Parry et al. 2006, pp. 11-12), though there has been little work on how creative recycling of fuel tax revenues might help to alleviate this concern or on how one might implement such recycling measures.

# **2.1. Model Assumptions**

_(i) Household utility_ . An agent, representing an aggregation over all individuals in the Mexico City metropolitan area, has utility function _U_ (.) defined by: 7
After the war, Akihabara became famous as an electronics wholesale district. Most of the goods initially on offer were surplus items discarded, or otherwise acquired, from the American occupation forces.Akihabara is still home today to a thriving trade in pirated software and boxes of computer parts and appliances that have "fallen off the truck".

In Japan's high growth reconstruction era, Akihabara's center of electronics gravity shifted from radios to home appliances. Then came personal computers and software. Akihabara accounted for nearly half of Japan's PC sales until 1995 when suburban mass retailers began selling household appliances at steeply discounted prices. Akihabara's dynamism today is driven by internet and data processing technologies, by animation, and by robotics.

_Information technology._ 742 software developers, internet ventures, and data processing companies cluster within a one kilometer radius of Akihabara station, the densest IT concentration in Japan, according to a March 2003 survey by the Ministry of Land, Infrastructure and Transport. The Tokyo Metropolitan Government is encouraging the transformation of Akihabara into the IT hub of Tokyo, and Tokyo governor Shintaro Ishihara is leading the effort. A 100 billion yen IT development project is underway adjacent to Akihabara Station on the former site of a public fruit and vegetable market. An IT Center, including 31 story and 22 story buildings, both to be completed by 2006, will house offices for IT related firms, convention halls, showrooms displaying the latest IT devices, and platforms for industry-university cooperation. The goal is to speed IT product development.

Adjacent to the IT Center, Kajima Corporation is constructing "The Tokyo Times Tower", a 40 story condominium, in collaboration with Sony Corporation. The tower is targeting IT devotees by offering "near future living space," a Sony interior design concept that integrates audio-visual devices with home networking technologies. A glass partition in the living room and an artificial marble wall in the bathroom can both be turned into home theaters through use of projector screens. Residents can watch the news while taking a shower or view movies on the ceiling while lying in bed with the use of a small, wireless, liquid crystal "airboard' controlling a plasma-display panel television (Nikkei Net 25 August 2003).

_Animation._ In the digital economy, content and software are far more profitable than hardware, and anime otaku are the newest pilgrims to Akihabara. Yesterday's PC outlets are today's purveyors of comic books, videodiscs, and game software. The most popular items sell in the millions. Akihabara's specialty shops now market garage kits for making synthetic-resin 30
15

# B. Instrumental variables

We use two stage least squares (2SLS) and three stage least squares (3SLS) to evaluate whether the predictable component of financial development is related to economic growth and the sources of economic growth. Since the 2SLS results are almost identical to the 3SLS results, we report the 3SLS and note differences in the text. We allow the constant to differ across decades but restrict the slope parameters to be equal across periods.<sup>1 5</sup> For instruments, we use LYO, LSEC, GOVI, PII, TRDI, and the predetermined values of the corresponding financial development indicators.

Table X summarizes the 3SLS results for the coefficients on our four financial indicators. (See Appendix Tables Xa-Xd for complete results.) The predictable components of (a) financial depth, (b) the relative importance of banks as opposed to central banks, and (c) the ratio of domestic credit issued to nonfinancial private firms to GDP are significantly related to each growth indicator. Although PRIVATE generally enters insignificantly in Table VIII, PRIVATE enters significantly in the growth equation when the regression includes dummy variables for Sub-Saharan Africa and Latin America. Inclusion of these continent dummy variables does not importantly alter the conclusions on the other financial indicators as shown in the Appendix, Table Xe. Thus, the predictable component of financial development appears strongly related to growth and the sources of growth. Interestingly, the predictable component of the other economic indicators - GoV, PI, and TRD - are not strongly linked with growth. (See Appendix Table Xa-Xd.)

# C. Sensitivity analyses

We find the results on the predetermined and predictable components of financial development to be fairly stable. As noted, the findings are insensitive to estimation technique. Inclusion of continent dummies or the a ge in the terms of trade tends to strengthen the results, while adding 15 Below, we discuss the results when all the coefficients are allowed to vary across decades.
In general, our ...ndings raise some intriguing analytical questions. The main question would be why changes in government spending may have an asymmetric e¤ect in recession. Understanding why the e¤ect may be asymmetric in good times is, in principle, much easier because in a situation of full employment, increases in government spending should have no e¤ect whereas reductions should. A second analytical question would be how to explain the di¤erent responses to ...scal contraction in industrial and developing countries.

In terms of policy prescriptions, our ...ndings stress the importance of computing the ...scal multiplier in recession and when government spending is going up (which is presumably the most relevant scenario for policy makers). Failure to do so may greatly bias the relevant estimate and hence misinform policy makers in their decision process.

# References

- [1] Auerbach, Alan J., and Yuriy Gorodnichenko, 2010. “Measuring the output responses to ...scal policy,”unpublished manuscript (UC Berkeley).

- [2] Auerbach, Alan J., and Yuriy Gorodnichenko, 2012. “Fiscal multipliers in recession and expansion,”NBER Working Paper No. 17447.

- [3] Barro, Robert, and Charles Redlick, 2011. “Macroeconomic e¤ects from government purchases and taxes,” Quarterly Journal of Economics 126, 51-102.

- [4] Blanchard, Olivier and Roberto Perotti (2002), “An empirical characterization of the dynamic e¤ects of changes in government spending and taxes on output,” Quarterly Journal of Economics 117 (4), 1329-1368.

- [5] Chan, K. S. and Howell Tong, 1986. “On estimating thresholds in autoregressive models,” Journal of Time Series Analysis 7, 179-190.

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a direct, adverse effect on innovation activity and growth, but also an indirect effect that operates in the same direction. We also show that if unit monitoring costs fall with the number of successful projects (as a result of information externalities, for instance), multiple equilibria may emerge–one of which, a middle-income trap, characterized by low wages in the design sector, a low share of the labor force engaged in innovation activity, and low growth. A sufficiently ambitious policy aimed at alleviating financial constraints–through the development of capital markets rather than government subsidies, which may be difficult to target effectively–may allow a country to move away from such a trap, not only by reducing the cost of finance but also by improving incentives to invest in skills and promoting the production of ideas.

The focus of our analysis is on middle-income countries, where we believe that accounting for the labor market effects of access to finance (or lack thereof) is particularly important to understand the interactions between financial intermediation, innovation, and growth. In these countries, the supply of highly-qualified labor remains relatively limited, creating therefore another constraint on innovation activity. By contrast, in developed countries the issue may be less about the quantity of highlyskilled workers and more about the allocation of talent–an issue that we addressed in a previous contribution–and the provision of adequate incentives to engage in risky entrepreneurial activities. Nevertheless, our analysis also has some relevance for slowgrowing industrial countries. Indeed, there is compelling empirical evidence to suggest that in many of these countries access to finance remains an equally important constraint on the innovation activity of small and medium-sized firms. Inadequate access to finance may therefore be the source of a slow-growth equilibrium, just as with the middle-income trap that we characterize in our analysis. In such conditions, a forceful policy aimed at promoting access to finance is also essential to escape from a lowgrowth equilibrium and put the economy on a path that would allow it to converge to a high-growth, high-innovation equilibrium.

The paper proceeds as follows. The recent evidence on access to finance and innovation is reviewed in Section 2. Although these studies pertain mostly to industrial countries, some also include developing countries.<sup>6</sup> The model is described in Section

> 6Empirical studies and enterprise surveys have indeed documented that lack of access to finance is

6
the land owned by the household in the past and the present, and the household's income, along with a set of village dummy variables.

# Data and Statistical Analysis

The data used in this paper are from a census survey of an endogamous sub-caste of potters spread across three villages, Halli, Beedu and Ooru<sup>8</sup> , located within 70 miles of each other also the state of Karnataka. Only 20 per cent of the adults in this community actually practice pottery, and most are day laborers with small-scale farming and silk rearing the other major activities. Halli and Ooru are multi-caste villages located within commutable distances of large towns. Beedu, a village consisting entirely of potters, is located in a "forward" district with relatively good schools and a long tradition of providing basic social services.

Table 3 provides some summary statistics about the sample. The average annual household income is 14,471 Indian rupees - about $1644 in PPP adjusted US dollars ($482 at the exchange rate prevalent at the time) - a little over half of Indian per capita household in PPP dollars, and well below the Indian poverty line of 45 rupees per person per day. The average land holding is very low at 0.59 acres. Education is also low, with the average maximum schooling of a person within a family at 4.54 years (the mean years of completed schooling among household heads is much lower at 1.6 years). Despite the low levels of education the villages have recently undergone a fertility transition with the average household consisting of 3 adults and 1.5 children.

Tables 4 shows that festival expenditures rise with income indicating that they are a "normal" good. They also rise with the maximum level of education in the family, the number of young children, and the number of girls of marriageable age. Thus, we can infer that private incentives are important determining factors. The amount spent on festivals by neighbors is not significant indicating that demonstration effects are not important. The level of expenditures display a fair amount of variation across two of the three villages with Beedu having significantly lower levels of festival expenditures than Halli, the omitted category. This is despite the fact that the households in all three villages in this sample belong to the same endogamous sub-caste and therefore observe the same religious and ritual calendar. This suggests that the private incentives

> **18** The names of the villages have been changed.

13
10

# IV. Capital Flight

The capital account is more than just borrowing and repaying external debt. For instance, in 1980 developing countries ran a capital account surplus of only about $68 billion, while their outstanding debt rose by $96 billion. In 1984, debt rose by about $69 billion, while the capital account surplus was only $14 billion.<sup>8</sup> The net inflow of direct investment, which would make the capital account more positive, was more than offset by accumulation of official reserves and, more importantly, by private purchases of assets in industrial countries, including capital flight.

Since evasion of taxes, unsustainable appreciation of the domestic currency, and exchange controls often motivate capital flight, statistics are sparse and uncertain. But the magnitudes are not trivial. Table 3 shows estimates of capital flight by two authors.9 Argentina, Mexico, and Venezuela account for most of the capital flight from the countries investigated. For those three, capital flight accounted for one-third to two-thirds of their total debt in 1984. For most countries capital flight has at least slowed since 1984, so its share in the total debt has probably not risen much since then. Most of the capital flight occurred when governments could easily borrow foreign exchange to sustain overvalued domestic currencies. More recent capital flight, for instance from Brazil, has mostly had to go through 8 World Bank Debt Tables 1987-88; IMF, IFS. Saudi Arabia, Kuwait, Middle East unspecified, and South Africa were subtracted from the IFS capital account total for developing countries, because the Debt Tables do not include them.

9 The two estimation methods in Khan and Haqu^ 1987 give similar results.
_Figure 2: Eligibility and willingness to be referred_ <!-- Start of picture text --> Salahaldin 2%1% 75% 21%<br>Ninawa 21% 11% 45% 23%<br>Kirkuk 1%2% 51% 46%<br>Diyala 1%1% 57% 42%<br>Anbar 8% 3% 58% 31%<br>Returnee 15% 8% 54% 24%<br>Displaced 15% 6% 46% 33%<br>Host 7% 5% 53% 35%<br>Rural 17% 9% 50% 23%<br>Household head document is missing 18% 5% 58% 18%<br>Cant afford basic need 15% 6% 55% 24%<br>No household member worked 18% 7% 52% 23%<br>Household head disabled 13% 6% 50% 31%<br>Female headed household 10% 4% 58% 28%<br>Registered for Covid-19 Minha support 23% 3% 61% 13%<br>Registered for government assistance 17% 8% 50% 26%<br>All sample 14% 7% 53% 26%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Referred and Willing Referred and Unwilling Not referred and Willing Not referred and Unwilling<br>Governorate<br>Status<br>Socioeconomics<br>Register<br><!-- End of picture text --> A third of eligible households for humanitarian support opting out of government support is indeed high and may pose problems both in program planning as well as meeting government developmental goals. First, the unwillingness to be referred would likely reduce the odds of successful referrals. Second, while some mechanisms could be used to replace these unwilling groups for example using the higher eligibility threshold to include lesser poor households form the humanitarian caseload, this, however, may not be encouraged in the spirit of reconciliation and national building after many years of conflict. There is a need to understand the grievances of these groups and tackle them to find appropriate solution. Knowing their characteristics is the first step. As seen in Figure 2, Ninawa governorate is more likely to host households that are eligible but not willing to be refereed (11 percent of the total sample of Ninawa), compared to other governorates, the percent is less than 3. They are more likely to be returnee households (8 percent) may also have applied for government assistance (8 percent) previously.

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<!-- Start of picture text --> Consumption habit persistence Inverse elast. of intertemp. subst. Inverse Frisch elast. of labor supply<br>1.5 1.5<br>6<br>1 1<br>4<br>2 0.5 0.5<br>0 0 0<br>0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.5 0 0.5 1 1.5 2 2.5 3 0.5 1 1.5 2 2.5 3 3.5<br>E.o.s between capital and labor for renew. E.o.s between capital and labor for fossil E.o.s between value-added and energy<br>8 8 6<br>6 6<br>4<br>4 4<br>2<br>2 2<br>0 0 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.2 0.4 0.6 0.8 1 1.2<br>E.o.s between value added and carbon E.o.s between high and low-skilled labor Import price elasticity<br>30 2<br>1.5 4<br>20<br>1<br>10 0.5 2<br>0 0 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 0.5 1 1.5 2 2.5 3 3.5 4 0.5 1 1.5 2 2.5 3 3.5<br>Export price elasticity E.o.s between renewable and fossil energy E.o.s between core goods and energy<br>10<br>1 0.1<br>5<br>0.5 0.05<br>0 0 0<br>-0.5 0 0.5 1 1.5 2 2.5 3 3.5 -10 0 10 20 30 40 50 -0.2 0 0.2 0.4 0.6 0.8<br>High-skilled wage adjustment cost Core good price adjustment cost Investment adjustment cost<br>0.02<br>0.015 0.02 0.3<br>0.01 0.2<br>0.01<br>0.005 0.1<br>0 0 0<br>-50 0 50 100 150 200 250 300 -50 0 50 100 150 -5 0 5 10 15 20 25 30<br>Renewable input adjustment cost Carbon input adjustment cost Export adjustment cost<br>0.6<br>0.8 0.4<br>0.6 0.4<br>0.4 0.2<br>0.2<br>0.2<br>0 0 0<br>-2 0 2 4 6 8 10 -2 0 2 4 6 8 10 -2 0 2 4 6 8 10<br>Calvo empl. param. Empl. elas. of low-skilled wage infl. Price infl. elas. of low-skilled wage infl.<br>60 1 1.5<br>40 1<br>0.5<br>20 0.5<br>0 0 0<br>0 0.2 0.4 0.6 0.8 1 -1 0 1 2 3 4 -1 -0.5 0 0.5 1 1.5 2 2.5 3<br>Output gap elas. of gov. cons. 3 Core infl. elas. of policy rate Output gap elas. of policy rate<br>4 8<br>3 2 6<br>2 4<br>1<br>1 2<br>0 0 0<br>-0.6 -0.4 -0.2 0 0.2 0.4 0.5 1 1.5 2 -0.2 0 0.2 0.4 0.6 0.8 1 1.2<br><!-- End of picture text --> Figure B1: Prior (gray) and posterior (black) distribution of structural model parameters. Dashed green lines represent modes of the posterior. Two chains of Random Walk Metropolis Hastings simulations, each with 50,000 iterations and 25,000 burn-in. Acceptance rate is around 0.25.

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Table B. Population Supply<sup>a,b</sup> (standard errors corrected for spatial dependence in parentheses)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Spatial GMM|Spatial OLS|Spatial GMM|Spatial GMM|Spatial GMM|
|Ln(incomeper capita)|2.539***|1.813***|1.846***|2.771***|3.072***|
||(0.624)|(0.359)|(0.476)|(0.613)|(0.879)|
|Ln(rural income opportunities:|<br>-5.536***|-4.152***|-4.873***|-5.638***|-6.040***|
|marketpotential)|(1.445)|(0.830)|(1.285)|(1.334)|(1.849)|
|Ln(ruralpop. supplymarket|6.231***|4.878***|5.615***|6.313***|6.719***|
|potential)|(1.376)|(0.788)|(1.223)|(1.276)|(1.755)|
|time dummies|Yes|Yes|No|No|No|
|Observations|369|369|123|123|123|
|Hansen J statistic<br>(overidentificationtest)|1.355||1.014|1.463|1.684|

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, ln(distance to São Paulo), ln(market pot. agric. land availability, 1970), port dummy, ln(per capita capital stock, 1970), southern region and time dummies.

- b. Coordinate variables are latitude and longitude. Cutoffs are 1.5 standard deviations of latitude and longitude (10.23, and 8.20), which correspond to about 900 miles.

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