rafmacalaba/datause-encoder-data huggingface dataset:
renew itself on a daily and generational basis) is subsistence production. The evolution of the independent, landowning peasant household has not altered the central role that subsistence production plays in rural communities in the Sierra. Answers in Table 3 should therefore be viewed as 'additional' income sources.

**lkble 3. Strategies to complement household income.**

|_Rank_|**_Melan_**|_Apunag_|_Maca Chico_|_Jatun_**_Era_**|_BeUavista_|_Membrillal_|_VUano_|
|---|---|---|---|---|---|---|---|
|1|Temporary<br>migration|Female &<br>child labor|Temporary<br>migration|Female &<br>child labor|Family<br>assistance|Permanent<br>migration|Cush crops|
|2|Female &<br>child labor|Temporary<br>migration|Domestic<br>animals|Domestic<br>animals|Domestic<br>animals|Temporary<br>migration|Subsistence<br>production|
|3|Land/labor<br>exchanges|Domestic<br>animals|Piece work|Temporary<br>migration|Permanent<br>migration|Small<br>business|Day labor,<br>murk<br>harder|
|4|VKaving|Land/labor<br>exchanges|Land/labor<br>exchanges|_|Small<br>business|Family<br>subsistence||
|5|Day labor|Cash crops||||||

Sousee: Field interviews fbr Rural Qualitative Assessmt (1994).

Temporary migration. Given the limitations in agricultural production and limited local employment opportunities, the most common element to earn additional income is temporary migration of one or several family members. Usually, young men and male heads of households migrate on a temporary and often cyclical basis. Most frequently, labor is provided in urban centers, and less often, in agriculture. In both cases, migration takes place during lulls in the cropping cycle on the small holding.

The migration rate for household heads ranges from 55 percent in Melan to 20 percent in Jatun Era, and young men migrate in similar proportions. Men from Melan travel primarily to Quito to work in construction (i.e., part of the informal sector) and to a lesser degree, to rural areas in the provinces of Esmeraldas (on the Coast) and Napo (in the Oriente). Most men migrate for a total of two to three months at a time, but rarely at a single stretch. They return home briefly (usually during weekends or at the end of the month) to remit their savings to their families. Men migrate mostly to Quito, to the regional centers like Ambato or Latacunga, or to the rice-producing region around Quevedo, on the Coast.' Female and child labor. Temporary male migration increases household labor and management responsibilities for women and children. The complementary nature of these two mechanisms is illustrated by their ranking as the top two mechanisms to supplement incomes in three of the four Sierra communities surveyed. Women have always played a central role in agricultural production in indigenous communities in the Sierra, and children assume

**21** For some, migration begins at a very early age. _"Since I was very young, I've migrated -- beginning at - age seven and I've stated migrating again because I can't support myfamily staying here"_ (household #2, Melan).

21
<!-- Start of picture text --> 1978:01:00<br>1979:01:00<br>1980:01:00<br>1981:01:00<br>1982:01:00 o<br>1983:01:00 y<br>3 1984:01:00<br>1985:01:00<br>S 1986:01:00<br>1 1987:01:00<br>a 1988:01:00<br>> 1989:01:00<br>1990:01:00<br>1991:01:00<br>§ I 1992:01:001993:01:00<br>1994:01:00<br>1995:01:00<br>1996:01:00<br>m<br>X 1997:01:00<br>L 1998:01:00<br>1999:01:00<br>2000:01:00<br>2001 :01 :00 _ _ _ _ _ _ _<br><!-- End of picture text -->
_Cross Country Evidence on Use of Technology for Identification, Detection and Collection_ To complement these causally identified studies at the country level, we zoom out to a crosscountry description of the relationship between the extent of technology use and a country’s tax performance using data from the OECD Inventory of Tax Technology Initiative’s 2023 Global Survey on Digitalisation. The survey catalogs the tax technologies used in 75 countries, capturing variables can be mapped to our framework to understand how countries use technology to boost their identification, detection and collection capacity.<sup>5</sup> For identification-related technologies, the survey collects whether the tax authority requires taxpayers to have digital identification with a unique identity number, and whether this ID is based on government-issued documents and/or biometric information, as well as whether taxpayers can register online. For detection, it collects whether the tax authority receives data from third parties such as trade partners, shares databases with other government bodies, requires taxpayers to submit e-invoices or to maintain online cash registers that directly report their sales to the tax authority, or uses artificial intelligence to conduct risk assessments or detect evasion. For collection capacity, the survey records whether taxpayers can file and/or pay taxes online, and whether they can request an extension or set up a payment arrangement online.

**Figure 2: Taxation Technology and Tax-to-GDP**

> 5 For more information, see: https://www.oecd.org/tax/forum-on-tax-administration/tax-technology-tools-and- <u>digital-solutions/.</u>

9
# **Table 5 – Impact of FD Law on Balance Sheets of Banks Given Their Exposure to Local Public Debt**

This table reports OLS estimates of the impact of the FD Law on the balance sheet of banks of varying ex ante exposure to local public indebtedness. The variable _Loansb,m_ is the total volume of outstanding loans (in logs) extended by bank b in month m. _Loans-NonGovtb,m_ is the total volume of outstanding loans (in logs) extended by bank _b_ in month _m_ to all borrowers excepting the government. _Loans-Govtb,m_ is the total volume of outstanding loans (in logs) extended to the government by bank _b_ in month _m_ . _IntRate-NonGovtb,m_ is the average interest rate (percent) charged by bank _b_ in month _m_ to all borrowers except government. _IntRate-Govtb,m_ is the average interest rate (percent) charged by bank _b_ in month _m_ to the government. _BankExposureGovb,Mar16_ is a measure of the exposure of a bank to local public entities in the month prior to the FD Law and is calculated as the ratio of bank lending to local public entities over total lending. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the bank-month level. Standard errors are reported in parentheses and are doubled clustered at the bank and month levels. *, **, *** denote significance at the 10, 5 <u>and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Loansb,m<br>(1)|Loans-Non-Govtb,m<br>(2)|Loans-Govtb,m<br>(3)|IntRate-Non-Govtb,m<br>(4)|IntRate-Govtb,m<br>(5)|
|---|---|---|---|---|---|
|Postm*BankExposureGovb,Mar16|0.01|1.26*|-1.18***|-0.02|1.23**|
||(0.03)|(0.71)|(0.37)|(0.31)|(0.60)|
|Observations|468|417|456|468|468|
|R-squared|1.00|0.96|0.94|0.95|0.76|
|Bank FE|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|Yes|

52
analysis. A policy focus only on entry into exporting will miss a fundamental aspect of the dynamics of exporting. A strategy that seeks to increase and sustain export growth rates should address constraints to growth of the intensive margin and especially the reasons for low survival rates of exports.

In this paper we have sought to extend the initial path-breaking studies of export survival by applying statistical survival techniques to a broad dataset of bilateral export flows with a high level of product and exporter-country detail. In particular we investigate exporters with a wide range of income levels including many low-income countries and countries in Sub-Saharan Africa. Unlike previous studies we formally test, and reject, the assumption of proportional hazards that underlies the standard Cox (1972) model that has typically been used in the literature. We discuss that unobserved individual heterogeneity is a likely cause of the failure of the proportional hazards assumption and estimate a Prentice Gloeckler (1978) model augmented by a gamma mixture distribution, to summarize unobserved individual heterogeneity. The results revealed that accounting for unobserved heterogeneity is indeed important as both coefficients and significance levels change dramatically. These findings also point to the importance of survival analysis using firm-level data to complement studies using product level flow data in modeling export survival.

Our analysis found that the initial size of an export flow is among the important determinants of its survival. The associated high hazard rate for initially small flows suggests caution in public policy interventions that are aimed specifically at exporters that start small. This confirms the reasoning in Rauch (2007), namely that broad institutional changes favoring small rather than large firms are likely to have a relatively small impact on export growth.

We also confirm that cultural and geographic ties between trading partners as well market size and exporting experience play an important role in export survival. In particular, the results showed that exporting experience is product and region specific. Whereas both regional and product specific experience appears to matter most for low-income 22
40 Table 17

||P<br>N|rojec<br>et Mi|ted Level<br>gration by|of Immig<br>Sex, Can|ration,<br>Emigratio<br>ada, 1986-87 to 2|n and<br>000-2001||||
|---|---|---|---|---|---|---|---|---|---|
||Immi|grat|ion<br>2|Em|igration<br>3||Net|||
|Year|Total|Male|Female|Total|Male Female|Total|Male|F|emale|
||||||(in thousands)<br>4|||||
|Low Migration|Assumptio|n||||||||
|1987-88|150|71|<br>79|64|32<br>32|86|38||48|
|1988-89|140|66|<br>74|65|<br>33<br>32|75|33||42|
|1989-90|140|66|<br>74|65|<br>33<br>32|75|33||42|
|1990-91|140|66|<br>74|66|<br>34<br>32|74|33||41|
|1991-92|140|66|<br>74|67|<br>34<br>33|73|32||41|
|1992-93|140|66|<br>74|67|<br>34<br>33|73|32||41|
|1993-94|140|66|<br>74|68|<br>35<br>33|72|32||40|
|1994-95|140|66|<br>74|68|<br>35<br>33|72|32||40|
|1995-96|140|66|<br>74|69|<br>35<br>34|71|31||40|
|1996-97|140|66|<br>74|69|<br>35<br>34|71|31||40|
|1997-98|140|66|<br>74|69|<br>35<br>34|71|31||40|
|1998-99|140|66|<br>74|70|<br>36<br>34|70|31||39|
|1999-00|140|66|<br>74|70|<br>36<br>34|70|31||39|
|2000-01|140|66|<br>74|70|<br>36<br>34|70|31||39|
|High Migratio|n Assumptio|n||||||||
|1987-88|150|71|<br>79|64|<br>32<br>32|86|38||48|
|1988-89|160|75|<br>85|65|<br>33<br>32|95|42||53|
|1989-90|170|80|<br>90|65|<br>33<br>32|105|47||58|
|1990-91|180|85|<br>95|66|<br>34<br>32|114|51||63|
|1991-92|185|87|<br>98|67|<br>34<br>33|118|53||65|
|1992-93|190|90|<br>100|68|<br>35<br>33|122|55||67|
|1993-94|195|92|<br>103|68|<br>35<br>33|127|57||70|
|1994-95|200|94|<br>106|69|<br>35<br>34|131|59||72|
|1995-96|200|94|<br>106|70|<br>36<br>34|130|59||71|
|1996-97|200|94|<br>106|71|<br>36<br>35|129|58||71|
|1997-98|200|94|<br>106|71|<br>36<br>35|129|58||71|
|1998-99|200|94|<br>106|72|<br>37<br>35|128|58||70|
|1999-00|200|94|<br>106|73|<br>37<br>36|;27|57||70|
|2000-01|200|94|<br>106|73|<br>37<br>36|<br>127|57||70|

'From June 1 of year t to May 31 of year t+l.

2Two immigration assumptions are offered, the low assumption is to reach 140,000 by 31988-89 and the high assumption is to reach 200,000 by 1994-95.

Only one emigration assumption is offered, that being a constant rate of emigration of 0.0025. The projected emigration levels presented in this table are those obtaine from a low growth scenario (low immigration assumption, TFR=1.2), and a high growth 4<sup>(high immigration assumption, TFR=2.1).</sup> Due to rounding, the data may not always add up to the totals.

Source: Statistics Canada, Demography Division, Population Projections Section.

"Population Projections for Canada, Provinces and Territories, 1986-2011."
_Table 6: Effects of BMI changes on medical consumption, by insurance status and income_

||(1)|(2)|(3)|
|---|---|---|---|
|Change in average BMI among household members|-0.968|-1.197|-0.700|
|aged 18+ in 1992/93|(2.68)|(2.87)|(1.36)|
|Health insurance||-0.206|-0.194|
|||(3.41)|(3.20)|
|BMI x Health insurance||1.036|0.835|
|||(1.33)|(1.06)|
|Poor 1992/93|||0.103<br>(1.93)|
|BMI x Poor 1992/93|||-1.182<br>(1.62)|
|_Estimates of BMI effects by insurance and income group_||||
|Poor uninsured|||-1.882<br>(-3.14)|
|Poor insured|||-1.047<br>(-1.20)|
|Non-poor uninsured|||-0.700<br>(1.36)|
|Non-poor insured|||0.135<br>(0.19)|

Note: t-statistics in parentheses. Regressions include all covariates listed in Table 2 and commune fixed effects, which were jointly significant.

The results in column 1 of Table 6 are for the full sample. Columns 2 and 3 suggest some heterogeneity in impacts, although the coefficients on the interaction terms are not significant at conventional levels. The estimated coefficients by subgroup indicate that the largest increase in medical spending following an adverse BMI shock is among the poor uninsured, the only group for which the impact is significant. The fact that the insured face no increase in medical outlays following an adverse health event is consistent with the generous coverage of the VHI program during the time period under consideration.

# - <u>BMI changes and changes in food and non food consumption</u>

Negative BMI shocks thus appear to be associated with reductions in household income and increases in medical spending, at least among poor uninsured households. The question arises: Can households hold their food and non-food consumption constant in the face of these changes?

9
# I. WHAT IS THE PROBLEM?

Population and output have been increasing rapidly in the primary cities of developing countries, such as Mexico City, Calcutta and Lima. In addition, the number of large cities is growing. There were 35 cities with population over 4 million in 1980, but by the year 2000, there will be 66, and by the year 2025, 135 (WHO, 1988). This growth has brought with it dramatic increases in vehicle ownership and vehicle-kilometers traveled. In Indonesia, the vehicle fleet tripled from 1970 to 1981, in Brazil it more than doubled, in Nigeria it increased 5 times. Nearly all of this growth has taken place in cities. For instance, from 1970 to 1980, the number of vehicles grew at an annual rate of 7.9% in Bangkok, 10% in Buenos Aires, 17% in Cairo, 5.6% in Calcutta, and 7.2X in Lima (World Bank, 1986). Using more recent data, in Mexico City, there were 1.1 million cars in 1975, with 2.6 million by 1989 (Faiz, et al, 1990). Vehicles per capita generally increased as well.

Of six developing countries reporting vehicle-kilometers-traveled (VKT) in 1977 and 1987, all experienced increases in VKT, ranging from 23% to 250% (UN, 1989a). Bus and truck traffic have also increased. Consider the modal split for San Paulo. In 1990, public transport made up 61.5% of total motorized trips, up from 58% in 1970. This slow rate of increase in share occurred along with very large increases in trips -- from 5.2 million person trips per day in 1970 to nearly 15 million in 1990 (Barat, 1990).1

1. This exceeds San Paulo's high rate of population growth. Note also that San Paulo built a metro and significantly upgraded suburban rail during this period.
one industry to another. Among those who switched, income risk was higher among those who moved to non-manufacturing sectors than those who switched within manufacturing sectors. In light of their ...ndings, Krishna and Senses (2009) conclude that the impact on labor income risk needs to be taken into account when calculating the costs of openness to international trade.

Robustness tests by Krishna and Senses (2009) reveal that controlling for o¤shoring causes the coe¢ cient on import penetration to increase. In addition, the o¤shoring variable is negative and signi...cant, suggesting that an increase in o¤shoring in a particular industry is associated with a decrease in income risk in that industry.

Artuç, Chaudhuri, and McLaren (2010) and Artuç and McLaren (2010) estimate and simulate the dynamic model of labor adjustment developed in Cameron, Chaudhuri, and McLaren (2007) and Chaudhuri and McLaren (2007) to assess the distributional e¤ects of trade shocks. The former study uses the US CPS data and the latter uses 2004-2006 data from the Household Employment Survey of the Turkish Statistical Institute. The studies estimate both the average cost of switching industries and the variance of idiosyncratic switching costs, and use the estimates to simulate a trade shock to the manufacturing sector. In both cases, the authors ...nd that, due to the high the costs of switching from one industry to another, the economy takes a decade to reach the new steady state after liberalization. During this time, workers move from the manufacturing sector to other sectors, wages in the manufacturing sector ...rst drop then rise as labor supply to that sector falls, and wages in other sectors at ...rst rise and then fall as labor supply to those sectors rises. However, throughout these ‡uctuations, the real wage of the manufacturing sector remains below that of the tari¤ steady state while the non-manufacturing sector real wage remains above it.

Importantly, the distributional e¤ects of the trade shock on lifetime expected utility are much smaller than the e¤ects on wages, once each worker’s future possible mobility and option value are taken into account. In particular, in many speci...cations import-competing 42
<!-- Start of picture text --> Density of the cyclicality coefficients<br>”<br>=<br>cno<br>a<br>Ss<br>4 2 0 2 4 6<br>Generated Cyclicality coefficients (using LGWOLS)<br>— Kernel density estimate<br>——— Normal density<br>kernel = epsnechnikov, bandwidth = 0.1851<br><!-- End of picture text --> <!-- Start of picture text --> Density of the cyclicality coefficients<br>”<br>=oén<br>a<br>o<br>-4 -2 0 2 4 6<br>Generateg Cyclicality coefficients (using LGWOLS)<br>~ :<br>— Kernel density estimate<br>——— Normal density<br>kemel = epsnechnikov, bandwidth = 0.2025<br>Bootstrap 200 replications<br><!-- End of picture text -->
**Figure 15**

# **Evolution of Contemporaneous Herding Statistic**

This figure presents the evolution of the average Lakonishok et al. (1992) herding statistic during the entire sample period (July 1996 to December 2005). The herding statistic is calculated using the asset-specific probability of buying an asset at a moment in time. Panel A and B present the evolution of the herding statistic for Domestic Equity and Domestic Corporate Bonds, respectively. The averages that are significant at the tenpercent level, according to the two-tailed t-test, are marked on the figure. Significant events that occurred during this period of time are also highlighted: the Asian crisis (July 1997), the Russian crisis (August 1998), the introduction of Fund E and the widening of the minimum return band (October 1999), and the establishment of the multi-fund regime (September 2002). Numbers represent percentages (results are multiplied by 100).

Panel A. Evolution of Herding Statistic - Domestic Equity (1996 - 2005) <!-- Start of picture text --> 10<br>Mean<br>8<br>Significant at ten percent<br>6<br>4<br>2<br>0<br>-2<br>-4<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-6<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br>Panel B. Evolution of Herding Statistic - Domestic Corporate Bonds (1996 - 2005)<br>25<br>Mean<br>20 Significant at ten percent<br>15<br>10<br>5<br>0<br>-5<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-10<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br> Herding Statistic<br> Herding Statistic<br><!-- End of picture text -->
an increase in effort can translate into better learning outcomes only when complemented with other inputs provided at home. An alternative explanation is that only students from relatively well-off backgrounds know how to translate increased effort into better outcomes.

We do find instead that boys and girls respond differentially to the intervention, with the latter reporting higher levels of effort after receiving the information package. Therefore the gender-differentiated effect on learning outcomes documented in section 4.2 can be potentially explained by the differential effect on effort.

# **5 Further Evidence on the Gender Heterogeneity**

The results presented so far show that girls respond to the provision of information by increasing their effort and, as a result, they display a large increase in test scores. For boys, we find instead small and not statistically significant increases in self-reported effort and learning outcomes. In this section we further discuss the differential response of boys and girls, and provide some suggestive evidence on the possible mechanisms behind these First, we study if both boys and girls update their beliefs in response to the information treatment. For this purpose, we rely on the information elicited as part of the 12th grade _ENLACE de contexto_ . Two different types of caveats should be taken into account when comparing the expected earnings collected at the baseline and at the follow-up. As already mentioned, the _ENLACE de contexto_ only collects information among 20% of the entire population of exam takers. Given the large share of students who dropped out before taking the 12th grade ENLACE, the populations of exam takers and non-takers might differ along both observable and unobservable characteristics. This has the potential to introduce a selection bias. Due to the randomized assignment of the intervention, the selection bias will not affect the internal validity of our results as long as it enters eq. 3 additively. Nevertheless, the external validity might be limited. Second, while the question regarding wage expectations included in the 12th grade _ENLACE de contexto_ reads exactly as the question included in the baseline survey, the answer only allows the choice between the six options described in section 3.4. In Fig. 3 we plot the distributions of expected earnings in the treatment and the control group both for boys and girls in the follow-up. Compared to the baseline, there is a higher proportion of boys and girls in the control group reporting an expected income in the bin where the observed average earnings fall. This might be the result either of the selection into the exam taking, or improved information as students approach the end of high school.

18