rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Poverty and vulnerability rates for different group in 2016**

|||Vulnerability (%)|Poverty (%)|Ratio of<br>vulnerability to<br>poverty|
|---|---|---|---|---|
|Regions|Tigray|32|27|1.2|
||Afar|44|24|1.9|
||Amhara|28|26|1.1|
||Oromia|40|24|1.7|
||Somali|60|22|2.7|
||Benishangul-Gumuz|36|26|1.4|
||SNNP|46|21|2.2|
||Gambella|42|23|1.8|
||Harari|28|7|4.1|
||Addis Ababa|9|17|0.6|
||Dire Dawa|36|16|2.3|
|Ecological|Moisture-reliable highlands|36|23|1.5|
|zones|Drought-prone highlands|31|21|1.5|
||Moisture-reliable lowlands|38|25|1.5|
||Drought-prone lowlands|56|27|2.1|
|Urban/rural|Urban|17|15|1.1|
||Rural|43|25|1.7|
|National||38|23|1.6|

Source: Author calculations based on HCES/WMS, 2016.

# **Sources and types of vulnerability**

As discussed above, vulnerability to poverty can be either poverty-induced (structural) which is driven by low average consumption of households (lower than the poverty line) or risk-induced (transitory) which is due to high consumption variability/volatility. The first two columns of Table 4 report the percentage of households that are poverty vulnerable and risk vulnerable, respectively. The results show that in the drought-prone lowlands, both poverty-induced and risk-induced vulnerability are higher than in the other agroecological zones. In the drought-prone lowlands poverty-induced vulnerability is relatively more important than risk induced vulnerability (ratio equal to 1.5) whereas in the other zones the ratio of poverty-induced vulnerability to risk-induced vulnerability is below one, suggesting that risk-induced vulnerability is more important. The ratios for Somali and Afar are 1.1 and 1.5, respectively. This attests to the unique nature of the drought-prone lowlands zone in comparison to the other agroecological zones and points in favor of policies and programs tailored specifically for the drought-prone lowlands. This is also consistent with the fact that the drought-prone lowlands lag in human capital indicators including education which are important determinants of welfare (World Bank, 2020).

17
60% of retail trade in Australia, Canada, Finland, Norway, Sweden and Switzerland, the 10 largest retail distributors represented less than 20% of the sector in the Czech Republic (16%) and Poland (2%). In contrast, market structure already was more concentrated in Hungary, with the first 10 enterprises accounting for 53% of the sector.<sup>3</sup> The rapid extension of competitive multinational chains in the retail distribution sub-sector in the Czech Republic since 1997 has not only increased competition but also introduced more efficient distribution and management methods.<sup>4</sup> Another evolving feature of market structure in many countries is the formation of joint purchasing groups, with food retailers tending to strengthen their contractual position vis-à-vis major food producers by signing co-operative agreements with other retailers.

**Design of detailed enterprise-level questionnaires.** Industry-tailored questionnaires were prepared, with separate questionnaires for road freight logistics providers and road freight logistics user enterprises. The questionnaires were developed and refined in the context of an initial empirical study undertaken to quantify the impact of innovations in the Mexican road freight industry on downstream business users.  From a particularly extreme degree of rigid regulation with a high degree of government interference, Mexico put in place beginning in 1989 a new policy framework for the road haulage industry based on free entry and market-based price setting.  Besides expected gains from reductions in road freight prices, a number of additional sources of gains were systematically quantified.  Faster and more reliable trucking has allowed user companies to offer new goods, both introducing previously unavailable products as well as making it possible for existing products to reach new areas. Importantly, the emergence of high-quality logistics service providers has enabled user companies to adopt better logistics practices.  Controlling for the effect of other exogenous

> 3 Ibid, Table A2.1.

> 4 In response to competition from multinational chains, several small but also large retailers have gone bankrupt, including Interkontakt Group, which until 1998 was the biggest retail firm in the Czech Republic. See OECD (2001), p 28.

7
in  exporting  firms  relative  to  domestic  firms  to  be  insignificant.  Thus,  our  findings  using matched  employer  employee  data  suggest  a  quite  different  picture  of  the  links  between trade  liberalization  and  wages  than  is  obtained  by  analyzing  the  data  at  a  more  aggregate (firm)  level.  Consistent  with  the  models  of  Helpman,  Itskhoki,  and  Redding  (2010)  and Davidson,  Matusz  and  Schevchenko  (2008),  we  find  that  the  workforce  composition improves  systematically  in  exporting  firms  in  terms  of  innate  (time  invariant)  ability  and  in terms  the  quality  of  the  worker-­‐firm  matches.   This  finding  also  serves  to  explain  the difference  between  the  results  at  the  firm  level  and  those  at  the  worker  level.  If  average worker  ability  improves  systematically  in  exporting  firms  following  trade  liberalization,  and this  change  in  not  taken  into  account,  it  will  appear  that  trade  liberalization  leads  to  a differential  wage  improvement  for  workers  at  exporting  firms  even  when  this  is  not  the case.

The  remainder  of  this  paper  is  organized  as  follows.  In  Section  2,  we  present  a  background discussion  on  Brazil’s  trade  policy  reforms  and  describe  the  data.  We  present  the  empirical methodology  and  estimation  results  for  the  aggregate  (firm-­‐level)  analysis  in  Section  3  and for  the  analysis  at  the  worker  level  in  Section  4.  Section  5  concludes.

# **2. Data   and   Policy   Background**

Our  main  data  are  administrative  records  from  Brazil  for  formal-­‐sector  workers  linked  to their  employers.  We  combine  this  worker-­‐level  information  with  complementary  data sources  on  firm-­‐level  exporter  status  and  information  on  industry-­‐level  trade  protection during  Brazil’s  main  trade  policy  reform  period.

# **2.1.   Brazil’s   Policy   Reforms**

The  1990s  were  a  period  of  dramatic  policy  reform  in  Brazil,  providing  a  particularly appropriate  setting  in  which  to  study  the  impact  of  trade  liberalization  on  wages.  As compared  to  the  gradual  process  of  globalization  in  many  developed  countries,  Brazil’s trade  reform  occurred  over  a  relatively  short  period  of  time,  and  with  substantial  cross-­‐ industry  variation.  Furthermore,  many  of  the  policy  reforms  were  arguably  unanticipated 5
# **Table of Contents**

|I.|Introduction ............................................................................................................................. 1|
|---|---|
|II.|Pooled Finance: Rationale, Origin, and Regulatory Context .................................................. 3|
|A|. Rationale ........................................................................................................................................... 3|
|B|.<br>Origin and Types ............................................................................................................................... 3|
|C|.<br>Regulatory Context ........................................................................................................................... 4|
|III.|Legal Structure, Management, Financing and Operation ........................................................ 5|
|A|. Legal Framework .............................................................................................................................. 5|
|B|.<br>Management ...................................................................................................................................... 8|
|C|.<br>Financing and Operations ............................................................................................................... 10|
||1.<br>Financial Sustainability ............................................................................................................... 10|
||2.<br>Review of borrower credit and legal compliance ....................................................................... 13|
||3.<br>Pooling mechanism: maturities, interest rates and yields ........................................................... 13|
||4.<br>Surveillance, cash management, and default remedies ............................................................... 16|
|IV.|Lessons for Developing Countries ........................................................................................ 18|
|V.|Conclusions ........................................................................................................................... 21|
|Ann|ex A: Overview of the Maine and New Hampshire Municipal Bond Banks ......................... 22|
|Ann|ex B: State Revolving Funds .................................................................................................. 24|
|Ref|erences ..................................................................................................................................... 26|

# **Figures and Tables**

|Figure 1 Municipal Bond Bank Funds Flow Model ................................................................................... 17|
|---|
|Table 1 Credit Ratings of Municipal Bond Bank Bonds ............................................................................ 11|
|Table 2 Sample Pooled Bond Structure ...................................................................................................... 14|
# **5 Results**

Section 2 highlighted the need to go beyond average effects in studying services trade agreements: due to the often asymmetric and nondiscriminatory nature of services liberalization, services RTAs do not necessarily provide substantial preferential treatment to partner countries. Taking this into account, it could be expected that having an RTA in services, on average, does not have a significant effect on GVC-trade or bilateral trade flows in general. Indeed, the results shown in Table 3 find basically no additional effect of having a services agreement on trade flows.

Table 3: Services RTAs and trade flows (average effect)

||(1)|(2)|(3)<br>GVC-exports|(4)|
|---|---|---|---|---|
||Gross exports<br>(total)|Intermediate<br>goods|Backward<br>(sourcing)|Forward<br>(selling)|
|RTA|0.130<sup>_∗∗∗_</sup>|0.133<sup>_∗∗_</sup>|0.124<sup>_∗∗_</sup>|0.138<sup>_∗∗_</sup>|
||(0.0386)|(0.0439)|(0.0446)|(0.0490)|
|SERV|-0.0208|-0.0190|-0.00370|-0.00440|
||(0.0390)|(0.0437)|(0.0453)|(0.0470)|
|Obs.|12320|12320|12320|12320|

Standard errors clustered by country pairs in parentheses. All specifications include exporter-year, importer-year, and country pair fixed effects, and are estimated using PPML.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001 The lack of the effect of services trade agreements may seem discouraging, but is in fact not surprising for the reasons described above. However, this does not mean that bilateral or regional agreements in services trade do not matter. Despite the lack of average effect, services agreements affect trade flows and GVC participation of country pairs in a heterogeneous matter depending on the countries’ income level or the specific provisions included in the services trade agreement.

## _Heterogeneous effects of services RTAs by income group_

The discussion in Section 2 conjectures an asymmetric impact of services trade liberalization for developed and developing economies. Consistent with the intuition, the empirical results in Table 4 show that the effect of services agreements are heterogeneous across income groups, with a significantly positive impact on exports and especially GVC-exports for developing countries. Compared to the baseline (NN), services agreements increase GVC-exports from developing to developed countries (SN) as well as between developing countries (SS), and the increase is proportionately larger than for total gross exports. In contrast, services agreements gener19
3,600 adult men and women having complete data in the panel (as compared to 3,900 and 5,200.) There were clear signs of lagged effects. Liver, kidney and CGI problems in 2000 affected male _SAH_ in 2002, though the effects were only significant at the 10% level. Spinal problems in 2000 had an effect on women’s (but not men’s) _SAH_ in 2002, and the effect was significant at the 5% level. Stroke and TB in 2000 lowered men’s _SAH_ in 2002, as did high blood pressure for women, though these effects were only significant at the 10% level.

The predicted values of _SAH_ scores from Table 3 can be interpreted as a weighted mean of the various specific health indicators in our data, in which the weights are chosen to give best fit with overall health-status. (We refer to the product of the probit regression coefficients for _SAH_ and the regressors as the “ _SAH_ score” and similarly for _SAW_ .) Figure 4 plots this weighted mean against normalized expenditure. We find only a small positive income effect.

We assume that _SAH_ and _SAW_ are functions of household expenditures, respondent income and a range of individual and household socio-economic characteristics that are likely to shift preferences, budget constraints and/or health production functions. In the case of _SAH_ , one can interpret this as a reduced form model, in which the specific health indicators in Table 3 are taken to be functions of these variables. In the case of _SAW_ , we interpret the “non-income” covariates as proxies for two main factors: differences in “needs” at given current income and differences in past or future living standards not captured in current incomes or expenditures.

Table 4 gives the OP for 2002 using as regressors a cubic polynomial of real expenditure, individual income, household size and demographic composition, age, education, unemployment and regional dummy variables. Similar results for all other variables of interest were obtained on dropping individual income and employment (given the aforementioned endogeneity concerns).

18
An additional important variable to explain learning achievement in public schools was school supervision by the principal and supervisor. The frequency of supervisors’ school visits has a significant and positive correlation with student learning. It is also consistent with the PARE experience, which indicates that the quality of supervisors and their frequency of school visits had significant and positive effects on student test scores (World Bank 1998). The type of post assigned to the teacher (short and long term) has a negative impact on learning achievement (mathematics test scores), particularly in urban areas. Students in schools with a high degree of supervision on the part of the school principal achieve better scores. Thus, differences in school organization and management could be important for student achievement. In this study it was found that the availability and maintenance of school facilities have a very modest impact on learning achievement.

Additionally, the impacts of each explanatory variable in elasticity terms were computed in order to compare the quantitative effects among all explanatory variables. As can be seen in Table 8, variables with the highest elasticity values include supervision, teacher's enrollment in _Carrera Magisterial_ and teacher's interest in students' learning.

# **_Carrera Magisterial_**

_Carrera Magisterial_ was aimed at raising the quality of basic education through teachers’ career development, presence in schools, and working conditions. This program represents an effort on the part of the government to provide better support for and recognition of the valuable work of teachers.<sup>9</sup> Results from the multivariate regression model show that at the national level and particularly in rural areas, enrollment in _Carrera Magisterial_ positively impacts learning achievement. Notice that being in the _Carrera Magisterial_ program increases a students achievement in mathematics by 1.87 percent (3.31 percent in rural areas—see Table 5 in annex). However, the level in _Carrera Magisterial_ is negatively correlated with learning achievement. Ultimately, the program may have good components that promote better teaching practices, but there is a pervasive incentive affecting teacher promotion. Results show that a large share of the teachers in basic education are relatively old and work in administrative tasks.

> 9 The _Carrera Magisterial_ Program, which contains several parts, is governed by the Comisión Nacional Mixta consisting of officials of the SEP and SNTE.

20
**Table A1: Least and Most Female-Intensive Sectors, U.S. Data**

|Least Female-Intensive|_FLi_|Most Female-Intensive|_FLi_|
|---|---|---|---|
|Logging|5.4|Other apparel and accessories|56.3|
|Coal Mining|5.7|Leather tanning and fnishing|56.3|
|Cement, concrete, lime, and|10.3|Retail bakeries|58|
|gypsum||||
|Sawmills and wood|11.3|Specialized design services|58|
|preservation||||
|Nonmetallic mineral mining<br>and quarrying|11.5|Cut and sew apparel|66.1|

**Appendix Table A2: Summary Statistics, U.S.-based measure of** _FL_

||Mean|Min|Max|SD|N|
|---|---|---|---|---|---|
|Manufacturing|29.7|10.3|66.1|13.6|78|
|Non-manufacturing|25.8|5.4|58|15.6|15|

36
# **Appendix  A.: Tariff calculations at the detailed tariff line level**

## **<u>Table A1.</u>** **_<u>Tariff rates at 2 digit level</u>_**

|**HS**||||Year|||
|---|---|---|---|---|---|---|
|**code**|**_Type of products_**|2001|2002|2003|2004|2005|
|**2digits**|||||||
|||70<sup>a</sup><br>|70|70|70|70|
|1|_LIVE ANIMALS_|3.9<sup>b</sup>|3.9|3.9|3.9|3.9|
|||_(2.1)_<sup>_c_</sup>|_(2.1)_|_(2.1)_|_(2.1)_|_(2.1)_|
|||256|256|292|292|300|
|2|_MEAT EDIBLE OFFAL_|20.2|20.8|29.3|28.3|29.1|
||_,_|_(6.9)_|_(7.4)_|_(21.3)_|_(19.4)_|_(20.7)_|
|||338|338|338|339|339|
|3|_FISH CRUSTACEANS_|10.3|11.2|10.4|10.2|10.1|
||_,_|_(4.0)_|_(20.5)_|_(4.9)_|_(2.9)_|_(1.6)_|
|||176|176|176|176|176|
|4|_DAIRY PRODUCTS_|16.2|15.9|16.1|16.1|15.8|
|||_(4.6)_|_(2.8)_|_(3.7)_|_(4.0)_|_(3.7)_|
|||33|33|33|33|33|
|5|_ANIMAL PRODUCTS NES_|8.2|8.2|8.2|8.2|8.2|
|||_(2.4)_|_(2.4)_|_(2.4)_|_(2.4)_|_(2.4)_|
|||42|42|42|42|42|
|6|_LIVE TREES PLANTS_|14.8|15.3|16.0|15.2|14.6|
||_,_|_(10.2)_|_(8.6)_|_(10.2)_|_(8.5)_|_(7.4)_|
|||121|121|121|121|121|
|7|_EDIBLE VEGETABLES_|14.9|16.0|16.6|16.5|15.9|
|||_(0.9)_|_(3.9)_|_(5.4)_|_(4.6)_|_(3.2)_|
|||137|137|137|143|143|
|8|_EDIBLE FRUIT NUTS_|6.9|10.3|11.2|10.6|10.3|
||_,_|_(3.3)_|_(9.3)_|_(11.9)_|_(10.1)_|_(9.7)_|
|||42|46|54|54|54|
|9|_COFFEE TEA SPICES_|6.0|6.7|7.4|6.9|7.0|
||_, ,_|_(3.8)_|_(6.0)_|_(5.9)_|_(4.3)_|_(4.8)_|
|||57|57|57|57|57|
|10|_CEREALS_|8.0|8.0|11.1|9.9|9.0|
|||_(2.5)_|_(2.5)_|_(9.4)_|_(5.9)_|_(4.1)_|

25
classification that happens to have an almost one to one classification to the ISIC rev 2. industrial classification.

Following equation (4), we interact the factor requirement with it’s wage. We used GDP per capita as a proxy of wage. This information was obtained from the World Development Indicators. Finally, capital requirement was interacted with a time trend and country fixed effects.

It is important to acknowledge that the availability of the Input Output tables implies a constraint on the number of countries that can be used to estimate the first stage implied by equation (4). A second constraint is given by the lack of time variation 9 of the IO tables because they are performed once in a while . Even though we have this two limitations, we expect that the variation across sectors and countries will enable us to correctly identify equation (4).

# **4 Empirical Results**

We know turn to our estimates of US import elasticities. In Table 4 the mean elasticity of substitution is estimated using Equation (1) and setting σ _s_ = σ for all sectors, but allowing for changes in US sector expenditures over tim (sector-year dummies). In e other words, all sectors have the same within constant elasticity of substitution, but the 10 elasticities between sectors could be different. To recapitulate, " _CIF Import Value_ " is the (log) CIF value of US's imports of commodity _j_ in sector _s_ from country _c_ at time _t_ ; " _CIF price_ " is the (log) instrumented CIF price paid by US consumer on imported commodity _j_ . All regressions include unreported country-product, country-year and

> 9 For specific information of the year of each IO table, see the GTAP manual, version 5.

The last two terms on the right hand are captured by the sector-year dummies.

10
multiple components of the stimulus, we aggregate the three programs into a single binary participation variable for the analysis.<sup>3</sup> Because our participation variable is imputed based on eligibility criteria rather than measured directly – the SES did not ask about participation in these programs – the effects we measure in the analysis are more of an intent to treat rather than an actual impact of participation. We are therefore examining the potential effect of the stimulus as if everyone who was eligible also received benefits under the program.<sup>4</sup> Aggregate statistics do show, however, that most eligible households participated in the stimulus.

Within some rounds, the SES data also collected information on households‘ perceptions of the economy. In the first six months of 2008 and 2009, for example, households were asked in the SES whether they believed the economy was in a worse situation compared to a year ago. Figures 4 and 5 show that just prior to the onset of the financial crisis in late 2008, urban and rural households across the board believed that the economy was worsening compared to the same point in 2007. By June 2008, less than 15 percent of rural and urban households both felt that the economy was doing better compared to 2007, a drop from about 25 percent in January of 2008. Rising oil and commodity prices helped fuel these sentiments, as indicated by Table 2, which shows that in 2008 price control was among the most important concern for households. By 2009, a greater share of households (about 35 percent) felt that the economy was doing better compared to in 2008, although this share slipped somewhat and flattened by mid-2009.

In this context, we can examine the coping strategies households adopt in a crisis. Table 2 presents summary data on how households expected to cope during a crisis, and shows that during the first half 2008 they were overwhelmingly inclined to reduce spending and limit their borrowing if

> 3 As reflected in Table 1, households might also be participating in similar programs (with similar eligibility criteria) as SP1 prior to March 2009.  In the analysis, therefore, we tested for whether the effect of the stimulus was any different if we also controlled for whether the household matched any of the eligibility criteria across all years (as a proxy for whether they would be participating in any similar programs at any other point of time). We found that the effect of the stimulus was unchanged.

> 4 This is a common problem with many impact evaluations, where even if a household is targeted they may not participate (or the extent of participation may vary among program households).

11