rafmacalaba/datause-encoder-data huggingface dataset:
_Question 20b: Transaction costs versus uncertainty in complying with government regulations_ This question was linked to the preceding one. Entrepreneurs who did not invest because of government regulations were queried whether their decision was based on regulations that were too expensive (but clearly known) or on regulations that were too unclear and uncertain to prevent reasonable investment planning.

If your answer was "yes", **could you** please specify which of the following two options better describes the nature of these problems: Costs of compliance are _too high,_ but clearly known L Costs of compliance are _too uncertain_ for investment planning **I** Other **I**

**Known**

**Unknown**

<!-- Start of picture text --> 82~~~~~~~~~~~~~~~~~~~~~~~~~~~2<br>82<br>15<br>MUB L DC ccl 9A "A CE C S Gs Va L cc SEX M E LAC S as<br>Percent of firms which said that costs of compliance Percent of firms which said that costs of compliance<br>are too high, but clearly known are too uncertain for investment planning<br><!-- End of picture text -->

**Percent of firms which said that costs of compliance are** **_too uncertain_ for investment planning**

In the developed countries costly regulations and red tape hinder investment. 40 percent of entrepreneurs responded that they had not invested because cost of compliance where too high, albeit clearly known. In developing countries the problem of high but certain cost is less important; on average less than 20 percent of businessmen responded in this way. On the other hand, almost 70 percent of entrepreneurs in developing countries said that they had not invested because the costs of compliance with government regulations where too uncertain to make for investment planning.

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collected rate was 9.5% in 2001, 9.7% in 2002, and 9.8% in 2003. The difference can be attributed to the fact that we did not take into account any exemptions other than the CIS free trade zone exemption.<sup>16</sup> We believe collected tariff rates more closely approximate the protection a sector receives and the incentives it faces. Using our estimated collected tariff rates, and based on a Rosstat mapping from the tariff line data of the Customs Committee to the sectors in our input output table, we calculated a weighted average tariff rate for the sectors of our model. The results of this procedure for each sector of our model are reported in table 12a.

Export tax rates are calculated from the 2001 input-output table of Rosstat and are reported in table 12a. Since we do not change export taxes in the counterfactual simulations, these parameters are less important to the results than the tariff rates.

# **Input-output tables**

The core input-output model is the 2001 table produced by Rosstat. The official table contained only 22 sectors, and importantly has little service sector disaggregation. In order to disaggregate the table, we used costs and use shares from our 35 sector Russian input-output table for 1995 prepared by expert S. P. Baranov. (For details see <u>www.worldbank.org/trade/russia-wto.) When we broke up a sector such as</u> oil and gas into oil, gas and oil processing, we assumed that the cost shares and use shares of the sector were the same in 2001 as they were in the 1995 table. For example, steel is an input to the oil and gas sector. Suppose in 1995, that oil purchased 55 percent of the steel used in oil and gas. Then we assume that in 2001, oil purchased 55 percent of the steel used in oil and gas.

# **Regional IO tables**

We generated input-output tables for the regions based on output data from the regions and the national input-output table. For each industrial sector we took national output from the national inputoutput table for 2001, and we used the data in Regions of Russia to allocate the shares of that output across regions. That is, from the Regions of Russia 2001, we have, by region; industry shares of output for the year 2000 (table 13.3); thousands of tons of oil recovery, including gas condensate, for the year 2000 (table 13.13); extraction of natural gas (in millions of cubic meters for the year 2000, table 13.14); thousands of tons of mined coal for the year 2000 (table 13.15). We also have external exports and imports by region, as well as the commodity structure of exports and imports by region for the year 2001

> 16 To calculate actual collected rate, we used the Ministry of Finance data on collected import duties as a numerator. As a denominator, we used the overall import volume less import from Belarus as reported by the Russian Customs Committee. The exclusion of the imports from Belarus is determined by the fact that the electronic dataset which we used in the calculations reported import volume without imports from Belarus.

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Figure 41.5. Fiscal Impulse of Federal Government (% change and deviation from potential output) <!-- Start of picture text -->
1.0<br>0.5<br>0.0<br>Fiscal  Impulse\<br>-1.5  _  Cyclical Compornent  of GDP<br>-- z  ~  'z  'U<br><!-- End of picture text -->

> Figure 4.6. Blanchard's Indicator of Fiscal Impulse

> **(Percentage of GDP)**

<!-- Start of picture text -->
1.5<br>0.5<br>0.0<br>n  i  ~~~~0 0u olo  a  -5  C* 0  (a 0o  Zod<br>-0.5  - Fis  Impulse<br>-1.0<br>.1.5<br><!-- End of picture text -->

> **Source:** Our elaboration using data from Ministerio de Economia de la Republica Argentina.

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exclusively until they reach the age of 6 months (see Column 1). Given that initially 83.1 percent of the mothers knew about breastfeeding practices, this represents an increase of 8 percent. Dietary diversity or consumption of more than four out of the seven food groups<sup>19</sup> among children older than 6 months also improved but not significantly. We find a significant improvement in the incidence of wasting – or weight-for-height – among the cohort of children aged 10 to 22 months at the time of enrollment:<sup>20</sup> wasting was reduced by 12.5 percentage points, which is a reduction of about 40 percent of wasted children in this age group. On the other hand for children aged between 22 to 46 months years of age at the time of enrollment,<sup>21</sup> the impact was not significant. We do not find any significant impact on the incidence of stunting and underweight, neither at the aggregate level, nor by age group. These results are consistent with studies that suggest that even if wasting outcomes are affected in the short-term, the incidence of stunting and underweight need a longer-term intervention (UNICEF, 2013).

**Table** **<u>7: Impact of</u>** **_<u>Shombhob</u>_** **<u>on nutrition outcomes</u>**

||**Mother's**<br>**knowledge**<br>**about**<br>**infant**<br>**feeding**|**Dietary**<br>**diversity**<br>**(6+**<br>**months)**|**Stunting**|**Under**<br>**weight**|**Was**<br>**22-46 months**<br>**when**<br>**enrolled**|**ting**<br>**10-22 months**<br>**when**<br>**enrolled**|
|---|---|---|---|---|---|---|
|It f_Shbhb_δ|0.063*|0.031|0.034|0.046|-0.036|-0.125*|
|mpac o_omo_()|(0.03)|(0.05)|(0.05)|(0.05)|(0.04)|(0.07)|
|(TC) t bli (β)|||-0.015|-0.023|0.059**|0.103**|
|- a asene|||(0.04)|(0.04)|(0.03)|(0.05)|
|Trend for non-recipients|||0.060|-0.083**|-0.086***|-0.079|
|(γ)|||(0.04)|(0.04)|(0.03)|(0.05)|
|Constant|||-0.222|-0.001|0.256|0.120|
||||(0.43)|(0.42)|(0.35)|(0.37)|
|Controls|||Yes|Yes|Yes|Yes|
|R2||0.117|0.052|0.036|0.049|0.093|
|Df|||1,536|1,594|1,552|606|
|Number Obs.|1,538|1,318|1,580|1,638|1,596|648|

_Significance levels: * p<0.10, ** p<0.05, *** p<0.01.Controls include: household head gender, age, education, household size, housing characteristics, asset ownership, remittances._ These results also lend some support to the widely held view that nutrition interventions

> 19 A standard practice recommended by Infant and Young Child Feeding Practices (IYFP).

> 20 Due to delays in program implementation, children aged 0-12 months at the time of the baseline survey were 10-22 months when the pilot implementation actually started.

> 21 Similarly, these children were 12-36 months old at the time of the baseline survey.

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Table 2: Imports of unprepared poultry (million US$, current)

|Year|Japan|Russia|European<br>Union|Arabian<br>Peninsula|Hong<br>Kong<br>(China)|Mexico|Others|Total<br>Trade|Total<br>Trade<br>(million<br>Tons)|
|---|---|---|---|---|---|---|---|---|---|
|1995|1,320|490|363|393|747|174|752|4,238|2,530|
|1996|1,301|509|477|379|833|213|1,131|4,844|3,770|
|1997|1,030|815|450|382|885|245|1,262|5,069|4,080|
|1998|963|563|418|408|806|226|1,291|4,675|4,550|
|1999|1,000|154|408|661|908|198|1,624|4,953|4,840|
|2000|899|366|502|674|835|257|1,652|5,185|5,480|
|2001|881|756|894|750|761|298|1,721|6,060|5,950|
|2002|959|816|722|675|650|246|1,729|5,795|6,180|
|2003|815|698|892|746|639|327|2,002|6,118|6,330|
|2004|749|663|860|805|520|386|2,163|6,146|5,660|
|2005|924|848|931|866|579|533|2,455|7,136|6,140|
|2006|743|922|863|864|660|599|2,764|7,413|6,496|

Source: COMTRADE database.

Note: Excludes intra-EU trade. Arabian Peninsula include: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia. United Arab Emirates and Yemen.

The impact of avian flu on international trade flows is more evident by examining the evolution of the exports of major exporters. Table 3 shows the rapid shifting in overall exports of unprepared poultry from HPAI affected countries to HPAI free countries after 2003. Thailand has been the most affected by these shifts. Thai exports of unprepared meat decreased from more than 600 million US$ in 2003 to almost zero in 2005. With a loss of about 300 million US$, China was also badly affected, especially in its export to Japan. Overall Chinese exports did not decreased as dramatically as those of Thailand because Chinese suppliers were able maintain a presence only in the Hong Kong (China) market by relying on regionalities.

As the overall demand in major importing countries was not substantially affected by HPAI, the loss of Southeast Asian suppliers was replaced by increases in domestic production and shifts in imports to HPAI free countries. Among these, Brazil has been 11
# **VII. Bibliography**

Ansar, S., A. Demirguc-Kunt, J. Hess, L. Klapper, & D. Singer. 2017. _The Global Findex Database: Measuring Financial Inclusion and the Fintech Revolution._ World Bank Publications, The World Bank Group: Washington, DC.

Asia-Pacific Economic Cooperation Secretariat (APEC). 2013. _Economic Impact of Submarine Cable Disruptions_ . APEC: Singapore.

Bachas, P., P. Gertler, S. Higgins, & E. Seira. 2018. “Digital Financial Services Go a Long Way: Transaction Costs and Financial Inclusion.” _American Economic Association Papers and Proceedings_ 208: 444-448 _._ Durbin, J. 1954. “Errors in Variables.” _Review of the International Statistical Institute_ 22:2332.

Garibaldi, P., & G. Violante. 2005. “The Employment Effects of Severance Payments with Wage Rigidities.” _Economic Journal_ 115: 799-832.

Geere, D. 2011. “How the first cable was laid across the Atlantic.” _Wired UK_ , < https://www.wired.co.uk/article/transatlantic-cables> Goldfarb, A. & C. Tucker. 2019. "Digital Economics." _Journal of Economic Literature_ 57: 3-43. Greene, H.W. 2003. _Econometric Analysis: Fifth Edition._ Pearson Education, New York University: New York.

Hausman, J. 1978. “Specification Tests in Econometrics.” _Econometrica_ 46: 1251-71.

Higgins, S. 2019. “Financial Technology Adoption.” _Northwestern University: Kellogg School of Management._ Hjort, J. & J. Poulsen. 2019. “The Arrival of Fast Internet and Employment in Africa.” _American Economic Review_ 109: 1032 - 1079 _._ International Labor Organization. 2019. “Indicator Description: Unemployment Rate”. ILOSTAT, < https://ilostat.ilo.org/resources/methods/description-unemploymentrate/> International Telecommunications Union. 2019. “Country ICT Data.” ITU, < https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx> Jack, W. & T. Suri. 2014. “Risk Sharing and Transaction Costs: Evidence from Kenya’s Mobile Money Revolution.” _American Economic Review_ 104: 183 - 223.

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There are also extreme differences across the income distribution in access to electricity. The general trend is increasing for successively higher income quintiles, indicating the regressive nature of electrification in urban and rural areas (Figure 7.3). In metropolitan Haiti a high and fairly equal access to electricity across the income distribution exists.

Figure <u>7.3: Access to Electricity by Income Quintile in Haiti, 2001 (percent)</u> <!-- Start of picture text --> 100<br>90<br>80<br>70 Metropolitan<br>Urban<br>60<br>Rural<br>50<br>40<br>30<br>20<br>10<br>0<br>1(poorest) 2 3 4 5(richest)<br><!-- End of picture text --> _Source:_ Own calculations based on HLCS 2001.

Figure <u>7.4: Access to Water by Income Quintile in Haiti, 2001 (percent)</u> <!-- Start of picture text --> 50<br>45 Metropolitan<br>40 Urban<br>Rural<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1(poorest) 2 3 4 5(richest)<br><!-- End of picture text --> _Source:_ Own calculations based on HLCS 2001.

42
cognitive and socioemotional and that digital skills are associated to some degree with cognitive and socioemotional skills. Pairwise correlations among those subsets confirm those patterns. Similar patterns emerge for Vietnam, Cambodia, and Thailand.

Our estimations have three main limitations. First, we proxy a skills profile for 20 percent of occupations that are not sufficiently listed in the job postings data set used to create a skills profile. These occupations represent a sizeable share of employment in the four studied countries: from 14 percent in Malaysia to 43 percent in Cambodia. We address this shortcoming by imputing skills profiles for these occupations based on similar occupations. Second, we derive skills use from requirements in job postings, which may omit skills that employers implicitly require rather than explicitly request. Employers do not specify the level of skills proficiency or use, so we equally weight all skills listed in the job posting. As a result, our skills profiles may not be complete, particularly omitting lower-level skills or under- (or over-) weighting the importance of certain skills. Third, we may be over-estimating the degree of digital-ness of the 127 occupations. Since we draw our skills data from on-line jobs vacancy postings, the jobs within each of the 127 occupations that are listed on the internet – and used to construct a skills profile that is a proxy for all jobs within an occupation – may be those that are more inclined to use digital technologies.

Overall, our findings show that all occupations require digital, cognitive, and socioemotional skills, suggesting that policy makers may need to reform the “basic package” provided by education systems in response to digital technologies in the workplace. Education system still need to teach technical and language skills, since those are in very high demand by employers. However, they may also want to rethink the “basic package” of education, namely, to continue teaching cognitive skills, increasing emphasis on socioemotional skills, and adding basic digital skills to the general curriculum. Learning to learn will become increasingly important as new technologies enter the workplace and workers need to adapt to them.

Finally, while advanced digital skills are increasingly emphasized in policy circles, they are only needed for a very small subset of jobs. The growth in demand for these jobs is growing very slowly in Southeast Asia, as comparison to the US. Even as economies grow into the next level of development, advanced skills will still be a minority of the skillset employers value. So, while advanced digital training may be a goal, the reality of the evolving landscape of jobs and technologies is more modest with a continued demand for the traditional cognitive, the increasingly explicit socioemotional, and the newest basic and intermediate digital skills.

36
interaction terms of a change in terms of trade with three state capacity variables (democracy, state fragility, and resource dependency) are also included later in Table 4 to test Hypothesis 7.

# _5.2.  Cross-country Regression_

Second, I use country-year panel data of contractual disputes and macroeconomic variables to highlight how each macro factor explains breach of contract over time.

where yct is a binary variable (disputes happened or not) to estimate the probability that disputes happened in country _c_ in year _t_ . As shown in the summary statistics of the country-year panel, about 9% of my countryyear observations experienced disputes in the past. Many disputes occurred during the 2002 financial crisis in Argentina (maximum disputes during that crisis was 34). The pooled OLS is applied with regional fixed effects κ and a vector of dummies for sectoral affiliations Dj as in Eq. (1).<sup>15</sup> In Eq. (3), Xc,t includes three types of macro shocks: changes in real GDP growth, terms of trade shock, and natural disaster to test Hypothesis 3. Natural disaster is a dummy which takes one if some types of natural disasters caused fatalities in each country-year. All these shocks would create substantial economic pressures in the local economy, and at the same time they could worsen the state capacity of the affected countries (see Miguel et al (2004) for the effect of climate shocks on local security situation). It is expected to affect the government's stance for the foreign investors in the country, leading to higher risk of BOC. To address lagged effects of these shocks on breach of contract, one-year lags of Xc are controlled.

Gt includes political regime and state fragility measures from Polity IV. Two resource dependency measures (aid and natural resource rents) are also included to test if higher resource dependency increases BOC risk as predicted by the resource curse story. Log of GDP per capita in 2000 is included to control for level of development. To remove non-linear time trend of dispute histories (as shown in Figure 1), linear and square terms of year trends are included in all specifications.

# **6. Results**

# _6.1. Obsolescing Bargaining_

> 15 A simple linear probability model is used to interpret within-country estimates in more straightforward manner, rather than using other non-linear panel model with unobserved heterogeneity.

14
they capture virtually the same context as characteristics of some households change slowly over time.

These _Xhm_ sets include characteristics of individuals, households, and dwellings. The set _Zm_ considers some of these variables to be aggregated at the municipality-level along with data on the coverage and availability of public services and infrastructure.<sup>7</sup> This helps raise the precision of the estimates by minimizing the ratio of the variance of the error _ηm_ relative to the variance of the total error _μhm_ , that is, the share of the variance of errors that results from unexplained differences across municipalities. The parameters of equation (1) are estimated using the whole sample in each round of the ENIGH. The income distribution is then simulated based on 200 repetitions in the corresponding census dataset, each covering Mexico’s total population. The only exception is the census data in 2015, which represent a sample of 5.9 million households; yet, this dataset has sufficient statistical power to provide reliable statistics at the municipality level.

Based on the simulated income distribution, poverty and inequality indicators were computed across municipalities and validated through several tests.<sup>8</sup> The income concept used is household net per capita income, which includes labor income, income from businesses owned by the household, nonlabor income, such as public and private transfers, and an estimate of the imputed rent of owner-occupied dwellings, selfconsumption, and in-kind transfers and gifts received. The measurement of poverty was based on the Foster et al. (1984) family of indexes by comparing this income concept with three poverty lines: food poverty, defined as the inability to acquire a basic food basket; capabilities poverty, defined as the inability to cover the value of the food basket, plus expenditures on health and education; and assets poverty, defined as the inability to acquire the latter plus expenditures on clothing, housing, and transportation. The municipalities’ inequality levels were computed through an array of well-known indicators such as the Gini coefficient.

This exercise yielded a robust, novel municipality-level dataset with income-based indicators that are comparable both over time and across 2,361 municipalities on which it was possible to compute reliable estimates on each data point over time. These municipalities represent 96 percent of Mexico’s current municipalities and cover approximately 98 percent of the country’s population. Summary statistics derived from

> 7Some of these variables are derived from the census datasets, while others are taken from administrative records collected in the National System of Municipal Information (SNIM for its acronym in Spanish) and the National Institute of Statistics and Geography (INEGI, for its acronym in Spanish).

> 8A detailed description of the small area estimation methodology used on each data point is available from the authors upon request.

9
of government intervention in trade has declined. Similar conclusions have been reached by many recent studies (Drabek and Laird, 1998; Finger and Schuknecht, 1999). This analysis pernits us to document them with individual country details collected on a systematic basis.

The timing of liberalization varied: In some countries, for example, Bolivia, Chile and Morocco the bulk of the reforms occurred in the 1980's; in others, such as Brazil, Dominican Republic and Zambia they occurred in the early 1990's and were then consolidated in the context of the URA. In still others limited progress has been made in recent periods.

At the same time the TPRs help identify the remaining issues in the reform agenda and some of the new challenges faced by developing countries. For example, a lot of the tariff bindings are at levels much higher than applied tariffs, creating a degree of uncertainty to exporters wishing to access these countries' markets as well as an opportunity for resurgent protectionism; while the overall use of nontariff measures has declined, the use of certain trade remedy measures such as anti-dumping is on the increase; Moreover, there is rising evidence of the difficulties institutions of developing countries, especially LDCs, are encountering in implemenfing WTO commitments in new areas such as Traderelated Intellectual Property Rights (TRIPS), Sanitary and Phytosanitary Measures, (SPS) and Technical Barriers to Trade (TBT).

The next two parts of this section are devoted to a review of developing countries' trade policies that directly affect imports and exports. In each case, there is a discussion of institutional issues. But no effort has been made to discuss other policies that affect trade, especially exchange rate or macroeconomic policies or domestic subsidies or taxes. In cases where more than one TPR has been prepared it is possible to document the changes relatively precisely and show some of them in quantitative terns. In others, the TPRs provide us with snapshots of the situation at the time the most recent TPR was prepared.

9