rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6. FDI inflows, average 2001-03**

<!-- Start of picture text --> % o f GD P<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Poland Hungary Czech Slovakia Bulgaria*<br>Rep<br><!-- End of picture text -->

**Figure 7. Trade Integration, average 2001-03**

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>Poland Bulgaria* Czech Hungary Slovak<br>Republic Republic<br><!-- End of picture text --> Source: WIIW and Eurostat *Data for Bulgaria refer to 2003-05 Source: Eurostat

*Data for Bulgaria refer to 2003-05

Examining the various component indicators of outward-oriented policies sheds light on the sources of the gap between Bulgaria and OECD CEE countries. These include explicit barriers to trade and foreign investments such as tariffs, discriminatory procedures against foreign firms and foreign ownership barriers, as well as regulatory barriers such as the absence of international harmonization or mutual recognition agreements.

While Bulgaria has achieved best practice standards in incorporating in legislation the principle of non-discrimination against foreign parties and has eliminated all other regulatory barriers to trade and investment (discussed in 1.3), progress can still be made in terms of tariffs and remaining obstacles to foreign investments.

# **_1.1 Tariffs_**

First generation reforms rapidly succeeded in eliminating all quantitative restrictions and the state monopoly over foreign trade (Kaminski, 2006) and the EU Eastern Enlargement project rapidly led to Bulgaria’s participation in a European free trade area. As a result Bulgaria’s tariff levels (measured here as MFN tariffs on industrial goods) are much lower in 2006 than those of comparator middle income countries and even slightly lower than some of the pre-accession OECD CEE (Figure 8). In particular, Hungary and Poland entered the EU with substantially higher tariff levels than Bulgaria’s in 2006. In addition, Bulgaria’s tariff levels have been lowered to those of the EU common market as of its entry into the EU as of January 1, 2007.

14
household’s production problem is to maximize revenues (since production efficiency is a necessary condition for utility maximization).<sup>14</sup> Catfish income is the product of catfish farm-gate prices, _p_<sup>_c_</sup> and catfish production, _qh_<sup>_c_.Similarly,agricultural income is the product</sup> of agricultural prices _p_<sup>_a_</sup> and quantities _qh_<sup>_a_.Householdincomeisthengivenby</sup> where _L_<sup>_i_</sup> _h_<sup>_, K_</sup> _h_<sup>_i_denotethequantitiesoflaborandcapitalallocatedtotheproductionofgood</sup> _i, i_ = _a, c._ Figure 3 presents a schematic representation of the equilibrium in household production. At any point in time, the (fixed) inputs available to the household (capital, labor, etc.) define a production possibility frontier, represented by the curve _ca_ , between catfish (denoted with _c_ in the horizontal axis) and agriculture (denoted with _a_ in the vertical axis). For given prices, efficiency in production requires tangency between the relative prices and the slope of this frontier. We assume that _p_<sup>1</sup> is the initial relative price of catfish and that, before the imposition of the tariff, inputs are allocated optimally, so that production is at point _q_<sup>1</sup> .

The imposition of AD duties implies an exogenous change in the relative price of catfish. Keeping everything else constant, optimality requires tangency between the frontier _ca_ and the new price vector. In Figure 3, when catfish prices decline to _p_<sup>2</sup> , production allocation would shift to _q_<sup>2</sup> . To study the welfare consequences of such a price change, differentiate (1) as follows: In a first best situation, and for a small price change, the last two terms cancel out because, with efficiency in production, _dqh_<sup>_a/dq_</sup> _h_<sup>_c_=</sup><sup>_−pc/pa_.Insuchcase,thewelfareanalysiscanbe</sup> based on the following first order approximation, popularized by Deaton (1989):

> 14 See Benjamin (1992) or Singh, Squire and Strauss (1985) for full models of optimizing agricultural households.

15
- “Moderate to high saline” river area is likely to increase from 8 percent at the baseline to 17 and 27 percent in the best and worst case scenarios, respectively.

- “Highly saline” river area is expected to increase from 35 percent at the baseline to 38 and 40 percent in the best and worst case scenarios, respectively.<sup>35</sup>

> 35 In the National Water Management Plan, the Water Resources Planning Organization in Bangladesh has categorized surface water salinity into the following groups: (i) <1dS/m: slightly saline; (ii) 1–5 dS/m: slightly to moderately saline; (iii) 5–10 dS/m: moderately to highly saline; and (iv) > 10dS/m: highly saline. This classification is for average dry season values (WARPO 2001). The conversion factor of dS to ppt depends on temperature. 1dS = 1.75 ppt has been used as a conversion factor in this analysis from the readings of salinity meters in coastal Bangladesh.

23
# **References**

- Barth, J., Caprio, G. and Levine, R. 2004, ‘Bank Regulation and Supervision: What Works Best?’, _Journal of Financial Intermediation_ , 13, pp. 205–48.

- Brown, D., Deardorff, A.V. and Stern, R.M. 1997, ‘Some economic effects of the free trade agreement between Tunisia and the European Union’, in A. Galal and B. Hoekman (eds), _Regional Partners in Global Markets: Limits and possibilities of the Euro-Med Agreements_ , Centre for Economic Policy Research, London, pp. 71–97.

- Clark, X., Dollar, D. and Micco, A. 2004, ‘Port efficiency, maritime transport costs, and bilateral trade’, _Journal of Development Economics_ , 75, pp. 417–50.

- Conway, P., Janod, V. and Nicoletti, G. 2005, ‘Product Market Regulation in OECD Countries: 1998 to 2003’, ECO/WKP(2005)6, OECD, Paris.

- Copenhagen Economics 2005, _Economic Assessment of the Barriers to the Internal Market for Services_ , Copenhagen Economics, Copenhagen.

- Dee, P. 2003, ‘Modelling the policy issues in services trade’, _Économie Internationale_ (94-95), pp. 283–300.

- Dee, P. 2005, ‘A Compendium of Barriers to Services Trade’, prepared for World Bank, available at <u>http://www.crawford.anu.edu.au/pdf/Combined_report.pdf.</u>

- Dee, P. 2007, ‘East Asian economic integration and its impact on future growth’, _The World Economy_ , 30(3), pp. 405–23.

- Dee, P. and Hanslow, K. 2001, ‘Multilateral liberalization of services trade’, in Stern, R. (ed.), _Services in the International Economy_ , University of Michigan Press, Ann Arbor, pp. 117–39.

- Dee, P., Hanslow, K. and Phamduc, T. 2003, ‘Measuring the cost of barriers to trade in services’, in Ito, T. and Krueger, A. (eds), _Services Trade in the Asia-Pacific Region_ , NBER-East Asia Seminar on Economics, Volume 11, University of Chicago Press, Chicago, pp. 11–43.

- Doove, S., Gabbitas, O., Nguyen-Hong, D. and Owen, J. 2001, _Price Effects of Regulation: International Air Passenger Transport, Telecommunications and Electricity Supply_ , Productivity Commission Staff Research Paper, Ausinfo, Canberra.

- Findlay, C. and Warren, T. (eds) 2000, _Impediments to Trade in Services: Measurement and Policy Implications_ , Routledge, London and New York.

- Gonenc, R. and Nicoletti, G. 2000, _Regulation, Market Structure and Performance in Air Passenger Transport_ , Working Paper No. 254, ECO/WKP(2000)27, Economics Department, OECD, Paris, 3 August.

- Gregan, T. and Johnson, M. 1999, _Impacts of Competition Enhancing Air Services Agreements: a Network Modelling Approach_ , Productivity Commission Staff Research Paper, Ausinfo, Canberra, July.

20
- `$` A lack of citizen understanding regarding the basics of climate science is an almost universal finding worldwide even though knowledge has increased over time. Especially notable is confusion between causes of climate change and ozone depletion, and confusion between weather and climate.

- `$` Americans know far less about climate change than their counterparts in the developed world

- `$` Accurate and complete understanding of information is not a pre-requisite for concern.

- `$` Concern is widespread around the world, but it may also be inversely correlated with the wealth and carbon footprint of a nation.

- `$` In some studies, more informed respondents reported less concern or sense of responsibility towards climate change.

- `$` People stop paying attention to global climate change when they realize that there is no easy solution for it. Many people judge as serious only those problems for which they think action can be taken.

# **_Information and Understanding_**

We begin first with what citizens around the world understand about climate change. The earliest major academic studies were carried out by Bostrom and co-authors, (1994), Bord, Fisher and O’Connor (1998), and Dunlap (1998). There have also been a series of public opinion polls in the U.S. We include these via work by Nisbet and Myers in 2007 and Brewer (2005) who publish systematic reviews of polling data on climate change in the U.S. which extend back into the 1980s. Psychologist Paul Slovic and interdisciplinary scholar Anthony Lessierowitz have also conducted recent work. All studies are in agreement in finding widespread misunderstanding regarding climate science.

In 1994 Bostrom and co-authors use a “mental model” approach to assess American understanding of climate science. They report “widespread misinformation regarding climate change in the general public, including confusion between ozone hole and global climate change and between weather and climate.” The authors also report confusion regarding causes of climate change: “automobile use, heat and emissions from industrial processes, aerosol spray cans, and pollution in general were frequently perceived as primary causes of global warming.” Two comparative international studies were published in 1998. Bord, Fisher and O’Connor (1998) review international survey data on public knowledge, concern, perceived risk and “willingness to pay” regarding global warming. While the authors find “solid awareness of 6
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lower excessive frictions would have no noticeable effect. As for the scale-dependent policies, improving conditions 20 percent in the smallest cities would have a very small effect, in no case improving welfare more than 0.1 percent. Doing the same in larger cities has, as expected, a greater effect. One important difference with China is that the welfare effects of any of these policies are much smaller.

**Table 4. Urban policies in Mexico**

||**W**<br>|**elfare differences(perce**<br>|**nt)**<br>|
|---|---|---|---|
|**Urbanpolicies**|**Efficiency**|**Amenities**|**Excessive frictions**|
|Improve in worst cities<br>by20percent|0.4|0.7|0.0|
|Improve in smallest<br>cities by20percent|0.1|0.1|0.0|
|Improve in medium-<br>large cities by20percent|0.5|0.3|0.1|
|Improve in largest cities<br>by20percent|2.1|1.1|0.5|

_Source:_ Authors.

Once again, the finding that targeting the largest cities has a greater impact is not surprising. As in China, if we were to control for the fact that improving conditions in larger cities benefits more people, we would find that the best policy targets the most lagging cities. Table 5 reports the results. Improving efficiency in the most backward cities improves welfare 0.2 percent, compared with 0.1 percent or less when targeting cities of different sizes.

**Table 5. Urban policies in Mexico for constant population-weighted characteristics**

|**Urbanpolicies**|**W**|**elfare differences(perce**|**nt)**|
|---|---|---|---|
||**Efficiency**|**Amenities**|**Excessive frictions**|
|Improve in worst cities<br>by20percent|0.2|0.1|0.0|
|Improve in smallest<br>cities by20percent|0.0|0.0|0.0|
|Improve in medium-<br>large cities by20percent|0.1|0.1|0.1|
|Improve in largest cities<br>by20percent|0.1|0.1|0.2|

_Source:_ Authors.

**_Mexico’s changing city-size distribution._** Until now we have compared urban systems across countries, but our methodology can also compare urban systems over time. When reducing spatial efficiency differences to the U.S. level, welfare gains in Mexico rise, from 0.2 percent in 1989 to 0.6 percent in 1994, and then drop again, to 0.3 percent in 2000 and 0.0 percent in 2005.<sup>12</sup>
**Section V: Sustaining a crossover business**<sup>**22**</sup>

The issues crossover entrepreneurs face in developing their businesses start with the establishment of the enterprise. Table 9 shows the primary business related challenges faced by crossovers and non‐crossovers when establishing their enterprise.

**Table 9: Main problem faced by women when starting their business**

||**Crossover**|**Non‐crossover**|**Difference**|
|---|---|---|---|
|Findingstaff willingto work for a woman|1.6|0.8|0.8|
|Findingright staff(woman not mentioned)|6.3|6.7|‐0.3|
|Low technical skills and experience|20.6|0.8|19.8***|
|Findingright suppliers|7.9|0.8|7.1***|
|Findinga location/premises|11.1|15.8|‐4.7|
|Accessingcredit|17.5|23.3|‐5.9|
|Balancinghome and work life|1.6|0.8|0.8|
|Distribution/deliveryofproducts to clients|3.2|0.0|3.2**|
|Findingcustomers willingto buyfrom a woman|3.2|0.8|2.3|
|Findingcustomers(woman not mentioned)|4.8|42.5|‐37.7***|
|Infrastructure and utilities|4.8|1.7|3.1|
|Corruption and bribes|1.6|0.0|1.6|
|Sexual harassment|0.0|0.8|‐0.8|
|Other|15.9|5.0|10.9***|
|N|63|120|183|

*** p<0.01, ** p<0.05, * p<0.1.

Some of the problems we might expect women who are pioneers in a sector to face are in evidence in this table. The most common constraint that crossovers mention is low technical skills. Here, the mixed methods data lines up with our earlier discussion of the KASSIDA data ‐‐ women's lower technical skills within these sectors persist. A smaller fraction of crossovers also cite issues of finding suppliers. When we ask them directly if their suppliers charge them more because of their gender, 13 percent of crossovers say yes, compared to 4 percent of non‐crossovers.

Clients and suppliers acknowledge that female business owners have lower technical skills which, for example, limit the range of products the crossovers can offer. According to these clients and suppliers, the crossovers “depend too much on their male employees to carry out hard tasks such as firing, lifting and delivering”. Furthermore, when asked “If you had a big contract, who would you give it to a male or

> 22 This section draws (except where noted) exclusively on the mixed methods data set.

25
# **3.4. Geographic concentration of activities at the regional level**

Given the difference in the stages of development between the coastal region and the inland region, we estimate the determination of the concentration of each category of industries at the regional level (coastal/inland).

Table 5 shows the sharp contrast in the determination of the concentration of the four representative industries in the coastal and inland regions. The role of openness is mixed. The results offer no support that large FDI inflow encourages the concentration of high-tech industries, which corresponds to the reality of the limited foreign investments in R&D in China. In the period of our study, most multinational firms were specialized in labor intensive activities. FDI did encourage the concentration of the non high-tech industries in the coastal provinces.

The relatively developed coastal provinces seem to be attractive to the two high-tech industries. However, an inland province with a relative high level of initial development is not attractive to these high-tech industries. It implies a strong tendency that the high-tech industries concentrate in the coastal poles. As to the non high-tech industries (chemical and bamboo), in the inland regions, the geographic concentration is independent of the level of development and most of the other variables listed above. By contrast, in the coastal region, provinces with a relatively low development level seem more attractive to the labor intensive industries. We argue that the diffusion of the labor intensive industries is limited within the coastal region.

**Table 5 – The determination of the concentration of some representative industries at the regional level**

||Chemical<br>and pr<br>manufact|material<br>oducts<br>uring (3)|Electro<br>telecomm<br>equip<br>manufact|nic and<br>unications<br>ment<br>uring (8)|Instrumen<br>and other<br>equip<br>manufact|ts, meters<br>measuring<br>ment<br>uring (12)|Timber p<br>bamboo, c<br>bibber a<br>produc|rocessing,<br>ane, palm<br>nd straw<br>ts (28)|
|---|---|---|---|---|---|---|---|---|
||coast|inland|coast|inland|Coast|inland|coast|inland|
|_t_<br>_j_<br>_FDIOVER_ ,|0.100*|0.027|-0.105|-0.034*|-0.073|-0.072**|0.201***|<br>0.005|
||(1.89)|(1.43)|(-1.51)|(-1.90)|(-1.30)|(-2.50)|(3.57)|(0.15)|
|1<br>, −<br>_t_<br>_j_<br>_yt_|-0.411***|<br>0.093|0.505**|-0.258***|0.576***|-0.363**|-0.656***|-0.165|
||(-2.69)|(0.93)|(2.52)|(-2.81)|(3.56)|(-2.38)|(-4.02)|(-0.82)|
|1<br>, −<br>_t_<br>_j_<br>_GAW_|-1.482**|-0.121|1.834*|-0.021|1.158|-0.022|-0.515|-0.257|
||(-2.01)|(-0.23)|(1.89)|(-0.04)|(1.48)|(-0.03)|(-0.65)|(-0.25)|
|_t_<br>_j_<br>_GPOP_,|-9.521**|-0.244|12.284**|-0.214|3.974|-1.851.|-4.981|-5.250|
||(-2.17)|(-0.07)|(2.13)|(-0.07)|(0.86)|(-0. 37)|(-1.06)|(-0.82)|
|Constant|2.915***|0.921***|-0.789|0.881***|-0.893|1.104**|3.768***|1.612***|
||(4.59)|(2.80)|(-0.95)|(2.90)|(-1.33)|(2.20)|(5.56)|(2.53)|
|No. of obs.|80|139|80|136|80|139|80|139|
|AdjustedRsq.|0.1548|0.0926|0.1478|0.3025|0.2459|0.3015|0.2389|0.011|

Note: _t_ -students are in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

17
we find that composition effects in financial openness may affect the magnitude of the RER undervaluation. More specifically, equity-related liabilities have negative and significant coefficient while loan-related liabilities have positive and significant coefficient in almost all specifications. Once more, liability dollarization did not seem to matter either. Finally, export concentration —as measured by the HirschmanHerfindahl index of export revenues— shows a positive and significant coefficient. This means that export pattern matters on the magnitude of RER undervaluation. The results on the ability of exchange rate flexibility to affect the magnitude of the undervaluation are mixed.

We test whether macroeconomic (and, more specifically, exchange rate) policies can generate a more persistent likelihood of exchange rate deviations. First, we test whether interventions can generate persistent RER deviations and, then, we test whether ― _de facto_ ‖ fixed or flexible exchange rate arrangements allow a faster speed of mean reversion. In general, we find that FOREX intervention can lead to greater persistence in the incidence rather than the magnitude of RER undervaluations (i.e. we obtain a statistically significant effect for the interaction term in our _Probit_ regressions and a negligible coefficient estimate in our _Tobit_ regressions). Hopwever, exchange rate regimes seem to play a role in generating persistent RER deviations. The _Probit_ analysis shows that the speed of mean reversion is slower for countries with fixed regimes in RER overvaluation.

34
**Table 3: Sensitivity to imputing missing values (1), including additional controls (2), and controlling for the incidence of conflicts (3) (Only SAR equations are reported)**

||(1)|(2)|(3)|
|---|---|---|---|
|Income (#)|-0.090 [0.035]**|-0.115 [0.045]**|-0.099 [0.035]***|
|Age (#)|1.088 [0.326]***|0.988 [0.285]***|1.045 [0.307]***|
|Age (#), squared<br>Female|-0.158 [0.047]***<br>-0.032 [0.028]|-0.139 [0.041]***<br>-0.027 [0.032]|-0.147 [0.045]***<br>-0.022 [0.032]|
|Single, never married|0.012 [0.026]|6.7e-03 [0.029]|0.014 [0.025]|
|Employment status||||
|Employed part time want full time|-9.4e-03 [0.053]|6.3e-03 [0.058]|-0.013 [0.060]|
|Employed part time don’t want full|-0.091 [0.026]***|-0.079 [0.031]**|-0.089 [0.028]***|
|Employed full time for self-empl.|-0.112 [0.038]***|-0.105 [0.034]***|-0.126 [0.034]***|
|Employed full time for an empl.|-0.086 [0.021]***|-0.074 [0.022]***|-0.087 [0.022]***|
|Highest education level||||
|Secondary to 3 year of tertiary|-0.108 [0.034]***|-0.128 [0.042]***|-0.130 [0.036]***|
|4 years of tertiary and beyond|-0.302 [0.066]***|-0.306 [0.058]***|-0.294 [0.059]***|
|Instruments for SAR||||
|Religion is important|-0.152 [0.073]**|-0.171 [0.070]**|-0.191 [0.074]**|
|Sacrificing one’s life for beliefs|1.321 [0.096]***|1.290 [0.127]***|1.308 [0.113]***|
|Additional controls||||
|Weighted Conflict Index|||1.7e-05 [7.4e-06]**|
|Community attachment index||-6.8e-04 [6.7e-04]||
|Community basics index||2.3e-04 [7.8e-04]||
|Civic engagement index||9.6e-04 [8.1e-04]||
|Satisfied with freedomto choose||-0.122[0.040]***||
|Country fixed effects|Yes|Yes|Yes|
|Locality fixed effects|Yes|Yes|Yes|
|Survey wavesfixed effects|Yes|Yes|Yes|
|Observations|37556|30192|29151|
|Log Pseudolikelihood|-102838.94|-83589.897|-81039.659|
|Wald test of indp.Eqns. (p-value)|0.063|0.046|0.038|

Notes: The reference categories for the employment status is “Out of workforce or unemployed”, for the highest education level is “Completed elementary education or less”, and for the locality fixed effects is “Rural area or on a farm”. X(#) and X(@)denote Ln(X) and Ln(X+1), respectively. We use Ln(X) for income to exclude individuals with zero income and Ln(X+1) for the number of children under 15 in the household to not exclude households with zero values on this variable. Standard errors are clustered by country/wave and robust to heteroscedasticity. Asterisks denote significance levels as follows: *** p<0.01, ** p<0.05, * p<0.1. Column 1 reports results with imputed missing values. Column 2 reports results with controls for conflict. Column 3 reports results with additional controls.

Furthermore, country-level time-varying omitted factors may also influence our results. It may be the case that support for violent extremism may be affected by terrorist incidents at the country/year level (See, among others, Blair et al., 2013; Lyall et al., 2013). For instance, wartime victimization may influence attitudes toward violent extremism. To control for this, we augment our baseline regression by controlling for the incidence of conflicts using the time-varying country34
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