rafmacalaba/datause-encoder-data huggingface dataset:
To further study potential biases in the DID estimates, Figure 17 presents the estimates of the industry fixed effects on the probability of occurrence of a plant closing event. Manufacturing is associated with a 14 percent higher probability of experiencing a plant closing, while agriculture is 36 percent less likely to experience this type of job separation. The results from figure 10 indicate the importance of controlling for workers’ industry of employment at the time of job separation. We re-estimate specification 2 with a subsample of workers for whom we can control for their industry of employment at the time of displacement and confirm that the results are robust to the inclusion of the industry of employment at the time of job displacement.<sup>11</sup> Furthermore, it is evident that plant closings are a phenomenon of agricultural sectors, particularly manufacturing. To the extent that higher quality workers and higher quality employer-employee matches are in the manufacturing sector where large enterprises operate rather than in agriculture, combined with the results regarding formality status of workers, these results lend further credence to the speculation that the DID estimates in particular are unlikely to be severely biased due to the omission of employer-employee fixed effects. Nonetheless, this remains a fertile area for future research.

**Figure 17: Industry fixed effects on probability of plant closing**

<!-- Start of picture text --> 0.50<br>0.35<br>0.20<br>0.05<br>-0.10<br>-0.25<br>-0.40<br>-0.55<br><!-- End of picture text -->

> 11 The ENOE collects information on the industry of employment at the time of job displacement for only a subsample of workers— those who experienced a job termination within the last two years of the survey interview.

27
- [20] Ho, Andreas. 2007. Patterns of Intergenerational Support in Grandparent-Grandchild and Parent-Child Relationship in Germany. Ageing and Society 27, 643-665.

- [21] Huang, Chong, Li Hongbin, Liu Pak Wai and Zhang Junsen. 2009. " Why Does Spousal Education Matter for Earnings? Assortative Mating and Cross-Productivity," Journal of Labor Economics 27(4).

- [22] Huang, Fali; Ginger Zhe Jin, and Lixin Colin Xu. 2012. Love and Money by Parental Matchmaking: Evidence from Urban Couples in China. American Economic Review Papers and Proceedings May 2012, 555-560.

- [23] Ikkink, Karen Klein, Theo van Tilburg, and Kees C. P. M. Knipscheer. Perceived Instrumental Support Exchanges in Relationships between Elderly Parents and Their Adult Children: Normative and Structural Explanations. Journal of Marriage and Family 61, 831-844.

- [24] Institute of Population Studies, CASS. Sampling Survey Data of Women's Status in Contemporary China. Beijing: International Academic Publishers.

- [25] Johnson, David, Lynn White, John Edwards and Alan Booth. 1986. " Dimensions of Marital Quality: Toward Methodological and Conceptual renement," Journal of Family Issues 71(3), 31-50.

- [26] Li, H.B., P.W. Liu, J.S. Zhang, and N. Ma. 2007. Economic returns to communist party membership: Evidence from urban Chinese twins. Economic Journal 117, 1504-1520.

- [27] North, Douglas. 1990. Institutions, Institutional Change and Economic Performance. Cambridge University Press.

- [28] Suen, Wing, William Chan and Junsen Zhang. 2003. " Marital Transfer and Intrahousehold Allocation: A Nash-Bargaining Analysis" Journal of Economic Behavior Organization 52, 133-146.

- [29] Weiss, Yoram. 1997. "The Formation and Dissolution of Families: Why Marry? Who Marries Whom? And What Happens Upon Divorce?" Chapter 3, in M.R. Rosenzweig and O. Stark eds., Handbook of Population and Family Economics, Elsevier Science.

- [30] Weiss, Yoram, and Robert Willis. 1997. " Match Quality, New Information and Marital Dissolution," Journal of Labor Economics 15(1), S293-329.

- [31] Xiaohe Xu. 1996. "Measuring the Concept of Marital Quality as Social Indicators in Urban China," Social Indicators Research 37(2), 189-206.

29
Table 33

|Dependent variable: N|eed of fisc|al adjustm|ent based o|n debt/rev|enue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.104***|0.107***|0.117***|0.107***|0.269***|0.086*|0.012|0.139***|
|(% of GDP)|(0.012)|(0.014)|(0.016)|(0.017)|(0.036)|(0.044)|(0.034)|(0.029)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-6.476***||-7.620***|-6.558***|-7.644***|-7.398***|-7.518***|
|||(1.116)||(1.485)|(1.475)|(1.487)|(1.483)|(1.483)|
|Inflation (t-1)||0.060||1.077*|1.061*|1.091*|0.968|1.098*|
|||(0.397)||(0.630)|(0.615)|(0.631)|(0.619)|(0.628)|
|U.S. Interest rate||0.027||0.005|-0.013|0.006|-0.010|0.003|
|||(0.029)||(0.038)|(0.039)|(0.038)|(0.038)|(0.038)|
|**Political factors**|||||||||
|Gini|||0.031**|0.028**|0.026**|0.019|0.030**|0.028**|
||||(0.012)|(0.012)|(0.012)|(0.021)|(0.012)|(0.012)|
|Rule of law|||0.115|0.152|0.093|0.157|-0.432**|0.148|
||||(0.104)|(0.118)|(0.119)|(0.119)|(0.217)|(0.119)|
|Margin of Majority|||-0.441|-0.360|-0.355|-0.354|-0.335|0.543|
||||(0.392)|(0.404)|(0.400)|(0.404)|(0.403)|(0.775)|
|Election's year|||-0.043|-0.058|-0.050|-0.058|-0.055|-0.062|
||||(0.103)|(0.106)|(0.107)|(0.106)|(0.106)|(0.106)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.228***<br>(0.041)||||
|Gini * Rigidity||||||0.001<br>(0.001)|||
|Rule of law * Rigidity|||||||0.024***<br>(0.008)||
|Majority * Rigidity||||||||-0.053<br>(0.039)|
|Observations|3,046|2,911|2,066|2,007|2,007|2,007|2,007|2,007|
|Number of ifscode|147|145|103|102|102|102|102|102|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in par<br>*** p<0.01, ** p<0.05|0.0235<br>entheses<br>, * p<0.1|0.0373|0.0276|0.0432|0.0581|0.0433|0.0478|0.0440|

- 62 -
Policy ReseaRch WoRking PaPeR 4377

# **Abstract**

A new literature on the nature of and policies for youth in Latin America is emerging, but there is still very little known about who are the most vulnerable young people. This paper aims to characterize the heterogeneity in the youth population and identify ex ante the youth that are at-risk and should be targeted with prevention programs. Using non-parametric methodologies and specialized youth surveys from Mexico and Chile, the authors quantify and characterize the different subgroups of youth, according to the amount of risk in their lives, and find that approximately 20 percent of 18 to 24 year old Chileans and 40 percent of the same age cohort in Mexico are suffering the consequences of a range of negative behaviors. Another 8 to 20 percent demonstrate factors in their lives that pre-dispose them to becoming at-risk youth – they are the candidates for prevention programs. The analysis finds two observable variables that can be used to identify which children have a higher probability of becoming troubled youth: poverty and residing in rural areas. The analysis also finds that risky behaviors increase with age and differ by gender, thereby highlighting the need for program and policy differentiation along these two demographic dimensions.

This paper—a product of the Latin American and Caribbean Region , Human Development Department—is part of a larger effort in the department to understand the challenges facing at-risk youth in the region and to design effective policy to support them. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Wcunningham@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>Table 4. Separate OLS estimation results by type of commodity</u>**

|Commodity type:|_cloth_||_equipment_||_food_||_mineral_||_goods_||
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.<br>|Std.Err.|Coef.<br>S|td.Err.|Coef.<br>|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|ln_speed_|-0.326|(0.039) ***|-0.445|(0.029) ***|-0.061|(0.019) ***|-0.599|(0.052) ***|-0.277|<br>(0.039) ***|
|ln_dist_|-0.038|(0.009) ***|0.024|(0.012) ***|-0.054|(0.006) ***|-0.100|(0.014) ***|-0.161|<br>(0.013) ***|
|ln_weight_|-1.003|(0.039) ***|-0.305|(0.025) ***|-0.781|(0.024) ***|-0.931|(0.046) ***|-0.544|<br>(0.040) ***|
|ln_elev_|0.077|(0.003) ***|0.070|(0.004) ***|0.017|(0.002) ***|0.106|(0.006) ***|0.064|<br>(0.006) ***|
|_D_elev_|0.017|(0.171)|-0.381|(0.375)|0.742|(0.578)|0.868|(0.328) ***|-0.059|<br>(0.067)|
|_crossborder_|-0.024|(0.009) ***|0.112|(0.011) ***|-0.013|(0.004) ***|0.058|(0.015) ***|0.127|<br>(0.018) ***|
|constant|4.873|(0.182)***|3.239|(0.158)***|4.082|(0.111)***|5.889|(0.270)***|4.620|<br>(0.210)***|
|Obs.|10,347||6,565||23,458||4,729||6,035||
|R-squared|0.4819||0.3655||0.3824||0.4220||0.4659||
|F stat.|65.32||34.48||131.00||...||97.37||
|No. of time dumm|y variables||||||||||
||131||131||131||131||131||

Note: The dependent variable is freight rate in logarithm, ln _p_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
- (1)Diagnosis rate γt: the time-varying share of daily infected individuals who are diagnosed with COVID-19.<sup>5</sup> We consider the logit transformation of γt and assume it follows:

- (2)Infection rate βt: the time-varying rate at which susceptible individuals are infected with the disease.<sup>6</sup> We assume that the infection rate follows the process given by:

where GP denotes the Gaussian process with mean function m(t) = xt⊺μ and covariance function based on Rasmussen and Williams (2006) and Zhou and Ji (2020).

- (3)Recovery rate α: the stochastic rate of recovery for infected individuals, the inverse α<sup>−1</sup> of which is equivalent to the average time to achieve full recovery after infection. The recovery rate is governed by the following equation:

We set α= 322.5 and v1α = 35 such that E(α−1) = 9.3 with the prior 95% credible interval in the range of 8.3 to 10.3 days. The mean infectious period of 9.3 days is chosen based on He et al. (2020) and Zhou and Ji (2020).

The unobserved number of COVID-19 cases is linked to the observed cases via the measurement function p(yt|xt, θ). The measurement function is a combination of the equation governing the dynamics of γt and the relationship Bt = γt(1 −α)UIt from the discretized SIR model in equation (1):

2 where η represents the regression coefficients, while σγ , the variance of the diagnostic rate, captures random fluctuations and reporting errors implicitly reflected in official COVID-19 case counts.

> 5 The diagnosis rate varies between 0 and 1.

> 6 The infection rate fluctuates between 0 and 1.

6
Figure 1.5a: Tractor production utilization rate in Russia and Belarus Figure 1.5b: Tractor production level in Figure 1.5c: Accumulated Russia and Belarus (1991=1) depreciation of fixed assets, % (machine building sector) <!-- Start of picture text --> 100 70<br>0.8<br>Belarus 68<br>80<br>0.6 66<br>60 Belarus Russia 64<br>Russia<br>40 0.4 62<br>60<br>20<br>0.2 58<br>0 56<br>0.0<br>54<br>1995 2000 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Sources: Rosstat, Belstat, World Bank staff estimates.

**In addition, there has been rapid growth in transport equipment production in Russia and in the penetration of Chinese products in the Russia market** . Clearly the twofold increase in heavy truck and bus production in China in the past five years (Figure 1.6a and 1.6b) dwarfs the relevance of Belarus’s transport equipment production.

Figure 1.6a Normalized production levels of heavy trucks (in units), 2005=100 Figure 1.6b Normalized production levels of heavy busses (in units), 2005=100 <!-- Start of picture text --> Heavy trucks<br>Heavy busses<br>400 300<br>350<br>250<br>300<br>250 200<br>200 150<br>150 100<br>100<br>50<br>50<br>0 0<br>2005 2006 2007 2008 2009 2010 200 5EU10 2006 Russia2007 200 8Belarus 2 009 China2010<br>EU10 Russia Belarus China<br><!-- End of picture text --> Source: Organization of Motor Vehicle Manufacturers, www.oica.net

**The increase in wages and the price of electricity and gas has also contributed to the erosion of the cost advantages of Belarus’s enterprises.** A privileged access to underpriced Russian energy supply has been greatly reduced with scheduled tariff increases (Table 1.7 and Figure 1.7a).<sup>18</sup> Moreover, the unit labor costs in Belarus have become higher than in Russia (Figure 1.7b).

Table 1.7: Average cost of electricity for industry <u>(USD/kWh)</u>

||**_2005_**|**_2006_**|**_2007_**|**_2008_**|**_2009_**|**_2010_**|
|---|---|---|---|---|---|---|
|Russia|$0.03|$0.03|$0.04|$0.05|$0.05|$0.05|
|Belarus|$0.05|$0.07|$0.09|$0.11|$0.11|$0.13|

Sources: Russia - Rosstat, Belarus – Ministry of Energy. Memorandum: Converted using average local currency unit to US$ exchange rates.

> 18 The recent (November 2011) gas agreement with Russia secures privileged gas import price for Belarus for 2012-2014 postponing but not cancelling an inevitable tariff increase in line with increase in Russian domestic tariffs.

10
al, 2010) we classified 18 percent of the businesses in the sample as “looking more like formal businesses before the program”.<sup>16</sup> This result is especially large for group 3, as businesses with characteristics similar to formal businesses in this group formalized 20 percentage points more than the same businesses in the control group.

Finally, businesses that received more than one visit from a tax inspector in the year prior to program implementation were more likely to formalize. This result, which is only significant for group 3, may suggest that the program was perceived as a way to limit tax harassment.

# **6. What prevents more firms from formalizing?**

We have seen that in the third treatment group, which combined all packages of incentives and in which the impact was the greatest, formalization rate was about 17 percent. This impact is greater than for similar programs in other contexts (Bruhn and McKenzie, 2014) in which formalization is also linked to taxes. But it means that even though this type of program had a significant impact, the majority of the informal firms still remain informal. This is true even if we consider specific sub-population like businesses operating outside the market or with an owner who went to secondary school, for which impact on formalization remained below 23 percentage points.

We think that some important factors could explain this result in our particular context:

- (1) The presence of other legal barriers to formalization;

- (2) The lack of success in changing the perception of formalization costs and benefits.

(1) Other legal barriers, like the lack of identification documents, may play an important role in explaining the results, as qualitative data from program beneficiaries and implementing agencies suggest. Moreover, data presented in Table 8 show that only 53 percent of business

> 16 Looking like a formal business owner is based on the predicted probability of being formal from a logit of formality status on baseline characteristics. This logit uses the data collected during the listing/baseline survey on 7,829 businesses who accepted the survey. Among them, 608 (7.8%) were formal at the time of the survey. We used the following baseline characteristics in the logit: operating in TOKPA market, gender, age, only primary education, only JHS or SHS level, higher level of education, operating in services, craftsman, business created less than 1 year ago, firm connected to electricity network, total number of employees, firm is doing some accounting, have done any advertising in the last 6 months, log of total amount of sales in an average week, log amount of last month profit, firm owner owns a bank account, the firm pays taxes, have done any advertising in the last 6 months (and controls for missing levels of these variables). Using the “predict” command in STATA, we end up classifying as “looking more like formal” 654 (18.2%) businesses out of the 3,600 in the study sample.

22
WPS3908

# Turkey’s Evolving Trade Integration into Pan-European Markets<sup>•/</sup>

## By

## Bartlomiej Kaminski<sup>*</sup> and Francis Ng<sup>**</sup>

**_Summary_** : This is an empirical paper seeking to identify the mode of Turkey’s integration into global markets in general and pan-European markets in particular as revealed in its trade performance. The analysis provides empirical support to the following observations. First, thanks to steady expansion of trade in goods and services since the mid-1980s, Turkey has become highly integrated into the world economy. Second, Turkey’s export performance in 1996-2004 in EU markets bears strong similarities to the aggregate performance of new EU members from Central Europe (EU-8). Similarities include dynamics, similar factors responsible for the increased presence in EU markets, factor content and the role of ‘producer-driven’ network trade.

Turkey, together with Hungary, Czech Republic, Slovak Republic, Slovenia, Estonia and Poland, stands as one of the top performers in ‘producer-driven’ network trade indicating participation in a new global division of labor based on production fragmentation.

The available evidence suggests an economic success story in the making. Export expansion owes a lot to improved policy environment and domestic liberalization. It is rather telling that the recent expansion has coincided with the implementation of most of the provisions of EU-Turkey CU Agreement, the completion of the removal of tariffs on trade in industrial products among the pan-European parties to the Pan European Agreement Cumulation of the Rules of Origin and improved macroeconomic stability after the 2001 crisis.

## World Bank Policy Research Working Paper 3908, May 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ • / Revised and updated version of a background report “Turkey’s Surprising Mode of Trade Integration into Pan-European Markets” prepared for _Turkey: A Country Economic Memorandum: Promoting Sustained Growth and Convergence with the European Union_ , ECA, World Bank, Washington, D.C. The authors are grateful to Subidey Togan and Aristomene Varoudakis for their comments on an earlier draft of this paper.

- / Department of Government, University of Maryland, College Park. E-mail: <u>bkaminski@gvpt.umd.edu</u>

> ** / Development Research Group -Trade, The World Bank, Washington, D.C. E-mail: <u>fng@worldbank.org</u>
- 53 -

10. There is growing concern, however, over the widespread practice of subsidizing consumer prices of fertilizer. First, it is impossible to truly delink producer and consumer prices because the difference between the two has direct fiscal implications. In addition, it is not clear that fertilizer subsidies are efficient from the overall perspective of agricultural policy. In many countries, fertilizer use is unbalanced and inefficient duo, in part, to its long history of low prices to farmers. There may be better methods to improve agricultural productivity; e.g., through a package of improved farming practices rather than through large-scale subsidies on one input -- or random subsidies on several. Further, non-price factors such as the transportation and distribution system for getting fertilizer to farmers often play an important role in its efficient application and use. Improvements in the distribution infrastructure, perhaps financed by an increase in fertilizer prices, could bring major gains _iA_ agricultural production. Even on the income distributional side, it is not clear that fertilizer subsidies benefit primarily poor **farmers.** They are cumbersome to target, and generally apply equally to all users, with proportionately more of the subsidy accruing to **the** larger ones.

# B. THE PRACTICE

# B.1 Project Sa=le

11. Our sample consisted of all five fertilizer projects (in India, **Yugoslavia,** China, Burkina and Nigeria) approved by the Bank during FY84 and FY85. Three were for new production capacity and two were for rehabilitation or energy savings at existing plants. All contained good coverage of the fertilizer pricing systems of the countries, at both producer and consumer levels.

# B.2 Calculation and Discussion of EfficiencX Prices

12. All SARs contained information on current borde; prices, which were compared with some or all of domestic production costs, ex-factory prices and farmgate prices. For the three cases reporting produetion costs, they were 97% and 88% of cif prices for urea, and 150% and 158% of cif ammonia or compound fertilizer prices. All SARs took the view that current cif prices are below the long-run equilibrium levels that should be considered when making long duration investments.

13. Ex-factory prices were higher than production costs in four of the five cases, where they were set on cost-plus basis. In the fifth case, Yugoslavia, ex-factory prices are set based on a number of factors including production costs, international prices and domestic prices in neighboring countries. This has resulted in financial losses for the fertilizer industry, but a 50% increase in ex-factory prices was planned to remedy this.

14. Most SARs provided a thorough analysis of the problems of the existing fertilizer pricing system -- and all five cases had major problems on efficiency grounds. The general solutions proposed were (i) to ensure the financial viability of fertilizer production by adhering to certain