rafmacalaba/datause-encoder-data huggingface dataset:
|Treatment|0.035**<br>(0.016)|0.061*<br>(0.033)|0.028<br>(0.018)|0.137***<br>(0.042)|‐0.018<br>(0.018)|0.081*<br>(0.039)|0.019<br>(0.028)|0.001<br>(0.067)|
|---|---|---|---|---|---|---|---|---|
|Observations|3,955|3,955|3,955|3,955|3,955|3,955|3,955|3,955|
|R‐squared|0.001|0.273|0.001|0.070|0.000|0.078|0.000|0.123|
|State FE|NO|YES|NO|YES|NO|YES|NO|YES|
|Month FE|NO|YES|NO|YES|NO|YES|NO|YES|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|
|Controls<br>Interacted|NO|YES|NO|YES|NO|YES|NO|YES|

**Note:** Robust standard errors in parentheses (White, 1980): *** p<0.01, ** p<0.05, * p<0.1. Columns (1‐2) are measured on the household level. Columns (3‐8) refer to per capita OECD adult equivalents. A full set of coefficients for control variables can be found in

Table 15 the appendix.

To capture this heterogeneity across consumption levels, quantile regressions are applied. Results are shown in Figure 4 and Table 3.

_Table 3: Results from quantile regressions of different outcome variables._

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome Variables|ln(Cons. Num.)|ln(Cons. Quant.)|ln(Cons. Val.)|ln(Cons. Cal.)|
|Q0.1|0.165**|0.342***|0.079|0.235*|
||(0.064)|(0.079)|(0.068)|(0.127)|
|Q0.25|0.058**|0.201***|0.198***|0.140*|
||(0.028)|(0.067)|(0.053)|(0.080)|
|Q0.5|0.018|0.136**|0.119**|0.042|
||(0.032)|(0.056)|(0.050)|(0.062)|
|Q0.75|0.047|0.114**|0.071|0.032|
||(0.034)|(0.050)|(0.051)|(0.067)|
|Q0.9|‐0.016|0.049|‐0.015|0.013|
||(0.028)|(0.050)|(0.054)|(0.064)|
|Observations|3,955|3,955|3,955|3,955|
|Month FE|YES|YES|YES|YES|
|State FE|YES|YES|YES|YES|
|Controls|YES|YES|YES|YES|
|Interacted Controls|YES|YES|YES|YES|

Robust standard errors in parentheses (White, 1980): *** p<0.01, ** p<0.05, * p<0.1. Column (1) is measured on the household level. Columns (2‐4) refer to per capita OECD adult equivalents.

13
30 Table 6: Annual changes in NACs, by country group, 1972-74, 1984-86, and 2005-08 (percent)

# (a) Rice

|**World exporters**|1972/73<br>-29|1973/74<br>-10|1984/85<br>18|1985/86<br>16|2005/06<br>-7|2006/07<br>8|2007/08<br>-29|
|---|---|---|---|---|---|---|---|
|<br>**World importers**|-24|-27|11|1|-3|3|-30|
|<br>High-income countries|-26|-19|23|35|-17|-10|-5|
|<br>Developing countries|-25|-23|10|-7|-3|8|-40|

# (b) Wheat

||1972/73|1973/74|1984/85|1985/86|2005/06|2006/07|2007/08|
|---|---|---|---|---|---|---|---|
|**World exporters**|-22|-6|2|43|5|-5|-5|
|**World importers**|-33|4|14|19|-5|-21|-14|
|High-income countries|-26|-2|17|27|-8|-30|-3|
|Developing countries|-34|3|3|12|-6|-3|-19|

# (c) All farm products

||1972/73|1973/74|1984/85|1985/86|
|---|---|---|---|---|
|**World exporters**|-12|-3|15|17|
|<br>**World importers**|-13|-6|9|8|
|<br>High-income countries|-8|-6|8|15|
|Developing countries|-13|-3|10|1|

a Unweighted averages of national NACs changes.

Source: Authors’ calculations based on updated NRA estimates from Anderson and Valenzuela (2008).
complications associated with reductions in nominal pay and to assign higher relevance to most of the possible reasons for avoiding wage cuts. Fears about lower effort and lower morale are systematically given as highly relevant reasons for avoiding wage cuts across firms of any type.

The survey also shed new light on the role of unions on downward wage rigidities. Managers subject to collective bargaining are more likely to avoid wage cuts because they fear those may harm the firm’s reputation as an employer (and consequently complicate future recruitment) and because of implicit insurance contracts with the workers. These results suggest a role for unions in enforcing implicit employment agreements and in spreading the information about job amenities in different firms among the labor force.

# **_References_**

- Agell, J. and Bennmarker, H. (2007), “Wage Incentives and Wage rigidity: A Representative View from within”, _Labour Economics_ , Vol 14, pp. 347-369.

- Agell, J. and P. Lundborg (2003), “Survey Evidence on Wage Rigidity and Unemployment: Sweden in the 1990s”, _The Scandinavian Journal of Economics_ Vol.105, No.2, pp.15-29

- Agell, J. and P. Lundborg (1995), “Theories of Pay and Unemployment: Survey Evidence from Swedish Manufacturing Firms”, _The Scandinavian Journal of Economics_ Vol. 97, No.2, pp. 295-307

- Akerlof, G.A. (1982), “Labor Contracts as Partial Gift Exchange”, _Quarterly Journal of Economics_ , 97, pp. 543-69.

- Akerlof, G.A., and J. Yellen (1990), “The Fair Wage-Effort Hypothesis and Unemployment”, _Quarterly Journal of Economics_ , 105, pp. 255–83.

- Altonji, J.G. and P.J. Devereux (2000), “The Extent and Consequences of Downward Nominal Wage Rigidity”, _Research in Labour Economics_ , Vol.19, pp. 383-431.

- Azariadis, C. (1975), “Implicit contracts and underemployment equilibria”. _Journal of Political Economy_ 83, pp. 1183-1202.

- Babecký, J., Du Caju, P., Kosma, T., Lawless, M., Messina, J. and Rõõm, T. (2012), “How do European Firms adjust their Labour Costs when Nominal Wages are Rigid?”, _Labour Economics_ , Vol. 19-N° 5, October 2012, pp. 792-801.

31
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more than 99 percent of the population in 171 of 193 countries, and we account for less than 95 percent of the population in only eight countries: Eritrea (94.5 percent of native speakers coded), the Islamic Republic of Iran (93.7 percent), Ethiopia (92.6 percent), the Lao Peoples Democratic Republic (90.2 percent), Timor-Leste (90.0 percent), Cameroon (89.1 percent), Chad (75.4 percent), and Papua New Guinea (32.0 percent).

Figure 1 characterizes the distribution of gender languages around the world. While many countries are dominated by either gender or non-gender languages, there is considerable within-country variation in Canada and the United States, Sub-Saharan Africa, South Asia, and the Andean region of South America. Across all countries, we estimate that approximately 38.6 percent of the world’s population speaks a gender native language.

# **3.2 Other Sources of Data**

Additional data for our cross-country analysis comes from several sources. Data on labor force participation, income, and population come from the World Bank’s World Development Indicators database. We use data on labor force participation in 2011, which is available for 178 countries. We also use data on primary and secondary school completion from the Barro-Lee Educational Attainment Data Set (Barro and Lee 2013), which is available for 142 countries. Data on gender attitudes comes from the World Values Survey and is available for 56 countries (World Values Survey Association 2015). Finally, we take several country-level geographic controls (average precipitation and rainfall plus suitability for the plough) from Alesina, Giuliano, and Nunn (2013). These data are available for 173 countries.

Data for our individual-level analysis comes from the nationally-representative Afrobarometer Surveys (Afrobarometer Data 2016). Afrobarometer surveys have been conducted in 36 African countries and are representative of the voting age population within each country. Given the salience of ethnolinguistic identities in many African societies, the Afrobarometer collects data on respondents’ native languages. We use data from four countries where gender and non-gender languages are indigenous and widely spoken: Kenya, 16
# Price and Quality Competitiveness of Socialist Countries' Exports

# by Zdenek Drabek and Andrzej Olechowski

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Results of Earlier Studies|2|
|III.|Manufacturing Costs|4|
|IV.|Measures of Price Competitiveness|7|
|V.|Treatment of Product Quality|7|
|VI.|Data|9|
|VII.|Estimation of Unit Value Indices|10|
|VIII.|Sensitivity Tests|12|
|ILX|Impact of EEC Protection on Price Competitiveness<br>of CPEs|15|
|_7C_|Effect of CPEs' Price Policy|17|
|XI.|Summary and Conclusions|19|
|Refere|nces|21|

* The authors are staff members of the World Bank and the Central Bank of Poland respectively. They would like to thank Professors Bela Balassa, Stuart Brown, and Kazlmierz Poznanski for their thoughtful comments, and Mr. Jerry Rozanski for his computer assistance.
Miracles in Calvo et al. (2006a). We then consider each of the 9 episodes in detail and re-visit the GDP and macro credit numbers in each country to examine if output began to recovery without a recovery in credit within a two-year period after the deepest point of the crisis (the trough). We categorize countries as _Phoenix Miracles at the macro level_ if GDP began to recover without a simultaneous recovery in credit and we classify countries as Non-Miracles at the macro level if both GDP and credit began to recover simultaneously.

We categorize countries as _Phoenix Miracles at the micro level_ if there was recovery in sales without a recovery in short-term borrowing (both sales and short-term borrowing aggregated across all firms in our sample in the country) within a two-year period after the deepest point of the crisis. We classify countries as _Non-miracles at the micro level_ if either aggregate sales never show a recovery or both sales and short-term borrowing recover simultaneously within a two year period after the deepest point of the crisis. For our firm-level analysis, we apply the same definition to classify each individual firm as a _Phoenix Miracle at the firm level_ if it experiences a recovery in sales without a recovery in credit. At the firm level we define credit both strictly as just short-term borrowing as well as a broader definition that includes short-term borrowing, long-term borrowing, and equity issuances.

# **_2.3 Sample Construction_**

For our sample of emerging markets, we collect firm-level accounting information from Bloomberg for over 2500 publicly traded firms in the above 3S episodes over a five-year window, two years preceding the trough of the output collapse to two years after the trough. In identifying if the countries are Phoenix miracles at the micro level, we restrict the sample to firms with non-missing sales and short term borrowing over the five years. In detailed firm-level analysis, where we identify individual firms as Phoenix Miracles, we further restrict the sample to 1326 firms across 5 countries after dropping firms with key non-missing balance sheet and cash flow statement information for each of the five years.<sup>8</sup> However, all our results are robust to allowing the size of the sample to vary as new firms are listed and incorporated into the database. It may be noted that Bloomberg does not remove bankrupt or de-listed firms. In addition to detailed balance sheet and income statement information, we also have information on the

> 8 Our sample size is comparable to sample sizes in other studies (e.g. Bleakley and Cowan, 2008; 2010) looking at crisis country episodes over the 1990s.

8
# **TABLE 2: COMPARING ESTIMATES OF LONG-RUN ELASTICITIES FOR PRICE INDICES**

||_Holtham (1988)_<br>_1967:II-1984:II_|_Gilbert (1989)_<br>_1965:I-1986:II_|_Borensztein &_<br>_Reinhart (1994)_<br>_1970:I-1992:III_|_Present Study_<br>_1960-2005_|
|---|---|---|---|---|
|Non-energy|—|0.12|0.11|0.16|
|Food|—|0.25|—|0.18|
|Non-food|—|0.12|—|0.33/0.04|
|Raw materials|0.08|—|—|0.04|
|Metals|0.17|0.11|—|0.11|

**Notes:** Holtham uses semiannual data, Gilbert and Borensztein & Reinhart quarterly, and the present study annual. Gilbert’s elasticities denote averages based of four specifications. Holtham’s raw materials elasticity is an average of two elasticities based on two sets of weights. The two non-food figures of the present study correspond to fertilizers and raw materials. ‘—‘ means not available.

**Source:** Holtham (1988), Gilbert (1989), Borensztein & Reinhart (1994), and author’s estimates.

**_— 14 —_**
4 score svstem normalizes the anthrop mnetric measures for these age-specific<sup>variances,</sup> There is , direct relationship between centiles and standard<sup>deviations. Minus 2 standard</sup> de\iations is identical to 2.r2th centile and -1.88 stlandard deLMations<sup>is equal to the third</sup> centile In growth monitoring, weight is usuallk plotted against age. Cutolts<sup>lor wAell-nourished and</sup> malnourished children are used as reterence curves, but the<sup>child'spreviousvromsthpaittrn</sup> becomes the real reterence. The child max, because of past nutritional<sup>insult,hesLunied and</sup> below the reference cur\e, but may be gro"ing at a normal rate. That child<sup>is notcurrcntlL at</sup> hiLh nutritional risk. If the child later experiences a sudden<sup>drop away from the expected</sup> growxth curve, the child needs alltntion to lorrect the cause of the problem.

A\HO. 1'JS(. op. cii
- [10] Borjas, George J. 2003. “The labor demandcurve is downward sloping: reexamining the impact of immigration on the labor market.” _Quarterly Journal of Economics_ 118(4): 1335-1174.

- [11] Borjas, George J. 2006. “Native internal migration and the labor market impact of immigration.” _Journal of Human Resources_ 56(2): 221-258.

- [12] Boustan, Leah Platt, Price V. Fishback and Shawn Kantor. 2010. “The effect of internal migration on local labor markets: American cities during the great depression.” _Journal of Labor Economics_ 28(4): 719-746.

- [13] US Bureau of Transportation Statistics. 2006. _Freight in America_ . United States Department of Transportation.

- [14] Buys, Piet, Uwe Deichmann and David Wheeler. 2010. “Road network upgrading and overland trade expansion in Sub-Saharan Africa.” _Journal of African Economies_ 19(3): 399-432.

- [15] Byrne, David and Eric Strobl. 2004. “Defining unemployment in developing countries: evidence from Trinidad and Tobago.” _Journal of Development Economics_ 73(1): 465476.

- [16] Cameron, A. Colin, Jonah B. Gelbach and Douglas L. Miller. 2008. “Bootstrap-based improvements for inference with clustered errors.” _Review of Economics and Statistics_ , 90(3): 414-427.

- [17] Card, David. 1990. “The impact of the Mariel Boatlift on the Miami labor market.” _Industrial and Labor Relations Review_ 43(2): 245-257.

36
# **III. Measurement Issues**

As pointed out earlier, the complexity of the processes surrounding both the demand and the supply of labor make it almost impossible to accurately measure discrimination. That being so, the most commonly used techniques are decompositions that rely on traditional and easy to procure labor market surveys. They are based on measures of discrimination derived from between-group comparisons of market outcomes for “favored vs. disfavored groups”, controlling for productivity-related individual characteristics (Salkever and Domino, 1997). Such analyses for the most part, look at earnings as the outcome of interest, while controlling for human capital endowments. Another way to get at discrimination is through audit studies, in which matched pairs of interviewees (actors) of different race or gender, respond to advertisements and attend interviews (see for example Yinger 1986, Neumark et al 1996). Well-known concerns with audit studies include imperfect matching (and training) of actors, the inability to double-blind (auditors may wish to please the researcher, and do not care about actually landing the job), expense (which limits both sample size and the scope of studies, which are typically focused on narrowlydefined occupations), and the need to deceive employers, which raises issues of informed consent (Kuhn and Shen, 2009).

Perhaps the most accurate way of measuring discrimination is through _experimental studies_ first conducted by Daniel (1968) and soon after by Jowell and Prescott-Clarke (1970). These type of studies simulate natural experiments to predict the behavior of individuals and groups in a particular setting. The spate of studies that followed have focused mainly on race and gender as the axes of discrimination, but there are increasing number of studies being conducted to test for discrimination based on ethnicity, immigrant or disability status and other axes. These studies do not attempt to provide any generalized picture of discrimination but have been instrumental in the widespread acceptance that discrimination exists. An increasing body of work suggests that in fact, people may not even be aware of the fact that they are discriminating or excluding and that a combination of social norms and practices “make people” behave in certain ways (World Bank 2014).

10
for women, but still sizeable. When it comes to the unemployment rate estimates, the estimated rank correlations also show improvements when comparing the SAE estimates to the direct estimates, increasing by 0.9 for men and 0.6 for women (from an admittedly low level). However, the estimated Pearson correlations show a marked decline, falling by 0.22 for men and 0.065 for women. This again reflects errors at the left and right tails of the true unemployment rate distribution, which the model does a very poor job of predicting.

|Table 5-Measure|s of Accurac<br>|y for Municip<br>|al Estimates<br>||
|---|---|---|---|---|
||(1)<br>Male<br>LFP|(2)<br>Female<br>LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
||Mean<br>(median)|Mean<br>(median)|Mean<br>(median)|Mean<br>(median)|
|**Estimated Relative Bias**|||||
|Direct|0.039|-1.017|-4.363|26.429|
||(0.306)|(-1.288)|(-59.069)|(-100.000)|
|SAE|-0.507|1.423|30.732|99.948|
||(-0.622)|(-0.726)|(9.651)|(40.522)|
|**Estimated mean absolute**|||||
|**error**|||||
|Direct|0.061|0.071|0.022|0.017|
||(0.046)|(0.054)|(0.015)|(0.009)|
|SAE|0.028|0.040|0.009|0.006|
||(0.022)|(0.028)|(0.006)|(0.005)|
|**Estimated Rank correlation**|||||
|Direct|0.471|0.623|0.363|0.324|
|SAE|0.541|0.750|0.497|0.264|
|**Estimated Pearson**<br>**correlation**|||||
|Direct|0.493|0.659|0.418|0.385|
|SAE|0.553|0.770|0.415|0.614|
|In-sample municipalities|1,072|1,072|1,072|1,072|

Only in-sample municipalities are included. Reported statistics are unweighted means, medians (in parentheses), or correlations across municipalities. Relative Bias is defined as 100 times the deviation of the estimated value and the true value divided by the true value. Rank or Pearson correlation is the unweighted rank or Pearson correlation between the estimated value and the actual municipal value.

The direct estimates of unemployment actually show negative estimated relative bias. This is likely driven by the fact that most AGEBs with a true unemployment rate of 1 percent are likely to have a sample with an unemployment rate of zero, leading to a consistent underestimate of the true rate. This is clear in Figure 2, which plots bias for male and female unemployment. Values of -100 indicate estimates of zero for unemployment. The direct estimates cluster disproportionately at this lower bound, indicating a large number of zeros. Although this averages out somewhat over municipalities (and different draws, which we return to below), the estimates for a single 19
number of NGOs as a proxy for outside options, and regressions in Appendix 2 use alternative outside option measures to check the empirical robustness. Fixed effect _μ_ purges region specific unobserved characteristics. To adjust seasonal working patterns (e.g., fishing in the peak season and farming in the slack season), it also controls for the months when interview took place.

Eq. (2) estimates career decision to continue fishing in 2009 conditional on those who were fishing in 2007 (see Table 3 for the career transition matrix).

where _q_ is the actual quality of aid boats; it equals one if the boat became unusable in 2009. Even in 2009, there would be a lack of job opportunities to become crew in villages with many bad boats, which might prevent households from remaining in fishing sector. If the aggregate shock posed a long-term constraint for fishermen’s job choices, β2 will be negatively signed. _X_ includes similar household characteristics as well as the fraction of fishermen in each village, a proxy for the size of fishing sector, capturing an agglomeration effect. l�sorig = 1� controls for sampling heterogeneity for the supplementary sample. _μ_ controls for the regional differences in the market environment and other unobservable factors.

Households' unobservable characteristics, such as mental conditions or risk preferences, might have driven job choices. To confirm the empirical robustness, endogenous switching regression is employed to account for the self-selectivity between two sectors. (see Appendix 3)

# _4.1  Endogenous self-financing_

Besides a risk-mitigation through labor supply responses, self-financing may be another optimal choice for agents to deal with the quality shocks depending on the level of wealth and/or an access to a private market. With this option, agents compare the relative payoffs of three options: fishing as crew (V1), fishing as an owner by buying a boat (V2), and working for non-fishing jobs (V3).

8
(4) firm productivity, _φit_ , which is unobserved by researchers but known to the firm when making import and investment decisions. The error term, _εit_ , captures measurement errors or unexpected productivity shocks.

As in OP, given the assumption that productivity evolves according to a Markov process, _φit_ is therefore monotonically increasing in investment when investment is larger than zero, allowing us to invert (10) to construct a control function for _φit_ : which can be substituted into (12) to arrive at the following specification: with where _φ_ ( _Iit_<sup>_W, Eit, K_</sup> _it_<sup>_W_) can be proxied semiparametrically using a higher-order polynomial function in</sup> the regressions. Although we cannot separately identify _D_ ( _Eit, Kit_<sup>_W_) and</sup><sup>_φit_, least squares estimation</sup> of (14) will still yield a consistent estimate of _κ_ . In practice, we estimate (14) for both processing and non-processing firms using Chinese customs transaction data from 2000 to 2016. For processing firms, we expect the import coefficient to be close to one, while for non-processing firms, we anticipate the coefficient to be significantly less than one.

# **3.3 Results of DVAR Estimation**

Equation (14) serves as the foundation for our industry-level estimations, where we allow _κ_ to vary between processing and non-processing firms within the same industry. We test the hypothesis that _κ_ for processing firms is significantly higher than for non-processing firms and, moreover, that _κ_ for processing firms is not statistically different from one. Table 1 presents the pooled regression results based on (14). Column (1) shows the least squares estimation with only firm and year fixedeffects, yielding an estimated _κ_ of 0.756. In Columns (2) and (3), we separate ordinary exporters from processing exporters. The import share for ordinary exporters is estimated at 0.352, while for processing exporters it is significantly higher at 1.112. Columns (4) and (5) incorporate a thirdorder polynomial function of investment, capital, and exchange rates to better control for unobserved productivity. The import shares are lower, suggesting the polynomial function effectively accounts for productivity. Importantly, the import share for processing firms remains significantly higher than for ordinary firms, and is not statistically different from one, as expected.

12