rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7: Family Management and Management Practices Sub-scores**

|**Model**||**MG2**|**OLS wit**<br>**MG3 Level of**|**h Country FE (C**<br>**MG4**|**ross-section)**||||
|---|---|---|---|---|---|---|---|---|
|**Outcome Variable**|**MG1**<br>**Problem**<br>**resolution**|<br>**Number of**<br>**performance**<br>**indicators**<br>**monitored**|**ease or difficulty**<br>**to achieve**<br>**production or**<br>**service provision**<br>**targets**|**Knowledge of**<br>**production or**<br>**service**<br>**provision**<br>**targets**|**MG5**<br>**Basis of**<br>**bonuses**|**MG6 Length**<br>**of focus of**<br>**production**<br>**targets**|**MG7**<br>**Promotion**<br>**of non-**<br>**mangers**|**MG8**<br>**Dismissal**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|All management is<br>family Y/N|-0.020|-0.082***|-0.033|-0.076***|-0.053**|-0.029|-0.040|-0.026|
||(0.020)|(0.019)|(0.024)|(0.024)|(0.027)|(0.027)|(0.027)|(0.027)|
|Constant|0.584***|-0.048|0.177**|-0.002|0.382***|0.229**|0.727***|0.313***|
||(0.073)|(0.068)|(0.082)|(0.069)|(0.094)|(0.095)|(0.081)|(0.111)|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Number of observations|9,015|9,015|9,015|9,015|9,015|9,015|9,015|9,015|
|Adjusted R2|0.168|0.219|0.129|0.142|0.139|0.168|0.132|0.152|
|Share of family<br>management (0 to 1)|0.025|-0.065**|-0.038|-0.098***|-0.052|-0.040|-0.058*|-0.027|
||(0.028)|(0.025)|(0.030)|(0.030)|(0.035)|(0.036)|(0.035)|(0.036)|
|Constant|0.579***|-0.045|0.179**|0.007|0.385***|0.233**|0.732***|0.315***|
||(0.074)|(0.069)|(0.082)|(0.068)|(0.094)|(0.095)|(0.081)|(0.111)|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Number of observations|9,015|9,015|9,015|9,015|9,015|9,015|9,015|9,015|
|Adjusted R2|0.168|0.215|0.128|0.142|0.139|0.168|0.132|0.152|

note: *** p<0.01, ** p<0.05, * p<0.1, Standard errors clustered at the strata level. Survey weights are employed. Controls are the same as in the base regressions in Table 2 26
areas are listed so that a given number of households can be randomly chosen from the list to be interviewed. The advantage of this approach is that it only requires knowledge of any FDP residing in an enumeration area, so that this person can be included in the sampling frame. The more accurate the number of FDPs in an enumeration area before listing households, the more efficient is the resulting sample. This approach requires appropriate enumeration area maps that can be easily defined through satellite images for camps, but might not be available across the country for FDPs living in host communities. Implementation can be expensive especially with limited knowledge about the number of FDPs in enumeration areas. However, area-based sampling has the big advantage of not requiring FDPs to register. In contrast, list-based sampling uses an existing list of all FDPs and randomly chooses a sample among them. If additional characteristics are available, like location or country of origin, the sample can be stratified. However, the sample will only be representative of registered FDPs. Hence, it is rarely used in the context of IDPs unless a proper registration system is in place.

To partly address sampling issues, the UNHCR and the World Bank have been cooperating to use the UNHCR or national refugee registration systems as initial master samples to conduct consumption surveys, similarly to what is done with censuses of regular populations. Initial experiments in this respect have been conducted in Jordan, Lebanon, Iraq, Uganda, Kenya, Ethiopia, and South Sudan, resulting in consumption surveys and poverty analyses of refugees and IDPs. Both the UNHCR and the World Bank are now also working with statistical agencies in multiple countries across the Middle East and North Africa and SubSaharan Africa regions to include FDPs in national sampling frames with initial efforts conducted in countries such as Chad, Jordan, Kenya, Niger and Uganda (e.g., World Bank 2021b).

However, the political economy and data privacy can make these efforts challenging. For host countries, FDPs may be only one of the many marginalized groups they may be concerned with. With limited budgets, statistical agencies need to justify prioritizing one group over another. Furthermore, data privacy is particularly relevant for FDPs. National sampling frames are constructed based on census information, including personal information such as addresses, phone numbers, names as well as GPS locations in some cases. The UNHCR and national governments generally work together to collect registration data but there is some information that may not be shared between the two sides. For example, national statistical agencies protect the national sampling frame and cannot share the underlying cartography that would be required for UNCHR to amend the sampling frame with counts of FDPs. Even if both agencies would be able to create a trusting relationship allowing close collaboration, questions remain on whether FDPs would need to agree with sharing their information with a government agency. This becomes even more relevant in the context 8
product quality. To address it, we exploit a new dataset including rich information from census data on all the products manufactured by all Chilean plants during the 1997-2003 period. We follow the empirical trade literature and use unit values (prices) of products to measure their unobserved quality or sophistication.<sup>2</sup> The second challenge concerns the difficulty in identifying causal effects of import competition on quality upgrading as upgrading can itself affect whether and how much foreign competitors choose to export to the domestic market. To address it, we rely on an effective trade barrier measure - transport costs - which capture differences in import competition across industries that are exogenous to quality upgrading.

Our econometric approach exploits variation in transport costs across 4-digit industries and over time and consists of regressions of product unit values on a lagged measure of transport costs, a set of plant and industry control variables, as well as product, plant, year, and industryyear fixed effects. Importantly, our specifications identify impacts by establishing comparisons of unit values across plants _within_ product categories. No attempt is made to distinguish higherquality from lower-quality products since differences across products in units of measurement and other characteristics preclude the direct comparability of their unit values.

Our main finding is that import competition has a positive and significant impact on plantlevel product quality upgrading. The magnitude of the estimated impact increases as our sample is progressively restricted to include all plants but only the products that they neither start producing nor discontinue producing during the sample period (continued products) and then to include only the plants in the sample during the entire period of analysis (continuing plants) and their continued products. This difference in magnitudes suggests that products with less

> 2 Iacovone and Javorcik (2008), Kugler and Verhoogen (2008), and Lelarge and Nefussi (2008) use data on unit values of domestic or exported products to proxy for product or export quality at the plant level, while Kiyota (2008) and Schott (2008) use data on unit values of exports to proxy for export quality at the country level.

2
consistent with program take-up measures based on process evaluation or administrative data.<sup>23</sup> Importantly, Table 1 documents substantial windfall effects: participation in formal apprenticeship is in part explained by substitution out of other forms of human capital investments, such as youth in the treatment group substituting out of traditional apprenticeships. 22.5 percent of youth in the control group participate in traditional apprenticeships, a proportion that is reduced by 18.5 percentage points in the treatment group. The windfall effect is of substantial magnitude. It can also be expressed in relative terms, as the share of youths exiting traditional apprenticeships (18.5 percentage points) relatively to the share of youths entering formal apprenticeships (71.2 percentage points), or 26 percent. In other words, for each formal apprentice placed, 0.26 youths substituted out of traditional apprenticeship. Overall, the net program impact on the share of youth in any form of apprenticeship over the course of the experiment is 52.8 percentage points.

The program also induced a small substitution out of other forms of human capital investments such as schooling or technical and vocational training (TVET). The participation in TVET in the control group is quite low (7.2 percent), but the program has a relatively large impact in reducing this proportion by 5.7 percentage points.<sup>24</sup> Last, the program also has a significant and negative impact on youths attending school. 20.5 percent of youth in the control group report going to school, and this proportion is reduced by 5.7 percentage points in the treatment group.

Despite these indirect effects, a large proportion of youth makes no human capital investment of any form in the control group (51.4 percent). Offering participation in the program sharply reduces this proportion by 36.3 percentage points, approximately half the program take-up. In the end, only 12.3 percent of youth in the treatment group do not make any form of human capital investment.

So far, we have focused on impacts on entry into apprenticeship between the baseline and

> 23Appendix A2 and Table A5 provide additional information on take-up measures from other data sources.

> 24To build human capital investment variables, we use a question in the follow-up survey about participation in apprenticeship and TVET, as well as a separate question about youth participation in public apprenticeship programs. We classify as participation in formal apprenticeship those who are either in apprenticeship or TVET and say they were registered in a public apprenticeship program. Although not perfect, and subject to some recall bias, this is the classification that is most consistent with information from the process evaluation survey, for the subsample of treated youth that can be matched in both surveys. See Appendix A2 for a more detailed discussion.

17
Table 14: Effects of forecast errors and forecast growth on worker compensation and worker composition: positive versus negative demand shocks

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log<br>monthly<br>wage|log<br>hourly<br>wage|log<br>monthly<br>base|log (1 +<br>overtime<br>pay)|log (1+<br>other<br>pay)|log total<br>hours|share<br>with a<br>degree|person<br>FE|
||||wage||||||
|Weighted forecast error|||||||||
|- positive shocks|-0.0013|-0.0014|0.0073|0.0338|-0.0528|0.0138|0.0113**|-0.0244|
|- negative shocks|(0.0097)<br>0.0119** <br>(0.0053)|(0.0098)<br> 0.0115**<br>(0.0053)|(0.0061)<br>0.0059<br>(0.0045)|(0.0394)<br>0.0233<br>(0.0179)|(0.0455)<br>0.0434* <br>(0.0244)|(0.0153)<br> 0.0371***<br>(0.0086)|(0.0052)<br>0.0007<br>(0.0027)|(0.0240)<br>-0.0530**<br>(0.0223)|
|Weighted forecast growth|||||||||
|- positive shocks|0.0031<br>(0.0035)|0.0020<br>(0.0035)|-0.0009<br>(0.0025)|0.0614***<br>(0.0211)|0.0077<br>(0.0214)|0.0081<br>(0.0063)|-0.0006<br>(0.0018)|-0.0016<br>(0.0129)|
|- negative shocks|0.0047<br>(0.0030)|0.0045<br>(0.0030)|-0.0001<br>(0.0024)|0.0230*<br>(0.0120)|0.0255<br>(0.0157)|0.0137*** <br>(0.0051)|-0.0007<br>(0.0016)|-0.0204*<br>(0.0123)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|12631|
|N (firms)|9306|9306|9306|9306|9306|9306|9306|6012|
|Adj. R<sup>2</sup>|0.0211|0.0230|0.0555|0.0225|0.00970|0.0601|0.0237|0.0491|
|RSS|472.2|471.4|248.5|15007|16660|1711|105.3|1545|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
13 of FFS system in place. Some countries have operated a pure per diem FFS system.<sup>17</sup> The incentive in this case depends on whether reimbursement rates are set above the marginal cost of an additional day. If they are, hospitals have an incentive to keep patients in longer; if they are not, hospitals do not.<sup>18</sup> Other countries have a FFS system where providers are paid only per procedure. In this case, the incentive is to limit ALOS and treat patients more intensively on the days they are in hospital.<sup>19</sup> _Beds and bed occupancy rates_ . Hospitals paid by historical budgets have a strong incentive to maintain high bed stocks since budgets are often based (and were based under the old Semashko system in the ECA region) on the number of beds.20 However, they have little incentive to keep their beds full. The effect on beds and bedoccupancy rates of shifting from budgets to FFS or to PBP depends on how the payment change affects the number of inpatient days; the latter is equal to the product of the number of admissions ( _N_ ) and ALOS ( _S_ ), which by the bed occupancy constraint must also equal the number of beds ( _B_ ) multiplied by the bed occupancy rate ( _R_ ) times 365: Our hypothesized effects on inpatient days of shifting from budgets to FFS and PBP are ambiguous: shifting to PBP is hypothesized to raise admissions but reduce ALOS, while shifting to FFS is hypothesized to raise admissions (albeit by less) and may

> 17 This was the case, for example, in Latvia (until 1996) and the Slovak Republic.

> 18 The latter supply response was identified by Frank and Lave (1989) in the context of the American states’ Medicaid reimbursement policies.

> 19 Macedonia (1991) and the Czech Republic (1995-96), for example, introduced “pure” per procedure mechanisms. The fact that most countries who implemented FFS in our sample actually opted for mixed per diem/per procedure mechanisms during the period of study (e.g. Croatia, Estonia and Romania) makes even more ambiguous the expected impact of our broad FFS category on ALOS.

> 20 It is therefore of little surprise that the vast majority of the former communist countries of the ECA region entered the 1990s with an oversupply of hospital beds and excess capacity in general. See, among other reports, the World Health Organization’s _Health Systems in Transition_ (HiT) series.
consideration of issues rela ting to the W orking Party on the Accession of Poland. The Agreement on Im plementation of Article VI of GATT 1994 has carried through the provision to the current Antidum ping Agreement. The provision allows discriminatory treatment in the case of countries that ha ve a com plete or substantially complete government monopoly over international trade and where all domestic prices are fixed by the state.

Authorities administering antidumping legislation and investigations have generally taken advantage of this provision to reject inform ation on costs and prices provided by those countries considered to be non-m arket economies. As an alternative to the use of such price and cost information, they make their decision based on cost and price inform ation from third party surrogate countries with market economic systems. In each case, the market economy country chosen is to be at a level of developm ent comparable to that of the non-market economy that is subject to th e antidumping investigation. In some cases, these investigating authorities have deve loped and used synthetic cost and price information.<sup>19</sup>

# **B. Criteria of Market Economy**

“GATT (General Agreement on Tariffs and Trade) was designed by market econom ies for market economies.”<sup>20</sup> However, there is not a standa rd criterion for the m arket vs. non-market economy distinction. A comparison of legal criteria for m arket economy treatment in US, EU, Mexico and Malaysia is listed in table 4.

**UNCTAD Criteria.** In the Glossary of Customs Terms of the United Nations Conference on Trade and Development (UNCTAD), UN CTAD defines a market economy and nonmarket economy in the following ways:<sup>21</sup>

A national economy of a country is a m arket economy that relies heavily upon market forces to determine levels of production, consumption, inve stment and savings without government intervention.<sup>22</sup> A national econom y is a non-m arket economy in which the government seeks to determine economic activity largely through a mechanism of central planning, as in the former Soviet Union, in contrast to a market economy, which depends heavily upon market forces to allocate productive resources. In a “non-market” economy, production targets, prices, costs, invest ment allocations, raw m aterials, labor, international trade and most other economic aggregates are manipulated within a national economic plan drawn up by a central planning au thority; hence, the public sector m akes the major decisions affecting demand and supply within the national economy.<sup>23</sup>

> 19 John H. Jackson, William J. Davey, and Alan O. Sykes, p.721-722, supra note 4.

> 20  Polouektov, supra note 11.

> 21 UNCTAD’s Glossary of Customs Terms can be found in the Automated Systems for Customs Data (ASYCUDA). The ASYCUDA is developed by UNCTAD, and takes into account the international codes and standards developed by ISO (International Organization for Standardization), WCO (World Customs Organization) and the United Nations. See www.asycuda.org in details.

> 22 Ibid, see ‘market economy’.

> 23 Ibid, see ‘non-market economy’.

14
asymmetrically expand their export products with their “core products” being the most responsive to new export opportunities.

This prediction is confirmed in our dataset, which covers a period of a significant decline in export costs faced by Mexican firms. To show this, we calculate the rank of export varieties (in terms of their export value) within each firm in each period t. Then we calculate the rank of export varieties in terms of the increase in their export value between time t and t + 1. The correlation between the two ranks is 0.67 for firms exporting at least two varieties and 0.63 for firms exporting three or more varieties. In Table 7, we demonstrate that this positive correlation holds if we regress the rank of the export expansion between t and t + 1 on the importance (i.e. rank) of the variety at time t, controlling for year and firm fixed effects. Both of these observations suggests that Mexican exporters tend to expand the most their most important export products.

Table 7: Expanding exporters focus on core products

|Explained variable|Rank of expansion of export product|
|---|---|
|Rank of Export Product|0.3***|
|(lagged)|(.011)|
|Year FE|Yes|
|Firm FE|Yes|
|Robust SE|Yes|
|R-Squared|.44|
|No. Obs.|14,118|

This pattern is confirmed by the transition matrix presented in Table 8. The table shows that the most important export variety (in terms of export value) at time t is in 62% of cases also the variety with the largest increase in the export value in the next period. For the second most important variety, this probability is only 47%, while for the fifth largest variety it goes down to 17%.

# 4.2 Fringe products

Products that appear more distant from core competencies are more likely to be dropped from export markets. The existence of product heterogeneity is confirmed when analyzing products that are more likely to be dropped from export markets. In fact, while exports of the most important varieties tend to expand, exports of the least important 15
**Table A12. Earnings, in real terms  (2014q1 set to 100)**

|||20|14|||201|5|||201|6|||20|17||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||<br>Plac|<br>e of resi|<br>dence|||||||||
|Minsk|100|107.1|103.7|108.3|109.8|110.6|108.3|113.0|97.8|100.6|98.3|99.3|92.8|98.6|96.5|100.2|
|Large cities|100|105.1|104.5|105.8|92.6|96.7|94.9|94.9|85.3|89.7|90.3|89.6|82.9|89.9|90.5|94.2|
|Small cities|100|107.3|106.6|104.8|85.3|90.3|88.9|87.4|78.0|84.3|82.9|82.5|76.5|82.3|83.2|87.2|
|Rural area|100|107.7|109.8|107.0|93.4|100.4|102.0|96.3|85.6|92.7|92.9|88.8|84.6|94.3|96.1|95.8|
||||||||Age||||||||||
|Child|100|105.8|106.5|107.3|97.1|102.7|102.3|102.7|87.8|94.5|95.7|94.9|86.1|95.2|97.2|100.7|
|working age|100|108.0|108.3|109.5|96.5|100.8|100.3|100.4|89.3|94.3|94.6|94.1|87.4|94.8|95.6|97.7|
|Retiree|100|103.6|98.9|98.9|97.3|98.7|95.5|93.2|86.2|90.3|85.1|85.0|86.9|93.2|89.7|96.1|
|||||Emp|loymen|t status|for wo|rking ag|e popu|lation|||||||
|Employed|100|107.0|106.9|107.0|95.5|99.4|99.1|98.6|88.5|92.9|92.4|91.9|87.5|94.0|93.9|96.5|
|Partly employed|100|104.3|106.6|100.3|85.0|94.5|100.2|88.9|79.1|86.9|88.5|87.4|69.8|79.1|99.8|87.0|
|Nonemployed|100|103.3|104.8|106.9|101.1|104.7|103.3|103.8|82.5|87.8|90.3|87.8|80.2|90.1|89.7|91.9|
|||||Ed|ucation|level f|or work|ing age|popula|tion|||||||
|high education<br>secondary|100|106.5|103.2|109.5|93.9|99.1|95.3|98.6|91.9|96.4|93.8|96.4|88.2|95.4|91.7|95.7|
|specialized|100|108.6|110.9|110.2|99.0|101.5|102.1|100.9|85.1|91.7|92.8|90.1|83.3|89.7|93.2|94.4|
|Other|100|108.8|110.5|108.7|94.8|99.3|101.1|98.7|87.8|91.9|94.3|93.2|86.3|95.0|98.3|99.9|
|||||||Type of|househ|old (HH|)||||||||
|HH without|||||||||||||||||
|children|100|105.9|104.3|104.3|94.3|97.4|95.5|94.2|87.2|90.7|87.9|86.8|83.7|89.1|87.8|90.8|
|Retiree HH|100|103.3|98.9|100.8|90.3|92.7|90.2|90.4|87.4|92.0|84.4|83.0|84.4|92.5|88.3|88.8|
|HH with children|100|106.8|107.0|108.2|97.9|103.1|102.5|103.1|87.7|94.1|95.9|95.2|86.1|95.1|97.0|100.0|
|1 child|100|109.3|108.2|110.7|96.9|101.9|100.9|101.8|86.8|92.4|93.9|94.8|84.6|93.4|95.3|97.1|
|2 children<br>3 and more|100|105.9|108.6|109.8|99.1|104.4|105.6|105.4|87.3|95.2|98.5|94.3|87.8|98.2|99.3|104.3|
|children|100|110.8|118.6|113.3|89.0|100.4|95.2|93.2|91.9|99.8|96.9|94.5|83.7|89.2|95.1|100.0|
|singleparent|100|99.9|98.0|102.4|93.4|97.4|93.8|100.9|83.9|92.3|84.6|86.4|83.7|93.1|95.2|98.1|

Source: Authors’ estimates based on HSS data.

39