rafmacalaba/datause-encoder-data huggingface dataset:
reflecting mainly the steady increase in the country's relative wealth (especially since the early 1980s). In contrast, persistent current account deficits have resulted in an increasingly large debtor position for the **U.S** after the mid 1980s.

# **_5._ Conclusions**

This paper proposes to consider external sustainability from the perspective of equilibrium in net foreign asset positions. According to this approach, an external situation is sustainable if two conditions are met. First, in the long run, international and domestic investors achieve their desired portfolio allocation of assets across countries. And second, in the short run, the current and capital-account flows are consistent with the reallocation of international capital to achieve the investors' desired stock positions.

Based on a standard Markowitz-Tobin model of portfolio diversification, the paper develops a reduced-form model of net foreign asset positions. The model yields a long-run equilibrium condition in which the ratio of **NFA** to the total wealth of domestic residents is a negative function of investment returns in the country relative to the rest of the world, a positive function of investment risk in the country relative to the rest of the world, and an inverse function of the ratio of foreign-owned to domestic-owned wealth. This long-run equilibrium condition guides the empirical analysis of the paper.

In order to estimate the equilibrium condition, the paper uses a newly constructed data set of foreign asset and liability stocks for a large group of industrial and developing countries spanning the period from the 1960s to the present (see Kraay, Loayza, Serv6n, and Ventura **1999.)** With these data and estimates for the total capital stock in each country, the paper obtains a measure of the wealth of domestic residents in a country. In addition, the paper develops summary _measures_ of country returns and risks. These are based on a comprehensive set of macroeconomic, policy, and institutional variables.

The econometric methodology of the paper is an application of the Pooled Mean Group estimator recently proposed **by** Pesaran, Shin, and Smith **(1999).** As a panel error-correction estimator, this method is well suited to the paper's objective given that it combines estimation of common long-run relationships across countries with the flexibility of estimating countryspecific short-run dynamics.

**26**
Table 5: Employment Effects of the Tax Cut for New Entrant and Incumbent Workers and Firms

||(1)<br>All frms|(2)<br>Employmen<br>Low TFP|(3)<br>t<br>High TFP|
|---|---|---|---|
|**Panel A: New entrant or incumbent workers**||||
|New entrant workers|0.0015<sup>_∗∗∗_</sup>|0.0014<sup>_∗∗∗_</sup>|0.0001|
||[0.0002]|[0.0002]|[0.0001]|
||_⟨_0.0425_⟩_|_⟨_0.0267_⟩_|_⟨_0.0159_⟩_|
|Incumbent workers|0.0038<sup>_∗∗∗_</sup>|0.0039<sup>_∗∗∗_</sup>|-0.0001|
||[0.0005]|[0.0004]|[0.0004]|
||_⟨_0.2873_⟩_|_⟨_0.1409_⟩_|_⟨_0.1464_⟩_|
|**Panel B: New entrant or incumbent frms**||||
|New entrant frms|0.0001|0.0002<sup>_∗_</sup>|-0.0001<sup>_∗∗∗_</sup>|
||[0.0001]|[0.0001]|[0.00004]|
||_⟨_0.0054_⟩_|_⟨_0.0045_⟩_|_⟨_0.0008_⟩_|
|Incumbent frms|0.0052<sup>_∗∗∗_</sup>|0.0051<sup>_∗∗∗_</sup>|0.0001|
||[0.0005]|[0.0005]|[0.0004]|
||_⟨_0.3247_⟩_|_⟨_0.1625_⟩_|_⟨_0.1622_⟩_|
|**Panel C: Firms established before or after 2012**||||
|Firms established after 2012|-0.0001|0.0002<sup>_∗_</sup>|-0.0003<sup>_∗∗∗_</sup>|
||[0.0001]|[0.0001]|[0.0001]|
||_⟨_0_⟩_|_⟨_0_⟩_|_⟨_0_⟩_|
|Firms existed in 2012|0.0053<sup>_∗∗∗_</sup>|0.0051<sup>_∗∗∗_</sup>|0.0002|
||[0.0004]|[0.0004]|[0.0004]|
||_⟨_0.3301_⟩_|_⟨_0.1670_⟩_|_⟨_0.1631_⟩_|
|_∗p <_0_._1, _∗∗p <_0_._05, _∗∗∗p <_0_._01||||

_Note_ : Table shows difference-in-differences estimates of the impact of the payroll tax cut on private sector employment based on estimating equation (9) for all firms (column 1) and separately for below-median (column 2) and above-median (column 3) TFP firms and for new entrant and incumbent workers and firms. In Panel A we study the change in employment for new entrants, who entered the labor market in the current year and so have less than 12 months employment, and for incumbent workers who have been continuously employed in the previous 12 months. In panel B we study the impact separately for new entrant firms, which were established in the current year and incumbent firms, which already existed in the previous year. In panel C we study separately the employment change at firms that existed before the payroll tax cut and at firms that were established after the payroll tax cut. In each panel the sum of new entrants and incumbents adds up to total employment and the employment rate in each of these categories (relative to the total population) in May 2012 is shown in angle brackets. In panel C the employment rate is zero because there is no employment in May 2012 at firms established after 2012. The difference-in-differences estimate compares the change in employment among the 55 to 57 age group that was affected by the payroll tax cut with the change in employment among the 52 to 54 age group that was not affected by the tax cut. Standard errors are reported in brackets, clustered at the age _×_ period level. ( _N_ = 9 _,_ 003 _,_ 984 individual-months) 58
Product standards can have a dual impact on costs, and therefore on trade. On the one hand, they may impose additional fixed costs (or even variable costs) on exporters as it may be necessary to alter production processes to adapt products to such standards in the importing country. Moreover, a producer willing to export to several markets may be confronted to idiosyncratic standards specific to each markets, and compliance with them may further inflate production costs.<sup>17</sup> Moreover, certification aiming to certify compliance with this set of rules can generate additional costs for the exporter.

On the other hand, product standards can potentially reduce exporter’s information costs if they convey information as to industrial requirements, or consumer tastes in the importing country, that would be costly to collect in the absence of standards. Compared to other sectors where the complexity of goods is lower, such as textiles and clothing, the information conveyed by standards in the electronic sector may be relatively more valuable,—as it would facilitate the performance and interoperability of devices and components— and the benefits of standardization seem to outpace its compliance costs.

# **4.1. Empirical Model**

To examine the differentiated impact of internationally harmonized and non-harmonized standards on EU imports of electronic goods, we use a standard gravity model of international trade applied to data on EU-15 imports from the rest of the world<sup>18</sup> . Individual EU standards in electronics often tend to be applied across numerous product lines, which make it convenient to aggregate the trade data to a higher level of generality.

As starting point, we take the micro-founded gravity model formulation of Anderson and Van Wincoop (2003, 2004) and adapt it to the electronics sector: where: _M ijtk_ = Country j imports from country i in category k for year t; _Yitk_ = Production of country i in sector k for year t; _E kjt_ = Expenditure of country j in category k for year t; _k_<sup>_Y_</sup> _t_ = Aggregate (world) output in category k for year t;  _k_ = Elasticity of substitution in category k;<sup>_t_</sup> _ijtk_ = Trade costs facing exports from country i to country j in category k for _k k_ year t; ;  _it_ = Country i’s output share in category k for year t;  _jt_ = Country j’s _k_ expenditure share in category k for year t; and  _ijt_ = Random error term, satisfying the usual assumptions. The inward resistance term,  _Pjtk_  1  _k_   _N_   _it k_  1  _itk_  _tijtk_ 1  _k_ , captures _i_  1 the dependence of country j imports on trade costs across all suppliers. By contrast, the

> 17 This assumption could be tested if a database similar to the one in the EU would exist for other markets. 18 Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, ,Sweden, and  United Kingdom.

11
change specific measures simply because they happen to be included in an aggregate indicator that they wish to influence. Of course, we do not argue that _onl_ y perceptions data are relevant for measuring governance. Rather, as we discuss in more detail in Section 2.2 below, our point is simply that this type of data brings valuable insights that can be used in conjunction with other types of information to measure governance across countries and over time.

The individual indicators from the 35 data sources are assigned to the six dimensions of governance and are combined into six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM). The statistical methodology converts the data sources into common units and constructs a weighted average that combines the information in each of the data sources. The methodology also produces margins of error that capture the unavoidable imprecision involved with measuring governance across countries. This imprecision is not unique to the WGI, but rather is likely to be pervasive in any efforts to measure governance and institutional quality across countries. A key attribute of the WGI is that these margins of error are explicitly reported alongside the estimates of governance and should be considered when comparing estimates of governance across countries and over time using the WGI. In contrast, in many other measures of governance and institutional quality, they are left implicit.

The WGI are designed to enable broad cross-country and over-time comparisons of perceptions of governance, reflecting the synthesis of views across many existing data sources, and with due regard for margins of error. At the same time, there is a wealth of information in the individual data sources themselves that can usefully be explored for understanding the factors behind overall patterns summarized in the aggregate indicators. For this reason, the component data of the WGI are readily available through the WGI website, and users are encouraged to consult this data alongside the aggregate indicators. In addition, the WGI are not designed to be a tool to evaluate specific governance reforms in individual economies – for this purpose, the WGI should be supplemented with more granular country-specific data and analysis that can shed light on the likely impacts of specific policy and institutional reforms.

This paper also updates and expands earlier evidence we have provided on three key methodological issues relating to the WGI: (1) the possibility that different data sources have correlated measurement errors, with implications for the weighting scheme and precision of the aggregate indicators; (2) the possibility that the aggregate indicators might change substantially with alternative reasonable weighting schemes, with implications for the robustness of the cross-country and over-time patterns in the six 2
_<u>Table 12: Sectoral effects of Nicaragua trade liberalization scenarios with perfect factor mobility</u>_

||<br>**Sectoral**<br>|<br>**tm**|**Impor**|**ts and**|**Local**|**Sales**|**Expor**|**ts and**|**produ**|**ction**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Output**||Δ**M**|**M/D**|Δ**S**|Δ**Pd**|ΔΕ**x **|**Ex/X**|Δ**Xp**|Δ**Px**|
|||||**FullU**|**nilater **|**al Lib**|**eralizat**|**ion**|||
|**Agriculture**|17|12|32|7|-2.6|-1.4|19|23|1.8|-0.6|
|**Food Processing**|15|5|9|21|-1.1|-1.2|4|28|0.1|-1.0|
|**Mining and Energy**|4|0|-4|48|-0.4|-0.6|10|12|0.9|-0.4|
|**Other Manufacturing**|12|3|3|57|-2.2|-1.3|8|21|-0.4|-0.9|
|**Services**|53|0|0|3|0.3|-0.2|3|2|0.4|-0.2|
|**Total**|100|3|4|23|-0.6|-0.7|10|12|0|0|
||||||**C**|**AFTA**|||||
|**Agriculture**|17|21|30|7|-0.9|1.4|-8|23|-2.3|1.0|
|**Food Processing**|15|8|5|21|0.3|0.6|27|28|7.1|1.0|
|**Mining and Energy**|4|2|1|48|0.4|1.6|0|12|0.4|1.6|
|**Other Manufacturing**|12|3|2|57|0.7|-0.2|5|21|1.4|0.0|
|**Services**|53|0|6|3|0.5|1.4|-4|2|0.4|1.4|
|**Total**|100|4|4|23|-0.6|-0.7|10|12|0.5|-0.5|

Sources: Author calculations from model results. Notes: The first two columns show pre-liberalization levels of sectoral outputs and tariff levels (in the top panel these are trade weighted averages of tariffs against all countries, in the bottom panel just against the U.S.). For a legend for Δ **M** , Δ **S** , Δ **Pd** , Δ **Ex** , Δ **Xp** , Δ **Px** see Table 10.

In the full liberalization case, unskilled workers experience an economy-wide increase in their real wages. In fact the positive 0.4% increment is an average of the real wage changes recorded in the segmented markets case. In that set-up non-farm unskilled workers were experiencing a 2.9% raise, but farm workers were facing a –3.9% cut in their wages. The contraction of agriculture does not help unskilled workers in the CAFTA case. Demand for their services increases in the non-farm sectors but not to a level sufficient to compensate for the job losses of the farm sector.

_<u>Table 13: Factor price changes with perfect factor</u>_ _<u>mobility</u>_

||Δ**P**|Δ(**P/CPI)**|Δ**P**|Δ(**P/CPI)**|
|---|---|---|---|---|
||**Full Liber**|**alization**|**CAF**|**TA**|
|**Skilled Labor**|1.1|2.5|3.0|2.5|
|**Unskilled Labor**|-1.0|0.4|-1.6|-2.1|
|**Capital**|1.8|3.2|5.2|4.7|
|**Sk/Unsk wage gap**||2.2||4.7|
|**_Price indexes:_**|||||
|**Food price index**|-2.2||-0.1||
|**Non food price index**<br>**CPI**|-0.9<br>-1.4||0.9<br>0.5||

34
# **ANNEX 3: DESCRIPTIVE STATISTICS**

# **Table A.3.1 Chile: Descriptive Statistics of the Included Variables**

|**Variable**|**Mean**|**Max**|**Min**|**Obs**<br>**(no**<br>**weight)**|**Obs.**<br>**(weight)**|
|---|---|---|---|---|---|
|Innovative|0.42|1.00|0|<br>706|<br>4742|
|R&D active|0.17|1.00|0|706|4742|
|Patent activity|0.10|1.00|0|706|4742|
|Introduce new products|0.58|1.00|0|706|4742|
|Introduce new processes|0.55|1.00|0|706|4742|
|Located in capital area|0.58|1.00|0|706|4742|
|<br>Export active|0.28|1.00|0|706|4742|
|Firm age (ln age)|2.88|5.55|0|704|4728|
|Firm size (ln size)|3.52|8.54|0|689|4584|
|<br>Universities|0.04|1.00|0|706|4742|
|Consultants|0.06|1.00|0|706|4742|
|Suppliers|0.15|1.00|0|706|4742|
|Other firms|0.03|1.00|0|706|4742|
|R&D department|0.12|1.00|0|706|4742|
|Innovation intensity|0.71|82.05|0|689|4584|
|Investment in innovation education|0.17|1.00|0|706|4742|
|Permanent innovation activities|0.18|1.00|0|706|4742|
|Knowledge of managers rated high|0.76|1.00|0|706|4742|
|Knowledge of supervisors rated<br>high|0.48|1.00|0|706|4742|
|<br>Knowledge of workers rated high|0.25|1.00|0|706|4742|
|Managers education and training<br>rated high|0.40|1.00|0|706|4742|
|Supervisors education and training<br>rated high|0.42|1.00|0|706|4742|
|Worker education and training rated<br>high|0.45|1.00|0|706|4742|
|<br>Food, Beverages and Tobacco|0.25|1.00|0|706|4742|
|Textile,<br>Wearing<br>Apparel<br>and<br>Leather|0.18|1.00|0|706|4742|
|Wood and wood products, furniture|0.10|1.00|0|706|4742|
|Paper, printing and publishing|0.07|1.00|0|706|4742|
|Chemicals, petroleum, coal, rubber<br>and plastics|0.13|1.00|0|706|4742|
|<br>Non metallic mineral products,||1.00||||
|<br>except products of petroleum and<br>coal|0.04||0|706|4742|
|Basic metal industry|0.02|1.00|0|706|4742|
|Fabricated<br>metal<br>products,<br>machinery and equipment|0.18|1.00|0|706|4742|
|<br>Other manufacturingindustries|0.02|1.00|0|706|4742|

Source: Data from the 3<sup>rd</sup> National Survey of Technological Innovation in Firms in Chile.

24
data, using 8,000 local (country-level) informants. The composite index is currently produced for 183 countries. The country rankings are newsworthy, with over 7,000 accumulated citations in Google News.

The World Bank‘s ― _<u>Country Policy and Institutional Assessments</u>_ ‖ (CPIA) attempt to assess the quality of a country‘s policy and institutional environment. This is not a mashup index, but it is used to produce what is arguably the most important of any of the mashup indices of development. The CPIAL has 16 components in four clusters: economic management (macro management, fiscal and debt policies), structural policies (trade, finance, business and regulatory environment), policies for social inclusion and equity (gender equality, human resources, social protection, environmental sustainability) and governance (property rights, budgetary management, revenue mobilization, public administration, transparency and accountability in public sector). These are all based on ―expert assessments‖ made by the Bank‘s country teams, who prepare their proposed ratings, with written justifications, which are then reviewed.

Two mashup indices are produced from the CPIA. One of them is simply an equally weighted sum of the four cluster-specific indices, with equal weights on their sub-components. This appears to be only used for presentational purposes. The second index puts a weight of 0.68 on the governance cluster of the CPIA and 0.24 to the mean of the other three components (and the remaining weight goes to the Bank‘s assessment of the country‘s ―portfolio performance‖). This ―governance-heavy‖ mashup index based on the CPIA is used in allocate the World Bank‘s concessional lending, called ―International Development Association‖ (IDA), across IDA eligible countries. The African Development Bank has undertaken a similar CPIA exercise to guide its aid allocation decisions.

The _<u>Environmental Performance Index</u>_ (EPI), produced by teams at Columbia and Yale Universities, is probably the most well known mashup index of environmental data. This ranks 163 countries by a composite of 25 component series grouped under 10 headings: climate change, agriculture, fisheries, forestry, biodiversity and habitat, water, air pollution (each of the latter two having two components, one for effects on the ecosystem and one for health effects on humans) and the environmental burden of disease.

6
49 Table 7. Contributions to total TRIs from various policy instruments in developing and high-income countries, 1965–2010 (percent)

|_Developing_<br>_countries_|1965-<br>1971|’72|’73|’74|’75|’76|1977-<br>1983|’84|’85|’86|’87|’88|1989-<br>2004|’05|’06|’07|’08|’09|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Import tax|9|13|3|2|10|11|9|10|13|14|16|11|9|5|6|5|3|5|
|Export tax|19|15|29|32|26|17|22|28|20|26|32|30|9|3|4|11|13|10|
|Import subs.|-3|-3|-8|-7|-3|-2|-4|-2|-2|-1|-2|-2|-2|0|-1|-2|-2|-2|
|**All measures**|**25**|**22**|**20**|**28**|**37**|**25**|**27**|**39**|**32**|**38**|**45**|**39**|**15**|**5**|**4**|**11**|**11**|**9**|

|_High-income_<br>_countries_|1965-<br>1971|’72|’73|’74|’75|’76|1977-<br>1983|’84|’85|‘86|’87|’88|1989-<br>2004|’05|’06|’07|’08|’09|’10|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Import tax|36|30|21|17|25|36|38|41|42|62|61|52|36|17|13|10|0|1|1|
|Export subs.|-4|-4|-2|-1|-2|-2|-3|-3|-5|-8|-8|-6|-3|-1|-1|-1|0|0|0|
|**All measures**|**31**|**25**|**16**|**13**|**22**|**33**|**34**|**37**|**37**|**54**|**55**|**48**|**33**|**15**|**12**|**9**|**0**|**1**|**1**|

Source: Updated from Anderson and Nelgen 2012a, based on NRA estimates from Anderson and Nelgen 2012b.

Note: International agricultural prices spiked upwards in 1974 and 2008 and downwards in 1986. “All measures” includes farm domestic support/tax measures. Trade tax equivalents of quantitative measures are included in the trade tax rows.
Policy Research Working Paper 10206

# **Abstract**

This paper uses labor force survey data for 1995–2020 to analyze the dynamics of job tenure in seven transition economies of Europe and a comparator country (Türkiye). The country-specific age-period-cohort decomposition demonstrates that, except in Albania, the job tenure of the cohort of workers entering the labor market in the 2000s is four to nine years shorter than that of workers who started working in the 1970s. This difference is at least twice as large as the difference in job tenure observed among workers from the same cohorts in European Union countries. These trends in tenure persist after accounting for changes in cohort composition, but they are significantly attenuated by controlling for differences in individual worker characteristics. These results suggest that the evolution of tenure in the transition economies of Europe is still driven mainly by the transition-induced structural change processes in the labor market.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, mlokshin@worldbank.org, and itorre@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