rafmacalaba/datause-encoder-data huggingface dataset:
||_Num_|_ber of cou_|_ntry-pairs with non miss_|_ing data_|
|---|---|---|---|---|
|**Year**|**GDP defator**|**CPI**|**Gross Trade data**|**Value Added data**|
|1970|139|103|215|-|
|1980|171|126|215|-|
|1990|206|166|215|189|
|2000|239|205|215|189|
|2010|248|230|215|189|
|2015|247|227|215|189|

- **_gvcb = Foreign Value Added + Foreign Double Counting Terms_** . This is the foreign content of exports and is sometimes referred to as the backward index of GVC participation.

- **_gvce = gvcb + DVARF_**

- This first index of GVC participation accounts for both backward and forward GVC participation and is the sun of foreign content in export as well as the domestic content of export that are re-exported further.

- **_gvc_broad_** = gross exports - _davax_fin_ .

- This second index of GVC participation is broader than the previous one as it also includes **_davax_int_** . This index measures all exports that are either intermediate inputs or foreign content. It only excludes from gross trade flows the share of domestic value added that is exported as final good and directly consumed by the destination consumers.

# **4 Methodology/Empirical Strategy**

While for the descriptive statistics in the first section the degree of global inflation synchronization was measured by the correlation of each country inflation and the World inflation, here we focus on the bilateral linkages and compute the correlation of country-pair inflation series.

10-year windows are used as benchmark due to the need to use no overlapping windows in the regressions. The use of 10, 15 and 20-year shows similar patterns in the evolution of the inflation correlations. The use of 10 and 15-year windows allows both to capture long-term and short-run fluctuation in the evolution of inflation correlation.

10
30 Table 1. Import-weighted Average Applied ad valorem Tariffs,<sup>a</sup> Primary Agriculture, Processed Food and Other Importable Goods, 1988 to 2004 (percent)

|Income group:|Sector:|1988-90|1995-97|2002-04|
|---|---|---|---|---|
|Low-income countries|Primary agric|15|10|9|
||Processed food|40|16|23|
||Non-agr goods|26|16|13|
|Middle-income countries|Primary agric|14|15|13|
||Processed food|27|18|17|
||Non-agr goods|20|11|7|
|High-income countries|Primary agric|3|12|15|
||Processed food|7|9|5|
||Non-agr goods|4|3|2|

> a Unfortunately the ad valorem equivalent of specific tariffs are not included in these estimates, which means they underestimate the total rates, especially for high-income agricultural products.

Source: UNCTAD TRAINS database, accessed from www.wits.org
This paper has analyzed Indonesian teachers’ labor supply under a theoretical framework that is based on a government-dominated market with government-set wage rate and demand for teachers. This framework could explain the phenomenon of the overall over-supply of teachers but shortage of qualified teachers in Indonesia. The results from the structural estimates constructed under the framework are particularly useful for looking into the impact of the latest teacher law on the future education profile of Indonesian teaching force.

Using a sample of workers with college education in the Indonesian Labor Force Survey, this paper has found that the relative wage rate of teachers and that of the alternative occupations significantly influence college educated workers’ decision of becoming teachers in Indonesia. The large-scale pay increase promised by the law for teachers with college education will have a significant effect on attracting a collegeeducated labor force to join the teaching force. It is estimated that the wage rate set in the latest teacher law will be able to increase the share of teachers approximately from 16 percent to 30 percent of the college-educated labor force. In addition, the new government-set wage rate can sustain a pupil-teacher ratio of 24-25 pupils per teacher with college education, but will require a more than 30 percent increase in the teacher salary bill.

Finally, we conclude by highlighting that attracting a high caliber labor force into teaching is from the quality concern in the first place. Even though there is general recognition that learning outcomes and education quality are influenced by the ability of teachers who guide the learning process, whether qualification in terms of educational attainment is an appropriate measure for teaching ability is debatable. Even though there 26
<!-- Start of picture text --> Catchment<br>| Information Rainfall-Runoff Global Tide Model<br>Rainfall, Evaporation Model<br>& water extraction<br>| Runoff d/s boundary<br>| Catchment Water level at<br>: Water Flow at<br>Primaryand ,fe aE (<br>|S Pea Hydrodynamic MiccaicLaee Hydrodynamic<br>Data d/s boundary Model u/s boundary Model<br>Sources |<br>Cross-section<br>| Water Flow Water Flow<br>Salinity Salinity<br>boundary at u/s Salinity Model TCC Salinity Model<br>ry d/s<br>Regional 1-D models Bengal Model<br>| . | 2-D Bay of<br><!-- End of picture text -->
# **QUALITY OF LIFE AND HUMAN DEVELOPMENT**

This section presents an analysis of the quality of life and human development in Pará. The quality of life index ( _indice de qualidade de vida-_ IQV) is a composite index containing 21 indicators of socio-economic well-being related to outcomes in sectors, such as education, health, and water supply developed by IPLANCE. The length of the bar in Figure 4.5 indicates the advance made in the state during 1991-1999. Hence, the bottom of the bar shows the IQV in 1991 and the top the IQV in 1999. Great advances have occurred throughout the period and the poorest states have made much greater advances than rich states in Brazil.

**<u>Figure 4.5:   The Quality of Life Index, 1991-1999</u>**

<!-- Start of picture text --> 0.85<br>0.80<br>0.75<br>0.70<br>0.65<br>0.60<br>0.55<br>0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>Source: IPLANCE data.<br>DF SP SC RJ RS PR MG ES RR GO MS MT AP AM PA RO TO SE RN BA AC PE CE PB PI MA AL Northeast Brazil<br><!-- End of picture text --> The quality of life improved in the Amazon during the 1990s. Pará experienced an increase of 13.5 percent in IQV, slightly below the improvement in the quality of life in Brazil as a whole (14.7 percent) and the North region (14.6 percent). However, Pará improved less than other states in the region, such as Tocantins (Figure 4.6). This indicates that a possible catch-up effect in the quality of life is not at play for all states in Brazil.

The Human Development Index (HDI) reveals general improvement has taken place in Pará but at a slightly slower pace than in other states **.** Figure 4.7 and Table 4.6a reveal that Pará’s human development indicator of 0.72 is slightly below the Brazil average of 0.76 in 2000. In 2000, Pará placed 0.12 points below Brasilia that has the biggest HDI in Brazil, and 0.9 points above Alagoas, the state with the lowest HDI. Furthermore, Pará has advanced less rapidly than other states during 1991-2000. The average Brazilian growth rate of the HDI was 10.9 percent and Pará advance less (8.2 percent) in the same period. In 1991, Pará was placed number 16 in the Brazilian ranking of HDI and during the 90’s it dropped one place in the ranking reaching the 17<sup>th</sup> place of the 27 states.

25
Hou, Y., and Png, I. P. L. 2022. “Learning to Let Go: A Field Experiment on Benchmarking and Entrepreneur Exit.” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4062428 Iacovone, L., Maloney, W. and McKenzie, D. 2022. “Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia.” _Review of Economic Studies_ , 89(1): 346-371.

Kremer, M., Rao, G. and F. Schilbach F. 2019. “Behavioral Development Economics,” in Handbook of Behavioral Economics: Applications and Foundations 1, Elsevier, vol. 2, 345–458.

Lemos, R. and Scur, D. 2018. “All in the Family? CEO Succession and Firm Organization.” PEDL Research Papers.

McKenzie, D. 2021. “Small Business Training to Improve Management Practices in Developing Countries: Reassessing the Evidence for "Training Doesn't Work." _Oxford Review of Economic Policy_ , 37(2): 276-301.

McKenzie, D. and Woodruff, C. 2014. “What Are We Learning from Business Training Evaluations Around the Developing World?” _World Bank Research Observer_ , 29(1): 48-82.

McKenzie, D. and Woodruff, C. 2016. “Business Practices in Small Firms in Developing Countries.” _Management Science_ , 63(9): 2967-2981.

Seither, J. 2021. “Keeping up with the Joneses: Economic Impacts of Overconfidence in Microentrepreneurs.” NOVAFRICA Working Paper.

15
# **1. Introduction**

Between 1989 and 2000, almost 2,300 firms with a market capitalization of over eight trillion U.S. dollars “internationalized” by cross-listing, issuing depositary receipts, or raising equity capital in major financial centers. These “international firms” account for more than 40 percent of the market capitalization of their home markets and in many countries value traded abroad exceeds domestic market activity. Yet, there are sharp disagreements over the causes and effects of internationalization.

To distinguish among theories of internationalization, we provide the first documentation of the evolution of Tobin’s _q_ and its components – corporate assets, market capitalization, and debt – before, during, and after firms internationalize. We also examine the time-series patterns of international firms relative to those of “domestic firms” (firms that do not internationalize) and thus abstract from country-specific factors influencing all firms within a country. To conduct the analysis, we compile a new database of over 9,000 international and domestic firms across 74 countries over the period 1989-2000, comprising almost 67,000 firm-year observations.

The major findings are as follows. First, on average, firms that internationalize at some point in the sample have higher _q_ s than firms that never internationalize, but this difference exists years before firms actually access international equity markets. Thus, a country’s higher valued firms are more likely to internationalize than its lower valued firms. Second, when comparing the average value of _q_ in the years before firms internationalize with the average value of _q_ in the years after they internationalize, we find that _q_ does not change after internationalization, nor does it change relative to that of domestic firms. Thus, internationalization is not associated with an enduring change in _q_ . Third, when tracing out the 1
17 point estimate in column 5 is 0.00875. The average number of discoveries per country-yearresource in our full dataset is 0.01. Simply as a means of gauging the magnitude of the estimated coefficient, this indicates an increase of 87.5% in the average number of discoveries. This already suggests a large impact of openness on discoveries.

**Table 3: The Impact of Liberalization on Resource Discoveries (First stage of 2SLS)**

|VARIABLES|(1)<br>SW/Wacziarg<br>Openness|(2)<br>SW/Wacziarg<br>Openness|(3)<br>SW/Wacziarg<br>Openness|(4)<br>SW/Wacziarg<br>Openness|(5)<br>SW/Wacziarg<br>Openness|
|---|---|---|---|---|---|
|Distance‐Weighted Average Openness, lagged|0.928***|0.831***|0.836***|0.833***|0.928***|
||(0.053)|(0.012)|(0.012)|(0.012)|(0.069)|
|Distance‐Weighted Growth Closed Economies, lagged|‐0.169***|‐0.098|‐0.113|‐0.116|‐0.172***|
||(0.053)|(0.086)|(0.086)|(0.086)|(0.052)|
|Country FE|/|/|/|/|YES|
|Year FE|YES|YES|YES|YES|/|
|Natural Resource FE|/|/|/|/|/|
|Country by Natural Resource FE|YES|YES|YES|YES|/|
|Year by Natural Resource FE|/|/|/|/|YES|
|Controls||(log) Price lagged|(log) Price lagged,<br>Discoveries Stock<br>lagged|<br>(log) Price,<br>Discoveries Stock<br>lagged,<br>Discoveries Stock|<br> <br>Discoveries Stock<br>lagged,<br>Discoveries Stock<br>Squared lagged|
|Observations|157,284|57,976|57,976|57,976|157,284|
|F‐Stat (2, 1801) Excluded Instruments|9192|2508|2560|2484|9388|
|Anderson‐Rubin Wald Test (p‐value)|0|0|0|0|0|
|R‐squared|0.6589|0.7157|0.7163|0.7165|0.6591|
|Error Clusterin|Year by Natural|Year by Natural|Year by Natural|Year by Natural|Year by Natural|
|g|Resource|Resource|Resource|Resource|Resource|

Note: The table reports the first stage of a 2SLS estimation where distance weighted average openness and distance weighted growth of closed economies are used as excluded instruments for a country’s SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include natural resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). The Anderson-Rubin Wald statistics allows for a test of the joint significance of the excluded instruments in a reduced-form estimation, where the null hypothesis is that the coefficients of the excluded instruments are jointly equal to zero. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

We now turn to the instrumental variable results, which are based on the identification assumptions discussed previously. Table 3 shows the first-stage of the 2SLS estimates. The coefficients on distance-weighted average openness and on distance-weighted growth of closed economies have the expected sign. The former is always highly significant and positive while the latter is only significant in columns (1) and (5) which allow for longer timer series. Overall, the high F-statistics (of the excluded instruments) indicate that the instruments are highly correlated with the endogenous variable.
_Note:_ Figures are based on the Europe and Central Asia extension of the Human Capital Index extension of (ECAHCI).

**Table 9 Gender-disaggregated estimates of the Europe and Central Asia extension of the Human Capital Index (ECA-HCI)**

||_Probab_<br>_survival t_|_ility of_<br>_o age 5_|_Educa_<br>_compo_|_tion_<br>_nent_|_Health co_|_mponent_|_EC_|_A-HCI_|
|---|---|---|---|---|---|---|---|---|
|_Subregion/country_|_Men_|_Women_|_Men_|_Women_|_Men_|_Women_|_Men_|_Women_|
|**Central Asia**|**0.978**|**0.983**|**0.433**|**0.434**|**0.920**|**0.961**|**0.390**|**0.411**|
|Kazakhstan|0.989|0.991|0.449|0.474|0.900|0.956|0.399|0.449|
|Kyrgyz Republic|0.979|0.983|0.424|0.442|0.909|0.962|0.378|0.418|
|Tajikistan|0.961|0.969|–|–|–|–|–|–|
|Uzbekistan|0.976|0.982|0.426|0.411|0.933|0.964|0.388|0.389|
|**Central Europe and Baltic**<br>**countries**|**0.995**|**0.995**|**0.503**|**0.549**|**0.909**|**0.957**|**0.456**|**0.524**|
|Bulgaria|0.992|0.994|0.431|0.457|0.914|0.953|0.391|0.432|
|Croatia|0.995|0.996|0.479|0.525|0.921|0.961|0.439|0.502|
|Czech Republic|0.996|0.997|0.523|0.570|0.921|0.957|0.480|0.544|
|Estonia|0.997|0.998|0.579|0.641|0.894|0.954|0.516|0.610|
|Hungary|0.995|0.996|0.487|0.509|0.910|0.953|0.441|0.483|
|Latvia|0.996|0.996|0.521|0.600|0.873|0.945|0.453|0.565|
|Lithuania|0.996|0.996|0.562|0.625|0.873|0.952|0.488|0.592|
|Poland|0.995|0.996|0.557|0.622|0.923|0.964|0.512|0.597|
|Romania|0.992|0.993|0.418|0.436|0.877|0.949|0.364|0.411|
|Slovak Republic|0.994|0.995|0.470|0.518|0.911|0.958|0.426|0.494|
|Slovenia|0.998|0.998|0.533|0.614|0.923|0.959|0.491|0.588|
|**Eastern Europe**|**0.991**|**0.993**|**0.517**|**0.551**|**0.860**|**0.942**|**0.441**|**0.515**|
|<br>Belarus|0.996|0.997|0.534|0.560|0.863|0.943|0.459|0.526|
|Moldova|0.982|0.986|0.419|0.443|0.888|0.953|0.366|0.416|
|Ukraine|0.990|0.992|0.521|0.558|0.857|0.940|0.443|0.520|
|**Northern Europe**|**0.997**|**0.997**|**0.570**|**0.643**|**0.921**|**0.950**|**0.523**|**0.610**|
|Denmark|0.995|0.996|0.575|0.647|0.912|0.940|0.522|0.606|
|Finland|0.998|0.998|0.553|0.644|0.907|0.949|0.500|0.611|
|Iceland|0.998|0.998|0.536|0.621|0.934|0.953|0.500|0.591|
|Norway|0.997|0.998|0.557|0.630|0.911|0.939|0.506|0.590|
|Sweden|0.997|0.998|0.584|0.648|0.939|0.962|0.546|0.622|
|**Russian Federation**|**0.992**|**0.994**|**0.582**|**0.623**|**0.840**|**0.917**|**0.485**|**0.568**|
|**South Caucasus**|**0.981**|**0.985**|**0.417**|**0.426**|**0.908**|**0.958**|**0.371**|**0.402**|
|Armenia|0.986|0.989|0.401|0.427|0.912|0.967|0.361|0.409|
|Azerbaijan|0.976|0.981|0.416|0.412|0.921|0.957|0.374|0.387|
|<br>Georgia|0.989|0.991|0.431|0.461|0.871|0.954|0.371|0.436|
|**Southern Europe**|**0.997**|**0.997**|**0.503**|**0.534**|**0.945**|**0.968**|**0.473**|**0.515**|
|Cyprus|0.997|0.998|0.574|0.604|0.944|0.974|0.541|0.586|
|Greece|0.995|0.996|0.499|0.540|0.929|0.961|0.462|0.517|

27
measure of transport costs that separates freight costs from insurance costs and thus improves upon that used by Bernard et al. (2006a) for U.S. industries and can confidently be considered exogenous to quality upgrading.

Our findings suggest that increased exposure to import competition, including that from China and India, may be beneficial by encouraging producers to follow the “high road” to competitiveness (Pietrobelli and Rabellotti, 2006). Taking into account the evidence provided by Iacovone and Javorcik (2008) that Mexican plants invest in product quality upgrading before they export, our findings suggest that over time plants - including those with no export experience - may be able to progressively target more sophisticated export markets. However, our evidence also suggests that import competition may be insufficient to enable quality upgrading where the technology gap between foreign competitors and local producers is high. Other policy tools will be necessary to encourage more radical innovation in products.

The remainder of the paper proceeds as follows. Section 2 describes the data and Section 3 presents the empirical specification. Section 4 discusses our main results, robustness tests, evidence of quality upgrading, and the imports-as-market-discipline hypothesis. Section 5 examines the differential impacts of import competition by type of exporting country and by type of plant and product. Section 6 concludes.

# **2. Data**

# **2.1. Plant Unit Values and Other Information**

In our analysis, we use a dataset with information on products at the plant level from 1997 to 2003 that is merged with the annual manufacturing census of Chilean plants with more than 10 employees (ENIA). Both datasets are provided and collected by the Chilean National Statistical Office. The products dataset includes information for each plant and year on the physical quantity 6
Table 15: Business dynamism and SOE concentration at the sectoral level (2d), 2016-2019.

||Entry rate|Exit rate|Share young|SD(growth)|Creation|Destr.|Net change|Realloc.|HHI|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Share of emp. in BOS|0.0355|-0.0677|-0.319**|-0.581|0.650***|-0.703*|1.353***|-0.0526|0.290***|
||(0.0785)|(0.0492)|(0.157)|(3.112)|(0.209)|(0.410)|(0.394)|(0.518)|(0.0463)|
|Average size|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Average age|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector emp. share|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector 2d|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|340|340|340|340|340|340|340|340|340|
|R-squared|0.943|0.843|0.982|0.443|0.892|0.754|0.640|0.883|0.992|

_Note:_ Dependent variables at the 2-digit industry level are, for each column, entry rate, exit rate, share of employment in firms up to 5 years of age, standard deviation of employment growth rate, gross job creation, gross job destruction, net employment change, job reallocation, and the Herfindahl–Hirschman index (HHI). The share of employment in SOEs is the fraction of employment in all SOEs relative to total employment. All models include year and sector-fixed effects. Data includes private sectors from RAIS, 2016-2019. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.
1989-2018 US Census imports data and showing that the low elasticity further decreases over time. To this goal, we first estimate the sea-distance elasticity of ad-valorem and unit-cost maritime transport costs _τodkt_ at the HS6 level _k_ : where subscripts _o_ , _d_ , _k_ , _t_ denote country of origin, destination port, product and year, respectively; _Y_ , ( _LANG_ ), ( _FTA_ ) are time-varying gravity equation variables from the “Dynamic Gravity Dataset" denoting GDP, common language, and free trade agreement, respectively; ( _SeaDist_ ) is the CERDI country level bilateral sea distance;<sup>17</sup> _WgtIModktodkt_<sup>istheratioofimportcharges(CIF)toimportweight</sup> measured in kilograms; and _θt_ and _νk_ are the year and HS6 product fixed effects, respectively. To explore how it varies over time, we then added the distance-year interaction terms, ln( _SeaDistod_ ) _× θt_ . The Chow-Test confirmed that it is time-variant since the interaction terms significantly improve the model. The full set of estimates is reported in Appendix Table 6.

Figure 9 illustrates the results with the year-specific elasticities. The left panel demonstrates that the average decline in the distance elasticity of ad-valorem transportation cost is approximately _−_ 0.003 per year or _−_ 0.09 over the entire thirty-year interval. The downward trend was even more pronounced for the distance elasticity of the per-unit transportation cost. As demonstrated by the right panel, this elasticity fell, on average, by _−_ 0.007 per year or _−_ 0.21 over the same three decades. To the best of our knowledge, the decreasing trends in these elasticities and the negative distance elasticity of the transportation costs have not been documented before. This trend is paralleled by the increasing share of low-income and remote countries in global trade. Since low-income and remote countries specialize in lighter goods (Lashkaripour, 2020), their growing share may cause the distance elasticity to decline. A more comprehensive explanation of the decreasing trend requires further investigation. To summarize:

> 17 See https://halshs.archives-ouvertes.fr/halshs-01288748/document.

16