rafmacalaba/datause-encoder-data huggingface dataset:
same as that using real GDP growth. While the change in the domestic unemployment rate variable is the preferred measure of the domestic macroeconomic shock in the analysis of high-income economies of Bown and Crowley (2013a), the lack of good unemployment rate data for China and India in particular means that those countries are excluded from any analysis using the unemployment rate; furthermore, it shortens the available time series of data for other countries. For this reason, we generally emphasize the results which use real GDP growth as the measure of the domestic economy.

Columns (3) through (7) demonstrate the robustness of our baseline estimates for 1995-2010 to various other checks. Column (3) replaces the set of bilateral importer-exporter indicators with a set of importer indicators and a separate set of exporter indicators. The IRRs for the variables of interest exhibit little qualitative change from the baseline specification. Nevertheless, this approach also allows us to report the IRR on the indicator that the trading partner is China. The estimated IRR of 9.09 indicates that, controlling for other factors, China’s exporters are roughly nine times more likely than the omitted exporting country (defined as the median targeted exporter in the sample) to face import protection through TTBs.<sup>18</sup> In column (4), we omit bilateral import growth in order to examine the possibility that identifying foreign-induced shocks through inclusion of both foreign real GDP growth and import growth may be collinear. While omission of imports does increase the size of the effect for the estimated IRR on the foreign real GDP growth slightly, the estimated IRRs on the other variables of interest are virtually unchanged.

Column (5) presents an alternative characterization of the dependent variable by narrowing it to consider only the bilateral count of products subject to the antidumping policy alone. Specifically, we redefine the dependent variable to be the bilateral count of products subject to new antidumping investigations that result in imposed import restrictions, thereby leaving out the other TTB policies of safeguards and countervailing duties. As Table 2 indicates, the count of products subject to new antidumping protection in a year is considerably smaller than that of all temporary trade barriers, averaging almost 1 fewer product per year per trading partner. Nevertheless, most of our key results in Table 3 continue to hold even when restricting attention to antidumping in isolation. In particular, the IRRs for the percent change in the bilateral real exchange rate and domestic real GDP growth are

> 18 In this particular sample, the median targeted exporter was Australia, which cumulatively had 120 distinct HS06 product-importer combinations hit with new TTBs during this period, compared to 1446 HS06 productimporter combinations for China’s exporters.

<mark>16</mark>
There are quite marked differences between the NPCs in the four sub-periods. The early period from 1965 to 1973 was characterized by high nominal protection which reflected high but declining domestic prices and very low international prices. The second period of negative nominal protection from 1974 to 1980 was dominated by massive gyrations in world free market prices which first surged more than three times above domestic prices, collapsed to levels below domestic prices, and finally almost doubled in 1980. During the third period of moderate positive nominal protection from 1981 to 1987, world prices once again collapsed and a prolonged period of low prices which bottomed in 1985 ensued. In the last period of negative or low NPCs between 1988 and 1994, real domestic prices and real international prices expressed in dollars did not change very much from their levels in 1987, but real reference prices increased substantially owing to the acceleration of the rate of devaluation of the Rupee which commenced in about 1987. This suggests that the positive nominal protection during a number of earlier years was to some extent offsetting exchange rate overvaluation.

Table 7: **Mill sugar: average state level NPCs on importable and exportable hypotheses for standard subperiods,1964-94**

|Phases|UP|**Impor**<br>:Mahara-l<br>:shtra|**table**<br>TN|All<br>**India**|UP|**Exporta**<br>:Mahara;<br>:-shtra|**ble**<br> <br>TN<br>l|**All India**|
|---|---|---|---|---|---|---|---|---|
|65-73|2.17|1.85|1.96|2.03|3.11|2.57|2.72|2.85|
|74-80|0.82|0.79|0.77|**0.80**|0.96|0.92|0.90|**0.94**|
|81-87|1.48|1.36|1.36|1.40|2.00|1.80|1.79|1.87|
|88-94|0.95|0.91|0.86|0.92|1.20|1.14|1.08|1.15|
|65-94|1.41|1.27|1.29|1.34|1.90|1.67|1.69|1.78|

Sources and Notes: See Annexes 1 and 3 for details. The averages are simple averages of the NPC estimates for the individual years.

During the period studied, nominal protection was considerably higher in UP than in Maharashtra and Tamil Nadu, where the NPCs were about the same. This difference shows up for all the subperiods, and on both the importable and exportable hypotheses. At first sight it might seem that the reason is that in most years levy prices were higher ( by about 5 to 10 percent ) in UP than in Maharashtra and Tamil Nadu, but this was offset by consistently higher free market prices in UP, so that in most years the average of the prices on free market and levy sales did not differ much as between the states. The principal reason is the much longer distance between the UP sugar growing areas and the ports (as represented by Calcutta) by comparison with the relatively short distances between the sugar areas in Tamil Nadu and Madras, and between the sugar areas in Maharashtra and Bombay. Because we assumed that UP as well as Maharashtra and Tamil Nadu sugar would supply the port cities, the considerably higher transport costs from UP mean that mills there would receive lower net prices if, in free trade conditions, they would either be competing with imports in these cities, or exporting. This is reflected in reference prices which are generally _5_ to 10 percent lower than the reference prices in 35
empirical literature. Studies that investigate the effect of trade policy on productivity often argue that the exogeneity of the trade liberalization shock helps to identify a linkage without worrying too much about the endogeneity of tariffs or other forms of trade policy. I account for such an argument in my theoretical and empirical models, and still obtain tariffs to be endogenous.

I employ a basic political economy of trade protection model and also introduce two different channels of protection that lead to unilateral liberalization once they are removed. The extra channels are meant to capture the perceived benefit of protection to the governments even in the absence of political economy concerns. I first keep the liberalization channel very simple to ensure that my results are not driven by any specific assumptions or complications and then give more structure to it by modeling a learning-by-doing argument. The main result from my theoretical models, simple yet compelling, is that despite an exogenous unilateral trade liberalization shock that is common across sectors, we obtain a differentiated effect across sectoral protection based on productivity. Based on my theory, I predict that more productive sectors receive more protection and that the extent of liberalization is less for sectors that experience a higher productivity increase.

Next, I test and confirm these theoretical results using production, trade, and tariff data at the 4-digit ISIC industry level for Colombia between 1983 and 1998. I keep all of my estimations closely related to my theory and account for all the potential endogeneity problems by using relevant instruments and methodologies.

Finally, I estimate a system of equations and show that by not accounting for the endogeneity of trade policy with respect to productivity, we might underestimate the positive impact of trade reform on productivity. Thus, correcting for the endogeneity bias does not overturn the results in the early empirical literature but makes them somewhat stronger for Colombia which would be interesting to test for different countries as well.

As a natural extension to this paper, it would be useful to carefully model the effect of tariffs on productivity and obtain a more structural simultaneous equations model considering all the interdependencies between tariffs, productivity, and their determinants.

27
countries is based on the prediction of the Case III of the companion paper. We argued in the companion paper that there may be other factors that are unrelated to either originating countries or reference countries that affect country fund prices. With such "imperfect substitution", we postulated that there may be a factor related to noise trading activity that would be common to all finds. We attempt to capture this factor through the construction of the global fund index based on our sample of NIFs.

# **m.3** - **Results**

To test the hypotheses outlined above, the following regressions were run for each ith fund since their time of inception: Tables 1 la and 1 lb report the relative importance of the domestic, U.S. and the (total sample) global factors for the NIFs from developed and emerging countries respectively. Specifically, we report the calculated F values and their significance levels for the six hypotheses under investigation. Similar results are reported in Tables I Ic and I Id with the total sample global factor being replaced by the subgroup (developed or emerging) global factor for the corresponding group of NIFs.

Let us first review the importance of the U.S. factor in explaining the price returns of developed market NIFs (Table I la). The U.S. market factors' importance is significant for 8 16
Table 2. Global income inequality and the between-country component <u>(benchmark year 2002)</u>

||(1)|(2)|(3)|
|---|---|---|---|
||Global inequality<br>between individuals|The between-<br>country<br>component of<br>global inequality|Share of (2) in (1)<br>(in percent)|
|Relative mean deviation|0.517|0.450|87|
|Coefficient of variation|1.751|1.278|73|
|Standard deviation of log of||||
|incomes|1.234|0.982|80|
|Gini coefficient|0.655|0.557|85|
|Mehran measure|0.783|0.683|87|
|Piesch measure|0.591|0.494|84|
|Kakwani measure|0.357|0.274|77|
|Theil entropy measure|0.835|0.579|69|
|Theil mean logdeviation|0.846|0.562|66|

Source: World income distribution (WYD) database. All incomes expressed in 2002 international dollars.

# **4. The relative importance of country vs. income class**

_Predicting global income position based on knowledge of country and class (in the aggregate)_ As we have seen, one’s position depends on two factors: allocation to country and allocation to income class. We can write for _i_ -th individual living in _j_ -th country: where Pij = income position (percentile) in the world income distribution, mj = mean country income, Gj = national inequality (say, Gini coefficient), and Cij = person’s income class in country _j_ , and εij = the error term.<sup>19</sup>

> 19 As mentioned, because of  the large size of ventiles for India and China, not all percentiles contain exactly 1/100<sup>th</sup> of  world population. As an alternative, we ran regressions where Pij is the exact position in the world income distribution (e.g., at the 63.4 percentile, not as a part of the 64<sup>th</sup> percentle). The results are almost identical. They are available from the author on request.

13
Page3of 3 Table 13 ... Perturbations of Germany's Real Goverrnmnt Expenditures SI Si SI Si Sl Si gO DC DC DC GNP PGRP RS ER PXN CUR8AL GDP GDP PGNP IN PCOI POIL SYNCO1.-O1 0.0079 0.0243 0.0050 0.0181 0.0208 0.0652 0.0079 0.0697 0.0198 0.0279 0.0236 0.0184 SYN(S, S) 0.2874 0.0433 0.0846 0.0572 0.0451 0.3429 0.2572 0.1355 0.0406 0.1628 0.0488 0.0167 L**5.*1) 0.0711 0.0002 0.0023 0.0000 0.0001 0.0105 0.0141 0.0446 0.0001 0.0343 0.0001 0.0001 Llrk-5.-1) 0.2394 0.2255 0.1957 0.0022 0.2262 0.1126 0.3639 1.2232 0.2602 0.5686 0.2147 0.1419 ... Perturbations of the United States Target Monetary<sup>Base</sup> Si Si St Si SI Sl NO DC DC DC GNP PGRP RS ER PXE CURBAL GOP GDP PGNP IN PCON POIL SYN(.1.1) 0.0225 0.1494 0.0440 0.0075<sup>0.0625 0.0390 0.0134 0.0223 0.0714 0.0091 0.0110 0.0112</sup> Sym(+S,.S) 0.0650 0.1170 0.0395 0.0061 0.0937 0.0309 0.0863 0.1116 0.0487 0.0838 0.0080<sup>0.0146</sup> $'TMC.10.*1 **0** .1981 1 0797 0.4670 0.0226 0.4916 0.0886 )<sup>0.1753 0.4978 0.7410 0.1142 0.0094 0.0320</sup> LIN10,1) 1.6270 1.6153 1.6390 1.5845 1..091 1.7119<sup>0.9033 1.7252 1.7366 0.6758 1.5659 1.6091</sup> LIN(-10.,1) 1.6533 1.6972 1.7225 1.5901<sup>1.6947 1.7241 0.9538 1.9066 1.7410 0.7674 1.5640 1.6147</sup> LIN(+10,+5) 0.1957 0.2273 0.0650 0.0397<sup>0.0656 0.0496 0.1549 0.2033 0.0619 0.0563 0.0310 0.0366</sup> LIN(*S,+1) 1.4828 1.5624 1.6148 1.5695 1.6061 1.7015<sup>1.02241.5905 1.7228 0.7252 1.5537 1.5960</sup> w
# The Transition to Export-Led Growth in South Korea: 1954-1966

by Stephan Haggard* Byung-Kook Kim** and Chung-In Moon***

# Table of Contents

|I.|The Political Economy of Import-Substitution: 1954-1960|3|
|---|---|---|
|II.|The Second Republic: April 1960-May 1961|11|
|III.|Military Interregnum: May 1961-December 1964|12|
||Restructuring Politics<br>Restructuring Business-State Relations<br>Restructuring the Economic Bureaucracy<br>Toward Policy Reform<br>Pressure from the U.S.|13<br>15<br>17<br>19<br>21|
|IV.|Economic Policy Under Park Chung Hee: 1964-1966|X|
|V.|Conclusion|33|
|Refer|ences|36|

> * Associate Professor, Department of Government, Harvard University

> ** Visiting Assistant Professor, Department of Political Science, Korea University *** Associate Professor, Department of Political Science, University of Kentucky
**Figure 4: Experiment 1 - Real GDP per capita growth**

**(int. rate is 50% lower; deviation from baseline)**

<!-- Start of picture text --> 0.5<br>0.45<br>0.4<br>0.35<br>Experiment 1A (all allocated to CG)<br>0.3<br>Experiment 1B (all allocated to IG)<br>0.25<br>Experiment 1C (50% allocated to IG, 50% allocated to CG)<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> 24
random sample of 50 individuals (for each hashtag-year-month). Accordingly, the share of female users (the dependent variable in our study) is derived from a random sample of up to 600 individuals for each hashtag-year (the unit of observation in our study).

Figure 1 shows the distribution of the share of female participation across hashtags for the period 2021 to 2023. Average female participation observed in our dataset for this period is 33 percent. Put differently, female participation is on average half of male participation. We could not find many sources to compare this estimate against. Two sources we could find report estimates of 35 and 43 percent ((DataReportal, 2023); (Statista, 2022)). Female participation is also seen to vary considerably across different issues (Figure 1). Yet, we rarely observe an even participation between men and women. For 90 percent of observations, female participation ranges from 11 to 58 percent.

# **3.4 Network Measures**

The clustering coefficient (Watts and Strogatz, 1998) measures the fraction of closed triangles in a network. That is, if someone has two network connections, what is the probability that those two connections are themselves connected. We take it as a measure of social support, because clustered networks mean that mutual acquaintances are likely to know each other and thus be able to work together to support a fellow friend.

The local clustering coefficient (i.e. the clustering coefficient for a specific network node) for a given graph _G_ = ( _V, E_ ) consisting of edges _E_ and vertices _V_ is calculated by summing the number of actual connections between nodes _j_ and _k_ , scaled by the number of potential connections _ki_ ( _ki −_ 1), where _ki_ is the number of nodes and _Ni_ in 11
Where they exist at all, corporate bond markets in the focus countries are undeveloped, with few issues, except in South Africa, where corporate issues had been growing faster than government issues in recent years. Due to factors including a lack of financial product diversity and the small number and size of issues, most of the corporate bonds in these countries rarely trade and are often held to maturity, with the investors tending to adopt a buy-hold strategy.

**Table 6.1 Overview of countries’ locally sourced infrastructure financing by financial instrument**

|Amount outstanding at end-|2006 or mostrecent available (US$millions)|||
|---|---|---|---|
||Bank loans /a<br>Government bonds /b|Corporate bonds|Equity issues|
|Benin|123.6<br>0|51.9|0|
|Burkina Faso|84.5<br>0|39.4|0|
|Cameroon|—<br>—|—|0|
|Cape Verde|107.6<br>—|n.a.|0|
|Chad|0.0<br>n.a.|n.a.|n.a.|
|Congo, Dem. Rep.|5.8<br>n.a.|n.a.|n.a.|
|Côte d’Ivoire|334.7<br>0|n.a.|133.4|
|Ethiopia|247.9<br>n.a.|n.a.|n.a.|
|Ghana|177.9<br>0|0|0|
|Kenya|574.5<br>0|64.8|3,408.0|
|Lesotho|20.9<br>0|n.a.|n.a.|
|Madagascar|67.6<br>n.a.|n.a.|n.a.|
|Malawi|17.4<br>0|n.a.|0|
|Mozambique|60.7<br>0|13.2|0|
|Namibia|117.1<br>0|298|0|
|Niger|66.5<br>n.a.|n.a.|n.a.|
|Nigeria|2,443.6<br>46.8|0|0|
|Rwanda|25.6<br>n.a.|n.a.|n.a.|
|Senegal|286.0<br>93.3|66.6|1,827.1|
|South Africa|6,274.9<br>762.8|6,841.3|48,148.7|
|Sudan|5.2<br>—|—|2,302.3|
|Tanzania|93.0<br>0|0|0|
|Uganda|74.9<br>0|7|0|
|Zambia|72.9<br>n.a.|7|0|

Sources: National finance ministries, national/regional central banks and securities exchanges.

a. Total outstanding loans to infrastructure sectors. Data for end-2006 with the exception of: Democratic Republic of the Congo (end-2003); Madagascar (end-2004); Namibia (end-June 2005); Tanzania (end-2005); Ghana (June 2006); South Africa (end-Sept. 2006); Zambia (May 2007); Chad (March 2006). b. The actual amount of government bonds financing infrastructure may be an underestimate as specific financing purpose for these bond issues is generally unavailable. — = Not available; n.a. = Not applicable (no such issues/no organized market).

# The importance of corporate bonds issued to finance infrastructure

Despite the generally underdeveloped state of these markets and significant variance in the extent to which corporate bonds are financing infrastructure across the countries, new corporate bond issuers operating in infrastructure sectors appear to be coming to the market, in at least one case constituting the 65
**Table 5b. Banks' Buffering Role: Informational and Contractual Complexity** **<u>(contd.)</u>**

||Log of # of<br>Exporters|Log of Export Value<br>per Exporter|Exporter Entry<br>Rate|Exporter Exit<br>Rate|<br>Exporter Turnover<br>Rate|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||_Pan_<br>|_el A: Product-Level Contra_|_ctual Complexity*C_<br>|_ountry-Level Rule_<br>|_of Law_<br>|
|1. Foreign Bank*Nunn|1.656***|0.330|-0.086***|-0.104***|-0.169***|
||(0.194)|(0.340)|(0.033)|(0.034)|(0.065)|
|2. Foreign Bank*Nunn*Rule of law|-0.936***|-0.654|-0.066|-0.069|-0.152|
||(0.281)|(0.477)|(0.059)|(0.061)|(0.121)|
|Observations|18,114|17,689|16,327|16,316|16,171|
|R-squared|0.880|0.544|0.471|0.465|0.508|
||_Panel B: P_<br>|_roduct-Level Contractual_<br>|_Complexity*Country_<br>|_-Level Strength of_<br>|_Legal Rights_<br>|
|3. Foreign Bank*Nunn|1.719***|0.646*|-0.058*|-0.079**|-0.119*|
||(0.237)|(0.381)|(0.034)|(0.036)|(0.068)|
|4. Foreign Bank*Nunn*Strength of legal rights|-0.176*|0.174|-0.002|-0.010|-0.019|
||(0.092)|(0.189)|(0.015)|(0.015)|(0.029)|
|Observations|18,114|17,689|16,327|16,316|16,171|
|R-squared|0.879|0.543|0.471|0.465|0.508|

Note: This table reports the effects of financial structures on the number, size and dynamics of exporters in 2-digit ISIC industries depending on the degree of informational and contractual complexity of products and countries. The financial structure is captured by the share of foreign bank assets in total bank assets in the country. The dependent variables are the number of exporters; the average foreign sales of exporters; the entry rate of exporters; the exit rate of exporters; the turnover (entry+exit) rate of exporters. All regressions include FinDev*ExtFin, FinDev*Nunn, Foreign Bank*ExtFin, Bank Concentration*ExtFin, country-year fixed effects and industry-year fixed effects, where FinDev is measured as all credit over GDP to control for total financial development and Bank Concentration is used to control for the concentration of the banking sector. Regressions in panel A also include Nunn*Rule of Law and FinDev*Nunn*Rule of Law. Regressions in panel B also include Nunn*Strength of legal rights and FinDev*Nunn*Strength of legal rights . The Nunn indicator of contractual complexity is defined in Section 3.3. The rule of law index is from the Worldwide Governance Indicators. The strength of legal rights is from Doing Business. The rule of law index and strength of legal rights are demeaned by the sample average. Robust standard errors, clustered at the country-year level, are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
**<u>Table 2: Imagery-based Outcome Variables for Control and Treatment Areas</u>**

||<br>**ND**|**VI**|**S**|**AVI**|**LS**|**WI**|**Rainfa**|**ll (mm)**|
|---|---|---|---|---|---|---|---|---|
||**Control**|**Treatment**|**Control**|**Treatment**|**Control**|**Treatment**|**Control**|**Treatment**|
|**Dry season**|**(Bega)**||||||||
|2005-09|0.384|0.388|0.226|0.226|0.110|0.120|116.65|121.65|
|2009-12|0.387|0.396|0.231|0.234|0.106|0.118|92.71|96.82|
|2012-16|0.363|0.376|0.211|0.216|0.078|0.094|141.67|146.24|
|**Pre-rainy s**|**eason (Kiremt)**||||||||
|2005-09|0.206|0.214|0.123|0.126|-0.015|-0.004|178.46|187.47|
|2009-12|0.202|0.211|0.121|0.124|-0.019|-0.007|169.81|177.22|
|2012-16|0.235|0.249|0.141|0.147|-0.009|0.006|264.62|275.89|
|**Rainy seas**|**on (Belg)**||||||||
|2005-09|0.476|0.489|0.297|0.305|0.232|0.253|1114.60|1121.44|
|2009-12|0.529|0.532|0.330|0.331|0.263|0.273|1164.46|1170.19|
|2012-16|0.464|0.477|0.291|0.299|0.223|0.239|1037.97|1048.21|

_Source:_ Calculated from Landsat 7 imagery using GEE.

_Note:_ Mean elevation and slope for control areas are 2,464 mm and 11.28 degrees, and for treatment areas 2,522 m and 11.76 degrees.

17