rafmacalaba/datause-encoder-data huggingface dataset:
_economic_ distance between two countries (geographic distance adjusted by time penalties caused by additional burdens posed by poor road conditions and congestion) and improving efficiency at border crossings.

# **_2. Preliminary Results:  Part 1_**

The first set of GMT estimations is organized as follows. The model is estimated using intraregional bilateral export flow data for the more integrated part of the European Union (EU15) and, second, for the Central America region. Estimations for both regions were first carried out in terms of total exports (measured in US$) and then for the disaggregated groups of goods: (i) Grains, including rice, maize, wheat, and barley, in both value (US$) and volume (kg) terms; (ii) Steel and Steel products, including ingots and other primary forms, iron and steel castings, tubes, rods, pipes and fittings, universals, iron and steel bars, rails and railway track construction materials, in both value and volume terms; and (iii) Processed Fruits & Vegetables in both value and volume terms. Estimations were carried out over a nine-year period from 2000 to 2008 controlling by country-pair fixed effects in a pooled regression. In the figures, results are presented at the 10-percent level of significance.

# **_2.1. Establishing a Benchmark for Central America’s Spatial Integration_**

The results obtained for the EU15 countries for total intraregional exports (Figure 3) correspond to what is expected according to the intuition underlying the GMT theory: the GDP of both the importing and the exporting countries is significant and positively related to export flows, while the distance between each pair of economies has a significant and negative impact on trade, whereby an increase in distance by 1 percent reduces the expected exports by 0.93 percent.

Similarly, as expected, adjacency represents opportunities for increasing exports within a highlyintegrated region. Expressed as the percentage change in total exports,<sup>8</sup> EU15 countries that share frontiers have 83 percent higher expected bilateral export flows than countries that do not.<sup>9</sup> Positive and significant effects associated with adjacency, although of different magnitude, have also been found in studies focusing on other European economic blocks - 56 percent (World Bank, 2005), the Latin America and the Caribbean Region overall - 174 percent (Carrillo and Li, 2002), and in large country-samples covering the rest of the world - 289 percent (World Economic Forum, 2008).

> 8 The discrete change in expected exports correspond to ∆�� <u>��</u><sup>�</sup> |������ �1 � ���<sup>�.�</sup> ���<sup>��</sup> �1 � ���<sup>��</sup> �1 ��<sup>�</sup> |������ ���<sup>�.�</sup>

> 9 Expressed as (���������1).

8
counterfactual income that households would have had in the absence of the transfer. Consequently, our estimates of ex ante income are likely to be too low, thus exaggerating the difference between ex post and ex ante incomes and leading to overstatement of the impact of the dibao program on incomes and on poverty. Despite this possible overstatement, we find that the impact of the dibao program on poverty rates is relatively small. In view of the small measured impact of the program even with possible overstatement, use of more complicated methodology that incorporates household responses is not warranted.

The CHIP household survey data contain ex post incomes, but not information on the amounts of dibao transfers received by the households.<sup>3</sup> Information about dibao transfers is, however, available at the village and county levels. The CHIP village‐level data contain information for 2008 and 2009 on the number of dibao and wubao households within the village and on the average dibao transfer per recipient within the village. Also, MOCA publishes county‐ level data on rural dibao participation and expenditures, which can be used to calculate county average dibao expenditures per recipient.<sup>4</sup> It is possible that county expenditures include some categories of government spending on the dibao program other than the dibao transfers to households; as discussed later, however, the county average dibao expenditures are quite similar to the village average transfers.

We use the village average dibao transfers and county average dibao expenditure amounts as proxies for household level dibao transfers. In this way we obtain two estimates of ex ante income for dibao households: one is equal to ex post household income per capita minus the village average dibao transfer, and the other is equal to ex post income per capita minus the

> 3 The data contain information on the total transfer income received by the households, including both private and public transfers, but without any breakdown by source or type of transfer.  We found no correlation between total transfers received by households and their dibao participation.

> 4 MOCA publishes county‐level dibao data on a monthly basis.  In our analyses for 2008 and 2009, we use year‐end (December) values of the MOCA county‐level dibao participation and expenditure levels to calculate monthly dibao expenditures per recipient.  To obtain annual dibao expenditures, we multiply the December amounts by twelve. These estimates therefore capture the level of transfers per capita attained by the end of the calendar year.  Since the MOCA county‐level data are not available for 2007, for 2007 we use the January 2008 county‐level data, multiplied by twelve.  We compared the January versus December values of the MOCA dibao variables for later years (December 2008 versus January 2009, and December 2009 versus January 2010) and did not find systematic differences.

11
TABLE 2: 2SLS, firm-level elasticity of substitution across suppliers

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|log<br>�<br>ˆ_p_<br>ˆ ̃_p_<br>�|0.5042|0.3945|0.4538|0.3409|0.4418|0.3535|
||(0.2129)|(0.0933)|(0.1389)|(0.1068)|(0.1982)|(0.1701)|
|_ε_|0.4957|0.6054|0.5461|0.6590|0.5581|0.6464|
|Obs|2854292|2028039|2028039|1966591|1020362|991187|
|K-PF|48.232|133.688|143.413|248.977|69.827|62.215|
|Seller IV|Y||Y|Y|Y|Y|
|Bilateral IV||Y|Y|Y|Y|Y|
|Feenstra term|Y|Y|Y|Y|||
|Buyer-time FE|Y|Y|Y|Y|||
|Product-time FE|Y|Y|Y|Y|||
|Buyer-product FE||||Y|||
|Seller-product FE||||Y||Y|
|Buyer-product-time FE|||||Y|Y|

**Notes:** 2SLS estimates come from estimating Equation (11). Time is monthly frequency. The set of common suppliers of buyer _b_ is Ω<sup>_∗_</sup> _i,bj,t_<sup>= Ω</sup><sup>_i,bj,t ∩_Ω</sup><sup>_i,bj,t_−1.That is, a supplier</sup><sup>_s_of buyer</sup><sup>_b_is considered</sup><sup>_common_if they traded in both the</sup> current and previous month. The first stage uses either bilateral or seller-level instruments, as reported in rows six and seven. Bilateral instruments correspond to Equation (17), and seller-level instruments correspond to Equation (16). The first row reports estimates associated with changes in relative unit values in logs. Standard errors are two-way clustered at the origin and destination state level and are reported in parentheses below each estimate. The third row reports the implied value for _ε_ , which is 1 minus the estimate on the first row. The fourth row reports the number of observations. The fifth row reports the Kleibergen-Paap F statistic from the first stage. A product is a 4-digit HS code, and the treatment period is March-May 2020. The table contains six columns. Each column corresponds to different combinations of instruments, fixed effects, and whether the Feenstra (1994) term is included. These combinations are reported in the last eight rows. All specifications include the controls mentioned in the paper.

# _6.2 Firm-level elasticities of substitution across products_

In Table 3, we report our estimates for the firm-level elasticity of substitution across products. In column (1), we show the OLS estimate of _ζ_ = 0 _._ 92, which reflects complementarity between product categories. In columns (2) and (3), we define products more granularly. In this case, the elasticities are around 0 _._ 80, which also reflects complementarity between products.

In columns (4)-(6), we report our estimates of _ζ_ under 2SLS estimation after using a weighted average of instruments across buyers’ sellers as discussed in Section 5.3. Our specification in column (4) reports a value of 0 _._ 69, reflecting that simultaneous negative demand and supply shocks during Covid-19 led to an underestimation of _ζ_ under OLS. This elasticity is higher than the 2SLS elasticity of substitution across suppliers for the same product ( _ε_ = 0 _._ 55), reflecting a lower degree 28
<!-- Start of picture text --> 1995 2000 2005 2010<br>Year<br>ZAF COL CHL<br>PER THA TUR<br>50<br>40<br>30<br>Total LVAX share (%)<br>20<br>10<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

Exports have become less important for wages in South Africa relative to total domestic production. Not only has the labor value added supported by exports declined relative to gross exports, it has also declined relative to total domestic labor value added (Figure 3). Growth in all the wages paid to produce final output including those to produce domestic inputs of final output—considering the linkages—was 11.8% annually, reaching $182.9 billion in 2011 (up from $59.7 billion in 2001). Growth in all the wages paid to produce exports was substantially lower (7.9% annually). As a result, $19 of each $100 of labor value added generated from total domestic production was exported in 2011, versus $27 in 2001.

This trend in labor value added reflects a broader trend whereby non-export production has outperformed export production, and exports have declined relative to domestic production. This is consistent with a general underperformance of South Africa’s exports over the past decade, which have made limited inroads into global markets and lagged behind those of peers in minerals, non-minerals and services (World Bank 2014, Matthee et al. 2016). This is also likely a by-product of Dutch Disease induced by the commodity boom and amplified by the relatively high labor content of mineral exports (discussed below), which boosts the spending effect of the commodity boom.

Nevertheless, exports continue to be more labor intensive than domestic production in South Africa, and the labor intensity has evolved similarly for both. As noted above, in 2001 each $100 of gross exports generated $41 of domestic wages, versus $27 of domestic final output. By 2011, this figure had reduced to $33 for exports and $21 for domestic final output.

10
services FDI than plants that are less heavy users of services.<sup>19</sup> To capture the intensity of services usage by plants, we compute the ratio of plant expenditures on the various services listed in Section 4 to plant sales. Information on a plant’s usage of foreignprovided services as separate from domestic-provided services would be the ideal measure to use. However, to the extent that domestic services providers improve their quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or knowledge spillovers - a plant’s total services usage can adequately capture the benefits which a Chilean manufacturing plant may derive from services usage for its _productivity_ growth beyond the contribution that services input growth per se generates for the plant’s _output_ growth.

To capture the presence of FDI, we compute net FDI inflows for each sector based on data from the Chilean Foreign Investment Committee by subtracting from annual FDI inflows the corresponding annual FDI outflows (which represent foreign investors’ repatriation of capital, profits, and dividends). Net FDI inflows however do not adequately capture the importance of FDI in a services sector and year because they neither account for past investments nor for the size of the sector. Thus, we cumulate net FDI inflows using the perpetual inventory method formula to construct an FDI stock for each services sector, as described in the Appendix.<sup>20</sup> Then, our measure of FDI penetration in a services sector is given by the ratio of the sector’s FDI stock to the sector’s output (GDP) obtained from the Chilean Central Bank.<sup>21</sup>

> 19 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to finance for industry growth. Our assumption implies that cost is the only limitation to purchase services and that there are no other restrictions preventing the access to certain services by certain users. 20 Our approach is similar to that followed for example to construct R&D stocks (Coe and Helpman, 1995). 21 Note that this measure differs from the traditional measures of the presence of FDI in a sector used in the FDI spillovers literature, i.e., the share of a given sector’s sales or employment accounted for by foreign firms.

15
Figure 4 presents a graph of these counterfactual PPPs against the 2011 ICP PPPs in logarithmic scale. The 45 degree broken line through the origin represents the benchmark case of equality between the two PPPs for each country. It can be clearly observed that for most part the 2011 ICP PPPs are lower than the counterfactual PPPs. From the table it is evident that almost the entire non-African region and some African countries constitute this part with the ICP PPPs lower than the corresponding counterfactuals. This result is analogous to the observation made in, for example, Ravallion (2014) that the 2011 ICP PPPs were lower than their counterpart in the 2005 ICP PPP. This feature translates into the PPP deflated GDP values, presented in Figures 5 and 6. While Figure 5 plots the per capita GDP values using counterfactual PPP deflator against those using the 2011 ICP PPP deflator, Figure 6 plots the corresponding aggregate GDP values. On superimposition of the line of equality between the two axes Figure 5 shows that for most part the per capita GDPs deflated by 2011 ICP PPPs lie above those deflated by the counterfactual PPPs. Figure 6 shows the overstatement of GDP by the 2011 ICP PPP in relation to the counterfactual PPP more clearly since the 45 degree line of equality between the two GDP values lies uniformly above the fitted line. This is analogous to the result noted in Ravallion (2014), Inklaar and Rao (2015), Jolliffe and Prydz (2015) on the overstatement of the 2011 PPP deflated real GDP in relation to the 2005 PPP deflated real GDP. While the temporal comparison between the 2005 and 2011 PPP and real GDP values in the above cited studies is with respect to the US dollar and the US economy, the comparison here relates to the same year (2011) and is with respect to the Indian Rupee and the size of the Indian economy. If one considers the present results in conjunction with the earlier ones, then we have robust evidence of understatement of the PPPs and overstatement of the PPP deflated GDP values in the 2011 ICP.

<mark>[Tables 6a and 6b about here]</mark> Table 6a presents the per capita annual <mark>GDP evaluated at 2011 ICP Consumption PPP and at counterfactual PPP with India as base and Table 6b presents the corresponding values for per capita aggregate consumption.</mark><sup>24</sup> <mark>The qualitative pictures portrayed by these tables are quite similar and mirror the comparative PPP estimates reported in Table 5. The revisions to the 2011 ICP PPP deflated GDP and consumption figures implied by the corresponding counterfactual</mark>

> 24 Tables B6a and B6b, in Appendix B, present the results in logarithmic scale.

27
21 _Qualifications.A_ word of caution is warranted in the interpretation of trends outlined in the paper. The above conclusions are preliminary in several respects. First, the sharp decline in agricultural employment on Java derives from one data source, namely the most recent National Labour Force Survey.<sup>3 4</sup> Evidence of this marked change needs to be corroborated with data from other surveys. Second, the analysis has been conducted at a highly aggregate level. There are still many sub-regional pockets of poverty and labour immobility in Indonesia. Clearly, special programs such as _Inpres Desa Tertinggal_ are still needed to help overcome these problems.

_Further Research and Policy Issues._ Further research is needed to examine local labour market processes and to substantiate some of the tentative conclusions outlined in the paper. Very little is known about the relationship between changing patterns of inter-regional labour mobility and their impact on wages and labour welfare in Indonesia. As labour mobility increases, the impact on changing economic structure and employment patterns on the job opportunities of migrants compared with local, non-migrant workers has become an important social and economic issue. This is particularly true in less developed regions where in-migration both to urban and rural areas has been intense - as in Southern Sumatra, parts of Kalimantan, Maluku, Irian Jaya and East Timor.

Two sets of issues are in urgent need of further investigation in a regional context. The first relate to the apparent accelerated shift out of agriculture in recent years. The second set of issues pertain to the factors driving high rates and new patterns of manufacturing employment growth, especially outside Java. One key question is the extent to which these developments have been increasingly influenced by national or sub-national rather than local structural change. Another is the extent to which different regional labour markets are affected by contrasting stimuli associated with economic growth and policy. Such factors include export-oriented industrial growth, rising agricultural productivity, and inter-regional and international migration.

With regard to specific regions, examples of key issues in need of further investigation include:

- Factors contributing to the marked slow-down in agricultural employment in certain regions such as Southern Sumatra, and the apparent dramatic decline in agricultural employment on Java

- The apparent more rapid increase in manufacturing employment and wages in densely populated Central Java than in the period of the oil boom.

34 A decline in the absolute numbers employed in agriculture had become apparent by around 1990. But the sharp decline was only observed in 1994.
**Table 1. Country Use of Non-redundant AD, CVD, SG, CSG at the Product Level, 1Q 2007 - 1Q 2009**

|||**Initiations**|||**Measures**||
|---|---|---|---|---|---|---|
|**Countries**|**2007**<br>**Total**|**2008**<br>**Total**|**1Q**<br>**2009**<br>**Total**|**2007**<br>**Total**|**2008**<br>**Total**|**1Q**<br>**2009**<br>**Total**|
|U.S.|14|12|1|3|13|3|
|EC|6|10|1|8|9|1|
|Canada|1|3|1|1|3|1|
|Australia|3|4|1|2|0|3|
|New Zealand|2|0|0|1|1|0|
|South Korea|6|3|0|0|6|2|
|Taiwan|0|0|0|1|0|0|
|Israel|0|1|3|1|0|0|
|Argentina|6|11|8|6|4|2|
|Brazil|8|10|0|11|5|1|
|India|14|21|9|11|11|4|
|Turkey|6|12|1|4|10|2|
|<br>China|1|6|2|6|1|0|
|South Africa|5|2|1|2|2|0|
|Pakistan|0|3|1|2|0|0|
|Colombia|1|6|1|7|0|1|
|Mexico|3|1|1|0|0|0|
|Peru|2|0|1|1|0|0|
|Venezuela|0|0|0|0|0|0|
|Ukraine|5|4|2|0|5|0|
|Others|1|9|1|2|1|0|
|_Developed countries total_|32|33|7|17|32|10|
|<br>_Developing countries total_|52|85|28|52|39|10|
|_Total_|84|118|35|69|71|20|

_Source_ : Compiled by the author from the _Global Antidumping Database_ . These are non-redundant AD, CVD, SG, CSG at the product level.

# **2.3 The targeted sectors**

Table 2 illustrates the product-level requests by sector for new import restrictions under these trade remedy instruments between 1Q 2007 and 1Q 2009. For the developed economy users, the industries most frequently resorting to these instruments are chemicals, iron and steel, and machinery, with 65 per cent of the developed economy initiations since 1Q 2008 occurring in just one of these three sectors. As these are the historically dominant sectoral users of trade remedies, the predominance of their use during the crisis is not surprising. Nevertheless, the recurrence of use in these sectors, and the 8
4 argues that depos insurance biases the banks against providing debt relief. Both straight debt relief, which affects a country's adjustment effort, and deit.buybacks tend to lower the value of the banks' contingent claim on the deposit insurance agency. Our empirical **evidence suggests** that heavily indebted countries will experience **irore** difficulty obtaining debt relief or repurchasing their debt than lightly **indebted countries and** that heavily exposed banks will **be more biased against debt releif and** repurchases than lightly exposed banks.

The remainder of this paper is organized as follows. Section 2 contains the empirical evidence. We present the **model in Section 3, and** also discuss the simulation results, and their implications for bank regulatory practice. Section 4 presents evidence on banks' stock market valuation. Section 5 contains a summary and conclusions.

# 2. **Does Bank Exposure Affect Secondary Market Prices** ?

## 2.1 A Cursory Inspection of the Data

While the debt obligations of a number of d:eveloping countries are being traded at large discounts, there are several developing countries with debt that appears to be at par. In this section we will first present some distinguishing characteristics of the two groups of countries. Second, discount and bank exposure data are presented for the countries for which debt is traded at a discount.

A cursory look at the data suggests that the debt of **countries with bad** repayment prospects is more likely to be traded at a discount in the secondary market. Repsyment prospects of countries have typically been measured by their general economic chararacteristics. Economic characteristics of borrowers that are identified as important measures **of**