rafmacalaba/datause-encoder-data huggingface dataset:
16 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# **5 Empirical results**

Table 6 and Table 7 present the estimates of the relationship between metropolitan fragmentation/coordination and city productivity premium. The results are pretty similar in both cases. Following Ahrend et al. (2014) we report in the first column the positive and significant impact of population on productivity premium, which implies that productivity is higher in larger cities. In this regard Ahrend et al. (2014) reported estimated elasticities that range from 0.016 (for the United Kingdom) to 0.063 (for the United States). In our study, we obtained an estimated elasticity of 0.08 for LAC cities. We also report the coefficients associated to the logged population density (i.e., elasticity of productivity with respect to population) and surface (i.e., elasticity of productivity with respect to area). The results show that an increase in population, while holding the area constant, and an increase in area, while holding population density constant, both have a positive and statistically significant impact on productivity. Finally, the difference between these two coefficients indicates that an increase in area, while holding the total population constant, generates elasticities from 0.04 to 0.07. This range is 0.02 higher than the elasticity range reported by Ahrend et al. (2014), 0.02 to 0.05.

The block of indicators for institutional fragmentation is reported in column (3) of Table 6 and Table 7. The results show opposite effects from fragmentation: on the one hand, the negative and statistically significant coefficient for the logged number of administrative units indicates that the presence of multiple local governments affects economic performance because of factors such as higher transactions costs, barriers to the diffusion of growth promoting policies, and other coordination failures (which is consistent with the centralist view). But, on the other hand, the positive and statistically significant coefficient for political fragmentation ( _no_au_100th_2010_ ) indicates that the presence of multiple local governments may lead to more responsive government to public needs (Nelson and Foster, 1999). Also, “smaller [local governments] make participation easier, make citizens feel more empowered and interested in their communities, and bring neighbors together” (Oliver, 2010, 65). Finally, the negative and statistically significant coefficient for _cc_pop2010_ue_ , indicates that an increase in central city domination may affect economic performance.
financing options of small firms. Our empirical tests address the broader question of the choice of public companies, with alternative financing venues (such as equity and public debt) to use secured L/Cs.

# **_4.1 Data Description_**

A sample was randomly drawn of 850 publicly traded manufacturing firms that were listed in Compustat in 1996 and were traded between the years 1991 and 1996.20 To identify firms with secured revolving L/C loans, we searched in LEXIS/NEXIS (a private legal information provider) for our sample firms for UCC liens outstanding against _accounts receivable.21_ The UCC lien includes the date of the filing, the debtor's name, the creditor's name, and a description of the collateral. The inclusion of this information allowed us to distinguish between loans secured by fixed and "floating" assets (such as accounts receivable). Firm-level data was collected from Compustat and CRSP.<sup>22</sup>

# **_4.2 Variable Descriptions_**

# _A. Size, Age, and Growth Variables_

We find in previous literature that size and age significantly affect a firm's ability to obtain external financing. This result is also predicted in our model, which finds that more informationally opaque firms should be more likely to require collateral. Summary statistics of our sample support these findings: The median size of assets of firms without secured L/Cs is $174.22 million while the median size of firms with secured L/Cs is only

> 20 This represents 65% of all manufacturing firms listed in Compustat (SIC codes beginning with 2 or 3). Summary statistics for this subsample are significantly insignificant from the full sample.

> 21 In order to claim priority to a secured asset in the case of default, a lender must legally file in court a UCC lien against the asset(s). Consequently, we can acertain that any outstanding liens would be included in Lexis/Nexis. In addition, SEC 10-K filings were checked to confirm that liens were not filed by a factor, an unpaid creditor, or for a securitization.

> 22 Financial ratios were corrected for outliers above and below 95% and 5%, respectively.

20
2009 data and the 2009 forecast that is the unpredicted piece of new import protection that one could associate as being related to the crisis. Furthermore, according to column (6), Argentina actually experienced a smaller increase in imports covered by TTBs in 2009 than the model predicts based on a time trend alone – i.e., not controlling for any of the sizable macroeconomic factors during the recession which would make conditions even more likely for an increase in TTBs. And Turkey’s _value_ measure actually fell slightly (‐9.25%) in 2009 compared to 2007 according to column (4), despite the model predicting a slight increase from the 2007 realized value.<sup>22</sup> Thus while there was an increase in import protection during the crisis – at least as measured by the stock of imported products subject to TTBs in 2009 being higher in 2007 – my interpretation of the preliminary evidence is that it is difficult to support a claim that the increase was _caused by_ the crisis, given underlying, pre‐crisis trends already apparent in the data.

# **4. Exporters and Foreign Use of Antidumping**

Thus far I have addressed the question of TTB use from the perspective of the policy‐imposing economies and the scope of their imported products likely to be affected by use of such policies over time. In this section I switch my focus to the _incidence_ of such policies, and I ultimately take the perspective of the exporters directly and negatively impacted by imposed TTBs. Furthermore, in this section I restrict my attention to the antidumping policy instrument.

I structure this section in two parts.<sup>23</sup> In the first subsection, I examine the use of antidumping from the perspective of each G20 policy‐imposing economy to identify trends and potential heterogeneity in the application of the policy across different categories of foreign export targets. In the second subsection, I re‐orient the analysis to the perspective of the exporters themselves. This allows

> 22 The substantial difference between the realized import shares for Turkey in 2009 based on equation (1) and (2) merits an explanation. While Turkey increased (on net) the number of HS‐06 products subject to TTBs  in 2009 relative to 2007, the particular HS‐06 products for which Turkey removed TTBs during this period were such a large share of imports that the value share measure of equation (2) declined slightly. The products were associated with Turkey’s removal of antidumping barriers on sizable imports of steel billets from Russia, Ukraine and Moldova that had been in effect since 1995.

> 23 In this section of the paper, and for reasons described in section 2.2.2 above, Mexico is the only major G20 user of such policies not included in the construction of data behind figures 4, 5, and 6 and table 5 discussed below (see again figure 2a).

23
# **Table 6.A. Model comparison: Bias of copper price forecasts**

(U.S. dollars per metric tonne)

|**Approach**|**Horizon**|**Consensus**|**Futures**|**Bivariate**<br>**correlations**|**BVAR**|**OEM**|**Machine**<br>**learning**|
|---|---|---|---|---|---|---|---|
|Consensus<br>|3<br>|-121|...<br>|...|...|...|...|
|Futures|3||7.8|...|...|...|...|
|Bivariate correlations|3||*|132.6|...|...|...|
|BVAR|3||||-69|...|...|
|OEM|3|||||-73.1|...|
|Machine learning|3||||||-49.1|
|Consensus|6|-111.6|...|...|...|...|...|
|Futures|6||29.1|...|...|...|...|
|Bivariate correlations|6|*|*|526.8|...|...|...|
|BVAR|6||||-91.9|...|...|
|OEM|6|||*||-92.1|...|
|Machine learning|6|||*|||-30.8|
|Consensus|9|-106.5|...|...|...|...|...|
|Futures|9||43.1|...|...|...|...|
|Bivariate correlations|9|*|*|875.4|...|...|...|
|BVAR|9|||*|-137.8|...|...|
|OEM|9|||*||-103.4|...|
|Machine learning|9|||*|||-27.8|
|Consensus|12|-93.4|...|...|...|...|...|
|Futures|12||52.1|...|...|...|...|
|Bivariate correlations|12|*|*|1105.1|...|...|...|
|BVAR|12|||*|-95.2|...|...|
|OEM|12|||*||-88.3|...|
|Machine learning|12|||*|||-29.5|
|Consensus|15|-70.9|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15||...|...|-297.6||...|
|OEM|15||...|...||-64.1|...|
|Machine learning|15||...|...|||3.4|
|Consensus|18|-36.3|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18||...|...|-193.7|...|...|
|OEM|18||...|...||-12|...|
|Machine learning|18||...|...|||43|
|Consensus|21|4.2|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations<br>BVAR|21<br>21|...|...<br>|...<br>|...<br>-699|...<br>|...<br>|
||||...|...|.|...<br>|...|
|OEM|21||...|...||43.4|...|
|Machine learning|21||...|...|||78.1|
|Consensus|24|51.6|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|-119.4|...|...|
|OEM|24||...|...||116.6|...|
|Machine learning|24||...|...|||103.6|

_Source_ : World Bank.

_Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate estimates of average bias for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to a t-test (bias).

42
concentration of imports in each of the Asian countries. Said this differently, it measures from how many countries an HS6 product is imported by each of our possible destination countries. Using notations, this concentration measure indicates the number of exporting countries, _k_ , from which a product _g_ is imported in each of our possible destination countries, _j_ , thus: where time subscripts are omitted for simplicity. In this expression, _s_<sup>_jk_</sup> _g_ = _m_<sup>_jk_</sup> _g_<sup>_/mg_,and</sup><sup>_mjk_</sup> _g_<sup>isthe</sup> import value of product _g_ imported by country _j_ from country _k_ and _ng_ is the total number of countries exporting the product _g_ .<sup>16</sup> To obtain a competition measure at the firm-country level, the next step is to match the above productspecific index with information on French exports at the firm-product-country of destination level over the period 1999-2005. This allows us to compute the average of the Herfindahl index at the firm level. To avoid compositional effects and potential endogeneity concerns, we take the average of this firm level index keeping constant the HS6 product range of French exporters at 1999, while allowing the concentration index to vary over time.<sup>17</sup> In this first measure, the number of foreign competitors is proxied by the number of countries exporting the same product line towards the Asian markets. To account for the competitive pressures that could emerge from other firms selling the same product line to the same destination, a second measure of competition is needed. To proxy foreign competition suffered from other firms, we introduce a second measure that captures the degree of competition due to the mass of French firms selling the same HS6 product in the liberalized market.<sup>18</sup> Using French Customs export data at the firm-product-country of destination level, we construct an Herfindahl index that this time captures the concentration of export shares of other French firms exporting same product to same country. The inverse of this index captures the degree of competitive pressures generated by French firms selling in that country. Similar to the pre13
<u>Table 3. Determinants of Urban Informal Types of Firms</u>

||Low ed|ucated self-em|ployed|High-ed|ucated self-e|mployed||Employer||
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(1)|(2)|(3)|(1)|(2)|(3)|
|Main owner, 25-54 years old|-0.097||-0.061|-0.011||-0.012|0.107||0.073|
||[0.054]*||[0.060]|[0.007]||[0.009]|[0.054]**||[0.059]|
|Main owner, 55 years or older|<br>0.073||<br>0.109|<br>-0.006||<br>-0.003|<br>-0.067||<br>-0.106|
||[0.078]||[0.087]|[0.031]||[0.031]|[0.082]||[0.091]|
|Main owner is female|<br>0.013||<br>-0.018|<br>0.022||<br>0.019|<br>-0.035||<br>-0.001|
||[0.047]||[0.051]|[0.011]**||[0.010]*|[0.049]||[0.052]|
|Primary education|-0.005||0.022|-0.001||-0.001|0.006||-0.022|
||[0.099]||[0.097]|[0.013]||[0.013]|[0.100]||[0.098]|
|Secondary education|0.041||0.068|-0.002||0.001|-0.039||-0.069|
||[0.096]||[0.092]|[0.014]||[0.014]|[0.097]||[0.093]|
|Vocational training|-0.752||-0.645|0.435||0.523|0.317||0.122|
||[0.086]***||[0.103]***|[0.111]***||[0.103]***|[0.140]**||[0.145]|
|University training|<br>-0.777||<br>-0.715|<br>0.600||<br>0.547|<br>0.177||<br>0.167|
||[0.086]***||[0.093]***|[0.205]***||[0.194]***|[0.207]||[0.194]|
|Firm age ==2-5 years||-0.032|-0.040||0.023|0.033||0.009|0.007|
|||[0.051]|[0.056]||[0.019]|[0.014]**||[0.050]|[0.058]|
|Firm age ==6-20 years||<br>-0.033|<br>-0.027||<br>-0.011|<br>0.008||<br>0.044|<br>0.019|
|||[0.062]|[0.068]||[0.012]|[0.010]||[0.063]|[0.068]|
|Firm age ==More than 20 years||0.049|-0.006||-0.008|0.010||-0.041|-0.003|
|||[0.066]|[0.086]||[0.023]|[0.009]||[0.065]|[0.089]|
|Retail trade (food)||0.159|0.085||0.002|-0.007||-0.161|-0.078|
|||[0.085]*|[0.084]||[0.016]|[0.008]||[0.084]*|[0.084]|
|Selling other goods||0.153|0.103||0.016|-0.002||-0.169|-0.101|
|||[0.093]*|[0.097]||[0.018]|[0.007]||[0.092]*|[0.096]|
|Provide services||0.010|-0.045||-0.011|-0.010||0.001|0.056|
|||[0.101]|[0.114]||[0.021]|[0.012]||[0.103]|[0.114]|
|Outside household premises||0.010|0.038||0.020|0.001||-0.030|-0.039|
|||[0.050]|[0.053]||[0.013]|[0.010]||[0.050]|[0.054]|
|Has a bank account||-0.395|-0.191||0.102|-0.019||0.294|0.210|
|||[0.087]***|[0.104]*||[0.051]**|[0.031]||[0.088]***|[0.107]**|
|Constant|0.869|0.721|0.780|-0.003|-0.012|-0.007|0.134|0.291|0.227|
||[0.103]***|[0.098]***|[0.133]***|[0.014]|[0.016]|[0.017]|[0.104]|[0.097]***|[0.132]*|
|Number of countries|3|3|2|3|3|2|3|3|2|
|Observations|1,151|1,793|1,129|1,151|1,793|1,129|1,151|1,793|1,129|
|R-squared|0.218|0.144|0.232|0.482|0.072|0.515|0.069|0.099|0.104|

Source: Own estimations using IES. Notes: Linear probability models estimated by Ordinary Least Squares. Omitted categories: owner 15-24 years old, owner with no education, firm 0-1 years old, manufacturing. Robust standard errors in brackets. * significant at 10%, ** 5%, *** 1% 34
Earlier studies also showed disparities in the geographic distribution of informal employment. For example, Manning (2018) observed that informal employment was highest in the outer islands of Kalimantan, Sulawesi, Sumatra, and other Eastern regions such as Nusa Tenggara, Maluku, and Papua. In contrast, informal employment was lowest in the densely populated islands of Java and Bali. These geographic patterns partly reflect the highly rural nature of informal employment, where many work as own-account workers in farming, fishing, and livestock (World Bank, 2010).<sup>12</sup> Nevertheless, there was also a significant number of informal workers in urban areas, who are primarily employed in retail trade, construction, and transportation (Manning, 2018; World Bank, 2010). As noted by the World Bank (2010), informal workers in urban areas had higher levels of education and income and were less likely to be poor than informal workers in rural areas. Overall, previous evidence suggests that low levels of human capital characterize informal employment in Indonesia and tend to attract more women than men (Manning, 2018; World Bank, 2010).

Much has been said about the differences in the labor market outcomes of formal and informal workers in Indonesia. For example, the World Bank (2010) estimated that informal workers earn 30 percent less on average than formal workers even after controlling for demographics, educational qualifications, and geographic location. Aside from this sizable earnings penalty, most informal workers do not have social insurance (e.g., health insurance, oldage pension) or employment benefits such as paid leave. While the Indonesian government has taken significant steps to expand the social safety net in recent years by allowing voluntary contributions by informal workers, coverage rates are far from universal. In 2017, for example, only 43 percent of informal workers had ever been enrolled in the national health insurance system (Muttaqien et al., 2021). Furthermore, more than 60 percent of enrolled informal workers

> 12 While agricultural workers are not always informal workers, there is a large overlap between the two groups. For example, the OECD (2019) estimates that nearly 94 percent of agricultural workers globally are informal workers.

16
domestic firms to new technologies, and more incentive to adopt them. In addition, fbreign presence induces greater competition in the corresponding industries, forcing inefficient firms to exit the industry. However, simply because the dispersion of productivity is narrower in sectors with significant foreign presence does not necessarily imply that overall levels of productivity should be higher in those sectors. Indeed, the regression in Model 3 which was performed on the level of TFP (not the deviation) reveals that foreign firms have a higher (and significant) level of productivity, but the presence of foreign firms in an industry does not cause a higher TFP level for firms in that industry (as can be depicted by the significant negative coefficient on SFORSH), although it does induce less deviation of firms from the efficiency frontier. This result indicates that if any productivity spillovers exist, they are negative. One possible explanation for this negative relationship is that foreign firms are attracted to sectors with a low level of productivity, i.e. sectors where foreign firms could exploit their comparative advantage<sup>1</sup> °. Firms with a high public share in ownership exhibit less deviation from the efficiency frontier and a higher level of productivity than firms with a lower share of public investment. This finding might indicate that, despite the financial crisis that resulted, the high-investment stategy followed by the government in the early seventies has allowed public firms to reach a larger size (therefore taking better advantage of scale economies) and obtain more technological capabilities compared to new, private firms. On the other hand, this result might be capturing high public equity sectors whiih are of national importance (such as phosphate derivatives) and where the government usually aims at reaching maximum productivity. Finally, note that the presence of public equity has a higher impact on productivity than the presence of foreign equity, as depicted by the elasticity of each variable.

'OFor a more extensive study on dynamic externalities from foreign investment in Morocco see Haddad and Harrison (forthc3ming).

18
14 through an expansion of trade. Fourth, when the inflation targeting dummies are introduced, there are fairly positive values associated with the adoption of IT practices. Here, the coefficients vary in size according to the measure employed in the regression to control for globalization and depending on whether IT policies are partially adopted (ITsoft) or fully adopted (ITfull). Trade openness related estimations render the highest IT coefficients and, overall, ITfull noticeably presents larger and more significant coefficients than ITsoft. The latter finding suggests that adopting a full-fledged IT regime results in higher per capita income gains than adopting IT policies only partially.

[Table 2 here] We report in Table 3 the results of estimating the static model for the sample of EMEs described previously. Most of the results are qualitatively similar to those we described above for industrial economies with one important difference. First, as before, controlling for globalization and IT improves the significance of the model considerably but not as much as in the estimates of industrial economies. The R<sup>2</sup> within increases from 0.110 in equation (1) to levels between 0.348 and 0.462 in equations (2) and (3). Second, the expected positive effects of the investment-to-output ratio remain and, with some variations, they are close in magnitude to those observed for industrial economies. In contrast with the findings for industrial economies, for emerging markets population growth estimates present in all cases negative and very significant coefficients. This result is consistent with emerging markets high population growth rates being larger than those of their aggregate output, a situation that ultimately affects income per capita growth. Third, we observe again positive and very significant effects of globalization on income per capita. While the highest coefficients remain those related to trade (varying
It is immediately apparent that the shape of the quasi non-anonymous curves is very similar to the shape of the anonymous GIC (Figure 1 (a)): they all display a supine _S_ shape driven essentially by very slow growth around the 80<sup>th</sup> and 90<sup>th</sup> percentile of the global income distribution and local maxima around the median of the income distribution and for the very top. However, if we compare the 1988-2008 results, it is clear that the gains among the country-deciles that were in the top 1% in 1988 were less than the gains we obtain by simply comparing income levels of the top 1% in 2008 and 1988. This is expected: not every country-decile that was in the top 1% in 1988 managed to have high growth over the next 20 years. Similarly, some country-deciles that were not among the top 1% in 1988 and have exhibited high growth are now (in 2008) in the top 1%. We find the equivalent result for the 1988 poorest country-decile whose growth rate was higher than what we found from the anonymous GIC. Furthermore, some of these differences might arise from restricting the sample to those countries which are present both in 1988 and 2008.

Over the 1988-2008 period, growth was the highest for those country-deciles around the 40<sup>th</sup> percentile of the 1988 global distribution, and lowest around the 85<sup>th</sup> percentile. Groups that were most successful come overwhelmingly from China and India; groups that were least successful are predominantly from the mature economies. Thus, 3⁄4 of the population that was between the 36<sup>th</sup> and 45<sup>th</sup> global percentile (inclusive of the 45<sup>th</sup> ) in 1988 belonged to the country-deciles, generally around the middle of national income distributions, from China and India. If we include other Asia too, the percentage of people belonging to this most successful group reaches 90%. Chinese deciles, for example, multiplied their incomes by a factor of 2.7-2.8.

In contrast, the country-deciles between the 81<sup>th</sup> and 90<sup>th</sup> (inclusive of the 90<sup>st</sup> ) percentile in 1988 are overwhelmingly from mature economies, and come from the lower halves of their national income distributions. Out of total 420 million people belonging to this group, about 365 million are from the mature economies (or differently, 135 out of 165 country-deciles). Even when we exclude from the mature economies those that in 1988 were Communist, the share of the “traditional” rich economies among this group is still very large: 78 percent of people. Some examples with particularly low real growth rates among rich economies include almost the entire lower halves of the income distributions in Austria, Germany, Denmark, Greece and the United 43