rafmacalaba/datause-encoder-data huggingface dataset:
Alcacer and Chung (2010) in our design of many these factors, and we refer interested readers to these papers for additional details on agglomeration metrics and their properties.

# _Population and Demographics_

Our initial explanatory measures naturally focus on basic traits of the district: population, age profiles of the population (demographic dividend), and population density. Our population control comes from the 2001 population census, and models several effects. First, and most important, it provides a measure of the size of local markets in terms of consumers. For some industries, especially in services, these local markets constitute the firm‘s primary product market. For other industries that are traded at a distance, this output feature of the local market is less essential. The overall size of the local district nevertheless represents an important measure for the overall surrounding economic activity (e.g., general availability of workers).

The population control will also reflect to some degree the supply of potential entrepreneurs. Most entrepreneurs start their businesses in their current local area: for example, Dahl and Sorenson (2007) document that over 70% of new firms in Denmark are founded in commuting regions where entrepreneurs were previously living. While some entrepreneurs move to new cities to start their businesses, this is mostly confined to niche, high-growth industries like biotech where a few dominant clusters form. Looking across all industries and types of firms, several studies have instead shown an underlying pattern where entrepreneurs are disproportionately located in their home regions compared to wage employees. This pattern has been shown in the United States, Italy, and Portugal (e.g., Figueiredo et al. 2002, Michelacci and Silva 2007). The population control will pick up some of this supply side effect.

We next consider the age structure of the district, which is also referred to as the ―demographic dividend‖ in the Indian context. The propensity to start firms changes over the lifetimes of individuals (e.g., Evans and Leighton 1989), and the age structure of a district can have an additional effect on the entry rate. For example, Bönte et al. (2009) document an inverted-U shape between regional age structures and entrepreneurship rates in Germany, and Glaeser and Kerr (2009) found high manufacturing entry rates in cities that had a disproportionate share of workers aged 20-40 years old. We construct the demographic dividend measure as the ratio of working age population to non-working age population using 2001 population census counts. The inverse of this measure is commonly known as a dependency ratio.

Third, we include a measure of population density. Higher population density again reflects some measure of local market size, but it also goes beyond to include the competition for local resources. While local sales are easier with a denser market, higher population density is also associated with higher wage levels and higher land rents. Density has also been linked to 11
greater detail the possible reasons for heterogeneous growth spillover effects across landlocked countries in different parts of the world.<sup>11</sup>

# **3. Defining a Country's Neighbors**

In previous studies, purely geographical criteria have frequently been used to identify a country's neighbors (Abreu _et al_ , 2004; Ads and Chua, 1997; Behar, 2008; Ertur and Koch, 2005, 2007; Moreno and Trehan, 1997; and Weinhold, 2002). Additionally, some studies have used trade data to estimate the strength of neighbor interactions (Easterly and Levine, 1998; Moreno and Trehan, 1997; and Weinhold, 2002). We use three alternative definitions of neighborhood by specifying three alternative neighborhood, or spatial, weights ( **W** ) matrices:<sup>12</sup>

#  **W1: Contiguity definition**

For  _wij_  _wii_ , _wij_ = 1 if countries _i_ and _j_ share a contiguity relationship; otherwise _wij_ = 0.<sup>13</sup>

#  **W2: Distance definition**

> 11 We have confined ourselves in this section to a discussion of literature which focuses more or less directly on the relationship between the growth of an economy and the growth of its neighbors and which uses parametric techniques. However, there also exist studies which have found evidence of strong crosscountry growth spillovers using non-parametric techniques (see, for example, Conley and Ligon, 2002) and which have identified spillovers from foreign R&D levels to domestic productivity (Coe and Helpman, 1995).

> 12 In all analysis we follow the standard convention of row-standardizing all **W** matrices, so that _N w_  1.  _ij j_  1

> 13 We assume that _wii_ = 0 to prevent a country's growth rate being included in the definition of the growth of neighboring countries and to avoid using a country's growth rate to predict itself in the analysis of sections 5 and 6.

11
**Table 7 : Earnings and Selection into the Private and Public Sectors of Wage Employment: 2004**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Public<br>sector log<br>monthly<br>earnings|Private<br>sector log<br>monthly<br>earnings|Log<br>monthly<br>earnings<br>in 2004|Selection<br>into public<br>sector<br>in 2004|
||in 2004|in 2004||(Probit)|
|Female|-0.214|-0.388|-0.302|-1.891|
||(3.22)**|(4.49)**|(5.60)**|(7.21)**|
|Married||||-0.020|
|||||(0.12)|
|Log age|5.361|4.085|3.961|-9.877|
||(1.66)|(1.89)|(2.34)*|(1.80)|
|Log age squared|-0.678|-0.485|-0.468|1.621|
||(1.50)|(1.58)|(1.95)|(2.08)*|
|**Education (reference=no formal schooling):**|||||
|Grades 1 to 10|0.338|0.157|0.254|-1.746|
||(2.42)*|(1.21)|(2.74)**|(5.66)**|
|Preparatory school|0.687|0.281|0.457|-4.095|
||(4.70)**|(2.01)*|(4.61)**|(7.49)**|
|Some tertiary education|1.077|0.769|0.886|-3.021|
||(7.07)**|(4.31)**|(7.93)**|(6.38)**|
|**Own occupation (reference=unskilled workers):**|||||
|Professional or technical|0.251|0.822|0.466|6.654|
||(2.37)*|(4.95)**|(5.27)**|(10.39)**|
|Admin or clerical|0.207|0.334|0.286|0.967|
||(1.86)|(2.63)**|(3.52)**|(5.15)**|
|Skilled production worker|0.292|0.482|0.426|1.777|
||(2.72)**|(4.29)**|(5.55)**|(7.82)**|
|Public2004|||0.492||
||||(4.64)**||
|public sector wage premium2004||||12.174|
|||||(9.72)**|
|Genresidualpublic2004|||-0.168||
||||(2.44)*||
|Selectivity term 1|-0.180||||
||(2.27)*||||
|Selectivity term 2||0.088|||
|||(0.62)|||
|Constant|-4.669|-2.532|-2.630|18.126|
||(0.81)|(0.68)|(0.89)|(1.85)|
|City dummies|Yes|Yes|Yes|Yes|
|Ethnicity dummies|Yes|Yes|Yes|Yes|
|Religion dummies|Yes|Yes|Yes|Yes|
|Observations|291|312|603|603|
|R-squared|0.49|0.45|0.51||
|Log likelihood||||-286|
|Pseudo R-squared||||0.31|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 35
# **Table A.25. The district fixed-effect RDD-DD regressions of individual-level outcomes with clustering the standard error at the** **<u>village level</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1144***|0.0538***|0.0330|0.5545***|0.3757***|0.3814***|-0.0080|1.2841***|
||(0.0292)|(0.0145)|(0.0221)|(0.1777)|(0.0808)|(0.0779)|(0.0290)|(0.2386)|
|(Poverty rate – 50) * Post-program|0.0070**|-0.0056***|-0.0038*|-0.0338|-0.0580***|-0.0547***|0.0075**|-0.0467*|
|period|(0.0034)|(0.0018)|(0.0022)|(0.0206)|(0.0117)|(0.0114)|(0.0031)|(0.0248)|
|Program * (Poverty rate – 50) * Post-|-0.0026|0.0046**|0.0026|-0.0002|0.0561***|0.0523***|-0.0102***|0.0504*|
|program period|(0.0036)|(0.0018)|(0.0024)|(0.0219)|(0.0119)|(0.0116)|(0.0035)|(0.0269)|
|Age|0.0243***|0.0040***|0.0104***|0.1132***|-0.0058**|-0.0059**|0.1571***|0.3874***|
||(0.0009)|(0.0004)|(0.0005)|(0.0051)|(0.0029)|(0.0029)|(0.0082)|(0.0345)|
|Age squared|-0.0003***|-0.0000***|-0.0001***|-0.0015***|0.0003***|0.0002***|-0.0081***|-0.0197***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0000)|(0.0000)|(0.0004)|(0.0015)|
|Male|-0.0653***|-0.0076***|0.0865***|0.9974***|-0.1495***|-0.1382***|0.0370***|0.1056***|
||(0.0050)|(0.0025)|(0.0038)|(0.0398)|(0.0186)|(0.0172)|(0.0073)|(0.0245)|
|Ethnic minorities (yes=1, Kinh=0)|0.1805***|-0.0990***|-0.0292**|-0.2248**|-0.2511***|-0.2432***|-0.1293***|0.7992***|
||(0.0194)|(0.0096)|(0.0125)|(0.1120)|(0.0781)|(0.0771)|(0.0195)|(0.0953)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2242***|0.0402***|-0.0947***|-0.6763***|0.7249***|0.6527***|0.2662***|-1.1919***|
||(0.0247)|(0.0083)|(0.0129)|(0.1290)|(0.0629)|(0.0614)|(0.0473)|(0.2023)|
|Observations|128,446|128,446|128,446|128,446|86,980|86,980|50,645|50,645|
|R-squared|0.190|0.066|0.174|0.154|0.096|0.091|0.195|0.330|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

75
# **4. Data**

I obtain issuance data from the Securities Data Corporation (SDC) Global New Issues database for the time period 1/1/1996-8/31/2005.<sup>15</sup> Following many empirical studies in corporate finance, I exclude regulated industries such as financial services and utilities firms. These firms do not operate in the same manner as unregulated firms and would likely confound the results if included. I also exclude those firms that have gone bankrupt because of the special set of issues that are included in capital structure determination when a company is failing. This exclusion follows the method of Asquith, Gertner, and Scharfstein (1994) who find that such situations generally cause a major restructuring of capital structure outside of the scope of financial constraint relaxation.

I collect over 49,000 domestic issuance observations of common stock, non-convertible debt, convertible debt, non-convertible preferred stock, and convertible preferred stock. The exclusion of international issuances is intentional due to the endogeneity between FPI and international issues. Only one issue per year is retained for each company to avoid upward bias in the results toward issuing capital. Dropping multiple issuance observations for firm years leaves 32,703 observations. Financials for the companies issuing domestically are hand collected from Reuters. This approach, although extremely time consuming, provides a much richer sample than afforded by SDC Platinum alone. Reuters provides financial information on all publicly traded firms for the majority of countries in the world and, as such, does not suffer from the bias toward large firms that other international databases such as Worldscope/Datastream/Research Insight do. In fact, according to Reuters, financials are provided for over 90% of the firms covered. A list of the cumulative firm year issuances by country is provided in Table 1.

(Insert Table 1 about here) The only firms not covered in Reuters are those that have gone bankrupt or have merged with another firm. Bankrupt firms are deliberately excluded from the sample as previously mentioned above.

10
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data been shown to affect both firm performance and tax reporting behavior of firms (Pommerehne and Weck-Hannemann 1996, Johnson, et al. 2000, Dollar, HallwardDriemeier and Mengistae 2005).<sup>27</sup> For this paper no suitable proxy for tax burden is available but we include a dummy for _corruption_ with a value one (zero) implying that a firm has (not) paid bribes or provided informal gifts in 2003. Also a dummy variable _credit_ is included equal to one if a firm faces a constraint to formal credit such as bank loans and overdraft. We also considered a list of variables approximating for the burden of regulation, such as the number of visits by agency inspectors, the time spent dealing with regulations by management, but they were insignificant in the empirical analysis and were omitted.

We are aware of the possible endogeneity problem with the IC variables _._ Firms may pay bribes to get away with underreporting and firms with larger official sales may have more access to finance than firms with smaller official sales. Dollar _et al._ (2005) have proposed taking the city-industry average of the IC variables to reduce the possible endogeneity.<sup>28</sup> We have used the same strategy, but found inflated estimates for the coefficients of the size and industry dummies due to severe multicollinearity problems. On the other hand, including the IC dummies directly in the regression does not affect the estimated parameters of the other variables much. This suggests that possible endogeneity bias, if any, is limited to the estimated coefficients of the IC dummies.

27 The quality of government institutions or legal systems has been shown to also affect informality (Pommerehne and Weck-Hannemann 1996, Loayza 1996, Johnson, et al. 2000, Dabla-Norris, Gradstein and Inchuauste 2008).

> 28 However as Dollar _et al_ . (2005) also point out that even the city-sector averages won’t be exogenous if more efficient firms self-select to better climate locations and the analysis does not fully control for all the forces driving the self-selection behaviors.

25
The pattern in Figure 1 suggests countries seeking to grow should target entry and expansion of exports in growing markets. This goal can be challenging, of course, as growth forecasts are notoriously difficult. An observed boom in coal exports induced by the loss of natural gas supply elsewhere does not mean that coal exports offer a long-term value proposition. Nonetheless, recent industrial policy initiatives have reflected this approach, targeting for instance electric vehicles. China and Korea, which today have captured this growth through exports, also anticipated it in their industrial strategies over a decade ago. The fastest growing four-digit products between 2018 and 2021, the most recent three-year period with global import data in COMTRADE, are roasted iron pyrites (SITC 2814); hemp, raw or processed but not spun (SITC 2652); and castor oil seeds (SITC 2235), all with global demand growing above 40 percent per annum. A challenge in targeting these industries is that they are commodities. In the electric vehicle example, countries targeted an industry in which it was feasible to develop a differentiated product that is protected to some extent from competition, given heterogeneity in consumer preferences.

# _International Competition_

The presence of tough competition suggests targeting growth or entry in an export sector is higher risk. Simple measures of the toughness of competition are the number of exporters (or the inverse of the Herfindahl index of national export shares). Atkin, Costinot, and Fukui (2021) observe that complex products are exported by a larger number of countries, raising a question about whether industrial policy targeting complex products rather than simple products can be successful. This result is a consequence of the fact that product complexity is defined based on whether the product is exported by countries that export many other products. Industrial policy targeting products exported by countries with high GDP per capita face the same issue, since countries with high GDP per capita export a greater variety of products (Hummels and Klenow 2005).

Of course, just because there are few competitors in a market does not mean it is easy to enter. Weak competition could reflect that the market leader has a significant cost or quality advantage, as is apparent for instance with solar panels from China. In such cases, a country considering entering that market can assess whether it can achieve similar cost and quality as the 7
# **Conclude Doha: It Matters!**

Bernard Hoekman, Will Martin and Aaditya Mattoo

## **DOHA: THE CHANGING CONTEXT**

The world is in the middle of a severe global economic crisis. The World Bank expects global GDP will decline by 2.9% in 2009.<sup>1</sup> Excluding China and India, developing nations' economies will shrink on average by -1.6%. Net private capital flows to developing countries will likely turn negative in 2009 – a more than US$800 billion drop from the peak of 2007. The decline in global foreign direct investment (FDI) flows that started in 2008 will deepen and spread to the developing world – with overall inflows projected to fall some 30% compared to 2008, the first time FDI has fallen more than 10% in a year since 1986. The value of remittances, another key source of external financing for many developing countries, will drop by over 7% this year.

Trade has been hit hard by increases in risk premia on lending (which raise the cost of finance) and the sharp fall in aggregate demand. Global trade volumes are expected to decline by some 10% in 2009 and values by closer to 20%. Many developing economies have been hit as hard as the high-income countries. While an unavoidable consequence of the collapse in demand, trade will be a critical dimension of the recovery, especially for the many developing countries that have small domestic markets and rely heavily on external demand as a source of employment in the formal sector and foreign exchange.

Maintaining an open trade regime and removing policies that impose negative spillovers on trading partners is an important part of the path for getting out of the crisis, in particular for developing countries. Given that a central focus of the Doha _Development_ Agenda is to promote the trade prospects of developing nations, the WTO membership can collectively make an important direct contribution to supporting a more rapid exit from the crisis by concluding the Doha Round. Concluding Doha can help by: (i) reinforcing policy stability and reducing the probability that governments will resort to protectionism – of paramount importance to developing countries given their reliance on trade as a driver of growth and a key mechanism to exit the crisis;<sup>2</sup> (ii) assisting governments to reduce fiscal expenditures once economies recover

> 1 All data reported are from World Bank, _Global Development Finance, 2009._

> 2 Research shows that the elasticity (or responsiveness) of trade to income has increased over time, from under 2 in the 1970s to over 3 in recent years (Freund, 2009).  Rapid drawing down of inventories in response to a revised outlook is part of the explanation.  In addition, a large part of GDP is services which contract less than

1
GO TO BOX 1.2 <!-- Start of picture text --> 7 There<br> 1.8<br>Account penetration by age group<br>Adults with an account (%), 2014<br>100<br>Age 25+<br>Ages 15–24<br>75<br>50<br>25<br>East Asia Europe & High-income Latin Middle South Sub-Saharan<br>& Pacific Central Asia OECD America & East Asia Africa<br>economies Caribbean<br>Source: Global Findex database.<br>THE GLOBAL FINDEX DATABASE 16<br><!-- End of picture text -->

> FIGURE 1.7

<!-- Start of picture text --> Account penetration by gender, 2011 and 2014<br>Adults with an account (%)<br><!-- End of picture text --> <!-- Start of picture text --> 2011 2014<br>East Asia & Pacific Male<br>Female<br>Europe & Central Asia<br>High-income OECD economies<br>Latin America & Caribbean<br>Middle East<br>South Asia<br>Sub-Saharan Africa<br>0 20 40 60 80 100<br>Source: Global Findex database.<br><!-- End of picture text --> Among developing regions, the Middle East continues to have a particularly large gender gap in relative terms, with women half as likely as men to have an account. South Asia has the largest gender gap on average in absolute terms, at 18 percentage points.<sup>7</sup> There has been some expectation that mobile money accounts might help close the gender gap in account ownership because of their greater affordability. But so far the evidence is mixed (box 1.2).

# _The youth gap_

Age is another characteristic that matters for the likelihood of having an account. Across all regions, young adults (ages 15–24) are less likely than older adults (age 25 and above) to have an account. While the gap in account penetration between these two age groups averages between 10 and 20 percentage points, the different levels of account penetration mean that its relative size varies enormously. The relative gap is smallest in high-income OECD economies and East Asia and the Pacific, at less than 15 percent, and largest in the Middle East, where young adults are less than half as likely as older ones to have an account (figure 1.8).

FIGURE 1.8 Overall, the gap in account ownership between young adults and older ones remained constant between 2011 and 2014. In developing economies, however, it widened slightly in absolute terms (even while remaining constant in relative terms) because older adults in East Asia and the Pacific and Europe and Central Asia were about 5 percentage points more likely than young adults to add an account. By contrast, in high-income OECD economies the gap narrowed slightly; here, account ownership increased among young adults while older adults were already universally banked.
**Figure 3. Cost per calorie by consumption vintile among the bottom half of the distribution**

<!-- Start of picture text --> 0.006<br>0.005<br>0.004<br>0.003<br>0.002<br>0.001<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Vintiles (Bottom half of the distribution)<br>Cost per calorie (R$/kcal)<br><!-- End of picture text --> _Source:_ Own calculations using POF 2017/18 data.

**Table 12. Poverty lines using 2nd and 3rd consumption deciles as reference group**

|**Total Poverty**|**Food**|**Lower Total**|
|---|---|---|
|**Estimation**|**Poverty**|**Poverty**|
|Parametric|R$251|R$443|
|Non-parametric|R$251|R$441|

_Source:_ Own calculations using POF 2017/18.

# Poverty lines based on alternative treatment of items in “lumpy” modules

In section 3 we described our approach to exclude lumpy items and relatively infrequent expenditures from the consumption aggregate used to determine the reference population, as well as from the nonfood component when estimating the total poverty line. In our main specification we exclude expenditures made on ceremonies, jewelry, property, vehicles, and construction, while in the following categories of goods and services, we exclude items that have average expenditures above R$500: toys and recreational goods and services, domestic electrical appliances, domestic nonelectrical appliances, and furniture. In a variation of this main specification, we exclude all toys and recreational goods and services; domestic electrical appliances; domestic nonelectrical appliances; and furniture, regardless of the average amount spent on these. Results are presented in Table 13 and show that poverty lines remain basically unchanged.

**Table 13. Poverty lines based on alternative treatment of items in “lumpy” modules**

|**Total Poverty**|**Food**|**Lower Total**|
|---|---|---|
|**Estimation**|**Poverty**|**Poverty**|
|Parametric|R$258|R$455|
|Nonparametric|R$258|R$452|

_Source:_ Own calculations using POF 2017/18.

20