rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Chinese exports increasingly compete with Latin American and Caribbean (LAC) 1 products in world markets. Competition in the US market is of particular importance. The United States has been Latin America's most important trade partner over the 2 postwar era. As a fraction of the region's trade with the world , trade with the United States stood at 60 percent in 2000, up from less than 47 percent in 1960, having grown continuously since the mid-1970s (see Figure 1). Latin America has also been an important trade partner for the United States, although there have been important fluctuations over the last three decades. As Figure 2 shows, total trade with Latin America fell in importance through the late 1980s, but has been picking up since then. What Figure 2 also highlights is the growing importance of US-China trade, which has come from representing an insignificant fraction of US trade to more than 5 percent nowadays.

The remarkable growth in US trade with China, and the challenges it portends for Latin American countries, are most impressive when we look at US import data; see Table 1. From 1990 to 2003, Latin American exports to the United States increased from $58 billion to $196 billion, growing, in real terms, at an annual rate of 6.9 percent. As US imports from the world as a whole grew at 4.8 percent over the same period, Latin America's share of the US market rose from 13.5 in 1990 to 17.5 in 2003. In the meantime, however, Chinese sales to the United States grew at a breakneck 16.6 percent annually, reaching $147 billion in 2003. China's export dynamism has allowed its share of US imports to increase four-fold to 13.2 percent in 2003.

Although Latin America as a whole had a fair export performance over the last decade, aggregate figures mask important differences among countries in the region. The lion's share of the growth in exports from Latin America, more than 80 percent, came from Mexico, a country that increased its share of the US market from 6 to 11.5 percent from 1990 to 2003. Over the same period, exports from Caribbean, Andean and

> 1 See Devlin et al (2005).

> 2 By trade here we mean the sum of exports and imports.

2
Table 4: IV Estimates of the Effect of Disruptiveness on the Probability and Elasticity of Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded<br>(1)|Last Funded<br>(2)|Ever Funded<br>(3)|Last Funded<br>(4)|
||Pa|nel a. Probabil|ity of Being Fund|ed|
|Disruptive|0.070**|0.076***|0.061*|0.066**|
||(0.030)|(0.024)|(0.035)|(0.029)|
|_R_<sup>2</sup>|0.144|0.123|0.145|0.125|
|Observations|2428|2428|2428|2428|
|Countries|9|9|9|9|
|||Panel b. Elast|icity of Funding||
|Disruptive|1.049**|1.091***|0.922**|0.965***|
||(0.412)|(0.301)|(0.454)|(0.337)|
|_R_<sup>2</sup>|0.163|0.142|0.164|0.144|
|Observations|2428|2428|2428|2428|
|Countries|9|9|9|9|
||Panel c. Elasti|city of Fundin|g Conditional on|Being Funded|
|Disruptive|0.422*|0.343|0.278|0.216|
||(0.231)|(0.288)|(0.320)|(0.493)|
|_R_<sup>2</sup>|0.437|0.436|0.439|0.436|
|Observations|548|481|548|481|
|Countries|9|9|9|9|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is either a dummy variable indicating if the firm received funding (panel a), or the IHS transformation of the amount of funding in US Dollars received in the last funding round (panels b and c). Standard errors clustered at the country level.

# **4.4 Disruptiveness and performance**

To analyze the correlation between the incorporation of disruptive technologies in firms’ products and services offering and firm-level performance, we rely on Pitchbook data, which includes more detailed information in terms of valuation and growth. Due to data restrictions, in this case, we are only able to compare Africa with LAC.

In Table 5, we estimate similar models to the ones shown in previous tables but using some performance-related indicators as dependent variables. As shown in panel (a) of the table, disruptive firms in Africa receive funding 0.66 years earlier than non-disruptive 24
# **III. Empirical analysis: the estimation set-up**

To empirically estimate the effects of increased patent protection on bilateral trade flows we use a conventional gravity model. Gravity models have been applied successfully to explain different types of international flows, such as migration, commuting, recreational traffic, and trade. Typically, they specify that a flow from country i to countryj can be explained-by supply conditions in country i, by demand conditions in countryj, and by forces.either assisting or resisting the flow's movement.<sup>1</sup> l Our depend variables are bilateral trade flows for 89x88 countries which were extracted from the United Nations Comtrade database. The data refer to 1989 total non-fuel and high technology trade. The rationale for using high technology trade flows besides total non-fuel trade is based on the a priori expectation that the effects of IPRs protection are stronger for knowledge-intensive trade. For a definition of our high technology aggregate, see Table 1.

Following earlier specifications of gravity models, our explanatory variables are GDP and population of both countries i andj, geographical distance between the two countries, a dummy variable which is one if the two countries share a common border and zero otherwise, and a dummy variable which is one if the two countries share the same language and zero otherwise.<sup>12</sup> The coefficients on GDP are expected to be positive and around unity (Anderson 1979); the coefficients on population are expected to be small and negative, representing economies of scales (Linneman 1966). Positive geographic and cultural distance are expected to have a negative influence on bilateral trade flows; that is the coefficient on geographical distance is expected to be negative. the coefficients on common border and language are expected to be positive. The data appendix gives more information on the countries included and the sources of all variables.

" Gravity models were developed based on intuitive reasoning rather than economic modeling. Due to their empirical success, there have been numerous attempts to shed some light on the economic underpinnings of the gravity equation. Linneman (1966) showed how the standard gravity equation can be derived from a quasiWalrasian general equilibrium model of export supply and import demand. Leamer and Stem (1970) showed how a gravity model can be derived from a probability model of trade patterns. Anderson (1979) suggested a theoretical foundation in terms of an expenditure system with goods differentiated bv countries of origin. Bergstrand (1985 and 1989) used a general equilibrium world trade model assuming utility- and profit-maximizing agent behavior and showed that the gravity model "fits in" with the Heckscher-Ohlin model of inter-industry trade and the HelpmanKrugman-Markusen models of intra-industry trade.

**12** See, for example, Tinbergen (1962), Linneman (1966), Aitken (1973), Pelzman (1977), and Primo Braga, Safadi, and Yeats (1994).

_5_
We only consider those periods where we have a full set of observations; i.e. we have information for each of the seven years before and after the surge starts. We took 7 years as the relevant time horizon, as in Hausmann, Pritchett and Rodrik (2005), however, for robustness purposes we also calculate results for 5-year periods and find the results very similar, and in Section V1, we discuss results for episodes that are sustained for 12-years.

To identify the surges, we use export data from COMTRADE covering the period from 1980 to 2005.<sup>8</sup> Also, since our objective is to focus on what happens in the manufacturing sector, we use the trade data reported under the SITC Rev. 2 classification (Chapters 5 to 8).<sup>9</sup> Appendix Table A1 describes all of the data used in the analysis in more detail.

After applying the criteria mentioned above, we obtain 92 episodes of export surges. The full list of episodes by country including the year when the surge starts can be found in Appendix Table A2.

Figure 1 shows the evolution of the average and median values of real manufacturing exports for developing and developed countries before and after the year when the surge starts (year 0) in the upper panel, and manufacturing export growth in the lower panel. For both developing and developed economies, the level and growth of manufacturing exports increases significantly after the surge starts. Figure 2, shows the rise of manufacturing exports during the surge, both as a percent of GDP and as a percent

> 8 In order to reduce the number of missing observations, we have used mirror data (imports) from all countries.  Because of noise in the data, we eliminate countries with annual trade below $1,000,000 during the period.

> 9 These chapters include all manufactured goods classified by materials, chemicals, miscellaneous manufactures and machinery and transport equipment. Also, we alternatively used the manufactured goods reported under the ISIC classification, chapters 31 to 39, and we obtained nearly identical results.

8
Steven Klepper 1996; Patrizio Pagano and Fabiano Schivardi 2003; Måns Söderbom and Francis Teal 2004; M. Angeles Diaz-Mayans and Rosario Sanchez 2008).

Given the indication that women entrepreneurs are more susceptible to difficulties in obtaining finance as well as inadequate business climate, we also control for a dummy variable equal to 1 if the firm reports access to finance as a major or a very severe obstacle and 0 if it reports the obstacle as no obstacle, minor or moderate obstacle ( _Finance obstacle_ ); and a dummy variable equal to 1 if the firm was inspected by tax officials last year and 0 otherwise ( _Inspected_ ).

The next firm-level control is a dummy variable equal to 1 if the firm is located in a large city and 0 otherwise ( _Large city_ ). Following Enterprise Surveys, a large city is defined as either the capital city or a city with a population of over 1 million. In our sample, about 37 percent of the firms are located in large cities. As indicated in the conceptual model, city size may influence the likelihood of the presence of women manager in a firm.

Our last set of firm-level controls includes (log of) age of the firm ( _Age_ ) and the proportion of a firm’s annual sales that are exported ( _Exports_ ). As we argued above, there could be a systematic difference in the likelihood of female top managers across countries depending on the age and export orientation of the firm. .

We also control for some important country-level variables as described in the conceptual model. We first control for (log of) GDP per capita, at constant 2000 US dollars ( _Income_ ). We use average values of the variable over the period 2007 and 2009 taken from World Development Indicators (WDI), World Bank. The variable serves as a useful proxy measure for various aspects of economic development that could be correlated with both women managers and women’s access to education.

It is natural to expect that ceteris paribus, countries with proportionately more women in the population to have proportionately more women in top management. At the same time, populations with more women could also reflect more favorable attitudes towards female children 16
~ _sd_ ( ∆<sup>_p_</sup> _t_ ) and _sd_ ( ∆<sup>_p_</sup> _t_ ) are compared, eliminating trend movements. The result is that the resulting to variability above a trending mean – see Gilbert and Morgan (2010, 2011).

Seasonality in food (and non-food) consumption can be analyzed in a similar manner. Write the level of consumption in period _t_ (month _m_ of year _y_ ) as _Ct_ = _Cym_ with year _y_ mean _Cy_ . As before, the conditional seasonal gaps can be calculated either from the level or the log data. Yet data on consumers’ expenditure are generally only available on an annual basis in developing countries and hence are uninformative in relation to seasonality. In some cases, households are surveyed two, three or occasionally four times in a year, but even if so, they would only give a bi-annual or quarterly (and not a monthly) picture and rarely are such surveys repeated over several years, especially not in Sub-Saharan Africa (Garcia-Verdu, 2014). Surveys over several years are necessary to identify a true, seasonal pattern, i.e. regular monthly deviations from the annual average observed across many years, as opposed to the intra-annual variation observed in one year, which comprises both the seasonal patterns as well as the year specific deviations from it.

Such data constraints are common to most studies on consumption seasonality in the literature. This paper also only has one (Malawi, Uganda) or two years of consumption data (Tanzania), allowing it to paint a snapshot of intra-annual variations in household consumption only, as opposed to true seasonal variations. Differently from most other studies, estimates of the monthly deviation from the annual average could be obtained in this case, by exploiting the fact that a nationally representative sample of the population was surveyed in each country. To do this, the following model was estimated: where _f/nf_ upper script stands for food or non-food, _i_ denotes the household, _m_ and _y_ the month and year of the interview, **Xiy** is a matrix of household characteristics that do not vary across seasons 11
**<u>Table 7: Corn income in Mali for different household groups, 2006</u>**

||Percentage of|households re<br>income|ceiving|Average i|ncome for all ho|useholds|Average income|for households wit|hpositive income|
|---|---|---|---|---|---|---|---|---|---|
||Auto|||Auto|||Auto|||
||consumption|Sales|Total|consumption|Sales|Total|consumption|Sales|Total|
|**All**|33.00%|6.34%|33.60%|21,979.48|9,259.97|31,239.45|66,654.80|146,112.90|92,967.98|
|**Residence area**||||||||||
|Urban|13.20%|1.99%|13.58%|9,014.41|8,227.37|17,241.78|68,407.95|414,377.10|126,956.00|
|Rural|44.80%|8.94%|45.55%|29,717.77|9,876.28|39,594.05|66,346.95|110,533.50|86,919.95|
|**Region**||||||||||
|Kayes|54.60%|10.74%|55.52%|33,016.04|8,182.98|41,199.02|60,483.50|76,166.44|74,205.51|
|Koulikoro|50.30%|5.72%|51.05%|27,992.64|2,512.82|30,505.46|55,597.68|43,894.40|59,757.40|
|Sikasso|71.20%|20.19%|72.98%|81,110.92|50,507.05|131,618.00|113,899.93|250,176.80|180,360.00|
|Ségou|23.80%|4.31%|24.30%|4,767.49|1,833.74|6,601.23|19,989.54|42,571.72|27,163.02|
|Mopti|14.50%|0.60%|14.51%|3,786.25|165.79|3,952.04|26,149.35|27,557.26|27,234.33|
|Tombouctou|9.60%|2.29%|10.47%|1,362.21|1,015.81|2,378.02|14,198.70|44,431.14|22,716.80|
|Gao|1.90%|0.00%|1.90%|885.74|0.00|885.74|46,733.75|0.00|46,733.75|
|Kidal|0.70%|0.00%|0.71%|50.33|0.00|50.33|7,040.00|0.00|7,040.00|
|Bamako|7.40%|1.45%|7.37%|2,334.42|963.08|3,297.50|31,674.81|66,305.63|44,742.41|
|**Quintile**||||||||||
|Q1|49.10%|13.97%|50.43%|24,508.26|20,431.49|44,939.75|49,951.54|146,204.30|89,112.31|
|Q2|42.60%|9.42%|43.39%|29,494.45|23,861.67|53,356.13|69,160.13|253,358.80|122,970.70|
|Q3|40.90%|7.80%|41.98%|29,720.05|6,475.10|36,195.16|72,713.82|83,054.02|86,221.02|
|Q4|29.30%|3.85%|29.51%|23,089.27|2,927.30|26,016.57|78,669.09|76,080.76|88,156.43|
|Q5|16.10%|1.49%|16.31%|9,666.79|1,391.71|11,058.50|60,090.62|93,440.88|67,795.41|

Source: Authors’ estimation using ELIM 2006.
Table 5: Information costs and asymmetric export responses to BRER – by type of product2003-2017

||Diff<br>|erentiated prod<br>|ucts<br>|Hom<br>|ogenous prod<br>|ucts<br>|
|---|---|---|---|---|---|---|
||Total|Intensive|Extensive|Total|Intensive|Extensive|
|VARIABLES<br><br>|(1)|(2)|(3)|(4)|(5)|(6)|
|lll<br>it<br><br>|-0.079***|-0.118***|-0.011***|-0.036***|-0.021**|-0.006***|
||(0.002)|(0.005)|(0.000)|(0.004)|(0.010)|(0.000)|
|lll<br>it<sup>× d</sup>it<br>|0.021***|0.026***|0.002***|-0.005|-0.020|0.000|
|<br>|(0.003)|(0.006)|(0.000)|(0.005)|(0.013)|(0.000)|
|<br>llit<br>∗|0.369***|0.553***|0.040***|0.257***|0.361***|0.023***|
||(0.004)|(0.007)|(0.000)|(0.007)|(0.014)|(0.001)|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Product FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,101,181|271,121|1,101,181|394,905|88,768|394,905|
|R<sup>2</sup>|0.297|0.445|0.262|0.268|0.411|0.237|

***, **, * imply significance at the 1%, 5% and 10% respectively. SE, adjusted for clustering at the HS6-year level, are reported in brackets.

Results are in line with that hypothesis. Column 1 reports the unrestricted model estimates for total exports of differentiated products. Changes in export quantities are associated with changes in BRER, but the association is lower for depreciations than for appreciations. When BRER appreciates by 10 percent, exports of differentiated products fall on average by 0.79 percent. When BRER depreciates by 10 percent, exports increase by 0.58 percent on average, making the elasticity of export quantities with respect to the BRER 27 percent lower for depreciations than for appreciations. Columns 2 and 3 focus on the intensive and extensive margin of exports. Both results show asymmetry in line with total export quantities. Exports of products actively sold to a trading partner are more elastic to BRER changes than overall exports (it is easier to increase or decrease existing shipments after conditions change than to establish new ones (or break them altogether)). They fall by 1.18 percent when BRER appreciates 10 percent, and increase by 0.90 percent when it depreciates by 10 percent. The probability of opening a new product-destination export line drops by 0.11 percent after a bilateral real appreciation of 10 percent, and increases by 29
# **<mark>Table C.4b: Comparison of modal class assignments across the 4-class and the 5-class models for relations with customers</mark>**

|Read by rows: from 4-class (row) t|o 5-class<br>Class|(column)<br>Class|<br>Class 3|Class 4|Class||
|---|---|---|---|---|---|---|
||1|2|||5|Total|
|Pure bilateralism|**89.42**|0.65|0.1|8.87|0.97|100|
|Bilateralism with private support|8.7|**51.75**|0.48|33.86|5.21|100|
|Bilateralism with weak support|1.54|6.28|**84.1**|0.15|7.94|100|
|Weak comprehensive governance|15.73|10.13|1.1|56.73|16.32|100|
|Total|54.58|14.17|11.65|15.94|3.66|100|
|Read by columns: from 5-class (col|umn) to<br>|4-class (ro<br>|w)<br>||||
||Class|Class|Class 3|Class 4|Class||
||1|2|||5|Total|
|Pure bilateralism|**94.43**|2.65|0.47|32.06|15.28|57.64|
|Bilateralism with private support|3.84|**87.98**|0.99|51.16|34.33|24.09|
|Bilateralism with weak support|0.38|6.02|**98.1**|0.12|29.52|13.59|
|Weak comprehensive governance|1.35|3.35|0.44|16.65|20.88|4.68|
|Total|100|100|100|100|100|100|
|Estimated class membership probab<br>|ilities an<br>|d standar<br>|d errors<br>||||
|Pure bilateralism|0.565|0.032|Class 1|0.4739|0.0422||
|Bilateralism with private support|0.145|0.025|Class 2|0.1152|0.0267||
|Bilateralism with weak support|0.242|0.000|Class 3|0.2205|0.0305||
|Weak comprehensive governance|0.050|0.008|Class 4|0.1579|0.0264||
||||Class 5|0.0325|0.0088||

Appendixes, page 21
which is more likely if secondary town job generation induces more migration and the larger is the reduction in their poverty gap, i.e. the larger are wos and ws<sup>4</sup> (and the smaller is _ts_ ).<sup>5</sup> In conclusion, from this extended H-T model with two destinations and explicitly accounting for migration costs, we learn that, in general, to assess whether to invest in cities or towns for poverty reduction, we need to take into account many interactions. Especially, the migration response to job generation as a result of the investment is key. The poverty outcome depends further intimately on where the poverty line falls in relation to the income levels in cities and towns. Finally, the poverty index chosen to evaluate the poverty effects can also matter.

For an empirically plausible income gradient scenario ( _wr < wos < woc < ws < wc_ ) and three different locations of the poverty line within this gradient, we have also derived the exact conditions under which one or the other policy will dominate. For convenience, Table 1 summarizes the findings, together with the associated poverty gradient. Given the importance of the migration response in determining the empirical outcome, migration costs and thus the proximity of the poor to towns versus cities, which is closely related to the migration cost they face (Schwarz, 1973), will play an important role (as will the difference in the income gaps between formal and informal employment across towns and cities).

To add perspective, globally, a larger share of the population lives closer to secondary towns than to cities. While 15.9 percent live within 1-3 hours from a secondary town (defined as ≤ 500,000 inhabitants), 9.9 percent live within 1-3 hours from a city (> 500,000 inhabitants); another 15.3 percent live in the rural hinterlands (at more than 3 hours from an urban center) (FAO, 2017). In Sub-Saharan Africa, which houses about half of the world’s extreme poor (World Bank, 2016), 22 percent of the population live within 1-3 hours from a secondary town, compared with 9 percent living within 1-3 hours from a city. About 1/3 of the rural population lives in the rural hinterland at more than 3 hours from an urban center (FAO, 2017). With 82 percent of Africa’s poor living in rural areas (Beegle et al., 2016) and many of these closer to towns than to Africa’s cities, it becomes important for global poverty reduction to understand how distance and migration costs interact in practice with the difference in income gaps between formal and informal jobs in those towns and cities.

> 4 Or the smaller the difference with woc and wc respectively (recall from (22): _wr < wos < woc < ws < wc_ ).

> 5 Compared to _tc_ –note that _dXs/dts<0_ .

13
Lorie (2003), investigating tax performance in CIS economies, regressed the tax revenue to GDP ratio on PPP GDP per capita for a large sample of developed, transitional and emerging market economies, with an additional regression which included a dummy variable for the OECD, graduating transition and CIS countries. The predicted tax revenue to GDP ratio for Tajikistan was 13.6% and 18.1% in the regressions with, and without, a dummy respectively. Tajikistan’s tax revenue had reached 16.6% of GDP in 2006, hence it is not out of line with what would be expected based on the country’s per capita income.

There are three potential sources of growth in domestic revenues as a share of GDP: First, the income elasticity of some tax handles; secondly, changes in tax policy (tax rates, thresholds, exemptions etc); and thirdly, improvements in tax administration.

**Table 2**

# **Domestic Revenues, 2000 and 2006 Outturns and Projections for 2011 and 2016; Percentage of GDP**

||2000|2006|2011|2016|
|---|---|---|---|---|
|DomesticRevenues|13.9|18.8|20.6|22.7|
|Tax Revenue|12.9|16.6|18.3|19.2|
|Impact of Taxadmin reform|n.a.|n.a.|0.7|1.4|
|Taxes excluding taxadmin reform|12.9|16.6|17.6|17.8|
|Individual Income tax|1.2|1.2|2.1|2.4|
|ProfitTax|0.6|0.6|0.6|0.6|
|Social Taxes|1.6|2.0|2.0|2.0|
|Sales tax(onexports)|3.3|0.6|0.6|0.6|
|VAT|2.5|7.4|7.8|7.7|
|ExciseDuty|0.5|0.7|0.7|0.7|
|CustomsDuty|1.4|0.9|0.9|0.9|
|Other Taxes|1.8|2.9|2.9|2.9|
|Non Tax Revenue|1.0|2.2|2.4|3.5|
|Revenuefrom HPPprojects|-|-|0.1|1.3|
|Other|1.0|2.2|2.3|2.2|

Source: 2000 and 2006, MOF and IMF; 2011 and 2016, authors’ projections 16