rafmacalaba/datause-encoder-data huggingface dataset:
products (14 percent of the consumption basket), followed by cereals - mainly corn - (9 percent), meat products (8 percent) and dairy products (6 percent). Vegetables, oils and fats, fruits, and sugar characterize the remainder 13 percent. Table 2.1 reports the trends of real prices for the 12 products as observed from the analysis of the household surveys.<sup>8</sup> Similarly, table 2.2 reports the average tariff for each good from 1989 to 2000.

Given the strong economic growth of the Mexican economy during the 1990s, it is not surprising that prices have declined substantially. All prices considered here with the exception of sugar decreased between 1989 and 2000. During this period, the decline of real prices has ranged from about 7 percent for cereals to about 40 percent for dairy products.<sup>9</sup> With the exception of the sharp economic downturn of 1995, real wages increased steadily in Mexico during the 1990s. Moreover, the data reveals that skilled wages have been increasing at a much faster rate than unskilled wages. Most of the literature contends that the widening wage gap is one of the effects of trade liberalization (Revenga 1997). Figure 2.2 reports the log of real wages in Mexico for the border region and the rest of the country. With the exception of the sudden shift due to the economic downturn and devaluation of 1995 when both skilled and unskilled wages declined dramatically, unskilled wages were stagnant while skilled wages increased during the period of analysis. As a result, especially in the regions away from the northern border, the skilledunskilled wage gap has been increasing.

Hanson (1997) explains this outcome in the framework of the recent literature on geography and trade as the result of the fact that unskilled jobs were more protected to start with, and therefore trade liberalization hurt them disproportionately. Wages are observed to be substantially higher (both skilled and unskilled). This is compatible with the geography and trade literature which predicts that a reduction in transaction costs will affect the regional structure of wages, so that industries concentrate geographically, and

> 8 To obtain real prices, nominal prices are deflated by the regional price indexes

> 9 Similar decreases in the prices of agricultural goods have been observed by Yunez-Naude (2002) on the basis of government statistics.

6
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| Past recessions, 25th-75th percentiles<br>—— Past recessions, median<br>—— Current crisis<br><!-- End of picture text -->
past eight years in over 100 countries with a consistent survey instrument.<sup>3</sup> The surveys try to capture business perceptions on the most important obstacles to enterprise operation and growth, but also include detailed information on management and financing arrangements of companies. Sample sizes vary between 250 and 1,500 companies per country and data are collected using either simple random or random stratified sampling. The sample includes formal and informal enterprises of all sizes, different ownership types and across nine sectors in manufacturing, construction, services and transportation. Firms from different locations, such as capital city, major cities and small towns, are included. Our empirical work relies on data for formal enterprises from 37 African countries and 49 countries from the rest of the world covering in total 35,000 firms during 2006 to 2009 (Appendix Table A1).<sup>4</sup> In addition, we focus on informal mostly micro-firms for a sample of 25 African countries.

The Enterprise Surveys offer several advantages for our purpose. First, the surveys collect comparable information for several firm characteristics across all the countries. This comparability allows us to document cross-country and within-country profiles of firms that have female ownership participation. Second, the surveys collect information on financing at the firm level as well as several other relevant firm characteristics. These include the firm size and age, human capital composition of the workforce, measures of technology adoption and firms‟ international openness, i.e. export activity and sources of capital. In addition, there is also detailed information on the firm‟s geographical location and its sector of activity (3digit-ISIC classification). Third, the surveys reach a substantial number of countries across all

> 3 See www.enterpriseseurveys.org for more details. Similar surveys were previously conducted under the leadership of the World Bank and other IFIs in Africa (RPED), and world-wide in 2000 (World Business Environment Survey).  Enterprise Surveys still go under different names in some regions, e.g. BEEPS in the Central Asia and Eastern European transition economies had first been launched in 1999 and was then modified to be comparable with the broader global initiative.

> 4 We lose 25 countries when using the share of working capital financed with external finance, mostly in Eastern and Central Europe and East Asia.  There are a total of 95 surveys but some countries enter the dataset in multiple years. Information collected is standardized across countries and covers formal establishments with 5 or more employees. However, Africa, East Europe and Central Asia, and Latin America include establishments with fewer than 5 employees. In Africa, there are an additional 25 standardized surveys covering micro-firms and targeting informal establishments.

6
The quality of the national road infrastructure also matters for timely delivery to global markets. Moreover, the use of the internet and a common language could also facilitate GVC participation. Studies show a stronger role of logistics performance for trade in parts and components than for trade in ...nal goods, and provide evidence that unpredictable land transport keeps most Sub-Saharan African countries out of GVCs.<sup>25</sup> Given that our sample includes a wide range of countries with diverse logistics infrastructure and language used, we are able to examine the impact of connectivity on GVC trade.

## 2.2.7 Macroeconomic Factors

Macroeconomic factors, in particular related to real exchange rates, can play a role for GVC participation. The degree of ...nancial development of countries is a source of comparative advantage but there is still limited understanding of whether ...nancial factors a¤ect GVC participation.<sup>26</sup> Our sample coverage of developed and developing countries allows us to study how these macroeconomic factors may impact trade and GVC participation.

# 3 Empirical Speci...cation

We examine the factors that in‡uence GVC participation by exploiting variation across countries and over time in GVC participation and in determinants. We estimate the impact of country decadal averages of the determinants on country decadal average GVC participation by least squares between e¤ects (LS-BE). The main speci...cation is given by:

> 25See Ansón et al. (2017) for estimates of the sensitivity of bilateral trade in parts and components and in ...nal goods to logistics performance. See Christ and Ferrantino (2011) for evidence on Sub-Saharan Africa. A positive correlation between broad infrastructure - including communication, electricity, roads, and power - and overall GVC participation in manufacturing is provided by Cheng et al. (2015). Linguistic proximity is shown to matter for bilateral GVC links in Buelens and Tirpak (2017) and Ignatenko et al. (2019). At a micro level, evidence on the role of unpredictability in imports’border clearance times, regional road density and internet access for ...rm export performance are shown, respectively, by Vijil et al. (2019), Rodríguez-Pose et al. (2013) and Fernandes et al. (2019b).

> 26See Beck (2003), Manova (2008), and Manova (2015). Exchange rate volatility is shown to be negatively related to bilateral GVC trade by Ignatenko et al. (2019).

10
some actor may be successful in terms of getting the signatories to stop fighting.<sup>25</sup> In order to ascertain whether a partial agreement is better than no agreement at all, it would be preferable to study all conflicts (i.e. also those conflicts where no settlement has been reached), and compare the conflict behavior of the signatories to that of all non-signatories.<sup>26</sup> However, this lies outside the scope of this study. What can be done is to look at the duration of peace in substantive terms by comparing the signatories with the non-signatories in this particular sample. Using the program Clarify, the expected duration of peace for signatories and non-signatories is estimated, holding all other variables at their median values (Tomz, Wittenberg & King, 2001).<sup>27</sup> The result shows that the difference in peace duration is quite substantial, in particular considering that the study only covers the post-Cold War period. The expected peace duration for a rebel group that has signed an agreement is approximately eight years, whereas the corresponding figure for a non-signatory is only about two years. In other words, being a signatory instead of a non-signatory adds another six years of peace.<sup>28</sup> The findings here thus indicate that the signing of an agreement makes a substantial difference for peace.

The results were obtained while controlling for a number of factors, and some of these are shown to significantly affect the risk that peace fails following a settlement. The models focus on different dimensions of peace, and hence, it is not surprising that the results are not the same across all models. I will briefly present a few of the most notable findings. The results on the settlement level show that the 25 Occasionally the signatories stop fighting already some time before the agreement is signed. This could lead one to suspect that the decision to stop fighting led to the signing of the settlement, and not the other way around. However, even if a group stops fighting before the signing of a deal, they may have done so in the anticipation of a future deal. The fact that the government agrees to sign an agreement and thereby granting concessions suggests that they continue to pose a military treat.

26 For instance, there might be some kind of selection effect taking place. This is, however, only a problem if there is some underlying factor that explains both why parties sign agreements and why they stick to peace.

27 Note that Clarify cannot be used with a Cox model and does not allow for stratification. Instead, I employed a Weibull model without stratification, and the expected values should be seen as indicative only. Moreover, since peace duration is measured one year after the agreement, I have subtracted one year from the values given by Clarify.

> 28 Looking only at the partial agreements, the expected peace duration is six years for the signatories, compared to only one and a half year for the non-signatories, hence, there is still a substantive difference.

18
corruption affect firm performance and are correlated with access to telecom services. Including them reduces sample size by between 6,500 and 11,000 observations and 9 and 11 countries.

3. _Dropping fixed-line and cellular subscriptions_ .

4. _Including one Internet variable at a time_ to avoid multicollinearity.

In all cases, the coefficients on the Internet variables remain positive, and significant with similar magnitudes after these changes.

We also re-run the regressions after dividing the sample into different groups: high-tech and low-tech industries; small, medium, and large firms; and exporters and non-exporters. The coefficients (in Table 2) remain significant in all cases. In most cases, the differences between groups are statistically insignificant. An exception is substantially and significantly smaller coefficients on the Internet variables for large firms than for small and medium-sized firms (SMEs). An interpretation is that cheap new communication technologies allow SMEs to expand their markets and improve technology to a greater extent.

# **Conclusions**

Evidence from worldwide firm-level data shows that Internet services in general—and web use by local businesses in particular—are remarkably robustly linked to labor productivity and firm growth. The results also suggest that the Internet is a general-purpose technology—it benefits firms of all varieties no matter the level of technology, size, and export oridentation. Moreover, it benefits SMEs more than large firms.

Because the Internet has high returns and is a general-purpose technology, developing countries might be able to catch up with rich countries by investing in broadband technologies. Only 4.4 percent of Internet subscribers in developing countries are broadband subscribers compared with 24.6 percent in developed countries (Kelly and Rossotto, 2012). The fact that the Internet benefits SMEs more suggests that it may be particularly useful in achieving mulitple policy goals such as efficiency (since SMEs create more jobs) and equity (since SMEs employ 6
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**<u>Figure F2: Unconditional quantile regression of height-for-age z-scores on food security measures, expanded controls (Nepal)</u>**

<!-- Start of picture text --> Per Capita Caloric Availability 1-SSR<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>1-SSEXR FCS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>95% Confidence Interval OLS<br>RIF-OLS<br>RIF-OLS<br>.6 .4<br>.4<br>.2<br>.2<br>0<br>0<br>-.2 -.2<br>.6<br>.3<br>.2 .4<br>.1<br>.2<br>0<br>0<br>-.1<br>-.2<br><!-- End of picture text --> 71
# **Figure 5: Employment transition matrices for women by employment status, formality and sectors between 2012 and 2018, by level of education**

<u>Women</u>

||Private informal|**37**<br>1<br>1<br>61|15% (17%)|
|---|---|---|---|
||Private formal|44<br>**10**<br>46|0% (0%)|
|No formal<br>|Public formal|5<br>5<br>**33**<br>57|1% (0%)|
|education|Unemployed|29<br>71|0% (1%)|
||Out of labor force|14<br>1<br>**85**|84% (81%)|
||Private infor<br>|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|
||Private informal|**28**<br>1<br>1<br>5<br>65|8% (11%)|
||Private formal|19<br>**9**<br>72|1% (1%)|
|Primary|Public formal|8<br>**66**<br>26|1% (1%)|
||Unemployed|14<br>8<br>**7**<br>71|1% (2%)|
||Out of labor force|10<br>1<br>2<br>**87**|89% (85%)|
||Private infor<br>|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|
||Private informal|**21**<br>5<br>4<br>5<br>65|5% (7%)|
||Private formal|10<br>**16**<br>21<br>7<br>46|1% (3%)|
|Secondary|Public formal|4<br>1<br>**86**<br>9|10% (11%)|
||Unemployed|10<br>1<br>2<br>**10**<br>77|11% (6%)|
||Out of labor force|6<br>4<br>2<br>7<br>**81**|72% (72%)|
||Private infor|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|
||Private informal|**29**<br>13<br>15<br>2<br>41|3% (5%)|
||Private formal|15<br>**44**<br>14<br>27|5% (5%)|
|Post-|2<br>Public formal|2<br>**84**<br>1<br>11|33% (32%)|
|secondary|Unemployed|6<br>4<br>7<br>**24**<br>59|16% (10%)|
||Out of labor force|4<br>3<br>4<br>12<br>**77**|43% (49%)|
||Private infor|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to women who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by educational levels. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

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GRDC Composite Runoff Fields V1.0 is 0.97 with 222 pair-wise complete observations out of 224 total. We tested also for the possibility of a basin area effect where small basins may have good correlation due to more concentration of gauging stations. We found the basin area variable is not significant. This result gives us confidence that the CLIRUN-II model results are reasonable and have the added advantage of a time-series for this analysis.

# **_Precipitation data_**

Precipitation data are available from Mitchell and Jones (2005) from the Climate Research Unit (CRU) and downloaded from the CGIAR website.<sup>iii</sup> These global data are a time-series from 1900-2000 at 0.5 grid. Mean precipitation is summarized by basin and by country-basin separately. The same procedure, as in the case of runoff, was used to calculate precipitation of basin and country-basin annual means and Coefficient of Variation (CV). The aggregated data are provided by running the algorithm for both the basin-country-polygons and the basin polygons. Precipitation is expressed in units of millimeters per year.

# **_Water variability variables_**

We were able to construct several sets of water variability variables for precipitation and for runoff. While our data allows calculation of precipitation at country-basin and at basin levels, the runoff variables could be calculated only at basin level. Technically, it is possible given the caveat that the country-basin will further split up the total area and will likely lessen the number of model observation(s) per basin-country. This reduction in observations gives room to a larger potential error and a lesser likelihood of actual gauge station observations in the basin for the model. We constructed the following variables: Mean precipitation for country1/basinj ( _MeanPb1_ ); Mean precipitation for country2/basinj ( _MeanPb2_ ); Mean precipitation for basinj ( _MeanPb_ ); Coefficient of Variation of precipitation country1/basinj ( _CVPb1_ ); Coefficient of Variation of precipitation country2/basinj ( _CVPb2_ ); Coefficient of Variation of precipitation basinj ( _CVPb_ ); Mean runoff for basinj ( _MeanRb_ ); Coefficient of Variation of runoff basinj ( _CVRb_ ).

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Table 11: The effect of road infrastructure on specialization

||Output Sp|ecialization|Employm|ent Specialization|
|---|---|---|---|---|
||||||
|**araes**|ols|iv|ols|iv|
|Road Effciency|4.092***<br>(0.906)|5.935***<br>(1.261)|2.735***<br>(0.829)|4.468***<br>(1.187)|
|Minimum travel cost to border|-2.089|0.340|-2.332|-0.0469|
||(3.075)|(3.217)|(2.399)|(2.694)|
|Minimum travel cost to port|6.172*|5.003|4.489*|3.382|
||(3.201)|(3.354)|(2.531)|(2.707)|
|Above median education|0.907**<br>(0.415)|0.917**<br>(0.394)|0.471**<br>(0.228)|0.480**<br>(0.234)|
|Above median population|0.977**|0.953**|1.033**|1.012**|
||(0.415)|(0.395)|(0.476)|(0.432)|
|Constant|-57.43***<br>(11.79)||-39.02***<br>(10.84)||
|Observations|4671|4517|4578|4410|
|Adj. R-squared|0.012|0.011|0.011|0.001|

_Note_ : * denotes significance at the 10% level, ** denotes significant at the 5% level, and *** denotes significance at the 1% level. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 150 and 300 km radii.

# **6 Conclusion**

Transport investments have the potential to stimulate growth through trade, structural transformation, agglomeration, and productivity. This paper uses extensive micro-geographic data and geo-referenced digitized maps of the transport network in Mexico over three decades to provide empirical evidence of the causal effect of improved access to markets following road improvements on local employment and specialization. In addition to shedding light on the effects of transport infrastructure (roads) on local economic development, this paper also provides evidence of the respective roles of domestic and external market access on the geography of economic activities.

To estimate the causal effect of roads on localities’ economic activity and their level of specialization, this paper addresses two important endogeneity issues, namely the potentially non-random placement of roads and the structural endogeneity of market access and market potential regressors, also called the recursion problem. For this, we adopt three separate idenWe use the so-called iv to address the common tification strategies. first ’doughnut’ strategy non-random placement of roads problem, by excluding surrounding localities up to 25 km. Second, we address the structural endogeneity of market access and market potential by using two raw measures of access to infrastructure (road count and road efficient length) that do not involve population or income (either as substitutes for market access indicators or as instruments).

Our empirical exercise sheds light on the causal effect of road improvements on the increas27