rafmacalaba/datause-encoder-data huggingface dataset:
a source of technological innovations than for other fi rms. The development in cooperation with client fi rms is a signi fi cantly more important source of technological innovations for minority foreign-owned fi rms than for domestic-owned fi rms, while the reverse is true for majority foreign-owned fi rms. Moreover, for majority foreign-owned fi rms the acquisition of technological innovations from any of the external institutions is signi fi cantly less important than for other fi rms. For exporters, development in cooperation with client fi rms and equipment or machinery suppliers is a signi fi cantly more important source of technological innovations, but so is the development and adaptation within the fi rm. Finally, for importers there is a signi fi cantly more important role of technology embodied in new machinery and equipment relative to non-importers and a less important role of technology development with client rms.

In sum, the fi ndings in this section show interesting patterns in technological innovations and their sources for fi rms that are integrated into global markets. The rest of the paper tests whether the bivariate correlations found so far resist more rigorous econometric estimation.

# 3. Empirical Findings

## 3.1. Main Estimates

Our empirical framework considers pro fi t maximizing fi rms deciding whether or not to engage in technological innovation. A fi rm decides to innovate if this decision is expected to increase its pro fi ts or, in other words, if the bene fi ts from this decision are larger than the costs. Let π<sup>∗</sup> ijc<sup>betheprofitsofafirm iinindustryjincountryc.Then,weassumethat:</sup> where Innovijc is a dummy variable that equals one if fi rm i reports engaging in technological innovation. Since π<sup>∗</sup> ijc<sup>is unobserved, Equation (3.1) cannot be estimated directly.Therefore,</sup> we assume that π<sup>∗</sup> ijc<sup>is a function offirm, industry, and country characteristics.In particular,</sup> we assume a linear form so that π<sup>∗</sup> ijc<sup>=βXijc+Ij+γIc+εijc,whereXijcisavector</sup> of fi rm characteristics, Ij are industry fi xed effects, Ic are country-level variables, and εijc 11
1.3 joint trade-fiscal reform: a case study

27 tariff revenue from Rs. 15.6 billion to zero; however, total effect on revenue is a net increase of 4 percent.

The message from the simulation results reported in Table 6 is that trade taxes can be reduced without adversely affecting total government revenues by implementing domestic taxes like VAT and excise duties effectively. In fact, if tariffs are used mainly for revenue-raising purposes (that is, not used to protect domestic industries) they could simply be replaced by excise taxes. Like VAT, excise taxes do not discriminate between domestic and international sources. They also do not fall under the purview of trade agreements, so countries under pressure to cut tariffs can simply switch to excise. This would just be a semantic change in nomenclature.

There is, however, a powerful assumption behind the advocacy of a switch in tax regime from tariffs to a broad-based consumption tax, namely, that countries have the capacity to enforce a complicated system like the VAT. One of the main arguments for reliance by poor countries on tariffs has always been that they are easier and less costly to collect at fixed border points.

As postulated in section 2, we need a larger VAT base to raise the same level of revenue in the presence of an informal sector. Piggott & Whalley (2001) show that VAT expansion can reduce welfare if it encourages suppliers to go underground to evade new taxes. The presence of the informal sector, however, may not dent revenue collection to the extent that the theory suggests. This is because a substantial share of revenue in poor countries is generated from VAT on imports which is usually collected at the border together with tariffs. In the Nepali data for 2008, for every rupee collected in tariff revenue, Rs. 1.7 was collected additionally in VAT and excise duty. This point is also made by Keen (2008) that the VAT (and withholding taxes) on imports actually acts as a tax on the informal sector. While the formal sector may claim tax credit on payments made at the border when they eventually pay income and other taxes, the informal sector does not, thereby minimizing loss to the exchequer.
authors emphasize that differences in prices, taxes, and market demand create economic opportunities that are exploited every day by agents living in border areas (Bach 1997, Nugent 2002). Although initially arbitrarily delineated, boundaries have become a reality, if only for economic life. Comparing communities on both sides of the Cˆote d’Ivoire-Ghana border, MacLean (2010) argues that patterns of social insurance differ and were shaped by colonial and post-colonial state action. Asiwaju (1976) and Miles (1994) develop similar arguments about the contrast between British and French colonial imprints at the Benin-Nigeria and the NigerNigeria borders, respectively. Finally, at the macroeconomic level, the inequality of income between African countries is larger than usually thought. According to Schultz (1998), the log variance of GDP per capita in PPP reached 0.415 in 1989 in Africa (including North Africa), which is by far the highest level of all regions of the world. Furthermore, this number is twice as high as it was in 1960 (0.213). Of course, income divergence between African countries may only reflect the divergence of their centers rather than their border areas. However, this possibility would be difficult to reconcile with a strong influence of neighbors on growth.<sup>1</sup> Drawing from a large set of household surveys covering the 1986–98 period, we estimate regression discontinuity designs at the borders of Cˆote d’Ivoire with Ghana, Mali, and Guinea, focusing on four welfare outcomes: consumption and children’s nutritional status (height-for-age), on the one hand, and the connection to electricity and access to safe water, on the other hand. When discussing the main results, we also examine household cash crop output and incomes. 1The insulating power of boundaries is not necessarily for the good. Englebert, Tarango, and Carter (2002) talk of political “dismemberment” and “suffocation”. Further, the “balkanization” of Africa is often believed to prevent the exploitation of returns to scale, despite efforts of regional trade integration. However, the impact of full political integration is different from a simple size merger (Spolaore and Wacziarg 2005).

4
15 Product. 8-_ The table presents information not only on social security programs for the public as a whole but also on programs which apply only to public employees and programs apart form social security which provide public health care and public (or social) assistance. These programs fall under the International Labour Office's definition of social security programs, and they may be categprized as social security in government financial statisti-.s, but they are not considered in the present paper apart from this table. 9 We note that systems for public employees are sometimes larger than broader social security programs. Where no flows are reported for public-employee systems, it is either the case that public employees are covered only under the general system or that the finances for such systems are not reported to the I.L.O.

As we focus on the first four columns of Table 5 we see that social security systems involve the largest share of an economy's product in Chile, 10 the higher-income European and Middle-Eastern countries, Singapore, and the industrial market countries. Social security involves a very small portion of the economy - less than 0.2 percent - in Nigeria, Bangladesh, Burma, Pakistan, Indonesia, and Thailand. Other figures for receipts and expenditures vary widely, with some tendency for richer countries to distribute a larger portion of their product through the social security system.

We also see in Table 5 that only three countries in our
||||Δ NRCI|||
|---|---|---|---|---|---|
||SE|Part‐time|diff|Equal sign trend|Reject H0|
|Rusia|0.182|‐0.396|‐0.578|no|yes|
|Georgia|0.033|‐1.215|‐1.247|no|yes|
|Kyrguiztan|0.054|0.325|0.271|yes|yes|
|Armenia|‐0.346|0.007|0.353|no|yes|
|Albany|0.311|1.021|0.710|yes|yes|
|Moldova|0.215|0.818|0.604|yes|yes|
||SE|Part‐time|diff<br>Δ RC|Equal sign trend|Reject H0|
|Rusia|‐0.089|‐0.359|‐0.270|yes|yes|
|Georgia|‐0.047|0.534|0.581|no|yes|
|Kyrguiztan|‐0.216|1.281|1.497|no|yes|
|Armenia|0.819|1.068|0.249|yes|yes|
|Albany|0.091|0.119|0.028|yes|no|
|Moldova|‐0.001|0.551|0.552|no|yes|
||||Δ RM|||
||SE|Part‐time|diff|Equal sign trend|Reject H0|
|Rusia|‐0.057|0.036|0.093|no|no|
|Georgia|0.097|1.441|1.344|yes|yes|
|Kyrguiztan|‐0.121|0.329|0.450|no|yes|
|Armenia|0.340|‐0.062|‐0.401|no|yes|
|Albany|‐0.484|‐1.171|‐0.687|yes|yes|
|Moldova|‐0.193|‐0.879|‐0.686|yes|yes|
||||Δ NRM|||
||SE|Part‐time|diff|Equal sign trend|Reject H0|
|Rusia|‐0.167|0.002|0.169|no|yes|
|Georgia|0.323|1.563|1.240|yes|yes|
|Kyrguiztan|0.126|0.173|0.048|yes|no|
|Armenia|0.216|‐0.123|‐0.339|no|yes|
|Albany|‐0.368|‐1.243|‐0.875|yes|yes|
|Moldova|‐0.172|‐0.951|‐0.778|yes|yes|

# 2. Temporary employment

Null Hypothesis: �Task��� �Task��� ���Task���� �Task����� Where: Task��� indicates the average measure of a task category (Non-Routine Cognitive Analytical – NRCA- Non-Routine Cognitive Interpersonal –NRCI-, Routine Cognitive –RC-, Routine Manual – RM-, and Non-Routine Mnual –NRM- ) for Temporary Employment in the Ending point of the analysis.

Task��� indicates the same average measure for Temporary Employment in the initial point of the analysis.

Task��� is the measure of task intensity for the Temporary employees in the ending point of the analysis.

Page 47 of 51
**Figure 2: Mobile penetration services in Djibouti is the lowest in MENA (connection penetration), even after factoring in the multi-SIM effect (subscriber penetration)**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>UAE Qatar Bahrain Israel Kuwait Tunisia Saudi Arabia Algeria Libya Morocoo Iran Oman Egypt Lebanon Turkey Jordan Syria Mauritania Iraq Sudan Palestine Yemen Somalia Comoros Djibouti<br><!-- End of picture text --> Source: Data from GSMA, The Mobile Economy Middle East & North Africa, 2018 Unfortunately, the (under)performance of the ICT sector can have significant macro-economic effects on the economy and poverty reduction (World Bank, 2016). For example, in rural Peru, the expansion of mobile network coverage boosted household real consumption by 11 percent (Beuermann _et al._ , 2012). The ICT sector also has great capability to enable job creation and innovation in the private sector with the corresponding increase in economic opportunities for the population. Moreover, the internet is also an enabler of higher labor productivity by means of lowering information and search costs. For example, the introduction of mobile phones in the grain markets of Niger led to farmers obtaining grain price information over the phone thereby reducing search costs by 50 percent (Aker, 2010) and reduced dispersion of grain prices across markets by 10 percent (Aker and Mbiti, 2010). People’s perceptions also reveal that access to the internet has led to an increase in consumer welfare by making several products and services available digitally. From the standpoint of both the government and the private sector, the internet can bring major benefits in the provision and delivery of services. Digital identification can also improve participation of and help for the disadvantaged groups in the country to become integrated. The advantages of a well-developed ICT sector are manifold and transformational.

In this paper, we use data from the Fourth Djiboutian Household Survey (henceforth referred to as EDAM4) to study the effects of introduction of competition in the telecom sector in Djibouti on the welfare of its residents. EDAM4 is a nationally representative survey conducted in 2017 in Djibouti. The sampling strategy of the EDAM4 was designed to produce representative indicators at several levels: national, urban, rural, by regions and by 5 districts in Djibouti city. In a first for the Djiboutian Statistical Office (DISED), the survey collected information on sedentary ordinary households and nomads, with a total of 4,474 households interviewed. Findings from the survey 4
# **Figure 8**

## **Variance decomposition over time, by degree of financial openness**

## **(a) Gross inflows**

<!-- Start of picture text --> Low financial openness High financial openness<br>1.0 1.0 1.0 1.0<br>0.9 0.9 0.9 0.9<br>0.8 0.8 0.8 0.8<br>0.7 0.7 0.7 0.7<br>0.6 0.6 0.6 0.6<br>0.5 0.5 0.5 0.5<br>0.4 0.4 0.4 0.4<br>0.3 0.3 0.3 0.3<br>0.2 0.2 0.2 0.2<br>0.1 0.1 0.1 0.1<br>0.0 0.0 0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share Global share Group share Country share<br><!-- End of picture text -->

## **(b) Gross outflows**

<!-- Start of picture text --> Low financial openness High financial openness<br>1.0 1.0 1.0 1.0<br>0.9 0.9 0.9 0.9<br>0.8 0.8 0.8 0.8<br>0.7 0.7 0.7 0.7<br>0.6 0.6 0.6 0.6<br>0.5 0.5 0.5 0.5<br>0.4 0.4 0.4 0.4<br>0.3 0.3 0.3 0.3<br>0.2 0.2 0.2 0.2<br>0.1 0.1 0.1 0.1<br>0.0 0.0 0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share Global share Group share Country share<br><!-- End of picture text --> 54
**Figure 23: Impact of the crisis on poverty**

# **Figure 24: Impact of the crisis on inequality**

<!-- Start of picture text --> 0.40 0.38 0.39 0.39 0.39<br>0.38 0.38<br>0.38<br>0.36<br>0.34<br>0.32<br>0.30<br>0.28 0.29<br>0.28<br>0.26 0.27<br>0.26 0.27 0.26<br>0.24<br>0.22<br>0.20<br>2011 baseline year year 1 year 2<br>(2012)<br>gini No crisis gini Crisis<br>Theil No crisis Theil Crisis<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011 The lack of growth is the main driver of the poverty increments in both urban and rural areas under the crisis scenario. The results of the Datt and Ravallion (1992) decomposition for year 2 show that higher poverty rates have mainly been caused by lower growth rather than distributional effects (Figure 25). However, in urban areas and at the national level, taking into account _only_ the effect of negative growth would understate the rise on moderate poverty levels since redistribution of income increases poverty. In the case of rural areas, however, ignoring the redistribution of income would overstate the rise in poverty.

**Figure 25: Datt-Ravallion decomposition – Benchmark vs. crisis scenario in year 2 (percentage change)**

<!-- Start of picture text -->
1.4<br>1.20<br>1.2 1.11<br>1.0<br>0.73  0.73<br>0.8<br>0.6 0.38  0.48  0.41<br>0.4<br>0.2 0.09<br>0.0<br>-0.2<br>-0.4<br>-0.33<br>-0.6<br>National Urban Rural<br>Total change Growth Redistribution<br><!-- End of picture text -->

Source: WB staff calculations based on data from ECH 2011 21
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# **Supplementary Appendix**

38
**Table 4. Summary of regressions adding one indicator at a time, ICA data**

|Dependent<br>variable|(1)<br>Operating<br>Income to<br>Sales|(2)<br>Any<br>Investment|(3)<br>Any<br>Investment|(4)<br>IK|(5)<br>IK|(6)<br>Ret.<br>Earnings<br>WK|(7)<br>Ret.<br>Earnings<br>WK|(8)<br>Ret.<br>Earnings<br>Invest|(9)<br>Ret.<br>Earnings<br>Invest|
|---|---|---|---|---|---|---|---|---|---|
|Usage of Credit<br>Products|-0.01|0.09***|0.10***|0.09***|0.10***|-101.41***|-97.03***|-193.82***|-195.94***|
||(0.684)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|N Dd|0.04***|-0.00|-0.00|0.00|-0.01|49.53***|48.54***|6.94|6.69|
|o eman|(0.001)|(0.862)|(0.891)|(0.983)|(0.772)|(0.000)|(0.000)|(0.793)|(0.813)|
||-0.11***|0.09***|0.07*|0.08*|0.09*|-76.96***|-87.44***|-10.95|-60.59*|
|Rejected|(0.000)|(0.010)|(0.059)|(0.095)|(0.061)|(0.000)|(0.000)|(0.733)|(0.090)|
|Savins Account|0.04***|0.01|0.01|0.04|0.04|9.79|8.38|36.65*|34.60|
|g|(0.007)|(0.714)|(0.758)|(0.103)|(0.118)|(0.158)|(0.251)|(0.075)|(0.114)|
|Financing<br>Obstacles|-0.01|0.03|0.05*|0.03|0.05*|-23.73***|-27.85***|-78.42***|-70.69***|
||(0.413)|(0.267)|(0.051)|(0.193)|(0.066)|(0.000)|(0.000)|(0.000)|(0.000)|
|Macro Obstacle|0.00|-0.04*|-0.03*|-0.00|0.01|1.15|-2.17|-35.59*|-24.56|
||(0.953)|(0.065)|(0.091)|(0.978)|(0.696)|(0.858)|(0.744)|(0.055)|(0.216)|
|Pli Obtl|-0.01|-0.01|-0.00|-0.03|-0.02|2.92|1.74|14.89|20.17|
|ocy sace|(0.238)|(0.508)|(0.853)|(0.129)|(0.364)|(0.614)|(0.771)|(0.373)|(0.262)|

Notes: Regressions 3, 5, 7 and 9 add operating income to sales as one of the controls, while others do not.

36
population and high rates of economic growth, and this should alleviate concerns around low ridership demand.<sup>44</sup> As of 2012, the total route length of the DM was 190 kilometers, and its ridership was about 2 million per day (DMRC 2012). In contrast, the San Francisco/Oakland area, has a population density that is slightly more than one fourth that of Delhi’s, and in 2014, the BART’s route length and average daily ridership was 167 kilometers and 0.4 million, respectively.<sup>45</sup> Moreover, Delhi is a polycentric city and the route design of the DM is ideal to serve daily commuters. It has a radial track layout, with major north-south and east-west corridors connecting different parts of Delhi to the border cities such as Gurgaon and Noida, which are the new centers of employment. Also, ridership is likely to increase further as new routes are completed. According to Litman (2014) rail transit is more appropriate in areas where development is more compact and noise and air pollution are serious considerations, while buses are more appropriate where travel is more dispersed. It seems to us that Delhi meets the criteria that favor having a metro. For these reasons we are optimistic about the viability of the DM.<sup>46</sup>

# VI CONCLUDING REMARKS

We study the impact of the Delhi Metro (DM) on pollution in Delhi between 2004 and 2006. We find that soon after the first extension of the Yellow Line of

> 44. The decadal growth in its population from 2001 to 2011 was 20.96 percent, and for the period 2007–2011, the annual compound growth of its Gross State Domestic Product was 10.1 percent (Govt. of NCT of Delhi 2012). 45. Data on population density for San Francisco was accessed from City Mayors Statistics. Source url: http://www.citymayors.com/statistics/largest-cities-density-125.html. Route length and daily ridership on the BART was from http://www.bart.gov/sites/default/files/docs/2014BARTFactsheet_Final%20011614.pdf. Both sites were last accessed on March 31, 2015.

> 46. Murty et al. (2006) carry out a social cost benefit analysis of phases I and II of the DM, covering a track length of 108 kilometers. Considering the estimates of financial flows during the period 1995–2041, they estimate the financial benefit-cost ratio to be between 1.92 and 2.30. They also estimate the capital costs of phases I and II to be 64,060 million and 80,260 million, respectively, and the net present social benefit from both phases to be 419.98 billion INR (in 2004–05 prices). Their calculations account for differences in shadow and market prices of unskilled labor; premiums for importing fuel; benefits accruing from reduced road congestion, accidents, and air pollution; and effects of redistribution of income among stakeholders. Their estimates are based on several assumptions regarding annual flows of costs and benefits during the entire lifetime of the DM. Evaluating the accuracy of these assumptions is beyond the scope of our study.

29