rafmacalaba/datause-encoder-data huggingface dataset:
76

# **Table A3 Dependent Variable: Growth Rate of Real GDP Information and Communications Technologies (ICT) Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A3.1**|**Reg A3.2**|**Reg A3.3**|**Reg A3.4**|**Reg A3.5**|**Reg A3.6**|**Reg A3.7**|**Reg A3.8**|
|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2301*****|**0.2264*****|**0.2251*****|**0.2310*****|**0.2128*****|**0.2285*****|**0.2300*****|**0.2275*****|
|**Growth of Labor**|**0.4506*****|**0.4614*****|**0.4617*****|**0.4521*****|**0.5694*****|**0.5181*****|**0.4435*****|**0.4545*****|
|**Initial GDP (1960)**|**-3.0532*****|**-4.0288*****|**-3.4424*****|**-3.4300*****|**-3.8747*****|**-3.4484*****|**-2.8316*****|**-3.1124*****|
|**HC15**|**4.4180*****|**3.7100*****|**4.9953****|**5.4972*****|**4.3613****|**4.0428****|**4.1537****|**4.3220****|
|**Log (Educational Quality)**|**0.5299**|**0.3848**|**0.3147**|**0.4798***|**0.2933**|**0.4761**|**0.5010**|**0.4420**|
|**Sq (HC15)**|**-0.9259***|**-0.9508*****|**-1.1712****|**-1.2625****|**-1.0208****|**-0.8913***|**-0.7351**|**-0.9313***|
|**Log (Patents)**|**0.2413*****|**0.2252*****|**0.2366*****|**0.2411*****|**0.1909*****|**0.1877****|**0.2667*****|**0.2288*****|
|**Computers**<br>**Internet Users**||**0.0082*****|**0.0082*****||||||
|**Hosts**||||**0.0017****|||||
|**Phones**|||||**0.0033*****||||
|**TV**||||||**0.0023**|||
|**Radio**|||||||**-0.0011***||
|**News**||||||||**0.0009**|
|**Constant**|**-4.6162****|**-2.8603***|**-4.1496****|**-5.2290*****|**-3.6660***|**-4.1642****|**-4.3658****|**-4.1638***|
|**R squared**|**0.5907**|**0.6646**|**0.6114**|**0.6034**|**0.6137**|**0.5958**|**0.6017**|**0.5936**|
|**Number of Countries**|**85**|**81**|**85**|**85**|**85**|**85**|**85**|**85**|
|*, **, *** denotes significance<br>Robust standard errors obtain|at the 90, 95 a<br>ed using White|nd 99 percent c<br>'s correction for|onfidence level<br>heteroscedast|respectively<br>icity.|||||
15 Tables 4.3 through 4.5 show the changes in PCE between 1985 and 1992 on a regional basis. For the country as a whole, real household expenditure per capita increased on average by 34 percent over the 1985-92 period. This is consistent with the real positive growth in GDP during this period, and with national account figures on aggregate consumption. The largest increase was recorded in rural areas with 46.8 percent despite a worsening in their distribution. However, other urban areas have also shown an increase in expenditure (25.6 percent) below the national average and even below rural expenditures. Table 4.4 and Table 4.5 show changes by agro-climatic belts together with rural/urban disaggregation. The largest increase in per capita expenditures is in the southern belt region of the country, where per capita expenditures increased from N 660.63 in 1985/86 to N 934.48 in 1992/93. This reflects an increase of 41.4 percent. This increase is followed by middle belt with 40.5 percent. The Northern belt shows the smallest per capita increase of 17.26 percent. Table 4.5 shows the regional changes by urban/ rural split. It shows that the increase in the Middle belt, was due to a large rural increase of 57 percent. The small increase in the Northern belt however, is caused by a small decrease in urban incomes. Nationally, all rural areas show an increase of 46.8 percent compared to 16.08 percent for urban areas.

**Table 4.3: Pattern of Real Household Per Capita Expenditures by Region**

||1985|/86||1992/93||
|---|---|---|---|---|---|
|**Region**|**PCE**|**Gini**|**PCE**|**Percentage**|**Gini**|
|||||**change**||
|Lagos Urban|762.67|0.340|748.62|-1.84|0.399|
|Other Urban|650.28|0.366|816.71|25.59|0.441|
|Rural|531.28|0.391|779.96|46.81|0.456|
|Nigeria|592.81|0.387|792.64|33.7|_0.450_|

**Table 4.4: Pattern of Real Household Per Capita Expenditures by Geographic Regions**

|**Geographic Region**|**1985/86 PCE**|**1992/93 PCE**|**Percentage change**|
|---|---|---|---|
|Northern Belt|540.58|633.91|17.26|
|Middle Belt|536.15|753.56|40.55|
|Southern Belt|660.63|934.48|41.45|
|Nigeria|592.81|792.64|33.71|
# **References**

Adasme, O., Majnoni, G., and Uribe, M., 2006. Access and Risk: Friends or Foes?: Lessons from Chile. World Bank Policy Research Working Paper 4003.

- Ayyagari, M., Beck, T., and Demirgüç-Kunt, A., 2007. Small and Medium Enterprises across the Globe. _Small Business Economics_ 29, 415-434.

- Beck, T. and Demirgüç-Kunt, A., 2006. Small and Medium-Size Enterprises: Access to Finance as a Growth Constraint. _Journal of Banking and Finance_ 30, 2931–2943.

- Beck, T., Demirgüç-Kunt, A., Laeven, L., and Maksimovic, V., 2006. The Determinants of Financing Obstacles. _Journal of International Money and Finance_ 25, 932-952.

- Beck, T., Demirgüç-Kunt, A., and Maksimovic, V., 2005. Financial and Legal Constraints to Firm Growth: Does Firm Size Matter? _Journal of Finance_ 60, 137-177.

- Beck, T., Demirgüç-Kunt, A., and Maksimovic, V., 2008. Financing Patterns around the World: Are Small Firms Different? _Journal of Financial Economics_ 89, 467-87.

- Berger, A. N., Kayshap, A. K., and Scalise, J. M., 1995. The Transformation of the U.S. Banking Industry: What a Long Strange Trip It’s Been. _Brookings Papers on Economic Activity_ 2, 155-219.

- Berger, A. N., Rosen, R. J., and Udell G. F., 2007. Does Market Size Structure Affect Competition? The Case of Small Business Lending. _Journal of Banking and Finance_ 31, 11-33.

- Berger, A. N. and Udell, G. F., 1996. Universal Banking and the Future of Small Business Lending. In: Saunders, A., Walter I., (Eds.), _Financial System Design: The Case for Universal Banking_ . Irwin (Richard D), Burr Ridge, IL, 559-627.

- Berger, A. N. and Udell, G. F., 2006. A More Complete Conceptual Framework for SME Finance. _Journal of Banking and Finance_ 30, 2945-2966.

- De la Torre, A., Martinez Peria, M.S. and Schmukler, S., 2008. Bank Involvement with SMEs: Beyond Relationship Lending. World Bank. Mimeo.

- Djankov, S., McLiesh, C.,  and Shleifer, A., 2007. Private Credit in 129 Countries. _Journal of Financial Economics_ 84, 299-329.

- IADB, 2004. _Unlocking Credit: The Quest for Deep and Stable Lending_ . The Johns Hopkins University Press.

IFC, 2007. Benchmarking SME Banking Practices in OECD and Emerging Markets.

- Keeton, W., 1995. Multi-Office Bank Lending to Small Businesses: Some New Evidence. Federal Reserve Bank of Kansas City _Economic Review_ 80, 45-57.

17
We calculate the z-score for a sample of 1,752 banks across 57 countries, with the number of banks included in our sample varying from a high of 315 in the United States and a low of 3 in the Republic of Korea, Oman, Sri Lanka, Sweden and Trinidad and Tobago.<sup>11</sup> We calculate the return on assets, its standard deviation and the capital-asset ratio over the period 1997-2003. In the regression analysis, we include the log of the z-score to control for non-linear effects and outliers.<sup>12</sup> Since z-scores might vary with the time period over which they are measured, we will test the sensitivity of our results to the time period over which z-scores are computed. All bank data are from Bankscope, a commercial database of financial statements of financial institutions around the world. While it does not provide a perfect coverage of banks, it usually covers around 90% of countries’ banking systems in terms of assets.

Table 1 shows a wide variation in bank fragility across countries. Here we present the unweighted average of z-scores across all banks for each country in the sample. The z-scores indicate that for banks’ losses to deplete equity, profits have to fall about fifty times below the average level of profits in the United States, but only less than two standard deviations in Korea. The average bank in the average countries has a z-score of 24.

# **3.2 Control variables**

We include several bank-level variables to control for bank characteristics that might influence the fragility of individual banks. Specifically, we include the log of total assets in U.S. dollars for the first available year. Larger banks might be better able to diversify risk and thus have more stable earnings, reducing their risk of insolvency. On the other hand, larger banks

> 11 We imposed a requirement of at least three banks per country.  Our results, however, are not affected if we use a broader cross-country sample including countries with data on less than three banks.  In order to have a sufficient number of observations to calculate the standard deviation of return on assets over time, we also require at least four years of data for each bank.

> 12 All our results are confirmed when we use the level of the z-score.

16
cubic feet per day in 2001. In the Burgos region production grew from 0.773 billion cubic feet per day in 1997 and is expected to reach 1.653 billion cubic feet per day by 2001. The Energy Ministry expects more natural gas production efforts in the exploration of known dry gas producing basins offshore, as well as in Macuspana, Veracruz, and Tampico-Misantla and Sur de Burgos, Tamaulipas; the installation of a cryogenic complex in Reynosa, near Burgos Basin; and the reduction of gas flaring by Pemex in the Gulf of Mexico.

**Figure 5. Moderate Scenario of Consumption and Net Production, 1998-2007** Thousands of cubic meters a day

<!-- Start of picture text --> 250,000 1<br>200,0000<br>150,000<br>100,000<br>50,000_<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>O-_ _ +<br>Electric Oil sector Industrial Residential and Vehicular Totl<br>commercial transport suppl<br><!-- End of picture text --> Source: Secretaria de Energfa, 1998 23
**Table 15: Distribution of employment in presumed comparative advantage industries, 2002 and 2009**

|**Two Digit Industry (ISIC Rev. 3.1)**||Workers|in enterp <br>% sha|rises in t<br>re in|he sample|Weighted|estimate of t|otal num<br>% shar|ber of w<br>e in|orkers|
|---|---|---|---|---|---|---|---|---|---|---|
||Number||total||Average<br>growth rate|Estimate|d total|total<br>of the<br>econo|<br>my|Average<br>growth rate|
||2002|2009|2002|2009|(%pa)|2002|2009|2002|2009|<br>(%pa)|
|**Primary sector:**|||||||||||
|Crop and animal production|13,966|7,045|10.3|3.6|‐7.1|39,802|319,621|2.4|4.8|100.4|
|Fishing and aquaculture|785|82|0.6|0.0|‐12.8|1,971|36,687|0.1|0.6|251.7|
|Extraction of crude petroleum||250||0.1|||1,766|0.0|0.0||
|Mining of metal ores|30|60|0.0|0.0|14.3|461|254|0.0|0.0|‐6.4|
|**_Primary sector total_**|14,781|7,437|10.9|3.8|‐7.1|42,233|358,328|2.5|5.4|106.9|
|**Manufacturing: **|||||||||||
|Food and beverages|26,255|45,743|19.3|23.6|10.6|103,589|157,449|6.2|2.4|7.4|
|Tobacco products|729|321|0.5|0.2|‐8.0|1,823|428|0.1|0.0|‐10.9|
|Textiles|2,573|4,310|1.9|2.2|9.6|5,748|30,165|0.3|0.5|60.7|
|Garments|496|611|0.4|0.3|3.3|24,613|63,172|1.5|1.0|22.4|
|Leather and leather products|242|788|0.2|0.4|32.2|2,778|6,612|0.2|0.1|19.7|
|Coke, refined petroleum products|14|199|0.0|0.1|188.8|42|498|0.0|0.0|154.9|
|Basic metals|737|1,108|0.5|0.6|7.2|1,341|8,652|0.1|0.1|77.9|
|**_Manufacturing total_**|31,046|53,080|22.9|27.4|10.1|139,932|266,976|8.3|4.0|13.0|
|**Other industries**|||||||||||
|Electricity, gas,steam and water|1,394|176|1.0|0.1|‐12.5|15,338|899|0.9|0.0|‐13.4|
|**Services:**|||||||||||
|Land transport; transport via|||||||||||
|<br>pipelines|3,681|1,999|2.7|1.0|‐6.5|10,328|17,774|0.6|0.3|10.3|
|Water transport|99|87|0.1|0.0|‐1.7|108|1,253|0.0|0.0|151.9|
|**_Total services_**|3,780|2,086|||‐6.4||||||
|**Grand total**|96,827|123,296|36.6|32.3|3.9|207,939|645,230|12.3|9.8|30.0|

49
# **Does Land Fragmentation Increase the Cost of Cultivation? Evidence from India**

**Klaus Deininger,**<sup>*** 1**</sup> **Daniel Monchuk,**<sup>******</sup> **, Hari K Nagarajan,**<sup>*******</sup> **Sudhir K Singh**<sup>*****</sup>

* World Bank, Washington DC

> ****** Cloudburst Consulting Group, Washington DC

> ******* Institute for Rural Management Anand, India

Key Words: Fragmentation, Inheritance, Cost, Production, Land, Endogeneity

# JEL : Q1, Q10, Q12, Q15

> 1 1818 H St. NW, Washington DC, 20433; Tel 202 4730430, fax 202 522 1151, email kdeininger@worldbank.org. We thank D. Ali, S. Jin, Madhur Gautam and Hans Binswanger for very useful feedback and comments.
Pakistan is 30 percent as compared to the average efficiency of 43 percent for gas power plants in the United States.<sup>2</sup> Aging infrastructure and overloaded transmission and distribution systems also contributed to high losses in the network. In fiscal year 2014, average transmission and distribution losses reached about 21 percent of all the electricity generated.

Another cause for Pakistan’s power shortages is upstream gas shortfalls. Gas plays an important role in power generation in Pakistan, constituting 25 percent of total electricity produced in 2014. Despite having large gas reserves, the low price of domestic gas has made gas supply increasingly fall short of demand in recent years. Specifically, natural gas was priced on average at 5 per million British Thermal Units (MMBTU) during 2009 and 2015 in Pakistan. In contrast, the price of liquefied natural gas was between USD 10 and 15 per MMBTU during the same period in the Asian market. Gas underpricing not only results in wasteful consumption, but also reduces investors’ interest in upstream exploration and production. Meanwhile, there are huge losses in gas transmission and distribution. The amount of unaccounted-for-gas, the difference between gas available for sale and gas actually sold, has reached 12 percent of total supply in 2016.

Facing supply shortfalls, domestic gas is being allocated to different sectors based on administrative orders. Although the economic benefit of using gas in power generation is expected to be among the highest in all sectors in Pakistan (USAID 2011), gas is diverted from power to other sectors such as fertilizer and transport. With gas resources waning, Pakistan has to increasingly rely on expensive imported fuel oil for power generation. Imported fuel oil is used for close to 40 percent of overall power generation in 2014, but the cost of this generation accounts for about 70 percent of the total energy cost. Reliance on oil-based power generation has resulted in higher cost of electricity and further worsened the circular debt issue.

Finally, although Pakistan has huge untapped hydropower and substantial wind and solar potential, development in renewable energy has been almost stagnant over the past decade.

> 2 United States Energy Information Administration. https://www.eia.gov/electricity/annual/html/epa_08_01.html

4
Table 7: Import impacts of BRI (change relative to baseline in 2030)

||BRI|LBD|BRI|UBD|BRI|UBBD|BRIU|BBTD|BRIU|BBTPD|
|---|---|---|---|---|---|---|---|---|---|---|
||%|$bn|%|$bn|%|$bn|%|$bn|%|$bn|
|World|1.4|462.4|3.4|1152.7|4.8|1648.6|5.5|1862.8|6.2|2108.4|
|BRI Area|2.9|386.1|5.4|735.8|8.7|1179.9|10.6|1437.4|11.3|1530.9|
|Cambodia|9.8|4.3|9.5|4.1|18.5|8.0|18.2|7.9|19.1|8.2|
|China|3.1|128.4|5.6|229.9|8.0|329.2|9.7|397.8|11.1|457.6|
|Indonesia|-0.1|-0.3|3.4|16.7|3.5|17.0|5.2|25.5|5.1|25.1|
|Lao PDR|2.6|0.5|5.7|1.1|37.8|7.5|38.0|7.5|38.5|7.6|
|Malaysia|16.2|72.7|18.5|82.9|19.5|87.3|20.4|91.7|20.5|91.8|
|Philippines|1.1|2.3|4.0|8.4|3.4|7.1|3.8|8.0|4.2|8.8|
|Thailand|4.1|20.7|21.4|108.7|26.8|135.9|27.5|139.5|27.6|140.0|
|Vietnam|3.1|14.5|3.2|15.2|2.4|11.3|4.2|20.1|4.6|22.0|
|Rest of East Asia|-0.2|-1.1|3.2|23.0|4.0|28.7|4.3|30.4|4.1|29.1|
|Bangladesh|5.8|6.6|7.4|8.5|9.4|10.8|18.2|20.8|19.5|22.2|
|India|2.6|30.7|4.4|52.8|5.9|70.9|10.1|121.7|11.3|135.5|
|Nepal|0.7|0.1|1.2|0.1|47.4|5.8|55.3|6.7|55.2|6.7|
|Pakistan|5.8|5.8|9.3|9.3|12.7|12.7|18.8|18.8|19.7|19.6|
|Sri Lanka|1.7|0.9|3.0|1.5|3.8|1.9|6.8|3.4|7.8|3.9|
|Rest of S. Asia|3.5|0.7|4.5|0.9|21.1|4.4|25.0|5.2|25.3|5.3|
|EU ECA|0.1|0.5|-0.1|-1.2|0.5|3.9|1.6|13.6|1.5|12.2|
|Russian Federation|2.0|12.2|2.8|16.7|9.3|55.3|11.3|67.3|12.3|73.5|
|Poland|0.3|0.9|0.3|1.1|0.4|1.5|1.3|4.5|1.3|4.6|
|Rest of E. Europe|0.6|1.3|2.2|4.7|4.9|10.6|5.4|11.6|5.1|11.0|
|Kazakhstan|2.3|2.8|3.4|4.2|39.9|48.9|41.1|50.3|40.9|50.1|
|Kyrgyz Republic|16.7|1.9|17.5|2.0|72.5|8.1|72.6|8.1|72.0|8.0|
|Rest of C. Asia|5.2|6.7|6.1|7.9|18.2|23.7|19.4|25.3|19.5|25.4|
|Iran,Islamic Rep.|2.0|3.2|7.4|11.9|14.2|22.6|22.4|35.8|22.8|36.4|
|Egypt,Arab Rep.|0.6|1.1|1.0|1.8|1.1|1.9|3.0|5.3|3.2|5.8|
|Turkey|8.5|34.8|8.5|34.5|10.0|40.9|12.7|51.8|12.8|52.1|
|Rest of MENA|1.5|29.6|4.3|83.7|11.2|216.4|12.8|247.7|13.3|256.7|
|Kenya|4.7|2.1|5.7|2.5|9.7|4.3|14.3|6.3|14.8|6.6|
|Tanzania|6.4|2.4|7.4|2.7|9.0|3.3|12.7|4.7|13.1|4.8|
|Non-BRI Area|0.4|76.3|2.0|416.9|2.3|468.8|2.1|425.4|2.8|577.5|
|Ethiopia|4.0|1.2|6.7|1.9|22.6|6.6|22.2|6.4|22.9|6.7|
|Rest of SSA|0.8|8.3|2.0|20.3|3.1|31.6|2.9|29.8|3.4|34.4|
|Lat. Am. & Car.|0.1|1.5|0.1|3.1|0.1|3.0|-0.1|-1.4|0.3|6.4|
|United States|-0.3|-10.9|4.6|189.5|4.3|175.6|4.1|168.5|5.1|208.9|
|Rest of High-inc.|0.4|19.3|3.1|135.6|3.4|148.1|3.1|135.3|3.9|170.4|
|Rest of W. Eur.|0.6|56.9|0.8|66.5|1.2|103.8|1.0|86.7|1.7|150.6|

- 29 -
Our study points to the importance of complementary policies such as the reduction of border delays and further tariff liberalization. Reducing the border delays within the BRI region by half brings about significant welfare gains. At the global level the welfare gains reach 1.1 percent, while in the BRI area the welfare gain increases to 2 percent as compared to 1.2 percent under infrastructure investment alone (as compared to the baseline in 2030). The tariff reductions generate a further boost to trade and enhance welfare gains. However, with the tariffs at a low level already, the additional gains are relatively small with an additional $60 billion and $110 billion under preferential and multilateral tariff reductions at a global level.

It should be noted that we abstract from the economic impacts of the infrastructure expenditures. These are unlikely to have major repercussions on the long-term real income gains but could affect the timing of the gains depending on the source of the financing, i.e. domestic versus foreign. Furthermore, there is significant uncertainty regarding the projects to be implemented under BRI and their potential for trade cost reductions. Our analysis uses the best available assumptions, but the impacts of the BRI will ultimately depend on the specific investments and their effectiveness in reducing trade costs.

# **References**

Atamanov, Aziz; Castaneda Aguilar, Raul Andres; Corral Rodas, Paul Andres; Dewina, Reno; Diaz-Bonilla, Carolina; Jolliffe, Dean Mitchell; Lakner, Christoph; Lee, Kihoon; Montes, Jose; Moreno Herrera, Laura Liliana; Mungai, Rose; Newhouse, David Locke; Nguyen, Minh Cong; Prydz, Espen Beer; Sangraula, Prem; Yang, Judy. 2019. March 2019 PovcalNet Update: What's New (English). Global Poverty Monitoring Technical Note; no. 7. Washington, D.C.: World Bank Group.

de Soyres, F., Mulabdic, A., and Ruta, M. (2018). The Belt and Road Initiative: A Structural Analysis, Unpublished Working Paper, World Bank.

De Soyres, Francois Michel Marie Raphael; Mulabdic, Alen; Murray, Siobhan; Rocha Gaffurri, Nadia Patrizia; Ruta, Michele. 2018 _. How Much Will the Belt and Road Initiative Reduce Trade Costs?_ . Policy Research working paper; no. WPS 8614. Washington, D.C. : World Bank Group.

Devarajan, S., J.D. Lewis, and S. Robinson (1990), “Policy lessons from trade-focused, two-sector models,” _Journal of Policy Modeling_ , 12(4): 625-657. DOI:10.1016/0161-8938(90)90002-V.

Hummels, D. and Schaur, G. (2013), “Time as a Trade Barrier,” _American Economic Review_ , 103(7): 2945-59.

International Trade Centre (ITC). Market Access Map (MAcMap). 2015. “Tariff Rates for 2014– 2031 between TPP Member Countries absent the TPP Agreement.” Prepared for the Global Economic Partnership Agreement Research Consortium.

Jolliffe, D., & Prydz, E. B. (2016). Estimating international poverty lines from comparable national thresholds. Journal of Economic Inequality. https://doi.org/10.1007/s10888-016-9327-5

- 20 -
# Table 2: Cotton Price Volatility

|Cotton Type|Period<br>l_______________<br>,Export|Average Monthly<br>Price|Standard<br>Deviation|Coefficient of<br>Variationa/|
|---|---|---|---|---|
|||I ..<br>-US¢---|--|--<br>---------|
|Central Asian|Aug 85-<br>Jan 93|<br>66.12|14.05|21.2*|
|Punjab SG 1505|Aug 88-<br>Jun 93|<br>69.10|11.77|17.2%|
|Chinese 329|May 85-<br>Jan 93|<br>70.14|14.30|20.41|
|Turkish Izmirant|Jan 85-<br>Apr 92|<br>74.99|14.59|19.51|
|FPA|May 85-<br>Jan 93|<br>66.64|12.78|19.21|

A/ Ratio of standard deviation to the mean.
or other means of providing electricity, a provider for the last-mile service to be linked to the network, and the household to be able to afford this service. Motorcycle and cars are durable goods whose economic impact is greatest with good-quality roads. Use of Internet also requires infrastructure access, the presence of services providers, and affordability.

The associated impacts of use and ownership variables on sectoral shares are large (Table 4). A 10 percentage-point increase in the number of households effectively using electricity is associated with a 3.6 percentage-point decrease in the share of agricultural employment, a 1.1 percentage-point increase in manufacturing employment and a 2.5 percentage-point increase in services employment. Having a car, and to a much lower extent, having a motorcycle have a large impact on sectoral shares. A 10 percentage-point increase in the number of households owning a car is associated with a 3.6 percentage-point decrease in the share of agricultural employment and a 4 percentage-point increase in services. A 10 percentage-point increase in the number of households effectively using Internet is associated with a 7.2 percentagepoint increase in the share of services employment. Both the use of mobile and land phones have strong impacts, with a 10 percentage-point increase in the number of respondents using a mobile phone (land phone) associated with a 1.9 (3.3) percentage-point decrease in agricultural employment shares. Finally, the impacts on employment and labor market participation as reported on the last column ’Not working’ are mixed.

Table 4: OLS results for Nigeria, Cameroon, Chad based on uses of roads, electricity and ICT, 2008-2018

||Agriculture|Manufacturing|Services|Not working|
|---|---|---|---|---|
|Electricity (share)|-0.373<sup>~~∗∗~~</sup>|0.114<sup>~~∗∗~~</sup>|0.259<sup>~~∗∗~~</sup>|-0.0485<sup>~~∗∗~~</sup>|
||(-25.46)|(16.60)|(21.21)|(-6.69)|
|Motorcycle (share)|0.0121|-0.0101|0.00669|0.0254<sup>∗</sup>|
||(0.55)|(-0.90)|(0.35)|(2.04)|
|Car (share)|-0.348<sup>∗∗</sup>|-0.0523<sup>∗</sup>|0.397<sup>∗∗</sup>|-0.0304|
||(-8.49)|(-2.12)|(11.47)|(-1.18)|
|Use of internet (share)|-0.102<sup>∗</sup>|0.00171|0.124<sup>∗∗</sup>|-0.123<sup>∗∗</sup>|
||(-2.08)|(0.06)|(2.85)|(-4.12)|
|Mobile phone (share)|-0.176<sup>∗∗</sup>|0.0967<sup>∗∗</sup>|0.0746<sup>∗∗</sup>|0.0230|
||(-6.07)|(7.00)|(2.95)|(1.14)|
|Land phone (share)|-0.402<sup>∗∗</sup>|0.00408|0.408<sup>∗∗</sup>|0.184<sup>∗∗</sup>|
||(-3.86)|(0.06)|(4.36)|(3.19)|
|Year + Country FE|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|
|R-squared|0.588|0.344|0.547|0.100|
|N. of observations|2369|2369|2369|2369|

_t_ statistics in parentheses

> + _p_ < 0 _._ 10, ∗ _p_ < 0 _._ 05, ∗∗ _p_ < 0 _._ 01

Notes.– All explanatory variables are shares of households with access between 0 and 1.

15
17

# Foonotes

- 1/ See for example, Neary and Ruane (1988), Neary (1988).

- 2/ The presentation follows closely Neary (1988). Wherever possible, our notation is the same as his.

- 3/ For example, in 1984 the foreign-owned share of the U.S. net capital stock was **1.4Z** and by 1988 this percent had risen six-fold. During the same period, the trade-weighted U.S. average tariff rate was 3.4Z on imports representing 5.6Z of gross output.

- 4/ The model is an extension of the model presented in de Melo and Tarr (1990) to include capital mobility.