rafmacalaba/datause-encoder-data huggingface dataset:
Table 1 Estimates of Structural Parameters

|Parameter|Estimate|Description|
|---|---|---|
|Discount factor _β_|0.95|Predetermined|
|Depreciation rate _δ_|0.0991|Computed from BEA and rm data|
|Expenditure shares|||
|Food and beverages|0.313|National accounts|
|Textiles and apparel|0.052|National accounts|
|Minerals|0.025|National accounts|
|Metals|0.025|National accounts|
|Other manufactures|0.211|National accounts|
|Non-traded goods|0.374|National accounts|
|Production function pa|rameters||
|_αL_|0.619<br>(0.036)|ACF regression|
|_αK_|0.283<br>(0.029)|ACF regression|
|_vm_|0.410<br>(0.003)|Computed from rm data|
|Stochastic processes of|shocks _A_|and _b_|
|_ρa_|0.902<br>(0.008)|AR(1) regression|
|_σ_<sup>2</sup><br>_a_|0.118<br>(0.00009)|AR(1) regression|
|_b_|0.318|AR(1) regression|
||(0.156)||
|_ρb_|0.777<br>|AR(1) regression|
||(0.116)||
|_σ_<sup>2</sup><br>_b_|0.044<br>(0.00011)|AR(1) regression|
|_P_(_bt_ =_high|bt_ =_high_)|0.827|Tauchen-Hussey approximation|
|_P_(_bt_ =_high|bt_ =_low_)|0173|Tauchen-Hussey approximation|

Notes: list of estimates and source of each parameter value. Standard errors in parenthesis.

36
wave that was conducted during the fourth quarter of 2020, and later compare with the 2017 and 2018 rounds. The data contains sociodemographic information for six-year-old and older household members who reside in Mexico.

An important feature of this survey is that the data allows us to identify men and women who use the computer at home to perform activities related to their work. In this paper, we refer to them as teleworkers.<sup>21</sup> Following Edwards and Field-Hendrey (2002), we restrict the sample to prime-aged men and women between 22 and 55 years old who are either working or out of the labor force. We identify their labor force and employment status using the question of whether they worked last week.<sup>22</sup> We restrict our sample to exclude unemployed individuals, students, pensioners, and persons with physical or mental limitations that prevent them from working.

Individuals can be in five exclusive states: out of the labor force, working on-site, working on-site in a condition of self-employed, telework, and telework in a self-employed situation. We identify teleworking category with two key questions related to the place and purpose of using the computer.<sup>23</sup> Table 1 shows that 71.1 (41.2) percent of men (women) worked on-site, 24.17 (17.35) percent teleworked, and 4.73 (41.45) percent were out of the labor force in 2020. The total sample includes 46.43 percent of men and 53.54 percent of women and as expected, a much larger share of women is out of the labor market than men. Interesting, between men and women, teleworking has the smaller gap between the shares of 6.82 percentage points, compared with being out of the labor force 36.42 percentage points. There is 15.87 percent of women working as self-employed, with 2.62 percent of them teleworking and 13.25 working on site. Of men, 19.94 percent work as self-employed, and almost one-fourth of self-employed men do teleworking.

> 21 There is no agreement in the literature on how to define telework. However, an accepted definition is to define teleworkers as workers who work from a remote work location and use information technology for operation and communication (See Sthanasiadou and Thriou, 2021, IRS 1996, Moon and Stanworth 1997).  The definition of telework is important as it could lead to wrong comparisons based on different base definitions.

> 22 We include as part of the labor force those individuals who reported working at least one hour during the reference period.

> 23 Questions p6_7_1 and p6_9_1 from the ENDUTIH. Specifically, question p6_7_1 asks about whether the computer is used at the house, and question p6_9_1 asks if the use is related to work activities.

9
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 32. Undercollection and losses in the power and water sectors, as percentage of GDP**

<!-- Start of picture text --> a. Power sector b. Water sector<br>0.7 0.18<br>0.16<br>0.6<br>0.14<br>0.5<br>0.12<br>0.4 0.10<br>0.3 0.08<br>0.06<br>0.2<br>0.04<br>0.1<br>0.02<br>0.0 0.00<br>Burkina Faso LIC-Nonfragile Burkina Faso LIC-Nonfragile<br>Collection inefficiencies Unaccounted losses Collection inefficiencies Unaccounted losses<br>Percenatage of GDP<br>Percenatage of GDP<br><!-- End of picture text --> _Source_ : Banerjee and others 2009.

## **Annual funding gap**

Burkina Faso’s infrastructure funding gap amounts to $123 million per year, or about 4 percent of GDP. The biggest funding gap is found in the water supply and sanitation sector, where the shortfall is $85 million; a $44 million gap is found in the transport sector and $35 million in the ICT sector (table 20). Additionally, if potential efficiency gains were fully captured in the power sector, it would benefit from a surplus of funds over identified needs of about $41 million per year. This surplus could be used within the power sector, but also allocated toward covering the funding gap in other infrastructure sectors.

## **Table 20. Funding gaps by sector**

<u>$ million per year</u>

||**ICT**|**Power**|**Transport**|**WSS**|Total|
|---|---|---|---|---|---|
|Needs|(119)|(142)|(144)|(208)|(613)|
|Spending|83|103|81|119|387|
|Within sector reallocation|0|0|20|0|20|
|Potential efficiency gains|0|80|0|3|83|
|**(GAP) or surplus**|**(35)**||**(44)**|**(85)**|**(123)**|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Potential overspending is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. WSS = water supply and sanitation; ICT = information and communication technology. — = Not available.

# What else can be done?

The funding gap can be addressed only by raising additional finance or, alternatively, by adopting lowercost technologies or less-ambitious targets for infrastructure development.

Burkina Faso has the potential of raising additional finance from the private sector. Over the early 2000s, Burkina Faso captured private investment commitments worth around 1.2 percent of GDP in the ICT sector, comparing favorably with many of its peers (figure 33). On the other hand, many countries 43
Rosen, R.J., P.R. Lloyd-Davies, M.L. Kwast, and D.B. Humphrey, 1989, New banking powers: a portfolio analysis of banking investment in real estate, _Journal of Banking and Finance_ 13, 355-366.

Roy, A.D., 1952, Safety first and the holding of assests, _Econometrica_ 20, 431-449.

Stiroh, K.J., 2004, Diversification in banking: is noninterest income the answer? _Journal of Money, Credit, and Banking_ 36, 853-882.

Templeton, W.K., and J.T. Severiens, 1992, The effects of nonbank diversification on bank holding company risk, _Quarterly Journal of Business and Economics_ 31, 3-17.

22
A Multi-Country Analysis of Multidimensional Poverty in Contexts of Forced Displacement Yeshwas Admasu<sup>[†]</sup> , Sabina Alkire<sup>[‡]</sup> , Uche Eseosa Ekhator-Mobayode<sup>[†]</sup> , Fanni Kovesdi<sup>[‡]</sup> , Julieth Santamaria<sup>[§]</sup> , and Sophie Scharlin-Pettee<sup>[‡]</sup> JEL classification: I32, J16, D63, D10, D74, O55 Keywords: forced displacement, multidimensional poverty, monetary poverty, Sub-Saharan Africa, refugee, IDP, internal displacement, gender inequality, female-headed households The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.

Acknowledgments: The authors are grateful for the guidance and comments received from Amalia Hadas Rubin, Andrew Peter Brudevold-Newman, Bilal Malaeb, Diana Jimena Arango, Jeni Klugman, Kathleen Beegle, and Lucia Hanmer at the World Bank, as well as Monica Pinilla-Roncancio and Corinne Mitchell at OPHI. The authors would also like to acknowledge the help of Utz Pape with the data used in this study. All errors remain our own.

Funding: This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

The data that support the findings of this study are openly available at the World Bank Microdata Library at https://microdata.worldbank.org/index.php/home.
29

**Figure 19:** GDP Estimates for the Three Scenarios Using Two Baselines a. Using 11MP Projections as the baseline b.  Using MA(3) model projections as the baseline

<!-- Start of picture text --> 10<br>10<br>9 9<br>8 8<br>S3<br>7 S2 7<br>S1 S3<br>6 6 S2<br>S1<br>baseline<br>5 5<br>4 4<br>GDP growth (%) - Baseline GDP growth (%) - Scenario 1 GDP growth (%) - Baseline GDP growth (%) - Scenario 1<br>GDP growth (%) - Scenario 2 GDP growth (%) - Scenario 3 GDP growth (%) - Scenario 2 GDP growth (%) - Scenario 3<br>Percent annual GDP growth Percent annual GDP growth<br><!-- End of picture text --> Source: Authors’ calculations and the various Malaysia Plans.

Note: 6MP, 7MP, 8 MP, 9MP, 10MP, and 11MP = Sixth, Seventh, Eighth, Ninth, Tenth, and Eleventh Malaysia Plan, respectively; S1, S2, and S3 = Scenarios 1, 2, and 3, respectively.

The simulations using GDP growth lend support to the notion that the overall GDP projection of 5.8% may be too optimistic. Therefore, the 2.4ppt increase from the baseline of 5% GDP growth in scenario 1 appears more plausible, although this is conditional on structural reforms that increase the competitiveness of domestic markets.

**Figure 20:** Total Factor Productivity Growth Rates, 1971–-2020

<!-- Start of picture text --> 4<br>3.6<br>3.5 3.3<br>3.2<br>3 2.7 2.9<br>2.4<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>TFP (official estimates) TFP (Baseline 11 MP)<br>TFP (Baseline ARMA(p,q))<br>Percent TFP growth<br><!-- End of picture text --> <!-- Start of picture text --> Percent TFP growth<br><!-- End of picture text --> Source: Authors’ calculations and Eleventh Malaysia Plan (11 MP).

Note: 6MP, 7MP, 8 MP, 9MP, 10MP, and 11MP = Sixth, Seventh, Eighth, Ninth, Tenth, and Eleventh Malaysia Plan, respectively; ARMA = moving average model; S1, S2, and S3 = Scenarios 1, 2, and 3, respectively; TFP = total factor productivity.

The higher productivity growth in scenario 2 and scenario 3 will require even more purposeful and aggressive reforms to reduce restrictions and increase the intensity of domestic competition.
**Figure 2: Average Tariffs**

<!-- Start of picture text --> 70.0% 0 - 0 Rich Countries<br>60.0% Globalizers<br>r 60.0% O Non-Globalizers<br>w 50.0%<br>> 40.0%<br>.30.0% -<br>4 20.0% -<br>10.0%<br>0.0%<br>1960s 1970s 1980s 1990S<br><!-- End of picture text --> Note: Rich countries refers to the 24 OECD economies before recent expansions, plus Chile, Hong Kong, Korea, Taiwan, and Singapore. Globalizers refers to the top one-third in terms of their growth in trade relative to GDP between 1975-79 and 1995-97 of a group of<sup>72</sup> developing countries for which we have data on trade as a share of GDP in constant local currency units since the mid- I 970s. Non-globalizers refers to the remaining developing countries in this group. Averages decadal and are population-weighted. Unweighted averages and alternative definitions of globalizers are reported in Table 3. Variable definitions and data sources are reported in Table 8.

29
These problems of administration and regulation are typically more acute in industries where there are numerous separate firms. In most countries, cars are manufactured by a few large firms and sold by their dealerships, and a buyer can be quite confident that the cars will meet with the standards required of them—ranging from pollution control to safety. However, second-hand car markets are characterized by thousands of small firms, so there is much greater uncertainty about these standards. In olden days, when milk was supplied to households by hundreds and thousands of small dairy ‘firms,’ adulteration in the form of dilution with water was rampant. A study of milk supply by small farmers and dairy producers in rural Bangladesh revealed that 100% of the milk samples were so adulterated (Chanda et al, 2012). Now, in the segment of the industry which is catered to by a few large firms, such regulatory violations are few and far between.

Examples of this kind can be found in many countries and in many sectors. Martin Wolf (2014) recently conjectured that “banking systems dominated by a few large institutions might be more stable than competitive ones.” Indeed, the traditional moneylender system violated usury laws with much greater abundance than large modern banks do.

The argument that we propose in order to explain this stylized fact is simple. Consider the hamburger industry. Suppose first that there are thousands of small burger shops, each one an independent firm. There is some regulation, R, about the ingredients of burgers that they are supposed to comply with. When one of these small firms is caught violating R, there is a limit to how much such a firm can be punished. The government can for instance, take away all its profits and may be even the owner’s car, but beyond a point there may be a limited liability constraint. Even without that, there are natural limits to the size of punishment on a small player. The limited threat of punishment is often inadequate to ensure compliance, using a standard crime-and-punishment argument (Becker 1968). Contrast the case where all those burger outlets are owned by one firm, MacDuck. If any of the outlets is caught violating R, the government can impose a much larger punishment on this large firm that has the capacity to pay the bigger penalty (in the extreme case, the entire profit from all its outlets). In other words, the limited liability constraint on punishments is much more binding on small firms than big firms.

However, the simple verbal statement is not enough. For example, we know from Bernheim and Whinston (1990) that in a repeated interaction with multiple contacts, good behavior is not always sustained by the threat of punishing one transgression by ending all interactions: the optimal response to such a strategy may instead be to transgress in all 3
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA for all countries. The change in wage rate is stronger in all countries except Senegal, where the effect is only slightly less than in the VAT case. The effect on firms’ income is also quite similar, although the situation reverses for Cameroon, which falls from a +0.1 percent to –0.11 percent.

**Table 14. Macro results of electricity investments, funded by income tax**

||||E|lectricity (in<br>|come tax fund|ed)||
|---|---|---|---|---|---|---|---|
|Variables|Definition|Benin|Mali|Senegal|Tanzania|Uganda|Cameroon|
|Ym|Aggregate household income|0.70|0.61|0.90|0.50|0.16|0.99|
|EV|Equivalent variation|0.64|0.59|0.84|0.49|0.16|0.90|
|S|Wage|4.52|0.99|2.00|0.77|0.28|1.59|
|Yg|Government income|13.97|3.03|5.61|5.17|2.98|6.04|
|Ye|Firm income|–0.10|0.50|0.41|0.65|0.06|–0.11|
|G|Total government expenditure|10.77|1.56|4.84|3.17|2.24|6.54|
|It|Total private investment|0.42|0.67|0.23|1.40|–0.45|0.40|
|E|Nominal exchange rate|–1.13|0.06|–1.09|–0.34|–1.96|0.80|
|GDP|GDP|0.67|0.63|0.83|0.72|0.18|0.80|

The strongest effects on the macro variable are on the nominal exchange rates. Previously all rates rose, but here they plummet in four countries. Uganda presents the starkest decrease, going from a 0.05 percent increase to a drop of 1.96 percent. Also, total investment in Uganda dwindled to –0.45 percent from what had been a positive effect of 0.16 percent. Overall, this funding option strongly resembles the VAT option, with the greatest difference found in the nominal exchange rates. The policymaker could therefore choose either option without creating major differentiated effects, at least at the macro level.

# The investment in telecom infrastructure

For our final investment option, we analyze telecom infrastructure investment. Here we analyze the foreign aid and import duties funding options. As shown in the elasticity table (see appendix), the two service sectors and the construction sector benefit the most. The two agricultural sectors also profit, followed by industries and mining/gas.

## **Investment funded by foreign aid**

We focus our comparative analysis here on the baseline and on road investment, as this option was also funded with foreign aid. The macro results are presented in table 15.

This simulation provides an interesting result. The impact on GDP is almost identical for all countries. The lower scale of the Uganda simulation aside, results range from a low 0.42 percent increase in Cameroon to a high of 0.55 percent in Senegal. This is the most minimal country difference in all of the simulations analyzed thus far. The effects are similar for household impact, although they are more differentiated than that observed for GDP (figure 3). Cameroon is the only exception to these similar results, with a positive effect on households twice as large as the GDP increase.

20
**Table 3**

**Number of Japanese emigrants to Brazil, 1908-1986**

|**Year **|**Number **|**Year **|**Number **|**Year **|**Number **|**Year **|**Number**|**Year **|**Number**|
|---|---|---|---|---|---|---|---|---|---|
|1908|830|1924|2 673|1940|1 268|1956|4 912|1972|352|
|1909|31|1925|6 333|1941|1 548|1957|6 147|1973|492|
|1910|948|1926|8 407|1942|0|1958|6 586|1974|239|
|1911|28|1927|9 084|1943|0|1959|7 123|1975|254|
|1912|2 909|1928|11 169|1944|0|1960|7 746|1976|1 126|
|1913|7 122|1929|16 648|1945|0|1961|6 824|1977|682|
|1914|3 675|1930|14 074|1946|6|1962||1978|584|
|1915|65|1931|5 632|1947|1|1963|2 124|1979|500|
|1916|165|1932|11 678|1948|1|1964|1 138|1980|562|
|1917|3 899|1933|24 494|1949|4|1965|903|1981|417|
|1918|5 599|1934|21 930|1950|33|1966|937|1982|329|
|1919|3 022|1935|9 611|1951|106|1967|1 070|1983|289|
|1920|1 013|1936|3 306|1952|261|1968|597|1984|261|
|1921|840|1937|4 557|1953|1 928|1969|496|1985|258|
|1922||1938|2 524|1954|3 119|1970|435|1986|363|
|1923|895|1939|1 414|1955|4 051|1971|452|Total|######|

Source: Carvalho (2003)

**Table 4:** **<u>Crop Share of</u>** **_Nikkeijin_ Farmers in Brazil**

||1912|1922|1932|1942|1958|
|---|---|---|---|---|---|
|Coffee|92.6|52.2|59|24.3|27.5|
|Rice|2.5|17.6|8.3|4.5|3|
|Cotton|1.2|12.1|14|39.2|20.5|
|Kinko|0.6|10.2|13|19.9|34.1|
|Other|3.1|7.9|5.7|12.1|14.9|
|Total|100|100|100|100|100|

Source: Kikumura-Yano, A. (2002).

27
<mark>Dupraz-Dopias (2020). COVID-19 shifts Peru’s internal migration into reverse. The New Humanitarian. 8 July 2020.</mark> Dutwin, D., and T. D. Buskirk (2017), “Apples to Oranges or Gala versus Golden Delicious?,” Public Opinion Quarterly, 81, 213–239.

<mark>Evans, J. R., & Mathur, A. (2005). The value of online surveys.</mark> _<mark>Internet research</mark>_ <mark>.</mark> <mark>Fan Li, Kari Lock Morgan & Alan M. Zaslavsky (2018) Balancing Covariates via Propensity Score Weighting, Journal of the American Statistical Association, 113:521, 390-400, DOI: 10.1080/01621459.2016.1260466</mark> <mark>FAO (2020). Migrant workers and remittances in the context of COVID-19 in sub-Saharan Africa. Rome. Last accessed on March 29</mark><sup>th</sup> <mark>, 2022. Link: https://doi.org/10.4060/cb0184en</mark> <mark>Ferri-García, R., & Rueda, M. D. M. (2020). Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys. PloS one, 15(4), e0231500.</mark> <mark>Garcia, R</mark> aphael ( <mark>2021). Can Brazil’s remote workers thrive outside big cities? BBC, January 2021. Last access on March 29</mark><sup>th</sup> <mark>, 2022. Link: https://www.bbc.com/worklife/article/20210121-</mark> <u><mark>the-challenge-of-remote-work-in-developing-nations</mark></u> <mark>Goel, S., Obeng, A., & Rothschild, D. (2015). Non-representative surveys: Fast, cheap, and mostly accurate.</mark> _<mark>Work Pap</mark>_ <mark>.</mark> <mark>Granello, D. H., & Wheaton, J. E. (2004). Online data collection: Strategies for research.</mark> _<mark>Journal of Counseling & Development</mark>_ <mark>,</mark> _<mark>82</mark>_ <mark>(4), 387-393.</mark> <mark>Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias.</mark> _<mark>Public opinion quarterly</mark>_ <mark>, 67(1), 79125.</mark> <mark>Andrew Kohut, Scott Keeter, Carroll Doherty, Michael Dimock, and Leah Christian. 2012. Assessing the representativeness of public opinion surveys. Pew Research Center, Washington, DC, 2012.</mark> <mark>Kapteyn, A., Smith, J. P., & Van Soest, A. (2007). Vignettes and self-reports of work disability in the United States and the Netherlands.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>97</mark>_ <mark>(1), 461-473.</mark> <mark>Lee, S. (2006). Propensity score adjustment as a weighting scheme for volunteer panel web surveys.</mark> _<mark>Journal of official statistics</mark>_ <mark>,</mark> _<mark>22</mark>_ <mark>(2), 329.</mark> <mark>Macinnis, Bo, Jon A. Krosnick, Annabell S. Ho, and Mu-Jung Cho. 2018. The accuracy of measurements with probability and nonprobability survey samples. Public Opinion Quarterly, Vol. 82, No. 4, Winter 2018, pp. 707–744.</mark> 25