rafmacalaba/datause-encoder-data huggingface dataset:
|||||||estimate is inadequate; Measurement<br>error|
|---|---|---|---|---|---|---|
|||1949 to|Cobb-||||
||US Nil||||||
|Sturm and De|atona|1985|Douglas;|OLS|Applying first-differencing method to Aschuer (1989a),<br>id lii i 026* f h S d 11** f|Endogeneity;<br>Reverse<br>causality;|
|Haan (1995)|Netherland<br>National|1960 to<br>1990|Log<br>Difference<br>Level||estmate output eastcty s . or te U an .5 or<br>the Netherland.|Measurement error|
|Holtz-Eakin<br>and Schwartz<br>(1995)|US States|1971 to<br>1986|Cobb-<br>Douglas; Log<br>Difference<br>Level|OLS|There is little support for claims of a dramatic productivity<br>boost from increased infrastructure outlays. The estimated<br>upper bound for the output elasticity is 0.10|Relying on assumptions; Aligned<br>with the weaknesses of growth model|
|Nourzad and<br>Vrieze (1995)|7 OECD<br>Countries|1963 to<br>1988|Log<br>Difference<br>levels|GLS|There is a statistically significant positive relationship between<br>public capital formation and the growth rate of labor<br>productivity. Estimated output elasticity is 0.059***|Endogeneity;<br>Reverse<br>causality;<br>Measurement error|
|Morrison and<br>Schwartz<br>(1996b)|US States|1970 to<br>1987|||Infrastructure investment provides a significant return to<br>manufacturing firms and augments productivity growth||
|<br>Morrison and<br>Schwartz<br>(1996a)|New<br>England<br>(Manufacturi<br>ng Industry)|1971 to<br>1987|Generalized<br>Leontief Cost<br>Function; First<br>derivative;<br>Log Levels|SUR|In the short run, public capital expenditures provide cost-saving<br>benefits that exceed the associated investment costs due to<br>substitutability between public capital and private inputs. Over<br>time, however, stimulating investment in private capital<br>increases economic performance more effectively than public<br>capital expenditures alone. Present value return of one-dollar<br>investment in public capital is 1.108 to 2.638|Reverse causality; Common trends;<br>External validity|
|Wang (2002)|East Asian<br>Countries|1979 to<br>1998|First<br>Derivative;<br>Levels|GLS|Spillover effects between private and public sectors coexist in<br>analyzing the interrelation between public infrastructure and<br>private production. Elasticity of the public infrastructure is<br>0.24***|Endogeneity;<br>Reverse<br>causality;<br>Measurement error|

# The dependent output variable is followed by the output to public capital stock/ investment elasticity value, where p<0.01 is denoted by ***; p<0.05 is denoted by **; and p<0.1 is denoted by *

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unemployed.<sup>2</sup>

# **3.1.2 World Bank Enterprise Surveys (WBES)**

The WBES are countrywide, representative samples of businesses and firms from all sectors of activity with at least five workers. They include information on enterprises’ establishments, assets, operations, sources of funding, structure of the workforce, and type of activities. We exploit data that were collected over the 2005-2018 period for coastal nations that had survey cycles before and after the date when submarine internet was acquired. These countries are Ghana, Kenya, Mauritania, Nigeria, Senegal, and Tanzania. We limit our sample to small- and medium-size enterprises (SMEs) that employ a maximum of 99 individuals. It is also important to note that while digitalized road maps are for the period before 2014, our household and firm-level data extend beyond 2014. Using household and firm-level data that extend beyond 2014 maximizes the number of observations, but this is justified because the African road networks have not changed much in recent years.<sup>3</sup> The WBES provide information on the location of firms that we first match with the location of cities and then from which we compute both the indicator of whether the establishment’s location is connected to internet, and the distance to the closest major road. After pooling all observations together, we obtain a sample of 15,033 firms. We define internet connectivity as being within a radius of two kilometers of an optical-fiber node. Firms’ hours of operation range between 10 and 84 hours, averaging 35 hours a week. They employ, on average, 16 full-time workers, and an average of 6 female workers. More than a quarter of firms, 26.85 percent, reported that telecommunications limitations are

> 2The characteristics of individuals in the sample are summarized in Table (A2) in the Online Appendix. Skilled occupations are distributed as follows: sales (38 percent); manual labor (18 percent); agricultural employee (16 percent); service sector (22.9 percent); professional, technical, or managerial occupations (10.90 percent); clerical occupations (2 percent). In our data, 80 percent of unskilled workers reported being self-employed in agriculture, with the remaining 20 percent working in domestic work or as unskilled, manual laborers. Among all respondents, 62 percent lived in rural areas, and 38 percent lived in urban areas. The ages of those who responded to the survey were as follows: 25 and under (39 percent), 25 to 50 (57 percent), and over 50 (0.2 percent).

> 3Figure (A1) in the Online Appendix shows the road networks in some West African countries have barely changed between 2008 and 2014.

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# **Figure 1: Labor force participation by race and immigration status. Males.**

<!-- Start of picture text --> 0<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br>-0.06<br>-0.07<br>-0.08<br>-0.09<br>Whites Blacks in Blacks Asians in Asians Hispanics Hispanics<br>out out out in out<br><!-- End of picture text -->

# **Figure 2: Labor force participation by race and immigration status. Females.**

<!-- Start of picture text --> 0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>Whites Blacks in Blacks Asians in Asians Hispanics Hispanics<br>out out out in out<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups and foreign born status (“in” for natives, “out” for foreign born) in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. The regression results are further detailed in table 2, columns 1 (males) and 5 (females).

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banking may therefore take more time to materialize. Data from a second follow-up survey expected in March 2016 will explore the impacts on these other outcomes.

One year after the program launch, all versions of the program had a significant impact on formalization rates. The impact was 9.1 percentage points in the first treatment group providing only registration simplification and information; 13 percentage points in the second group, which also provided business training, counseling and bank support services; and 15.8 percentage points in the last group, which also added to the previous group’s benefits the provision of tax mediation services. Differences between groups are all significant, suggesting that businesses valued the additional incentives provided. These impacts are slightly higher than those measured in studies conducted in other contexts, especially given the fact that formalization is linked to tax registration in Benin. The formalization rate in the control group was less than 1 percent, suggesting that in the absence of the program, this type of informal businesses would not usually formalize.

The program had a higher impact on male businesses owners, those with more education, operating outside Dantokpa Market, in sectors other than trade, and that before being offered the incentives to formalization had characteristics similar to businesses that were already formal. Program impacts in all these sub-populations were significantly higher than in the rest of the sample, but formalization rates still did not exceed 23 percent. As a result, the majority of the informal sector is likely to remain informal, even after this policy change.

Two factors may explain why we did not observe greater impacts. First, the lack of proper identification was a barrier: only 53% of business owners had a passport or an ID card, the official documents required to formalize. Second, despite the impact on formalization, the program did not impact much the perception of formalization for many firms. For example, significant shares of beneficiaries still considered registration time and cost excessively high, and procedures complicated.

The remaining sections of the paper are organized as follow: Section 2 presents the potential benefits and costs associated with formality in Benin, section 3 details the _entreprenant_ program, the sampling, the study design and the data. Section 4 describes program implementation during the first year and take-up on program components. Section 5 presents 5
of higher cereal prices will be uniformly negative.

To compare per capita expenditures levels across rural and urban households, we adjust for cost-of-living difference using a new set of poverty lines produced for NSS61 by the World Bank. Panel (a) of Figure 11 highlights the rural-urban wealth disparity, showing an increasing urban representation in higher per capita expenditure percentiles. This fact largely explains the shape of the national CCIC in panel (b), which is merely a weighted average of the corresponding urban and rural curves displayed alongside. Given that rural households are made worse off relative to urban households more or less uniformly along the whole range of the wealth distribution and that the urban weight increases with wealth, the national CCIC must become increasing steep as well.

# **5.5 Digression: Economic growth and Engel’s Law**

First-order comparative statics implies that the welfare cost of a price increase is proportional to (minus) the initial net consumption bundle, an approximation which is probably quite accurate for a change in cereal prices of just ten percent.<sup>31</sup> Climate change, however, will not increase cereal prices overnight, so the question arises: What is the right ”initial” net consumption bundle? So far, we have used the value of _p_ ( _y − x_ ) _/c_ observed in 2004-05. But, as a result of (distribution-neutral) economic growth,<sup>32</sup> household incomes will also rise over the next three decades and, by Engel’s law, drive the food share, _px/c,_ downward. Consequently, the farther off in the future the cereal price increases actually take place, the lower the (instantaneous) welfare cost is likely to be.

Suppose, then, we take the other extreme position and assume that cereal prices rise overnight, not in 2005, but only in 2040. In other words, we replace the 2005 cereal share with the predicted cereal share in 2040. Assuming that Engel’s law applies, and that the other component of the net consumption ratio, _py/c_ , stays constant, this calculation should give a lower bound on the true welfare cost of the cereal price increase. Consider, for simplicity, the quadratic Engel curve specification used by Banks, et al. (1997). If consumption grows at an exponential rate of _g_ for _H_ years,

> 31Banks et al. (1996) find that even for a price increase approaching 20 percent errors in first-order welfare change estimates are tolerably small (less than 10 percent of the true welfare change based on full demand system estimation).

> 32In terms of our model, where _c_ = _z_ + _px_ = _πa_ + _wτ_ (1 _− κ_ ), assume that land and labor productivity, _π_ and _w_ , are rising over time at a common exponential growth-rate _g_ , so that total per capita expenditures, _c_ , is forced to follow suit.

25
43.6 in 2008.<sup>34</sup> Using the SPL, we estimate a global headcount rate of 42.3 in 1990 which drops to 33.4 in 2008 (or from 43.6 in 1990 to 33.7 in 2008 when using the SPL with a lower bound). Our societal poverty measure is lower overall and declines at a faster rate. The higher aggregates from Ravallion and Chen originates from the fact that their preferred specification results in a higher average poverty line than the SPL. The difference in levels can also be inferred from comparing the SPL panels in Figure 5 with the panel for the weakly relative poverty line.

By design, the SPL used in estimation of societal poverty is increasing with growth, and this is reflected in Figure 7 which shows the values of the line over time. The populationweighted average SPL over all countries increases from about $5.20 in 1990 to $6.70 in 2013. Among upper-middle-income countries, the mean SPL almost doubled over the same time period, increasing from $2.80 in 1990 to $5.50 in 2013. The relative line has increased in all regions, but faster in fast-growing regions (Table 4). In 1981, the mean SPL in South Asia was equal to the international poverty line of $1.90 and the SPLs in East Asia and the Pacific, and Sub-Saharan Africa were just slightly higher. Due to strong economic growth in East Asia and Pacific, the mean line has more than doubled since then to $5.60 per day in 2013. In contrast, Sub-Saharan Africa, which has experienced much weaker overall growth, has an estimated mean societal poverty line of $2.20 per day in 2013, essentially unchanged since 1981.

The profile of societal poverty by region and income groups and over time is given in Table 5.<sup>35</sup> Similar to regional profiles of absolute poverty, Sub-Saharan Africa stands out as a region with substantially higher rates of societal poverty. While societal poverty has declined in

> 34 Implemented with the latest data and at 2011 PPPs with the poverty line as max(1.9, 1.9/2+0.5*mean) we the estimate are 49.9% in 1990 to 44.1% in 2008. 35 We use the World Bank’s classification of countries in regions and income groupings. For details about the World Bank income classification, see Fantom and Serajuddin (2016). For regions used by the World Bank see: http://www.worldbank.org/en/country.

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**Table 1 Labor Force Characteristics for Cambodia and Sri Lanka**

|||**C**|**ambodia**|||**Sri Lank**|**a**|
|---|---|---|---|---|---|---|---|
||**2004**|**2007**|**2008**|**2009**|**2002**|**2006**|**2008**|
|**_Employment and Education_**||||||||
|**Females in labor force (percent)**|50|49|48|50|32|31|34|
|**Years of education**|5.51|6.07|6.05|5.98|8.44|8.56|8.81|
|**Years of education for females in T&G**|<br>6.12|6.26|5.8|6.39|10.03|10.01|10.18|
|**Years of education for men in T&G**|7.55|8.62|7.22|7.28|10.2|10.33|10.46|
|**Average years of education in T&G**|6.39|6.67|6.10|6.55|10.08|10.11|10.26|
|**_Employment Share of the Industry_**||||||||
|**Agriculture (percent)**|58|49|43|53|20|12|18|
|**T&G (percent)**|4|5|6|5|6|6|6|

_Source:_ Authors’ calculations are based on Cambodia Socio-Economic Surveys and the Sri Lankan 2002 and 2006 Household Income and Expenditure Surveys and the 2002 and 2008 Labor Force Surveys _Note:_ T&G = textiles and garments.

# _4.2. Unit Values_

In both Cambodia and Sri Lanka, unit values show declining trends following the end of the MFA. Sri Lankan apparel export unit prices to the United States declined sharply after the MFAphase-out from $59 to $42 per dozen items over the 2004–08 period (see figure 3). A slight rise in EU unit values over 2004–05 was followed by a moderate decrease (Staritz and Frederick 2011b). In spite of this decline, Sri Lanka’s apparel exports have generally experienced higher unit values than those of other Asian apparel exporter countries, reflecting the higher valueadded production that Sri Lanka specializes in. Unit values of apparel exports from India and Sri Lanka to the EU are higher than those of Asian competitor countries, including Bangladesh, Cambodia, China, Pakistan, and Vietnam (Tewari 2008).

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Policy Research Working Paper 9386

# **Abstract**

Senegal has experienced a rapid expansion in fixed and mobile broadband internet infrastructure over the past decade. This paper examines the relationship between access to broadband internet and household welfare between 2011 and 2018 by integrating the latest two rounds of household budget surveys with data on the location of fiber-optic transmission nodes and coverage maps of 3G mobile technology. The results show that 3G coverage is associated with a 14 percent increase in total consumption and a 10 percent decline in extreme poverty. These results are robust to controlling for household demographics and other spatial characteristics, such as region fixed effects, road density, nighttime lights, and elevation above sea level, as well as for access to complementary digital infrastructure, such as 2G coverage or fixed broadband internet. The findings are also robust to an instrumental variable approach that relies on distance to 3G coverage in neighboring areas. These effects are larger among households in urban areas and households headed by men or younger cohorts. Although they are in the same direction, the welfare effects of proximity to fixed broadband infrastructure are not statistically significant.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tmasaki@worldbank.org and crodriguezc@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
paternity leave, and the remaining 7 decided not to answer; mainly due to lack of time. In Round 2 in June and July 2012 all of the original 48 experts replied, after two reminders.<sup>11</sup>

# **4. Results**

# **4.1. Willingness-to-Pay (WTP) Results from Rounds 1 and 2**

The distribution of the 49 experts (where one expert answered in Round 2 only) from European countries is shown in Table 1, and reflects to some extent the countries that have the largest numbers of environmental valuation practitioners. We see that 29 of 49 experts come from Northern and Central Europe, 12 from Southern Europe and 8 for Eastern Europe. The table indicates the level and differences in WTP by country (for one of our questions, on mean WTP for Plan A in the first questioning round; see below). The table also indicates average income levels in the four indicated regions, both regular (non-adjusted) incomes and purchasing power parity (PPP)-adjusted incomes; where the latter are less dispersed than the former.

**Table 1: Geographical distribution of the environmental valuation experts (N = 49) that participated in the European Delphi survey (and the countries they assessed specific WTP estimates for).**

|**Country group**|**Country (number of**<br>**experts)**|**Stated Mean WTP for**<br>**Plan**<br>**A,**<br>**Round**<br>**1;**<br>**euro/household/year**|<br> <br>**Mean Group GDP**<br>**(PPP-adjusted GDP),**<br>**euro/capita/year**|
|---|---|---|---|
||Denmark (2)|36.0||
|Nordic countries|Finland (3)|28.7|51,100|
|(total number of<br>experts = 11)|Norway (3)|65.0|(32,500)|
||Sweden (3)|31.7||
||Austria (2)|57.5||
||Belgium (1)|45.0*||
|Northern and Central<br>E|Germany (3)|88.3|36,200|
|urope<br>(total number of|Ireland (2)|34.0|(29,500)|
||Netherlands (3)|38.3||

> 11 One additional expert, who answered only Round 2 of the survey, is also included in Table 1.

7
**_Energy demand models for policy_** **_<u>formulation</u>_**

The derived demand functions can be obtained from Shepherd’s lemma _X i_  _C_ /  _Pi_ (2) Although these functions are non-linear in the unknown parameters, the factor cost shares (Mi = PiXi/C) are linear in parameters. i = PiXi/C) are linear in parameters. = PiXi/C) are linear in parameters. iXi/C) are linear in parameters. Xi/C) are linear in parameters. i/C) are linear in parameters. /C) are linear in parameters. _M i_  _i_   _ij_ (ln _Pj_ ) for i = factor inputs, j = factor inputs, i # j (3) _j_ Although these functions are non-linear in the unknown parameters, the factor cost shares (Mi = PiXi/C) are linear in parameters. i = PiXi/C) are linear in parameters. = PiXi/C) are linear in parameters. iXi/C) are linear in parameters. Xi/C) are linear in parameters. i/C) are linear in parameters. /C) are linear in parameters.

These share equations are estimated to obtain the parameters. Only n-1 such equations need to be estimated as the shares must add to 1.

The own price elasticity of factor demand is obtained as follows: Source: Pindyck (1979).

Many studies routinely used this flexible functional form in demand studies until late 1980s.<sup>24</sup> The preference for this functional form derived from the theoretical underpinning of the function, the flexibility of avoiding pre-specification of any particular relationships, and the imposition of minimum restrictions on the parameters.

> 24 See Borges and Pereira (1992), Christopolous (2000), Dahl and Erdogan (2000), Siddayao et al (1987), Saicheua (1987), Kim and Laby (1988), Mahmud and Chisti (1990), and Buranakunaporn and Oczkowsky (2007).

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Table 1. Construction of the measurement of the content of tasks

|Task|Elements of the tasks (t)|
|---|---|
|Non-routine cognitive<br>(analytical)|Information analysis<br>Creative thinking<br>Interpretation of information for others|
|Non-routine cognitive<br>(interpersonal)|Establishment of personal relationships<br>Leadership, management and motivation of staff<br>Training /development of others|
|Routine / cognitive|Importance of repetition of the same task<br>Importance of being exact or precise<br>Beingstructured|
|Non-routine manual|Operating vehicles or operating machines<br>Spending time using your hands to handle, control or<br>feel objects<br>Manual dexterity<br>Spatial orientation|
|Routine manual|Rhythm determined by the speed of equipment<br>Control of machines or processes<br>Spending time doing repetitive movements|

Source: Compiled based on Acemoglu and Autor (2011) After assigning each attribute to each task, and these to the information from the surveys, the values of each element t are normalized in order to enable information to be comparable across time, using the following formula: Where � is the combination of the 16 tasks listed in Table 1 for the individual � and �� and �� represent, respectively. The weighted average and the standard deviation of the task � in the total of the period 1995-2015 are calculated in the following manner:

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