rafmacalaba/datause-encoder-data huggingface dataset:
are to the poverty line would mean giving different amounts to different poor people, which proves at times to be politically infeasible and administratively costly. Second, the objectives of the social protection programs may go beyond immediate consumption and short-term poverty eradication. Many elements of social protection are indeed not intended to be targeted to the actual poor, but to protect non-poor against the risk of becoming poor, or help the vulnerable to improve their resilience (e.g. contributory pensions, unemployment benefits, universal health insurance etc.). In the developing world represented by 56 countries in ASPIRE, only 23.6 percent of all social protection beneficiaries are present among the poorest quintile.

Nevertheless, important criterion for assessing the adequacy of any social protection program is its ability to alleviate poverty and ensure equity (see for instance Grosh et al’s (2008) discussion of targeting efficiency). That is, while a BCR of 1 is an unattainable ideal benchmark, countries may ensure adequate protection of the poor and vulnerable by ensuring that at least some resources flow to the poor. And indeed, on average the BCR for the SP system as a whole is above zero, with a maximum value of 0.4 (the higher levels are achieved by countries as diverse as Timor-Leste, West Bank and Gaza and Rwanda) – meaning that for each dollar of transfer at least 40 cents is going to fill the poverty gap.

Even programs that are not specifically focused on the poor can achieve good performance in targeting the lower end of the income distribution. We examine this through ASPIRE, focusing on how well different types of programs reach _the poorest quintile_ . Figure 6 shows the BCR varies across types of programs. With a mix of social protection programs some countries are achieving relatively high degrees of efficiency for some programs- the BCR is above 0.5 in almost 40 programs across countries.

# **FIGURE 6: GRAPH OF BENEFIT-COST RATIO FOR DIFFERENT TYPES OF SOCIAL PROTECTION PROGRAMS**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0-0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Benefit-cost ratio<br>Cash Transfers Social Pensions<br>Family/Child or disability allowances Conditional Cash Transfers<br>In-Kind Other Social Safety Nets<br>School feeding Public Works or Cash-for Work<br>Number of programs<br><!-- End of picture text --> Source: Authors calculations. Note: BCR for poverty line set at the lowest quintile cut-off. Benefit-cost ratio is the reduction in poverty gap obtained for each $1 spent in Social Protection and Labor programs.

17
on labor demand.

Table 4: Estimates of the Digital-Technology Adoption Effects on Jobs

|VariableofInterest|||WB|ES||
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
||Coef.|0.341|||0.303|
|Change in Export Status|St.Dev|(0.082)|||(0.066)|
||T-test|[4.167]|||[4.578]|
|ChaneinEmail|Coef.||0.240||0.220|
|g <br>Adti|St.Dev||(0.077)||(0.068)|
|opon|T-test||[3.109]||[3.212]|
|ChaneinWebsite|Coef.||0.227||0.215|
|g <br>Adti|St.Dev||(0.046)||(0.040)|
|opon|T-test||[4.919]||[5.418]|
|ChiM’|Coef.|||-0.539|0.004|
|ange n anagers<br>i|St.Dev|||(0.175)|(0.001)|
|experence|T-test|||[3.074]|[2.981]|
||Coef.|0.083|0.062||0.034|
|Change in TFPR|St.Dev|(0.022)|(0.021)||(0.015)|
||T-test|[3.841]|[2.955]||[2.293]|
|_R_<sup>2</sup>||0.073|0.081|0.053|0.105|
|_N_|||7,9|26||

Note: Table 4 presents the results from estimating equation 4.8 for the pool sample. For each of the estimated specifications, we use changes in estimated TFPR assuming the same corresponding specification as in Table 3. The estimation controls for sector, country, and time fixed effects. The “exporting status” takes value 1 if the firm sells a product in international markets; “email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “website adoption” takes value 1 if the firm has a business website; “managerial experience” takes value 1 if the firm has a manager with years of experience above the country-median. “Employment” measures full-time employees; “Exporting status” takes value 1 if the firm sells a product in international markets; “Email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “Website adoption” takes value 1 if the firm has a business website; “Manager’s experience” takes value 1 if the firm has a manager with years of experience above the country-median.

Table 4 shows that changes in digital-technology adoption, exporting, and accumulation of managerial experience have positive and statistically significant effects on jobs. For our preferred specification, which is the one displayed in Column 4, the largest effect comes from exporting (30 percent, approximately), followed by digitization (21 percent for each variable), and managerial experience (0.1 percent). Interestingly, in all the specifications, the TFPRrelated effect is positive and statistically significant, meaning that TFPR improvements are labor-augmenting. However, this does not necessarily means that the effect is positive for all the sectors, as Table 3 displays pooled regressions, which are a weighted-average of the sector-specific ones. Sector-specific regressions, which are available upon request, show that 27
# **Table 2b. The Effect of Informality on Poverty, controlling for government expenditure/GDP**

|**_Dependent variable: Povert_**|**_y Headc_**<br>|**_ount index_**<br><br>OL|**_, latest y_**<br><br>S|**_ear_**<br>||I|V||
|---|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|Initial Government Expenditure|0.0031|0.0096|0.0114|0.0063|0.0033|0.0157*|0.0224***|0.0123|
|(% of GDP, 1985)|0.51|1.58|1.09|1.01|0.37|1.86|3.44|1.54|
|Schneider Shadow Economy|0.0075*||||0.0240***||||
|(% of GDP)|1.95||||2.97||||
|Heritage Foundation Informal Market||0.2135***||||0.2470***|||
|(ranging 1-5: higher, more informality)||4.41||||3.47|||
|Self Employment|||0.0091||||0.0230***||
|(% of total employment)|||1.51||||3.08||
|Non-contributor to Pension Scheme||||0.0064***||||0.0076***|
|(% of labor force)||||4.95||||3.41|
|Constant|-0.1130|-0.7019***|-0.2911|-0.3624***|-0.7887**|-0.9201***|-0.9325***|-0.5467**|
||0.59|3.33|0.99|2.86|2.45|2.77|3.34|2.56|
|No. of observations|48|48|32|43|40|41|29|37|
|R-squared|0.12|0.33|0.13|0.36|-|-|-|-|
|Hansen J Statistic (P-value)|-|-|-|-|0.25|0.14|0.52|0.61|

Notes:

1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients.

2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively. 3. For IV regressions [5] to [8],

- Endogenous variable: each of four informality measures.

- Instruments: four determinants of informality (Law and order; Business regulatory freedom; Average Years of Secondary Schooling; Sociodemographic factors).

4. See Appendix 1 for definitions and sources of variables.

Source: Authors’ estimation 20
observed data from indicator k. The parameter βk captures the slope of the relationship between true but unobserved governance and the observed data in indicator k. Since unobserved governance has a standard deviation of one, βk is the increase in the observed data associated with a one standard deviation improvement in unobserved true governance. Finally, the standard deviation of the error term, σk captures how informative the observed indicator of governance is about true governance. If σk is large (small), the observed data provides a very imprecise (precise) signal of true governance.

# _The Conditional Distribution of Governance_

The assumptions of the UCM detailed above allow us to summarize our knowledge about the unobserved level of governance in each country by calculating the distribution of unobserved governance _conditional on the observed data for each country_ . Let y j = (y 1j,..., y jK j ) denote the observed data for country j from each of the Kj data sources in which it appears. Since we have assumed that unobserved governance gj and the error terms εjk are jointly normally distributed, this implies that the joint distribution of the observed data, yj, and unobserved governance, gj, is also normal. This in turn means that the distribution of governance _conditional on the observed data for each country_ will also be normal. Our estimate of governance for each country will simply be the mean of this conditional distribution, and the precision of this estimate will be the standard deviation of this conditional distribution.

Applying standard results for the multivariate normal distribution, the conditional distribution of gj given the observed data yj is normal, with the following mean and variance: In particular, the mean of governance conditional on the data available for country j, that we use as our estimate of governance, is just a weighted average of the standardized scores from each data source. The weights applied to each data source are inversely words, data sources that provide a more precise signal of governance receive a greater weight in the conditional mean, and hence in our estimate of governance. The variance of the conditional distribution summarizes how reliable or precise is this estimate of governance. This variance is smaller the larger is the number of data sources available for each country, and the more precise are each of those data sources.

We refer to the expression in Equation (2) as the "governance estimate" for each country, and the square root of the variance in Equation (3) as the "standard error" of the 98
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

**Table 13. Illustrative investment targets for infrastructure in Ghana**

||Economic target|Social target|
|---|---|---|
|ICT|Install fiber optic links to neighboring capitals and submarine cable|Provide universal access to GSM signal and<br>public broadband facilities|
|Irrigation|Develop additional 15,000 hectares of irrigation schemes with high<br>rates of return (>12% IRR)||
|Power|Develop 1,400 MW of new generation capacity|Raise electrification to 76 percent<br>(100 percent urban and 37 percent rural)|
|Transport|Achieve regional connectivity with good quality two-lane paved road<br>Achieve national connectivity with good quality one-lane road|Provide rural road access to 80 percent of the<br>highest-value agricultural land, and urban road<br>access within 500 meters|
|WSS||Maintain current access rates for water supply and<br>achieve Millennium Development Goals for<br>sanitation|

Sources: Mayer and other 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009.

Meeting these illustrative infrastructure targets for Ghana would cost $2.3 billion per year over a decade. Capital expenditure would account for 60 percent of this requirement. Meeting growing demand for power will require an estimated $1.3 billion per year to install almost 1,400 megawatts of new generation capacity and expand electrification. The water and sanitation sector is the area with the highest spending needs: clearing the sector rehabilitation backlog, as well as sustaining the the Millennium Development Goal target for water and achieving the same target for sanitation, will require over $0.4 billion per year, of which capital expenditure accounts for 70 percent. Requirements for transport and ICT, while less than the amounts needed for power and water and sanitation, are also high in absolute terms—approximately $0.25 billion per year for each (table 14).

**Table 14. Indicative infrastructure spending needs in Ghana for 2006 to 2015**

<u>$ million per year</u>

|Sector|Capital expenditure|Operations and<br>maintenance|Total needs|
|---|---|---|---|
|ICT|162|34|196|
|Irrigation|29|30|59|
|Power|631|624|1,255|
|Transport|174|133|307|
|Water supply and sanitation|306|129|435|
|Total|1,302|950|2,252|

Sources: Mayer and others, 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models that are available online at http://www.infrastructureafrica.org/aicd/tools/models.

Ghana’s infrastructure spending needs are especially high relative relative to the size of its economy—as much as 21 percent of the country’s GDP in 2006 (figure 19). Investment alone would absorb approximately 12 percent of GDP, not far from the 15 percent China invested in infrastructure 34
- 10-

Table 4b. New Registration of Motor Vehicles, 1978 to 1987

||Cars|Motor-<br>cycles|Public<br>convey-<br>ance|Goods<br>vehicles|Trailers<br>and<br>cara-<br>vans|Special<br>pur-<br>pose<br>vehicles|Public<br>ser-<br>vice<br>vehicles|Tractors<br>and<br>mecha-<br>nized<br>equipment|<br>Total|
|---|---|---|---|---|---|---|---|---|---|
|1978|8,052|3,011|1,451|3,328|14|783|55|1,024|17,718|
|1979|8,513|3,528|1,154|2,313|5|366|53|563|16,495|
|1980|7,283|3,335|1,166|2,079|1|201|81|422|14,568|
|1981|11,128|1,852|1,764|2,361|69|219|27|827|18,247|
|1982|5,993|1,616|1,046|1,893|3|237|15|645|11,448|
|1983|2,908|1,965|1,952|1,864|4|159|26|386|9,264|
|1984|3,121|2,270|369|1,991|10|83|3|377|8,224|
|1985|5,192|2,155|852|2,734|40|300|7|319|11,599|
|1986|4,199|2,793|4|,270|7|488|30|385|12.172|
|1987|6,414|2,589|4|,875|16|517|25|413|14,i749|
|Total||||||||||
|1978-87|62,803|25,114|37|,462|169|3,353|323|5,361|134,584|
|Total||||||||||
|1980-87|46,238|18,575|29|,216|150|2,204|214|2,774|100,371|

_Source:_ Quarterly Digest of Statistics (1978-85) and Statistical Service Accra (1986-7).

The vehicle fleet is very unevenly distributed throughout Ghana. Over half the vehicles obtain certification in Accra; another 7 percent in Tema, and one sixth in Kumasi. Less than one quarter of all vehicles are scattered over the rest of the country. The uneven geographical distribution of vehicle ownership is accompanied by a skewed distribution of road utilization. The Ghana Highway Authority is in charge of preparing the traffic report. The reliability of their data in the past was questionable, and independent counts on certain sections indicate that the GHA figures are understated by 25-30 percent. Steps have recently been taken to ensure greater scrutiny and reliability.

Traffic on interurban roads is quite sparse. The 1988 traffic counts (Table 5) indicate that only 14 percent of trunk roads carried over 750 vehicles per day, contributing 60 percent of veh-km (the most traveled 2 percent, with average daily traffic (ADT) in excess of 3,000 vehicles, contributed 15 percent of traffic). On the other hand, the two-thirds least traveled roads (with an ADT of less than 300 vehicles) accounted for less than one fifth of traffic. Most of the heavily traveled roads are surfaced, but only half of them are in good condition. There is a positive association between traffic and the paving of the road and its condition (ADT for paved roads is 720 vs. 150 for unpaved ones; the ADT for good and poor roads are 680 and 290, respectively), but a quarter of the traffic uses unpaved and poor roads.
price levels. However, although cattle is a traditional export of northern countries and kola nut is a traditional export of forested Cˆote d’Ivoire, many goods flow one way or another across the border depending on market demand (e.g., cereals such as rice or millet, textiles, spare parts, etc.), so persistent price differentials should be limited (Labaz ́ee 1993). At the national level, World Bank (2012) figures for the year 1993 indicate that the price level of private consumption was lower in Mali by a factor of 0.81; Heston, Summers, and Aten (2012) report a ratio of 0.75. Even if they prevailed at the border, these orders of magnitude would not cancel out the large BDs in real consumption. The devaluation of the CFA franc in 1994 creates an additional difficulty in the comparison because the Malian income survey was implemented in the middle of this year (between March and June), whereas the Ivorian survey was conducted over 1992 and 1993. As already mentioned, monthly prices were used to correct for inflation. The average deflator for cash expenditures in Mali (i.e., the ratio of 1993 prices to current prices) is 0.90, indicating a 10% inflation rate six months after the 50% devaluation. Again, we checked that the Cˆote d’Ivoire advantage was preserved even when we do not deflate the consumption and income variables for Mali.

Furthermore, the BDs for the share of stunted children are again consistent with consumption figures, whereas no difference in national averages is observed. In addition to income, climate and ecology strongly determine height in Africa (Moradi 2012). In West Africa, all anthropometric data confirm that people from the savannah are taller than people from the forest because of the protein intake they obtain from milk and meat (cattle breeding is constrained in the South by the presence of the tsetse fly). In the Malian survey, children from the North were taller than children born close to the Cˆote d’Ivoire border. Thus, when restricting 28
# **Introduction**

It takes 116 days to move an export container from the factory in Bangui (Central African Republic) to the nearest port and fulfill all the customs, administrative, and port requirements to load the cargo onto a ship. It takes 71 days to do so from Ouagadougou (Burkina Faso), 87 days from N’djamena (Chad), 93 from Almaty (Kazakhstan), and 105 from Baghdad. In contrast, it takes only 5 days from Copenhagen, 6 from Berlin, 16 from Port Louis (Mauritius), 20 days from Shanghai, Kuala Lumpur or Santiago de Chile.

We introduce and utilize new data on trade costs from the World Bank’s Doing Business report. The data are collected from 345 freight forwarders, port and customs officials operating in 126 countries. We use data on the average time it takes to get a 20foot container of an identical good from a factory in the largest business city to a ship in the most accessible port.

Our goal is to estimate the effect of time costs on trade volumes. A standard tool is the gravity equation.<sup>1</sup> There are, however, two concerns with the use of a gravity equation that we address. The main concern is that the volume of trade may directly affect trade facilitation. The marginal value of investment in trade facilitation is higher when the trade volume is large since cost savings are passed on to a larger quantity of goods. In addition, many time-saving techniques, such as computerized container scanning, are only available in high-volume ports. Thus, while more efficient trade facilitation stimulates trade, trade also generates improved trade facilitation.<sup>2</sup>

> 1 Wilson et. al. (2004) and Nordas and Piermartini (2004) use a standard gravity equation to estimate how trade facilitation costs and infrastructure affect trade flows using a gravity model.  They use data on port efficiency from the Global Competitiveness Report.  These data are based on perception surveys and may not be comparable across countries.

> 2 In a related paper, Hummels and Skiba (2004) provide evidence that trade volumes affect the timing of adopting containerized shipping and reduce shipping costs.

2
**Figure 22: Timor-Leste: Training/Insitution Valued by Employers of Formal Enterprises While Hiring**

<!-- Start of picture text --> 6 1.9 4<br>Short Course (technical/vocational) 3 8 .0 6<br>6 5 .9 9<br>SLTA<br>3 4 .0 1<br>7 3 .2 8<br>Technical/Vocational Training Cent ers 2 6 .7 2<br>7 3 .2 8<br>STM/ High School 2 6 .7 2<br>8 3 . 2<br>Indonesian Univ/Polyt echnic 16 .8<br>9 0 .2 8<br>Timorese Univ/Polyt echnic 9 .7 2<br>0 10 20 30 40 50 60 70 80 90 100<br>P e r c e nt<br>Ye s No<br><!-- End of picture text --> Source: UES, 2004 66. (d) <u>Barriers to the Development of a Skilled Workforce: The survey contained a</u> question on factors that constrain enterprises in maintaining a fully proficient work force. Supply side factors dominate among the constraints to developing a trained workforce for formal enterprises (Table 18). The constraints seem to be most daunting for manual, service and technical workers. Lack of funding is a common theme for each category of workers. However, in the case of manual and service workers, and less so for technical/professional and administrative workers, there also appears to a serious dearth of suitable courses and training organizations. Employers also think that in comparison with managerial and administrative workers, manual and service workers come into the firm with low qualifications. On the other hand, time constraints do not appear to be significant, indicating that if funding and appropriate training were available, workers would have the time to undertake training.

**Table 18: Factors** **<u>constraining development of proficient workers - formal enterprises</u>**

||Manual<br>workers|Service<br>workers|Admin<br>workers|Tech/ profess<br>workers|Managerial<br>workers|
|---|---|---|---|---|---|
|Lack of funding|25.9|24.3|10.9|15.8|35.8|
|Lack ofqualified workers|25.1|22.5|10.1|12.4|6.9|
|Lack of suitable courses relevant<br>to upgrade the staff|24.9|21.7|13.8|10.7|10.9|
|Lack of appropriate training<br>organizations|16.4|19.2|10.1|9.5|8.9|
|Lack of knowledge of training<br>providers|11.5|11.5|6.9|8.5|7.7|
|Lack of time for training|13.4|11.5|6.7|7.5|14.8|

Source: UES, 2004 30
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