rafmacalaba/datause-encoder-data huggingface dataset:
_Table 12: Estimates for ‘not high-income, low-growth’ regions that declined around the Great Recession._

|Dependent Variable||∆GD|Ppcrt||
|---|---|---|---|---|
|Decline in Average YearlyGDP Per Capita|No||Yes||
|Regions|All|All|Not High-Income|and Low-Growth|
||(1)|(2)|(3)|(4)|
|log(GDPpcrt−1)|-0.219****|-0.305****|-0.253****|-0.254****|
||(0.007)|(0.016)|(0.021)|(0.021)|
|Population Changert|||||
|< 0|Ref.|Ref.|Ref.|Ref.|
|≥0|0.002|0.018****|0.012***|0.012***|
||(0.001)|(0.004)|(0.005)|(0.005)|
|log(Employment Densityrt−1)|<br>0.040****|<br>0.035*|<br>0.066***|<br>0.068***|
||(0.010)|(0.018)|(0.025)|(0.025)|
|log(Share GVA Agriculturert−1)|-0.105*|-0.036|-0.052|-0.060|
||(0.059)|(0.150)|(0.166)|(0.165)|
|log(Share GVA Industryrt−1)|0.018****|0.048***|0.033*|0.034*|
||(0.005)|(0.016)|(0.018)|(0.018)|
|log(Share GVA Constructionrt−1)|<br>0.014****|<br>0.057****|<br>0.047****|<br>0.046****|
||(0.003)|(0.007)|(0.007)|(0.007)|
|log(Share GVA Market Servicesrt−1)|0.028****|0.100**|0.060|0.060|
||(0.008)|(0.041)|(0.051)|(0.051)|
|log(Share GVA NonMarket Servicesrt−1)|<br>-0.015**|<br>0.011|<br>-0.026|<br>-0.023|
||(0.007)|(0.025)|(0.035)|(0.035)|
|log(Patentsrt−1)|0.001*|0.004*|0.005**|0.005**|
||(0.001)|(0.002)|(0.002)|(0.002)|
|log(Inward FDIrt−1)|0.004***<br>|0.001<br>|-0.009<br>|...|
||(0.001)|(0.003)|(0.006)||
|Max Inward FDIrt−1|||||
|None|...|...|...|Ref.|
|Headquarter|...|...|...|0.014<br>|
|||||(0.009)|
|Innovation|...|...|...|<br>-0.010|
|||||(0.017)|
|Production|...|...|...|-0.001|
|||||(0.005)|
|Logistics, Distribution, Transportation|...|...|...|-0.006|
|||||(0.010)|
|Marketing and Sales|...|...|...|0.002|
|||||(0.005)|
|Region FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|No. Observations|14,854|3,640|1,428|1,428|
|No. Regions|1,061|260|102|102|
|R<sup>2</sup>|0.36|0.56|0.45|0.45|
|Adjusted R<sup>2</sup>|0.31|0.52|0.39|0.39|
|<br>Model F Statistics[p-value]<br>|<br>193.73[0.000]<br>|<br>147.55[0.000]<br>|<br>46.47[0.000]<br>|<br>39.44[0.000]<br>|
|Notes: *G< 0.1; **G< 0.05; ***G< 0.01;<br>Declining regions are those with an average ye<br>than) greater than the corresponding value de<br>those with an average yearly GDP per capita o<br>growth regions are those recording a long-run<br>|****G< 0.001.<br>arly GDP per capit<br>fined over the sub<br>ver the period 200<br>growth rate of GD<br>|Robust standard e<br>a over the pre-cris<br>sequent period 20<br>3-2017 lower than<br>P per capita betwe<br>|rrors are reported in<br>is period 2003-2007<br>08-2017. Not high-i<br>the 90% of the sam<br>en the years 2003 an<br>|parentheses. (Not)<br>(equal to or lower<br>ncome regions are<br>ple average. Low-<br>d 2017 lower than<br>|
|the 50% of the sample average. The variables<br>variables are defined in logarithm terms, excep<br>|capturing patents<br>t for the dummy v<br>|and inward FDI ar<br>ariable capturing<br>|e defined per 100,0<br>population change b<br>|00 inhabitants. All<br>etween timesNand<br>|
|N−1, and the categorical variable included in<br>FDI that interested a region, for which the cate|specification (4) th<br>gory used as refere|at captures the m<br>nce is no inward F|ost relevant business<br>DI received.|activity of inward|

28
25 magnitude of the trade damage from a violation of commitments under a trade agreement. Models of the global market for particular farm products also often adopt such assumptions, for want of reliable or agreed econometric estimates of those elasticities for each country. Moreover, these indexes have the advantage over formal supply/demand models in that they can be expressed in time series form and thereby reveal trends and fluctuations over long periods, rather than just providing a snapshot at a point in time which is typical of comparative static commodity models. Once reliable price elasticity estimates do become available, the theory in the first section of the article can be used to provide more accurate estimates of the TRI and WRI.

# **References**

Anderson, J.E. and G. Bannister. 1992. “The Trade Restrictiveness Index: An Application to Mexican Agriculture.” Policy Research Working Paper 874, World Bank, Washington DC, March.

Anderson, J.E. and J.P. Neary. 2005. _Measuring the Restrictiveness of International Trade Policy_ . Cambridge: MIT Press.

Anderson, K. and Associates. 2009. _Distortions to Agricultural Incentives: A Global Perspective, 1955 to 2007_ . London: Palgrave Macmillan and Washington DC: World Bank (forthcoming).
Third, reductions in tariffs by partner countries<sup>will have important market access effects</sup> for India. Further reductions, particularly in tariffs<sup>on textiles and clothing, are likely to</sup> improve India's terms-of-trade and yield sizable<sup>welfare benefits.9</sup> One particularly important aspect of these market access<sup>effects, and perhaps the most</sup> compelling reason for seeking cuts in tariffs in<sup>the textiles, clothing, and leather sectors,</sup> relates to _trade diversion._ In the current environment,<sup>such reductions are necessary to</sup> arrest the trade diversion that MFN traders such<sup>as India have suffered as a result of</sup> regional agreements. We present one example - in<sup>relation to NAFTA -which brings</sup> home starkly this impact. It is likely that there are similar<sup>effects in the EU market</sup> stemming from preferential arrangements between the<sup>EU on the one hand and Eastern</sup> European and Mediterranean countries on the other.

**Chart 3: Exports of India and Mexico to**<sup>**North American Market, 1990-1998**</sup>

<!-- Start of picture text --> 9 See Hertel and Martin (1999).<br>Clothing Exports to US and Canada<br>8 ~~~~~~~~~114<br>7 NAFTA 6<br>2<br>1 __2<br>0 Manuliacturina ~ toU8n ~ aaa[ ~ nci<br>1990 1991 1992 1999 1994 1995 1996 1997 1998901<br><!-- End of picture text --> <!-- Start of picture text --> Clothing Exports to US and Canada<br>c~~~~~~~~~~~~~~~6ld 12<br>1 4<br>4O<br>TU Sxol ag xol oU n afd<br>9219 99 M 19 9719<br><!-- End of picture text --> <!-- Start of picture text --> NWnufactiuring Exports to Us and Canada<br>Soue LN COMTRA<br>.0<br>60<br>a2 30<br>20-2<br>10<br>1990 1991l 1992 1993 1994 1995 1998 1997 1998<br><!-- End of picture text --> <!-- Start of picture text --> MiuamctA.ng Exports toUS and Cargda<br>6<br>9 6<br>2~~~~~~~~~<br>6<br>I<br>1990 1991 1992 1999 1994 1995 1996 1997 199<br><!-- End of picture text --> _Soume:_ **UN CAOMvTRADE Databas**
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|---|---|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Renewable Energyin the Rural Market Project|Lucia Spinelli|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Water Sector Reform Project|Manuel G. Marino|
|Financial Sector Adjustment Loan Project|Stefan Alber-Glanstaetten|
|Financial Sector Adjustment Loan Project|Stefan Alber-Glanstaetten|
|Financial Sector Adjustment Loan Project|Stefan Alber-Glanstaetten|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
|Pollution Management Project|Horacio Cristian Terraza|
the poorest education outcomes.<sup>6</sup> The selection of districts for the program was based on the literacy rate (among those age 10 and above), as measured by the 1998 Pakistan Population and Housing Census. Of the 34 districts that Punjab had at the time, the 15 with literacy rates below 40 percent were selected for the program (table 2). Over time, as the number of districts in Punjab increased from 34 to 36,<sup>7</sup> the number of program districts increased to 16, and the number of nonprogram districts to 20.

Districts selected for the program had poor school participation outcomes (see table 1). Measured in 2003, preprogram school participation rates for children of primary and secondary school ages were lower in program districts than in nonprogram districts, for both boys and girls. Moreover, the gender difference in preprogram school participation rates was greater in program than in nonprogram districts, particularly among children of secondary school age. In program districts, 59 percent of boys of secondary school age were enrolled in school, compared with 41 percent of girls—a difference of 18 percentage points. In nonprogram districts, the corresponding gender difference was only 4 percentage points. By 2014, near the end of our observation period, school participation rates had increased for both boys and girls and in both program and nonprogram districts, but large gender differences in school participation rates remained for children of secondary school age in program districts (figure 3).

When initiated in 2004, the program covered girls enrolled in government schools in grades 6–8. In 2005, it was expanded to also cover girls enrolled in grades 9–10. Under the program, households were offered a cash benefit of 200 Pakistan rupees (PRs) a month ($3.40) for each girl enrolled in grades 6–10 in a government school, conditional on an

> 6 The first distribution of cash benefits was initiated in January 2004, based on enrollment and attendance information for the October–December 2003 quarter.

> 7 Nankana Sahib was formed from the division of Sheikhupura in 2005, and Chiniot from the division of Jhang in 2009.

10
answering those questions. Nevertheless, it is worth pointing out that when it comes to rigorous evaluation of private sector policies in Africa, there has not been much in the way of either experimental or structural work – so there is plenty of scope for both types going forward – and hopefully this paper will help researchers considering either approach.

I begin section 2 by establishing a number of facts about the private sector and the firms participating in private sector programs in different African countries. Most importantly, the number of medium and large firms is relatively small in most countries, as are the number of firms participating in any given firm-level intervention. Moreover, these firms are quite heterogeneous in terms of employment size, industry, and sales levels. Section 3 examines the implications of these facts for the power of experiments to detect impacts of firm policies, and for the ability of structural models to credibly estimate firm behaviors. The number of firms and heterogeneity of these firms poses challenges for both approaches. In section 4, I then discuss a way forward, which involves focusing attention on a smaller, more homogeneous sample of firms and collecting a lot more data on these firms. Section 5 then concludes.

# **2. Background on Small, Medium and Large Enterprises in Africa**

Before discussing which methods of evaluation and estimation are likely to be most appropriate for assessing policies directed at firms in Africa, it is useful to establish some facts about the likely populations of interest for such studies.

# **2.1 Fact 1: The number of SMEs and large firms in many African countries is relatively small.**

I begin by first asking how many firms there are of different sizes, as collected through industrial censuses in selected countries. These censuses differ somewhat in their coverage: some only cover manufacturing and some exhibit an incomplete coverage of home-based enterprises. Nevertheless, they give a sense of the scale of small, medium, and large scale enterprise activity.

Table 1 shows that there are many firms with fewer than 10 workers in most African countries, and there are more of such firms in wholesale and retail trade than in manufacturing. Within the category of firms with fewer than 10 workers, the vast majority have at most one or two workers. For example, in Mauritius, out of the 91,980 units with 9 or fewer workers, 70.6 3
Table 1. Minutes spent in the last 24 hours (main activity), among those reporting nonzero time in each category

||A<br>Wo|<br>mong those reporti<br>men|<br>ng nonzero tim<br>Me|<br>e:<br>n|
|---|---|---|---|---|
||Obs.|Minutes|Obs.|Minutes|
|Activity categories:<br>|||||
|(1) Unpaid work|||||
|(1a) Domestic|||||
|Buy food or other items<br>|420|41.1<br>|60|42.2<br>|
|Cook, prepare food/drink|1492|86.0***|189|61.9***|
|Clean the house, wash/iron|1242|66.0***|422|50.0***|
|Household maintenance, improvements|32|113.6|37|189.7|
|Paying bills, planning household finances|22|58.5|21|58.3|
|(1b) Water and fuel collection|||||
|Collecting firewood|284|57.3**|152|72.6**|
|Water collection|39|47.2|41|42.7|
|(1c) Childcare (for children <17 years)|714|218.2***|283|121.0***|
|(1d) Elderly care|59|110.6|19|69.4|
|(2) Off-farm and/or agricultural work<sup>(2)</sup>|||||
|(2a) Wage|432|473.4|605|476.6|
|(2b) Non-farm enterprise (NFE)<br>|427|426.9|361|410.6|
|(2c) Agriculture|||||
|Farming|410|236.0***|477|268.9***|
|Livestock|467|75.9***|448|102.5***|
|Fishing|46|150.6|147|215.3|
|Hunting/gathering food<br>|6|109.9|8|111.3|
|(2d) Making goods (furniture, pottery, baskets, clothing)|38|86.1|5|118.6|
|(2e) Work for other households for free: exchange labor|33|169.0|33|183.3|
|(3) Leisure|||||
|Personal care|1822|47.7**|1539|49.5**|
|Watching TV/listening to radio/reading|980|139.1*|871|147.0*|
|Exercising|105|54.8|168|60.7|
|Social or religious activities, hobbies|419|160.1|373|165.4|
|(4) Traveling and commuting|1117|73.1***|1104|86.4***|
|(5) School and homework|220|82.0|66|114.9|
|(6) Sleeping/resting|1976|658.3|1647|657.9|
|(7) Eating/drinking|1960|75.9|1637|76.3|
|(8)Other<br>Notes:|197|196.2|150|205.7|

(1) Sample includes men and women self-reporting in the LSMS+ time diary module, aged 18 and older. All estimates weighted by household sampling weight.

(2) Significant differences in minutes spent across men and women (separately for full sample and among t>0) are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 9
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Annual funding gap

South Sudan’s annual infrastructure funding gap amounts to $879 million per year, or roughly 15 percent of GDP (table 22). The largest funding gap—equivalent to 40 percent of South Sudan’s needs—is attributable to the water and sanitation sector. Transport comes a close second, with its needs representing 35 percent of the total. Power comes a distant, yet very significant, third.

The estimation of the funding gap assumes two key issues. First, existing spending is allocated to areas where spending needs have been already identified. From this perspective, some resources could hypothetically be reallocated from sector A to sector B to increase the economic returns to that spending. But there is not much potential in South Sudan for reallocation. Second, funding gap estimations assume the country can eliminate the existing inefficiencies in spending overnight. From this perspective, the funding gap estimate is a lower bound. Yet, funding gaps look implausibly large when measured relative to the existing annual spending flows.

To cover the annual infrastructure funding gaps, the country would essentially need to more than double its current level of infrastructure spending from 7.5 to 14.6 percent of GDP.

**Table 22. Funding gaps by sector**

|$ millions per year|ICT|Irrigation|Power|Transport|WSS|Total|Total as<br>share of<br>GDP|
|---|---|---|---|---|---|---|---|
|Spending needs|(99)|—|(209)|(711)|(348)|(1,367)|(22.7)|
|Existing spending|50|—|31|366|4|452|7.5|
|Efficiency gains|0|—|1|35|0|36|0.6|
|Funding gap|(49)|—|(177)|(310)|(343)|(879)|(14.6)|

_Source_ : Derived from Foster and Briceño-Garmendia (2009).

_Note:_ Estimates use the base scenario of spending needs. Potential overspending across sectors 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; GDP = gross domestic product. —.= Not available

# What else can be done?

South Sudan’s funding gap is very large relative to the size of its economy, and enormous with respect to its current level of spending. Nevertheless, there are a number of options for making it more manageable, including raising additional finance, taking additional cost-cutting measures, and lengthening the period of time allotted for meeting the illustrative infrastructure targets. In any case, given the magnitude of the gap, difficult decisions will have to be taken regarding the prioritization of the different investments.

There are, however, realistic prospects for increasing the flow of resources to infrastructure. Although not all components of the required infrastructure platform are suitable for private finance (particularly roads, water, and sanitation), other components may be (for example, ICT, power generation, and ports). Challenges for attracting private investors are conspicuous particularly due to the country risks linked to the instability of the country.

44
# **Table 4 Productivity Growth in the United States, by Sector**

<u>(annual percentage change)</u>

|_Sector_|_1987–9_<br>_Total factor_<br>_productivity_|_5_<br>_Labor _|_1995–2_<br>_Total factor_<br>_productivity_|_001_<br>_Labor _|
|---|---|---|---|---|
|Private nonfarm business|0.6|1.0|1.4|2.5|
|Goods|1.2|1.8|1.3|2.3|
|Services|0.3|0.7|<br>1.5|2.6|

_Source_ : Triplett and Bosworth 2004.

It is perhaps no surprise that we underestimated the game-changing and revolutionary nature of ICT, which is a very good example of what has come to be characterized as general purpose technology (GPT), like electricity. Of course, overestimation of the significance of ICT led to unrealistic expectations—the euphoria about the New Economy—and the technology bubble, which burst in 2001. Nevertheless, it is likely that the new information technology has not peaked, that there remains considerable scope for harnessing it for productivity growth and the empowerment of people worldwide.

The significant role of technological progress can be illustrated using the classic formulation of Solow (1957), which augments the standard Cobb-Douglas production function (constant returns to scale) with a parameter for technical change: where Yt is output of a country in year t, Kt is the capital stock in year t, Lt is labor force in year t, ∝ is the parameter representing returns to capital as a factor of production, and τ is a parameter representing technical change.

The equation can be reexpressed in per labor terms ( _y_ = _Y_ / _L_ ) to bring out the role of capital deepening and productivity: 30
**Technology sophistication measure (Technology readiness)** The pre-pandemic technology index used in this paper reflects the sophistication of the most widely used technology in a business function, as described in Figure A1. Following ( _17_ ), based on the experts’ assessment, we order the technologies in each function _f_ according to their sophistication, and assign each a rank _rf ∈_ 1 _,_ 2 _, ..., Rf_ , from least to most advanced. Because several technologies may have the same sophistication, the highest rank in a function _Rf_ may be smaller than the number of possible technologies _Nf_ . We define the relative rank of a technology as _r_ ˆ _f_ = _Rrff−−_ 11<sup>.Notethat</sup><sup>_r_ˆ</sup><sup>_f∈_[0</sup><sup>_,_1].Thetech-</sup> nology sophistication of business function _f_ in firm _j_ is a monotonic increasing function of the relative rank of _the most widely used technology_ of firm _j_ in function _f_ ( _r_ ˆ _f,j_ ). For example, our baseline sophistication measure is

The technology sophistication measure is scaled between 1 and 5. For example, if the firm is mostly using a frontier technology (e.g., ERP for business administration), it takes the value of 5, while if it is mostly using the most rudimentary technology (e.g., handwritten processes for business administration), it takes the value of 1. The information of the average index and its distribution across quintiles, combined with the standard deviation, provided in Table 1 may provide a good intuition of the specific technologies used by those firms. For example, the average of 1.1 for firms in Q1 with a standard deviation of 0.02 suggests that these firms are mostly relying on manual methods to perform GBFs with little variation among them. ( _17_ ) provides several robustness checks to show the measure is robust to alternative plausible cardinalizations of the ordinal rankings.

# **A2. The FAT and COV-BPS questionnaires**

The management questions and the questions on skill of the top manager in the FAT are:

- Does this establishment provide formal incentives for workers in the form of money, gift, or recognition to suggest and/or use better ways to produce goods or provide services? Yes/No.

24