rafmacalaba/datause-encoder-data huggingface dataset:
**For analytical purposes, services can be categorized into six production process stages.** Figure 3.5 provides a breakdown of the manufacturing production stages and the services associated with each stage (based on Low and Pasadilla, 2015).<sup>12</sup> The production stages and examples of services are: (1) establishment (including construction services and real estate); (2) pre‐manufacturing (R&D and special design services); (3) manufacturing (testing and engineering services); (4) post‐manufacturing (distribution and transport); (5) post‐sales (repair and maintenance services); and (6) back‐office (other business services such as accounting, insurance, legal and management services).

**Figure 3.5: Production stages of the value chain where services are added**

||License;<br>Construction;<br>Real estate|R&D; Special<br>design services;<br>tech services|Engineering services;<br>cleaning; technical<br>testing; waste & envir<br>services|Transport;<br>distribution; whole<br>sale, marketing;<br>installation|Repair;<br>maintenance;<br>finance and<br>renting|Accounting;<br>insurance;<br>finance; legal;<br>management|
|---|---|---|---|---|---|---|
|Establis|hment|Pre‐manufacturing|Manufacturing|Post‐manufacturing|Post‐sales|Back‐office|

Source: author’s elaboration based on high‐level categorization from Low and Pasadilla (2015). Note: for a full list of detailed services sector by NACE 3‐digit for production stages, see Table A2 in Annex 1.

**Most service activities within Turkish manufacturing firms are in the post‐manufacturing stage.** Turkish services affiliate activity can be analyzed in terms of the number of affiliates as well as the revenue associated with each affiliate (Figure 3.6). (The Turkish firm‐level data distribute the revenue of the firm to the affiliates based on their employment share inside the firm. In other words, the revenue share is equivalent to the employment share of the services affiliate within the entire firm.) The left‐hand panel shows the breakdown based on the number of affiliates and the right‐hand panel gives the breakdown based on the employee‐weighted share of revenue that these services affiliates generate. Both breakdowns show that post‐manufacturing services are the largest type of services produced by Turkish manufacturing firms. This category includes transportation and distribution services. Back‐office services and establishment services are the next largest categories.

**Figure 3.6: Share of number of affiliates and revenue of manufacturing firms (2014)**

<!-- Start of picture text --> No. affiliates Share revenue<br>6.2% 13.7% 2.3%<br>17.7%<br>8.5%<br>AgricultureEstablishment 0.2%1.0% Other<br>4.5% Goods Establishment<br>Pre manufacture<br>1.6%<br>Other Manufacturing 34.7% Pre manufacture2.1%<br>Back office Manufacturing<br>13.4% Back office<br>Post sales<br>Post manufacture Post manufacture<br>Post sales<br>4.4%<br>48.1% 33.2%<br>8.3%<br>Source: EIS data from Turkey; author’s calculations.<br><!-- End of picture text --> 12 A full categorization of all services into production stages by their 3‐digit NACE code is presented in Table A2 in Annex 1.

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Figure 3 <!-- Start of picture text --> CHAGEeIt GDP AD INORT GROWTH FOR TRADE AL COUNTRIES<br>30 Turke * Ghana<br>20 Tunisia * 4Chil*<br>* .8raziL<br>1 CoLombia<br>0Mexico<br>Panama, SenegaL<br>-0 # *B48urundi<br>Cote d Ivoire.<br>-20<br>-30 CKenyal * =<br>-30 -20 -10 0 to 20 30<br><!-- End of picture text --> aGDP = Percentage point improvement in the GDP growth rate 3-years after the first trade _ Loan compared to 3-years before. A1 1MP = Percentage point improvement in the import growth rate 3-years after the first trade Loan compared to 3-years before.

Source: World Bank data.

In the first half of the 1980s, import compression was the dominant force behind the negative resource balance-GDP relationship. Figure 3 shows the strong positive link between import growth and GDP growth. Imports can affect GDP growth in at least two ways. One is through the effect of imports on the domestic production of competing goods. Increased competition from imports hurts inefficient production and leads over time to a more efficient structure of domestic production.
Let there be _N_ + 1 countries in the model, indexed by _i_ = 0 _,_ 1 _,_ 2 _, . . . ..N_ , where country 0 is considered the reference country. Each country _i_ then follows the _V ARX_ ( _p, q_ ) model: for _t_ = 1 _,_ 2 _, ..., T_ . Here, _ki ×_ 1 matrix _yi,t_ represents the endogenous domestic variables and _ki_<sup>_∗×_1matrix</sup><sup>_y_</sup> _i,t_<sup>_∗_representsthecorresponding(weakly)exogenousforeignvariables.</sup><sup>_k_</sup> and _k_<sup>_∗_</sup> are the numbers of domestic and foreign variables respectively, _ai,_ 0 is a _ki_<sup>_∗×_1vector</sup> of fixed intercepts and _ai,_ 1 is a _ki_<sup>_∗×_1vectorofcoefficientsonthedeterministictimetrends.</sup> _p_ and _q_ are the lag lengths of the domestic and foreign variables respectively. They are selected according to the Schwartz Baysian (SB) criterion. Finally, _ui,t∼_ iid (0,<sup>�</sup> _ui_ ).

Foreign variables are calculated as the weighted average of the rest of the countries’ values of that variable. More specifically, where _wi,j,t_ is a weighting factor that captures the importance of country _j_ for country _i_ , _N_ with � _wi,j,t_ = 1 and _wi,i,t_ = 0. Most of the GVAR literature use fixed trade weights based _j_ =0 on bilateral trade volumes. However, these may be subject to temporal changes, particularly for the time period considered in this paper as it contains the periods of financial crisis of 2007-08 and the Asian financial crisis of 1997. As a result, using a fixed weight might mislead the results. In order to take account of these changes during the sample period, time-varying weights are used to construct the foreign variables in the country-specific models. These are constructed as three-year moving averages to smooth out short-run business-cycle effects in the bilateral trade More compactly, setting _pi_ = _max_ ( _p, q_ ), equation 1 can be written as: 5
Duflo, E. and R. Pande. (2007). “Dams”. _Quarterly Journal of Economics_ , 122 (2): 601–646.

- Duranton, G. and M. Turner. (2011). “The Fundamental Law of Road Congestion: Evidence from US Cities”. _American Economic Review_ , 101(6): 2616-2652.

- Duranton, G. and M. Turner. (2012). “Urban Growth and Transportation”. _Review of Economic Studies_ , forthcoming.

- Fernald, J.G. (1998). “Roads to Prosperity? Assessing the Link between Public Capital and Productivity”. _The American Economic Review_ , 89(3): 619–638.

- Fernandes, A. and A. Pakes. (2008). “Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data”. NBER Working Paper No. 14178.

- Fernandes, A. and G. Sharma. (2012). "Together We Stand? Agglomeration in Indian Manufacturing”. World Bank Policy Research Working Paper Series 6062.

- Ghani, E., A. Goswami, and W. Kerr. (2012a). “Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing”. NBER Working Paper No. 18524.

- Ghani, E., A. Goswami, and W. Kerr. (2012b). “Is India’s Manufacturing Sector Moving Away from Cities?”. NBER Working Paper No. 17992.

- Ghani, E., W. Kerr, and Stephen O’Connell. (2013). “The Exceptional Persistence of India’s Unorganized Sector”. World Bank Working Paper No. 6454.

- Gupta, P., R. Hasan, and U. Kumar. (2008). “What Constrains Indian Manufacturing?”. ICRIER Working Paper No. 211.

- Gupta, P. and U. Kumar. (2010). “Performance of Indian Manufacturing in the Post Reform Period”. Working Paper.

-

- Holl, A. and E. Viladecans Marsal. (2011). “Infrastructure and Cities: The Impact of New Highways on Urban Growth”. Universitat de Barcelona‐Institut d’Economia de Barcelona Working Paper.

- Hsieh, C. and P. Klenow. (2009). “Misallocation and Manufacturing TFP in China and India”. _The Quarterly Journal of Economics_ , 124(4): 1403-1448.

-

- Hsu, W. T. and H. Zhang. (2011). “The Fundamental Law of Highway Congestion: Evidence from Japanese Expressways”.  Chinese University of Hong Kong Working Paper.

- Kochhar, K., U. Kumar, R. Rajan, A. Subramanian and I. Tokatlidis. (2006). “India’s Pattern of Development: What Happened, What Follows?”. IMF Working Paper No. 06/22.

- Lahr, M., R. Duran and A. Varughese. (2005). “Estimating the Impact of Highways on Average Travel Velocities and Market Size”. Unpublished mimeo.

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Gennaioli, Nicola, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer.

2011. “Human Capital and Regional Development.” NBER Working Paper No. 17158.

Grogger, Jeffrey, and Gordon H. Hanson. 2011. “Income maximization and the selection and sorting of international migrants.” _Journal of Development Economics_ 95: 42–57.

Grossmann, Volker, and David Stadelmann. 2008. “International mobility of the highly skilled, endogenous R&D, and public infrastructure investment.” IZA Discussion Paper No. 3366.

Grossmann, Volker, and David Stadelmann. 2011. Does international mobility of highskilled workers aggravate between-country inequality? _Journal of Development Economics_ 95: 88–94.

Hanson, Gordon H., Kenneth Scheve, and Matthew J. Slaughter. 2009. “Individual Preferences over High-Skilled Immigration in the United States.” In _Skilled Immigration Today: Problems, Prospects, and Policies_ , eds. Jagdish Bhagwati and Gordon Hanson, 207–46. Oxford University Press.

Hunt, Jennifer, and Marjolaine Gauthier-Loiselle. 2011. “How Much Does Immigration Boost Innovation?” _American Economic Journal: Macroeconomics_ 2:31–56.

Iranzo, Susana, and Giovanni Peri (2009). Schooling Externalities, Technology, and Productivity: Theory and Evidence from U.S. States, Review of Economics and Statistics 91, 420–31.

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## _Source:_ AWP 2011.

# **What are the main drivers of project selection?**

Several mechanisms are intended to ensure project selection with roads being built according to developmental priorities: the usual planning tool in the sector (HDM-4<sup>10</sup> ), which identifies the priority economic investments to finance based on road deterioration and traffic, in addition the RoadSIP II Strategy<sup>11</sup> and the Sixth National Development Plan which identify the priority projects.

The major criteria for the selection of road projects tend to be poverty reduction and support to economic growth. In assessing the 2008 – 2011 contract data, the authors sought to determine any correlations between road allocation and a number of variables including population, political support and poverty in order to identify the most plausible driver of project selection in Zambia.

Population, a proxy for agglomeration and economic growth, is negatively correlated with road allocation (see Graph 5). Over the period (2008 to 2011), the most populous province, Copperbelt, received among the lowest allocation. By contrast, Western province, which has almost half the population size of Copperbelt, has received more than 18 times the road allocation. Less populated provinces seem more likely to benefit from larger road projects in Zambia.

**Graph 5: Correlation between road allocation and population**

<!-- Start of picture text --> Western<br>Northern<br>Eastern<br>North Western Southern<br>Lusaka<br>Luapula Central<br>Copperbelt<br>6 8 10 12 14<br>population<br>roads Fitted values<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Notes:_ Y axis is the province road commitments over total road commitments (measured in percentage). X is the province population share over total Zambian population (measured in percentage).

10 Highway Development Model.

11 RoadSIP I was succeeded by RoadSIP II, covering the period 2004 to 2013. The Government issued a revised Road Sector Policy in 2003 to guide expenditure. Road Fund resources were to be allocated in the following order of priority: 1. routine and periodic maintenance of all core roads classified as being in good and fair condition; 2. counterpart funding of donor-funded and rehabilitation programmes; and 3. administrative costs for RTSA, NRFA and RDA (Republic of Zambia 2003:10) _._ 12
Weighted revenues reported in SMS surveys for women in the control group show no significant differences in any bi-weekly period. Had the treatment caused negative effects for women in the control group, we would expect to see persistent declines in revenues for women in the control group over time as revenues grow for treated women. The high-frequency SMS data provide reassurance that the treatment did not lead to substantial general equilibrium effects that negatively impacted women in the control group.

The final channel through which negative spillovers may have occurred is on the extensive margin: women in the control group who did not have a business at baseline may have delayed their entry into entrepreneurship because they knew that they could participate in SBS at the end of the study. Although we cannot fully rule out this channel for negative spillovers, it is worth noting that a third of women in the control group who did not have a business at baseline report having a business at midline, and half open a business by endline.

# **6 Discussion and Conclusion**

Our study shows that skills are a relevant barrier to economic inclusion among a self-selected sample of rural women. A program that teaches entrepreneurship and business skills is effective at increasing the number of women entrepreneurs, increasing the number of businesses that each woman runs, and improving the profitability and resilience of their businesses relative to the control group. Despite positive impacts on a range of entrepreneurship outcomes, we find no evidence that the program reduces poverty over the period of our study.

Our results show that skills-based programs for women can be successful in increasing women’s incomes in rural areas and helping women cope with negative shocks, two core objectives of rural development efforts. We find that treated women’s revenues remain stable throughout the first COVID-19 lockdown in Uganda, while revenues among control women drop significantly. Although our results suggest that entrepreneurship can significantly improve the resilience of low-income women in rural areas, it is worth noting that there are limitations to the external validity of our results. The COVID-19 lockdown affected the entire country and severely restricted the movement of goods and people, but it did not directly affect agricultural productivity. Events like droughts that reduce agricultural productivity may be more difficult for rural entrepreneurs to navigate because they can lead to broad-based reductions in local demand. Starting a different business or shifting the focus of a business may be less effective at coping with reductions in local demand.

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# **Appendix 4 – Descriptive statistics and regressions table for section 5 on Exposure**

_Table 17: Flood exposure by (1) expenditure, (2) expenditure and elevation._

||(1)|(2)|
|---|---|---|
|Expenditure|-5.19e-06|1.03e-05|
||(1.91e-05)|(1.94e-05)|
|High elevation (b|ase)|ref.|
|Low elevation||1.072***<br>(0.363)|
|Constant|-0.191|-0.976***|
||(0.220)|(0.293)|
|Observations|1,008|1,008|
|Prob > F|0.788|0.0459|

Table 18: Descriptive statistics for households affected or non-affected by the floods, and significant differences.

||Unaffected|Affected|Difference||
|---|---|---|---|---|
|Real per capita annual expenditure|6,175|6,061|-114.7||
|Age|46.1|45.0|-1.1||
|Household size|3.2|3.7|0.5|***|
|Male-headed household|56.2%|54.4%|-1.8%||
|% of households in low elevation areas|52.0%|75.7%|23.7%|***|
|Tenure situation|||||
|Owner|11.0%|21.3%|10.3%|***|
|Own dwelling but not land|3.9%|5.3%|1.4%||
|Rent from private person|45.9%|33.3%|-12.6%|***|
|Rent from relative|2.4%|5.0%|2.7%|*|
|Rent free from relative|31.6%|27.3%|-4.3%||
|Other|5.2%|7.9%|2.7%||
|Dwelling Type|||||
|Separate house|1.8%|0.3%|-1.4%|**|
|Semi-detached house|5.5%|5.6%|0.0%||
|Flat/apartment|1.3%|1.1%|-0.1%||
|Compound house|90.9%|92.9%|1.9%||
|Huts|0.6%|0.1%|-0.4%|*|
|Roof material|||||
|Metal sheet|48.0%|44.0%|-4.0%||
|Slate/asbestos|47.5%|52.2%|4.7%||
|Cement/concrete|4.5%|3.8%|-0.7%||
|Wall material|||||
|Mud bricks/earth|2.8%|9.6%|6.8%|***|
|Wood|3.4%|5.5%|2.1%||
|Metal sheet|1.9%|1.1%|-0.8%||
|Cement/concrete<br>Floor material|91.9%|83.9%|-8.1%|***|

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# Crosi-sectional Analysis of Factors Affecting Growth in Developing Countries: Examination of Methodological Problems

A large number of studies of international cross-sectional data have been conducted during the lost 20 years in an attempt to explain the growth rate or other variabies in the developing economies. The purpose of these studies is to permit scientific comparison of national experiences, as the basis for policy recommerdations. In the absence of systematic and scientific international comparisons, judgments are likely to be based only on partial comparisons and subjective evaluations. Moreover, if international cross-sectional studies are to serve for economic policy, their conclusions must be solidly established. Yet it seems that, in the present state of the literature, these conclusions are relatively fragile and sometimes contradictory.

The conclusions depend on the methodological choices made. What definition is used for economic growth? Is it measured over a long or a short period? Does the investigation concern all countries or a homogeneous subset? Does it seek to evaluate all factors affecting growth or, on the contrary, to bring out the impact of some particular factor? Is the aim to measure the overall impact of one or another factor or to identify the mechanisms through which it affects growth? Is this done using a reduced form model or a structural model?

The purpose of the present work is obviously not to resolve all the methodology problems posed by cross-sectional studies. Nor is it to present an exhaustive balance sheet of the cross-sectional analyses that contribute to knowledge of factors affecting growth and of the efficacy of economic policies. Our analysis is based on some 30 cross-sectional studies selected using various criteria. The reference studies used here are recent studies (since 1982), written in English or French, that directly explain growth (excluded are studies designed solely to explain certain factors affecting growth, such as the saving rate, the investment rate, the rate of export growth, etc.). The reference studies, moreover, either use a large range of independent variables or display originality in the choice of factors affecting growth or in the way they have analyzed them (cf. table 1).

The methodological questions posed by cross-sectional analysis have been grouped into two categories. The first series of questions concern the choice of the dependent variable (i.e. the growth indicator adopted), the countries and the period over which it is measured, that is, the characteristics of the sample. The second series of questions concern the independent variables, i.e. their selection, their
projections have been addressed in this study using actual input data. This allows focusing on the predictive capacity of each variation of the proposed micro-simulation method. In addition, using the last available household data as a comparator is tricky because the comparison could potentially attribute specific outcomes to that projection when these outcomes could result from other unrelated factors occurring simultaneously.

Secondly, the simulation relies on behavioral models built on past data that reflect the pre-existing structure of the labor market and household incomes, plus the relationship of these factors and their relationships with demographics as they stood before the expected change. Consequently, the simulation assumes these structural relationships remain constant over the period projections are made. The further back the baseline year is from the present, the more questionable this assumption will likely be.

Thirdly, the model is limited in its ability to account for shifts in relative prices between different sectors of the economy because of external shocks. One such example is the general equilibrium effect of a change in the terms of trade between agriculture and other sectors. In the absence of a CGE model, it is nearly impossible to model changes in terms of trade between economic sectors explicitly.

A fourth consideration is that the model does not consider the geographic mobility of factors (labor or capital) across time. Thus, all individuals are assumed to remain in their place of origin, even as their labor force status changes or their employment sector alters. Usually, this assumption seems like an abstraction from the truth in a stable environment and would only matter when the results are disaggregated spatially or across rural and urban areas.<sup>17</sup> Fifthly, the simulation component of the model relies on random draw using a pseudorandom number generator in computing the allocations of individuals into labor status, as well as labor earnings of new workers and workers who are changing sectors. The model is configured such that the seed of this random number generator is the same over different runs, so generally, the results will be reproducible. However, small changes in data may lead to changes in outcomes where the random components are required and hence in the results.

Finally, it should be noted that this simulation has not incorporated other transmission channels through which households might be impacted. An example of such impacts is a fall in school retention, educational learning, and childhood nutrition caused by a suspension of school (and school feeding programs).<sup>18</sup>

> 17 Considering measures against the Covid-19 pandemic, such as curfews, lockdowns, and quarantines, the assumption seems plausible.

> 18 See World Bank (2020).

12
For advertising we employ the 1982, 1987, and 1992 input-output tables from the Bureau of Economic Analysis (BEA). Two input industries are used to calculate advertising: advertising, and signs and advertising displays. We supplement the advertising data from input-output tables with data from Schonfeld & Associates.

# 7 Empirical Findings

We ...rst focus on assessing the impact of tangible sunk costs. Table 1 presents the ordinary least squares (OLS) estimates of equations (15) and (18), along with the values of the ordinary and adjusted R<sup>2</sup> statistics. As noted above, to estimate (15) we employ the four-...rm concentration ratio, thus setting m = 4. Separate regressions were estimated for the census years 1982, 1987, and 1992. Overall, these results strongly support the hypothesis that sunk costs in‡uence the size distribution of ...rms.

The results of equation (15) indicate that the intensities of the rental and secondhand� markets� and the rate at which capital depreciates are signi...cant determinants <u>Q4i � Qi</u> of ; the di¤erence in mean size between the four largest and all ...rms in Qi industry i. We ...nd that the stronger is the second-hand market for the capital employed in an industry, the faster is the rate at which such capital depreciates, and the easier is the leasing of that capital (i.e., the lower is the sunkenness of the capital employed in the industry), the smaller is the dispersion in ...rm size within the industry. Moreover, the parametric restrictions implied by the model are not rejected by these data, which provides a measure of con...dence in the model and the measure of sunkenness proposed in (8). The likelihood ratio test indicates that the maintained hypothesis H01 : b1 = b2 is not rejected at the 5 percent level in all three temporal cross-sections (1982, 1987, and 1992).<sup>11</sup>

> 11 For the 1982 cross-section, F = :07 < F :195;396<sup>=3:87,andtheprobabalitylevelatwhicha</sup> standard F-test rejects the restriction is .79; for 1987, F = :07 < F1<sup>:95</sup> ;426<sup>= 3:86andtheprobabality</sup> level at which a standard F-test rejects the restriction is .78; for 1992, F = :18 < F1<sup>:95</sup> ;425<sup>=3:86,</sup> and the probability level at which the standard F-test rejects the restriction is .67.

13