rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 45,000<br>40,000<br>—ee a<br>—25,000iY ae<br>— AL Kalman (billions of<br>2005 pesos)<br>— UE<br>10,000<br>5,000<br>()<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text --> <!-- Start of picture text --> 12.0%<br>-rr AN<br>/ ~ UY<br>6.0% —AL Kalman / real GDP<br>== AL Kalman / KS<br>4.0%<br>¢poetttt tenn e_ -<br>_- o” we. ~.,<br>2.0% mewn wee wee w ewe<br>0.0%<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text -->
Policy ReseaRch WoRking PaPeR 4433

# **Abstract**

Using a census of all workers in private establishments in the formal sector in Mexico to track workers and establishments over time, this paper presents the first Mexican worker and job flow statistics. The data allow for comparing these flows across time, space, and worker characteristics. Although many patterns are similar to those documented in developing countries, the analysis uncovers patterns that have potentially important policy implications. The authors compare the results to the literature, illustrate how the statistics change during times of reform and crisis, and present novel findings that contribute to the broader literature on worker reallocations.

This paper—a product of the Enterprise Analysis Unit, Financial Private Sector Development Department—is part of a larger effort in the Financial Private Sector Development VPU. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dkaplan@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
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> - 26 -

3.07 A major factor in the economic justification was out-migration of skilled workers from Egypt, Jordan, Pakistan, Bangladesh and Indonesia to the oil-based economies of the Middle East. By the time of the seventh Pakistan project, worker remittances were the largest single source of foreign exchange. A significant portion of the training investment supported the continuing supply of such labor. The return of workers as the oil-exporting economies contracted led to labor *'arket instability in the 1980s. In Jordan, it produced an oversupply of workers in certain skills categories that, together with continuing demand for skilled workers, led the government to establish a labor market information system and re-orient the number and size of courses of different kinds in vocational training centers. In contrast, in Pakistan, returning workers appear to be easily absorbed by the economy, with many choosing to establish their own businesses (Pakistaa Sector Study, 1988).

3.08 The most prominent rationale in the low income countries of Sub-Saharan Africa was the need to replace costly expatriate managers and skilled workers. The same rationale was important in Morocco and Algeria. Economic justifications for the low-income countries of Sub-Saharan Africa were also based on projections of growth. Economic analysis tended to be less complex in these smaller economies, and to be too optimistic. In the Cameroon, for example, a secondary vocational school was built to provide skilled workers for intended industrial expansion in the northern region. Industrial development was delayed, and graduates had to leave the region to find employment.

3.09 Equity Justifications: Only one of the sample of components was justified primarily on equity grounds: investment in boarding places for Tanzanian nonformal centers and technical secondary schools was intended specifically to increase the access of rural clients to the former and women to the latter. Increased access for rural groups to skills training was, however, an ubiquitous secondary justification: it was mentioned in connection with at least one (but not necessarily each) component in each country in the sample. Access for women figured prominently in the justification of seven of the 76 projects; most of these were in the EMENA region (Jordan, Turkey, Egypt, Pakistan, YAR, Barbados).

# Planning

3.10 Virtually all of the sampled component investments were planned or. the basis of projected manpower requirements. In the majority of cases these were forecast from admittedly inadequate data, with appropriate caveats regarding the indicative nature of the estimates. Such estimates were then compared with the output of existing training systems and investments rationalized as (usually partially) closing the gap. In a majority of projects these estimates were made by Bank staff or UNESCO consultants.

3.11 In a number of cases manpower requirements were derived not from global forecasts, but from enterprise surveys (Ecuador I, Uruguay I, Cameroon II), and other indicators of employment needs. In this
research and development (R&D), among other things. Several papers argue that small firms are more likely to be financially constrained and that these constraints might get relaxed as firms grow and as countries develop financially.<sup>12</sup> Other papers study whether firms in China and India are financially constrained. In China, state-owned enterprises seem to have better access to finance and thus seem less financially constrained (Chow and Fung, 1998, Li et al., 2008, Poncet et al., 2010, Guariglia et al., 2011, Hale and Long, 2011b). In India, smaller firms seem to be more financially constrained (Love and Martinez Peria, 2005 and Oura, 2008). The results in our paper show that new capital market financing is related to higher growth and investment for publicly listed firms. This seems consistent with financial constraints affecting even the large, publicly listed firms that arguably have access to formal markets.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 analyses the development of capital markets in China and India and how firms use them to raise financing. Section 4 studies the dynamics of firms around the use of capital markets. Section 5 concludes.

# **2. Data**

To analyze the capital market financing and performance of firms in China and India, we assemble a new and comprehensive firm-level data set covering firms’ security issuances in capital markets around the world as well as balance sheet data. Our data on capital raising activity come from the Thomson Reuters’ SDC Platinum database, which provides transactionlevel information on new issues of common and preferred equity and publicly and privately

> 12 See Kumar et al. (1999), Cooley and Quadrini (2001), Guiso et al. (2004), Beck et al. (2005, 2008a, 2008b), Mitton (2008), Musso and Schiavo (2008), and Arellano et al. (2012), among many others.

7
|share||||
|---|---|---|---|
|Absolute latitude|0.08|4.3e-06|0.00003|
|Fraction Muslim|0.08|0.00035|0.00215|
|Population growth|0.08|-0.00603|0.07020|
|**Market**<br>**capitalization**|**0.07**|**3.8e-07**|**4.7e-06**|
|Fraction Buddhist|0.07|-0.00021|0.00167|
|Fraction Catholic|0.07|-0.00012|0.00102|
|Ethnolinguistic<br>fractionalization|0.07|0.00028|0.00171|
|Primary school<br>enrolment|0.07|0.00026|0.00278|
|**Market turnover**|**0.07**|**4.5e-07**|**3.7e-06**|
|Fraction Jewish|0.07|0.00017|0.00230|
|Area|0.07|1.3e-08|1.3e-07|
|Fraction speaking<br>foreign language|0.07|0.00011|0.00089|
|Fraction<br>Protestants|0.06|0.00007|0.00107|
|Fraction of pop.<br>speaking English|0.05|-0.00011|0.00103|
|Equipment<br>investment|0.04|0.00026|0.00815|

To provide robustness checks, we again perform the estimation with alternative priors.<sup>6</sup> Figure 4 illustrates the comparison. The implications of

> 6. We also perform estimations using an alternative MC3 sampler, but the differences in posterior statistics are

39
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Cameroon > Road investments > Macro results**

||||Reduction in<br>other ublic|Roads (|% variatio<br>Imort|n)<br>Forein|Income|
|---|---|---|---|---|---|---|---|
|Variable|Definition|Reference|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|5,409,614.00|0.38|0.76|0.84|1.07|0.71|
|EV|Equivalent variation||0.38|0.71|0.78|1.12|–0.90|
|s|Wage|1.00|0.31|1.15|1.34|1.84|1.04|
|yg|Government income|1,500,596.00|0.69|5.85|5.85|5.85|5.85|
|g|Total government expenditure|656,249.00|–5.68|6.11|6.11|6.11|6.11|
|ye|Firm income|2,118,815.00|1.16|0.19|–0.03|–0.62|0.32|
|It|Total private investment|1,213,585.00|2.02|0.72|0.46|–0.27|0.01|
|GDP|GDP|6,395,129.00|0.71|0.70|0.70|0.70|0.70|
|e|Nominal exchange rate|1.00|–0.27|–0.28|–2.21|–6.31|–0.62|

# **Cameroon > Road investments > Sectoral results**

||||Reduction in<br>||Roads<br>|||
|---|---|---|---|---|---|---|---|
||||other blic||Imort|||
|Variable|Sector|Base|pu<br>expenditure|VAT|p<br>duties|Foreign aid|Income tax|
||Crop agriculture|1,088,074.00|1.34|0.36|0.23|–0.02|–0.28|
||Export agriculture|419,908.00|1.53|0.52|0.41|0.25|0.55|
|Va|Mining and gas|623,479.00|1.59|1.55|1.48|1.32|1.55|
|Value added or|Industries|1,266,964.00|1.18|–0.03|0.18|0.37|0.47|
|output|Construction|108,698.00|8.70|6.84|6.52|8.14|7.93|
||Private services|2,265,916.00|0.72|0.06|0.10|0.01|–0.01|
||Public services|622,090.00|–4.25|3.31|3.19|3.50|3.46|
||Crop agriculture|1.00|–0.36|0.36|0.49|0.56|–0.15|
||Export agriculture|1.02|–0.37|0.52|1.01|0.50|–0.47|
||Mining and gas|1.04|–0.28|–0.33|–0.09|–6.34|–0.72|
|pq<br>Market prices|Industries|1.11|0.10|1.87|2.15|–0.86|–0.12|
||Construction|1.00|0.16|1.57|1.76|–0.30|0.09|
||Private services|1.03|0.10|1.15|0.83|0.61|0.05|
||Public services|1.00|0.24|1.28|1.43|1.18|0.70|
||Crop agriculture|1.00|0.84|0.45|0.46|0.64|–0.48|
||Export agriculture|1.00|0.61|–0.54|–0.58|–0.40|–0.59|
|r<br>|Mining and gas|1.00|1.21|0.73|–1.69|–6.68|0.72|
|Rental rate of<br>capital|Industries|1.00|1.67|–0.54|0.17|1.17|0.61|
||Construction|1.00|9.51|8.18|7.98|10.50|9.38|
||Private services|1.00|0.84|0.35|0.60|0.93|0.09|

49
Information on investment and years of schooling, needed for data on capital and education, is often missing in the early periods of WDI. Thus, we partially rely on data from Bosworth and Collins (2003) and follow the same approach to update their data.<sup>24</sup> We use their data on capital formation, which were originally based on the Penn World Table (Summers and Heston, 1991) and later updated. We then augment the data set by adding 13 SSA countries, and update the entire data up to 2010.<sup>2526</sup> In addition, like Bosworth and Collins (2003), we use the average of Barro-Lee and Cohen-Soto for education.<sup>27</sup> The information on saving and investment as a percent of GDP comes from WDI, which collects the National Accounts of each country. Likewise, capital transfer such as FDI, ODA, and remittances are obtained also from WDI to examine the trend of the Balance of Payment.

Finally we look into data that may affect TFP growth. We first use UN country-level data on sectors to assess changes in the contribution of each sector to output and capture the trend of structural transformation. The UN GDP data are disaggregated according to the following sectors: agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); manufacturing (ISIC D); construction (ISIC F); wholesale, retail trade, restaurants, and hotels (ISIC G-H); transport, storage, and communication (ISIC I); and other activities (ISIC J-P). We use the broad category of three sectors: agriculture, industry, and service. In addition, to examine whether improved freedom and political stability have played a role in TFP growth, we use the Freedom House Index from 1980 to 2010 that captures political rights and civil liberty.<sup>28</sup>

> 24 Barry Bosworth and Susan Collins kindly supplied us with updated data, which included 19 SSA countries for the period of 1960 – 2007.

> 25 For the recent period that both Bosworth and Collins (2003) and WDI cover, we compare the data on gross fixed capital formation from the two sources and find that they both lead to a similar level of capital formation.

> 26 Due to a limited availability of data, a few countries do not have information up to 2010. The final year of observation for Burkina Faso is 2006; Central Africa Republic, 2009; and Democratic Republic of Congo, 2009.

> 27 See Barro and Lee (forthcoming) and Cohen and Soto (2001). An exception is that we use Lutz data for Chad. See Lutz et al. (2007).

> 28 The Freedom in the World survey provides an annual evaluation of the progress and decline of freedom in 195 countries and 14 related and disputed territories.

10
- 35 _

its tax rates or raising its cadastral assessment since either would incre.se its revenues relative to the equivalently assessed cadastral base.<sup>2 7</sup> 28 36. Secondly, the "fiscal effect" formula is too narrowly focussed both as to the taxes covered and the types of municipalities. By focusing on property taxes, it not only fails to encourage the development of other tax bases but may actually encourage tbe substitution of property taxes for other types of taxes with little overall gain in local resource mobilization. To forestall this probletm, consideration should be given to including all municipal revenue sources in the formula, even while continuing to use cadastral values as tne (actual or potential) tax base. Moreover the application of "fiscal effort" to small municipalities alone is undoubtedly undesirable. Large municipalities (above 100,000 inhabitants) bulk large in total local government finance and the lack of an incentive for effort invites them to substitute national resources for local resources.

- 27/ By using uniform assessments (or uniform relations between assessed values and market values) across different (small) municipalities, no municipality would stand to gain from undervaluing its property or lose from increasing assessed values. In fact, communities would only stand to gain from increasing assessments both because their owni revenues would rise as would the transfer received. The recommended sharing formula would necessitate some agency--such as the Instituto Geografico Agustin Codazzi (IGAC)--provide information so that the uniform or equivalent assessed value of property in each municipality could be calculated.

- 28/ Note that as they stand all three formulas take into account factors of "need" (as represented by population share) and "fiscal effort" (each community's tax performance relative to some average performance) but all ignore the question of "taxable capacity" (i.e. differential per capita tax bases) and hence make no attempt to compensate for differences in capacity. In addition, regardless of which of these sharing formulas were used, the problem would remain that each municipality was to be compared with other municipalities in the same department, thus raisin8 the possibility of unequal treatment of municipalities across departments. This potential anomaly in the application of the formula was corrected in 1987 and now small municipalities are being compared nationwide.
76 Xinhua News Agency 2012c; _Daily News_ (Tanzania) 2011, 2012a, 2012b; Platts 2011f; Tanzania Daily News 2012; _East African_ 2010; IMF 2012f; EPPO 2012a, 2012b; Thai News Service 2010b, 2011b–2011e, 2011g, 2011i, 2011j, 2012a, 2013a–2013c; Platts 2012d, 2012h; Africa Energy Intelligence 2011; Togosite 2011; IMF 2011b; Platts 2011d, 2011h; IACtHR 2002; Business News Americas 2011a; _Economist_ 2013-2-9; AP 2010b; AP 2011-7-30; Info.VN 2012; Platts 2010; Asia Pulse 2011a; TuoiTreNews 2013; IHS 2005; Reuters 2010b; _International Oil Daily_ 2011; _Yemen Times_ 2012; Reuters 2011d; IHS 2010a; All Africa 2012a; IMF staff country reports from 2009 to 2012; Government Web sites and announcements; Web sites of national oil companies and central banks. a √ under P signals the presence of at least one operating refinery. b √ under E signals a net exporter in 2010.
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 5.1  Regional spending needs by sector**

||Powe|r|Transp|ort|ICT|||Total||
|---|---|---|---|---|---|---|---|---|---|
||||||||Total|Total|Total|
|**Country**|Investment|O&M|Investment|O&M|Investment|O&M|investment|O&M|(inv+O&M)|
|Benin|||2|8|||2|8|10|
|BurkinaFaso|||3|15|0.59|0.03|4|15|19|
|Cote d'Ivoire|27||3|18|0.25|0.01|31|18|48|
|Gambia,The||7|1|1|||1|8|9|
|Ghana|5||9|20|0.31|0.02|14|20|34|
|Guinea|786|80|28|24|0.78|0.04|815|104|919|
|Guinea-Bissau|5||2|5|0.55|0.03|7|5|12|
|Liberia|2||10|6|1.03|0.05|13|6|19|
|Mali|26||24|16|||50|16|66|
|Mauritania|1||14|5|||15|5|20|
|Niger|1||9|18|2.36|0.12|12|18|30|
|Nigeria|2|137|21|72|0.24|0.01|23|209|232|
|Senegal|3||8|19|||11|19|30|
|SierraLeone|||7|5|0.88|0.04|8|5|13|
|Togo|||0|2|||0|2|2|
|**ECOWAS**|**858**|**224**|**141**|**234**|**7**|**0.35**|**1,006**|**458**|**1,464**|

Source: AICD calculations.

*empty cells indicate that AICD does not estimate any spending for investment or maintenance of a specific sector

**Figure 5.1  Spending for regional infrastructure as a share of GDP**

<!-- Start of picture text --> Guinea<br>Guinea-Bissau<br>Liberia<br>Gambia, The<br>Mali<br>Mauritania<br>Sierra Leone<br>Niger<br>Burkina Faso<br>Senegal<br>Ghana<br>Cote d'Ivoire<br>Benin<br>Nigeria<br>Spending Needs for regional infrastructure as a % of GDP<br><!-- End of picture text --> Source: AICD calculations.

67
<!-- Start of picture text --> Figure 3.2a: Engel Curve for Cereal<br>0.25 \<br>NY<br>Oo x wstsee<br>@ 0.10 NS<br>0.05 ‘<br>— Kernel \<br>---- 95% Cl<br>0.00 4°77 OLS<br>11 42 43 44 45 46<br>Log Food Expenditure<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3.2c: Engel Curve for Oil<br>0.15<br>sn<br>Sen<br>a ST<br>0.05 SS<br>mse<br>— Kernel<br>---- 95% Cl<br>0.00 4 “7 OLS<br>1 12 13 14 15 16<br>Log Food Expenditure<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3.2b: Engel Curve for Meat<br>0.5 | — Kernel<br>*-- OLS -<br>a ween oe ee ere<br>@ 024 eee<br>o4<br>00<br>"1 12 13 14 15 16<br>Log Food Expenditure<br><!-- End of picture text -->

<!-- Start of picture text --> Figure 3.2d: Engel Curve for Tubers<br>04 i ee<br>fe a ae<br>0.3 MLEBE Sit,Nee<br>a / _<br>/ N,<br>04 SS<br>— Kernel<br>---- 95% Cl<br>00 477 OlS<br>14 12 13 14 15 16<br>Log Food Expenditure<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3.2e: Engel Curve for Other Foods<br>0.4 — Kernel<br>---- 95% Cl “<br>--- OLS a“<br>c Co<br>a Ses foe"<br>o4 ee<br>0.0<br>1 12 13 14 15 16<br>Log Food Expenditure<br><!-- End of picture text -->
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Botswana’s share of roads in good condition is among the highest in southern Africa**

<!-- Start of picture text --> South Africa<br>Mauritius<br>Swaziland<br>Malawi<br>Namibia<br>Botswana<br>Mozambique<br>Madagascar<br>Zimbabwe<br>Zambia<br>Angola<br>Lesotho<br>0% 25% 50% 75% 100%<br>Percentage Main Road Network Length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD Road Sector Database on southern Sub-Saharan African countries.

**Table 2. Botswana’s road indicators benchmarked against Africa’s low- and middle-income countries**

|Indicator|Unit|Resource-<br>rich countries|**Botswana**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|98|**31**|278|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|128|**43**|318|
|GIS rural accessibility|% of rural pop. within 2 km of all-season road|20|**15**|31|
|Main road network condition [2]|% in good or fair condition|68|**80**|86|
|Rural road network condition [3]|% in good or fair condition|61|**73**|65|
|Classified paved road traffic|AADT|1,408|**858**|2,451|
|Classified unpaved road traffic|AADT|54|**95**|107|
|Primary network overengineering|% of primary network paved with 300 AADT or less|15|**23**|18|
|Perceived transport quality [4]|% firms identifying transport as major business<br>constraint|27|**13**|18|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries. _Note:_

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary networks.

[3] Rural network is generally defined as the tertiary network and does not include the unclassified roads.

[4] _Source:_ World Bank—IFC Enterprise Surveys on 32 Sub-Saharan Africa countries. GIS = geographic information system; AADT = average annual daily traffic.

During its recent economic boom, the country has given priority to developing and maintaining surface connectivity. It has allocated substantial resources to expansion and rehabilitation of the existing network. Based on the physical configuration and condition of Botswana’s road network, it is possible to estimate the resources that would be needed to maintain the network in good condition and properly rehabilitate it as per the road life cycle. These maintenance and rehabilitation norms, when compared against existing spending patterns, give a sense of how Botswana has succeeded in securing resources for road preservation (figure 7). As the benchmarking shows, Botswana spends a significant amount over the 11