rafmacalaba/datause-encoder-data huggingface dataset:
# **1. The Importance of Services in the World Economy and Global Value Chains**

**Services account for the majority of GDP and employment globally, and they are growing faster than manufacturing.** The services sector ranges from traditional areas such as construction, utilities, transport, and real estate, to business services such as R&D, design, engineering, sales, marketing, finance, insurance, and accounting. Services represented around 61 percent of global GDP (in terms of value‐added) and 63 percent of employment in 2015, while manufacturing accounted for only 13 percent of GDP and 23 percent of employment. Additionally, between 25 and 60 percent of employment in manufacturing firms can be found in service support activities.<sup>2</sup> Worldwide, annual growth of services value‐added over the last five years has been about 3.7 percent, compared to 2.3 percent in manufacturing. Research has shown that developments in services explain divergent economic growth performance across countries (Timmer et al., 2008, 2007).

**Services also account for a large and growing share of exports in value‐added terms.** Services exports measured in gross terms as a share of total world gross exports have remained at around 20 percent since 1980. However, in value‐added terms (which is more indicative of their true importance), services have increased from below 30 percent to above 40 percent (see Figures 1.1 and 1.2). One important factor behind this growth in value added is services’ role as inputs into other economic activities, mainly manufacturing.

<!-- Start of picture text --> Figure 1.1: Gross exports of goods and services Figure 1.2: Value‐added exports of goods and<br>as a percentage of total world gross exports services as a percentage of total world value‐<br>added exports<br>80 82 80 79 80<br>69<br>60 60 61<br>57<br>40 40 43<br>39<br>31<br>20 18 20 21 20<br>0 1980 1995 2009 0 1980 1995 2009<br>Goods Services Goods Services<br>Gross exports % of total world gross exports<br>Value-added exports % of total world value-added exports<br><!-- End of picture text --> Source: Heuser and Mattoo (2017) based on Johnson and Noguera (2016).

**Adding services embodied in manufacturing exports brings the overall contribution of services to exports to close to two‐thirds.** Services exports can be decomposed into services directly exported (i.e. exports of service companies) and those embodied as inputs. Services have a double role of being a final good for _direct_ export or an input, which is therefore exported _indirectly_ through other (manufacturing) sectors. According to Miroudot and Cadestin (2017), services inputs, whether domestic or foreign, account for about 37 percent of the value of manufacturing exports (based on a sample of OECD countries). However, services inputs can also be produced by manufacturing firms besides being bought externally.

2 Such as R&D, engineering, transport, logistics, distribution, marketing, sales, after‐sale services, IT, management, and back‐office support (Miroudot and Cadestin, 2017).

4
strikes and violent public protests, and prompted the government to swiftly reintroduce subsidies (Bazilian and Onyeji, 2012; Siddig et al., 2014).

# **5. Set-up of the illicit activities model**

The basic structure of this model builds on Hosoe et al. (2010), who offer a small open economy CGE model featuring a representative household, two sectors, and a government. The model developed for the purpose of this study is a static small open economy model, featuring multiple representative households (representing five income quintiles), firms, and a government. The government collects taxes on production, factor usage, and imports, as well as direct taxes on households, and disburses FFS.

Moreover, the model adds several non-standard features: A small group of households is modeled to engage in fuel smuggling activities. The profits of smuggling depend on the price differential between the domestic and international price of fuel, which is determined through the level of subsidies paid. The model also allows for the evasion of production and factor taxes. In order to represent different policy options for using reform revenues, the model considers (1) government expenditure, (2) direct cash transfers, and (3) reductions of pre-existing taxes, such as labour or production taxes.

# _5.1. Tax evasion: Representation in the model_

In this model it is assumed that firms choose to evade taxes on different factors and on production, while incurring a real cost of evasion. Formally, factor taxes _τh,j_<sup>_f_onfactor</sup><sup>_h_andsector</sup><sup>_j_areevadedattherate</sup><sup>_ef_</sup> _h,j_<sup>.Similarly,production</sup> taxes _τj_<sup>_z_are evaded at the rate</sup><sup>_ez_</sup> _j_<sup>.In line with Liu (2013), tax evasion activities</sup> incur real costs _F_<sup>_loss_</sup> and _X_<sup>_loss_</sup> : _h,j j_ This setting implies that the cost of tax evasion is measured as a share _c_ ( _e_ ) of either factor inputs _Fh,j_ (in the case of factor tax evasion) or production _Zj_ (for production tax evasion). Parameters _A_<sup>_f_</sup> _h,j_<sup>,</sup><sup>_Az_</sup> _j_<sup>,</sup><sup>_N_</sup> _h,j_<sup>_f_,and</sup><sup>_N_</sup> _j_<sup>_z_characterisethe</sup> cost function and are determined during calibration. The total net benefit to firm _j_ from evading factor taxes is<sup>�</sup> _h_<sup>_Fh,j_(</sup><sup>_τ f_</sup> _h,j_<sup>_ef_</sup> _h,j_<sup>_p_</sup> _h_<sup>_f−c_(</sup><sup>_ef_</sup> _h,j_<sup>)).</sup> Evasion losses reflect the extra (unproductive) “self-input” due to evasion efforts. Specifically, if a firm engages in evasion activities, a share of its resources will be directed towards this evasion activity rather than production (e.g. labour hours). This unproductive evasion activity means that fewer resources are available for producing output, thus resulting in the output loss _Xj_<sup>_loss_</sup> .

8
|P005911|1994|1999||14 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P005911|1994|2000|3/29/2000|16 Closed|Yemen,Republic of|
|P005911|1994|2001|3/27/2001|18 Closed|Yemen,Republic of|
|P005911|1994|2002|3/19/2002|20 Closed|Yemen,Republic of|
|P005911|1994|2003|1/23/2003|22 Closed|Yemen,Republic of|
|P005911|1994|2004|1/16/2004|24 Closed|Yemen,Republic of|
|P005911|1994|2005|4/25/2005|26 Closed|Yemen,Republic of|
|P005912|1996|1996||3 Closed|Yemen,Republic of|
|P005912|1996|1997||6 Closed|Yemen,Republic of|
|P005912|1996|1998||8 Closed|Yemen,Republic of|
|P005912|1996|1999||10 Closed|Yemen,Republic of|
|P005912|1996|2000|6/13/2000|13 Closed|Yemen,Republic of|
|P005912|1996|2001|5/30/2001|16 Closed|Yemen,Republic of|
|P005912|1996|2002|5/31/2002|19 Closed|Yemen,Republic of|
|P005912|1996|2003|6/17/2003|22 Closed|Yemen,Republic of|
|P005912|1996|2004|12/17/2003|23 Closed|Yemen,Republic of|
|P005912|1996|2019|5/7/2019|24 Closed|Yemen,Republic of|
|P005920|1997|2000|6/27/2000|3 Closed|Argentina|
|P005920|1997|2001|5/25/2001|5 Closed|Argentina|
|P005920|1997|2002|6/13/2002|7 Closed|Argentina|
|P005920|1997|2003|6/16/2003|9 Closed|Argentina|
|P005920|1997|2004|6/17/2004|11 Closed|Argentina|
|P005920|1997|2005|4/19/2005|13 Closed|Argentina|
|P005920|1997|2006|6/1/2006|14 Closed|Argentina|
|P005920|1997|2007|12/27/2006|15 Closed|Argentina|
|P005920|1997|2007|6/20/2007|16 Closed|Argentina|
|P005920|1997|2008|12/13/2007|17 Closed|Argentina|
|P005920|1997|2008|5/13/2008|18 Closed|Argentina|
|P005920|1997|2009|12/18/2008|19 Closed|Argentina|
|P005920|1997|2009|5/29/2009|20 Closed|Argentina|
|P005920|1997|2010|12/19/2009|21 Closed|Argentina|
|P005920|1997|2010|1/20/2010|22 Closed|Argentina|
|P005920|1997|2011|2/23/2011|23 Closed|Argentina|
|P005920|1997|2012|12/28/2011|24 Closed|Argentina|
|P005920|1997|2013|7/9/2012|25 Closed|Argentina|
|P005920|1997|2013|2/23/2013|26 Closed|Argentina|
|P005920|1997|2014|11/17/2013|27 Closed|Argentina|
|P005920|1997|2014|5/17/2014|28 Closed|Argentina|
|P005920|1997|2015|12/24/2014|29 Closed|Argentina|
|P005930|1977|1985||0 Closed|Argentina|
|P005932|1977|1985||0 Closed|Argentina|
|P005932|1977|1986||0 Closed|Argentina|
|P005934|1978|1985||0 Closed|Argentina|
|P005934|1978|1986||0 Closed|Argentina|
|P005934|1978|1987||0 Closed|Argentina|
|P005934|1978|1988||0 Closed|Argentina|
|P005935|1979|1985||0 Closed|Argentina|
<!-- Start of picture text --> 100 100<br>°<br>* - ° sy . 7<br>Ad 600 e©ee30 @ °<br>80 °<br>°®, ®e° é e g 80 © @ @ @o ee<br>_ eee sé = le oe) © eo @ °<br>2 & ° £<br>s ° ° & ©SO@ 00 @& fo)<br>3 fo} %o bd e 2<br>222 680le ee, PY 5 60\jepeceweewee @<br>eero}ro} @eC@”°e@@eC@”°e@@@ eo@@@ ° © 53ssss3ss @ @ ome)<br><!-- End of picture text --> <!-- Start of picture text --> 100 100<br>°<br>* - ° sy . 7<br>Ad 600 e©ee30 @ °<br>80 °<br>°®, ®e° é e g 80 © @ @ @o ee<br>_ eee sé = le oe) © eo @ °<br>2 & ° £<br>s ° ° & ©SO@ 00 @& fo)<br>3 fo} %o bd e 2<br>222 680le ee, PY 5 60\jepeceweewee @<br>eero}ro} @eC@”°e@@eC@”°e@@@ eo@@@ ° © 53ssss3ss @ @ ome)<br>fy sa e ® ° 2 sd<br>F= I<br>& 40{® %° o e e@ : 2 40p0e0¢ @<br>a © © ec ° £<br>© oe ® re ®<br>2 eo © z3<br>20 2 20 \e °<br>*, ates° le<br>to) °<br>0 0<br>10,000 20,000 30,000 40,000 50,000 60,000 10,000 0: 30,000 40,000 50,000 60,000<br>GDP per capita (US$) GDP per capita (US$)<br>. Gi = ye<br>: aie } Risk to well-bei<br>: .eajA =< — 4 .= (% of GDP perSH<br>_<br>‘¥ | sad 7S WY ~ Poofh 1§ 0. 03 0-0. 35 0<br>a od : me Mer. I No data<br>aN, 0 : io<br><!-- End of picture text -->
WPS3945 to this issue below when we examine the effect of migration on labor market participation.

# 4.2 Child Labor Market Participation and Days Worked

Migration has a strong dampening effect on child labor market participation, regardless of whether we look at an indicator for whether the child works or the reported number of days worked over the survey year. Overall, days worked fall by about 66% (from 27 to 10 for boys and 27 to 9 for girls) (Tables 4 and 5). Unlike all three measures of schooling, however, there are no gender differentials in labor market activity. Children in non migrant households work about 27 days over the period of a year, and migration reduces labor market activity for both boys and girls by roughly the same number of days, though boys are more likely to report some work. Interestingly, the presence of older siblings is signi fi cant and reduces labor market activity for both boys and girls (see Appendix Table A3). Finally, accounting for selection into migration serves to strengthen the migration effect substantially, suggesting, again, that there is signi fi cant negative selection into migration.<sup>19</sup> While these results are consistent with our results on schooling, given the strong negative correlation with school attainment and labor market activity, they do not suggest that the poorer performance of boys in school retention or accumulated schooling can be ascribed to increased labor market activity. Of course, the increased work burden may come from increased responsibilities for domestic activities and home production that are captures poorly in the data.

Although these results are strong, the IV estimates we present above cannot really account for the time sensitivity of educational investments. In particular, we cannot separate the impact of migration among children within a household. However, in reality such differences must be important if our argument is correct. Children who have already reached an age beyond which school enrollment is unlikely before the fi rst migration episode for the household or who have been withdrawn from school due to resource constraints prior to migration are unlikely to catch up to children whose crucial schooling decisions were made post migration and the ensuing increase in resource fl ows. To this extent, our IV estimates are thus likely to understate the true impact of migration on schooling. To examine these differences more carefully, we now turn to our second strategy.

> 19 This effect only increases if we use the less restrictive de fi nition of work (not reported here) and, again both boys and girls appear to gain equally on average. For days worked per year, IV estimation of the migration effect with tobit and tehsil fi xed effects (not reported here) generated very similar results.

18
condom brands and it also distributes low cost, high quality, condoms, both in rural and urban areas. Since the launch of this program, called Condom Social Marketing (CSM) in 2001, PSI has tripled the number of condoms that are sold and the number of shops where condoms can be purchased.

# **3 Data and Descriptive Statistics**

The data used for this study come from the 2004 Lesotho Demographic and Health Survey, collected by the Ministry of Health and Social Welfare (MOHSW) in collaboration with the Bureau of Statistics (BOS) in Lesotho and ORC Macro International.

The LDHS was conducted using a representative sample of women and men of reproductive age living in 8,592 households in the ten districts of Lesotho, both in urban and rural areas. The survey utilized a two-stage sample design. In the first stage, 405 clusters (109 in the urban and 296 in the rural areas) were selected from a list of enumeration areas from the 1996 Population Census frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey. All women age 1549 who were either permanent household residents in the 2004 LDHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, in every second household selected for the survey, all men aged 15-59 years were eligible to be interviewed if they were permanent residents or visitors present in the household on the night before the survey. The final sample includes 7,095 women between 15-49 years of age and 2,797 men between 15-59 years old.

## [Insert figure 1]

Figure 1 shows the age profile of HIV prevalence for males and females in Lesotho. 26 percent of females in Lesotho are HIV positive, compared to 18.8 percent of males. In many other sub-Saharan African countries the prevalence of HIV is considerably higher among women then men, reflecting that, biologically, the probability of transmission from male to female is substantially higher than from 6
empirical investigations have branched out into a number of directions, including ...rm-level analysis, new approaches to trade in tasks and o¤shoring, and, to a limited degree, implicit contracts.

# 3.2 Empirical Work on Heterogeneous Firms and Bargaining.

In part because the literature on ...rm heterogeneity is so new, and in part because the data demands for testing these theories are quite high, not many studies are available in this area. To take ...rm and/or worker heterogeneity into account properly, information at both the ...rm and individual employee level is typically required, suggesting the need for matched employeeemployer datasets. In this section, we review several recent papers that have succeeded in contributing to this literature.

The pioneering work on trade and income inequality with heterogeneous ...rms actually predates the theory. Bernard and Jensen (1997) study the Annual Survey of Manufactures from the US Census Bureau to decompose the large rise in average skilled wage premia that occurred over the 1980’s. They show that a substantial fraction of the increase occurred between plants, in other words, by intra-industry shifts in the allocation of workers from plants with lower skill premia to ...rms with higher premia. This between-plant e¤ect is larger than the within-plant e¤ect (which is merely a rise in the skill premium for any one ...rm over time). Indeed, by some measures, the between-plant e¤ect is completely dominant (Table 5). Further, it occurs entirely among ...rms that export, and vanishes when the sample is restricted to ...rms with only domestic sales. This was an early indicator that trade might cause an increase in wage inequality through within-industry e¤ects, a mechanism very di¤erent from HeckscherOhlin, and alerted the ...eld that ...rm heterogeneity may have something important to do with the e¤ect of trade on inequality.

More recently, Menezes-Filho and Muendler (2007) combine insights from the Melitz (2003) model with worker heterogeneity to provide a compelling empirical example of the importance 30
our results: corporate taxes and cyclicality. Djankov et al. (2008) present data on effective corporate tax rates across 85 countries and find that effective corporate tax rates have an adverse impact on entrepreneurial activity leading to less demand for VC. Groh et al. (2010) also include taxation as a component of their PE attractiveness index. Similarly, in un-tabulated analysis, we also test if taxation plays a role in how much buyout capital is invested across countries but do not find any statistically significant relationship regardless of the corporate tax data we use.<sup>24</sup> Private equity managers have to deliver returns to their investors and hence might be looking for opportunities to buy companies at a discount when economies are going through downturns. To test this, we create an across-country and time recession dummy based on jumps in the unemployment rate and include it as a control variable in our country-level models. We do not find a significant association between this recession dummy and buyout activity, suggesting that there is no evidence of PE bottom-fishing during times of dislocation.

Another interesting question is if the response of buyout investments to institutional factors differs across industries as the level of institutional involvement may vary across industries. To explore this, we repeat our industry-level specification on subsamples created based on various industry groupings. One such difference we find evidence for is that the effect of regulatory environment on buyout activity seems to be more pronounced in non-traded versus traded industries, and in services versus goods industries. This might reflect that private equity managers have to deal with institutions more when making investments into portfolio companies from these industries and thus respond more to regulatory factors. However, it should be noted that these

> 24 We use other data on corporate taxes from different sources like the OECD and the World Bank’s Doing Business data. Our results remain unchanged if we include corporate tax rates as an additional control in our models. The reason we do not include it in the main analysis is that the tax data are either only available for a subset of countries or a subset of years and lead to a big drop in the number of country-years in the sample.

24
WPS3937

# **The Handshake: Why Do Governments and Firms Sign Private Sector Participation Deals? Evidence from the Water and Sanitation Sector in Developing Countries**

### **Olivia Jensen**

London School of Economics

**Frédéric Blanc-Brude** King’s College London

### **Abstract**

This paper uses a new dataset, “WATSAN,” of Private Sector Participation (PSP) projects for water and sanitation in developing countries to examine the determinants of the number of projects signed per country between 1990 and 2004. The new dataset improves on existing sources in particular in its coverage of projects with local investors and provides adequate data for crosscountry regression analysis.

We use a negative binomial regression model to investigate the factors influencing the number of PSP projects in a sample of 60 developing countries with 460 PSP projects. The regression results provide support for the hypotheses that PSP is greater in larger markets where the ability to pay is higher and where governments are fiscally constrained. We test several indicators of institutional quality and find that these are generally significant in determining the number of projects signed per country. Measures of the protection of property rights and the quality of the bureaucracy emerge as the most important institutions that encourage PSP. Rule of law and the control of corruption are significant, albeit at a lower level, while the quality of contract law and political stability are not robustly significant.

#### **Comments are welcome. Corresponding authors:**

Olivia Jensen Frédéric Blanc-Brude London School of Economics and Political Science King’s College London **Development Studies Institute Department of Management** Houghton Street Franklin-Wilkins Building London WC2A 2AE 150 Stamford Street +44 7985 796 404 London SE1 9NN <u>o.jensen@lse.ac.uk</u> +44 7985 934 330 <u>frederic.blanc-brude@kcl.ac.uk</u>

## World Bank Policy Research Working Paper 3937, June 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The authors would like to thank Antonio Estache, Roger Strange and Stephane Straub and participants of the Applied Infrastructure Research Conference (Berlin, October 2005) for their helpful comments.

1
4 The survey data are rather unique. Because of widespread metering of households with a piped water connection, the consumption data have a high degree of accuracy that is not often found. In addition, the dataset is complemented by a large set of socio-economic and health variables.

# **2.1. Piped households**

Among the surveyed households, 38 percent had a private connection to the piped network (for further purposes of the study, we removed from the sample the 84 households who did not report any monthly water use). Of the households with a private connection 23 percent had an in-house private connection, 19 percent had (only) a yard connection, and 58 percent had both. Piped households consume on average about 135 liters of water per capita per day from the piped network.

Piped households had to pay SLK 8,415 (equivalent to US$87 in 2003) in order to get a private connection to the piped network (including road cutting, pipe laying, meter installation). This represents about half of the monthly wage for a piped household.

Water from the piped network is charged through an increasing five-block tariff. The same tariff applies to all piped households in our sample. Marginal price varies from SLK 1.25 per cubic meter in the lowest block (for any unit below 10 cubic meters per month) to SLK 45 per cubic meter for any unit above 25 cubic meters per month. Households are almost equally distributed across the five blocks.<sup>4</sup> The water bill, which includes a fixed fee of SLK 50, is sent every month to each household connected to the piped network. The typical or median monthly water bill is SLK 89, while a typical household spends SLK 10,300 on household expenses – suggesting that the costs of piped water supply makes up less than 1 percent of household expenditure. The typical water bill for the poor (defined as a piped household with an income falling in the first quartile of the income distribution) is SLK 72 (which represents about 1 percent of household expenditure).

Piped households have been asked to give their opinion about the quality of the piped water service. Overall, 25 percent of households with piped water declared themselves to be

> 4 Block 1: [1-10 m3], price is 1.25 SLK/m3; block 2: [11-15 m3], 2.50 SLK/m3; block 3: [16-20 m3], 6.50 SLK/m<sup>3</sup> ; block 4: [21-25 m<sup>3</sup> ], 20.00 SLK/m<sup>3</sup> ; block 5: [>25 m<sup>3</sup> ], 45.00 SLK/m<sup>3</sup> .