rafmacalaba/datause-encoder-data huggingface dataset:
4. Guidelines for good practices in the design of laws and implementation methods should be devised, and should include assessments of the specific procedures that are applied in different countries and mappings from existing guidelines to those recommended by major international organizations, such as the World Bank and UNICITRAL. This type of analysis should identify the important and desirable features of laws and procurement processes—including, for example, the reliance on standards and norms that apply to procedures along the lines that were developed for construction procurement in South Africa (see Watermeyer, 2004a, b). Related to this, a critical assessment of the costs and benefits of existing guidelines that are applied by institutions, such as the major development banks, would be very useful. What do these guidelines actually achieve? Are they outdated given the rise of the Internet and the potential for government to business portals to introduce market forces into the procurement process through real-time reverse auctions and the like?

5. The factual record on outcomes from procurement regimes in developing countries (including the number of contracts put out to tender, indicators of the use of selective or limited tendering, the use of preference margins in contracts, the number of firms submitting bids for a contract, the propensity to award a contract to the lowest bidder, and the propensity to source from abroad or from domestically favored groups) should be assembled. Of course, countries differ markedly in the amount of data that they collect and make available publicly, but with the growing use of computers in procurement systems, more developing countries are assembling such data. Subject to any concerns about confidentiality and the like, bringing these data together would facilitate policy analysis. Data on the observed outcomes from state procurement could be complemented by information obtained from surveys of business people and others’ perceptions of the “fairness” or otherwise of national procurement systems.

6. Given the role that discriminating between suppliers for state contracts plays in many nations’ procurement regimes, more research is needed that identifies the costs and benefits of such discrimination, especially as it relates to set-aside policies and to international trade. At present opinion is divided as to the merits of such discrimination. A balanced analysis and account of the pros and cons of such measures that draws upon available empirical research would help both policymakers 23
-  29  -

a 19-percent increase in total EU-destined exports), and their share rose from 24 percent to 32 percent. But it fell to 28 percent in 1994. Since the share of unskilled labor intensive products in EU-oriented exports of Czech Republic, Hungary and Poland had also been contracting since 199293, there was nothing unusual about it.<sup>15</sup> However, while 1992-93 witnessed what turned out to be historically peak levels of the shares of unskilled labor intensive products in EU-oriented exports from these countries, Bulgaria’s pattern has been diverging. The 1994-96 witnessed a major realignment in the export growth pattern, with skilled labor and capital intensive products emerging as major levers of Bulgaria’s export expansion, turned out not to be sustainable (Figure 4).<sup>16</sup>

**Figure 4:** _Factor intensities of Bulgaria EU-oriented exports in 1991-2003(in percent)_

<!-- Start of picture text --> 45<br>40 Capital<br>35 Intensive<br>30<br>Skilled<br>25 Labor<br>20<br>15 Natural<br>Resource<br>10<br>5 Unskilled<br>0 Labor<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Based on EU Data from UN COMTRADE Statistics.

Bulgaria’s pattern in terms of export intensities has become a complete reversal of patterns observed in Hungary or Poland as well as in other transition economies in that period. This was a repetition of the patterns observed during the initial stages, albeit on a much larger scale, as the share of unskilled labor intensive products rapidly expanded to levels well above the earlier peak in 1993 and their expansion extended over a much longer period of time. The share of both skilled labor and capital intensive products in Bulgaria’s EU exports after increases in 1994-95 has subsequently, whereas unskilled labor intensive drove Bulgaria’s exports until 2001, with their share rising to 40 percent and stabilizing at this level.

Bulgaria’s idiosyncratic factor intensities extended to total exports still in 1996 (first year for which reliable foreign trade data are available). Given the relative endowments in capital across regional peers, one would not expect such a significant presence of capital intensive products in Bulgaria’s total exports. Yet, the share of capital intensive products was higher in Bulgaria’s exports than in total exports of all other comparators in 1996 (Table 21). This may come as no surprise vis-à-vis SEE-4, but it is rather startling when cast against much more developed CEEC-8 economies taken as a bloc. The share in CEEC-8 exports was four percentage points lower than in Bulgaria’s, while the share of unskilled labor products four percentage points larger. In a similar vein, unskilled labor intensive products had much larger presence in both Croatian and Romanian exports than in Bulgaria’s.

15 See the calculations reported in World Bank 1999.

16 The value of total EU-oriented exports increased 85 percent in 1993-96. The value of skilled labor intensive products quadrupled and that of capital intensive exports. Simultaneously, the share of unskilled labor intensive declined
Equation (7) is estimated based on national “use” (input-output) tables between 2000 and 2014 for China and India – countries which experienced high rates of growth in services value added between 2000 and 2014.

# <u>Results</u>

The contribution of final demand to the growth of professional, scientific and technical services between 2000 and 2014 in India – at almost 90 percent – indicates that the expansion of the sector was largely “stand-alone, i.e. direct transactions between service providers and final consumers. In fact, among the different components of final demand, net exports accounted for 66 percent of the subsector’s growth compared to 20 percent for consumption and 3 percent for investment (Table 9). This is indicative of India’s success in the export of offshore professional services, starting with BPO services such as contact and call centers, which laid the foundation for higher value-added services such as software, finance and accounting (Nayyar 2012).

In contrast, the contribution of final demand to the growth of professional, scientific and technical services in China between 2000 and 2014, was 24 percent and that of (net) exports was only 1 percent. The contribution of intermediate demand in the growth of these services was therefore paramount, but this was not driven by links with the manufacturing sector. In fact, what seemed to matter most was the input of professional scientific and technical services into mining, utilities and construction as well as other services (Table 9).

Final demand mattered less for the growth of other high-productivity services. For example, it accounted for 50 percent of the growth in wholesale and retail trade in China and India between 2000 and 2014 (Table 7). In the case of transportation and storage services, final demand mattered less, accounting for approximately 31 percent of the sector’s growth in China (Table 8). Furthermore, the large contribution inputs into manufacturing value added within the intermediate demand component indicates that the growth of these services subsectors might depend more on a manufacturing core. Services input into manufacturing value added accounted for, respectively, 62 percent and 38 percent of annual average wholesale and retail trade services value added growth between 2000 and 2014 in China and India. The input of these wholesale and retail trade services into other sectors made a substantially smaller contribution to its growth (Table 7). The growth of transportation services presents a similar picture.

That these services may “need” a manufacturing core to develop does not take away from the fact that transportation and distribution services are vital inputs into the production of manufactured goods. Hence, to the extent that services are embodied in manufacturing, there will likely be a symbiotic relationship between the two sectors. For example, input into manufacturing accounted for 59 percent of the annual average value-added growth in transportation and communication services between 2000 and 2014 in India, while manufacturing input into these services accounted 13
In section 3, we continue by illustrating that the welfare implications of real exchange rate targeting may depend on the relevant circumstances. In a neoclassical framework, real exchange rate targeting is typically Pareto-inefficient. However, there may be distributional implications that favor competing exporters, and harm importers. In contrast, Keynesian models and models of export externalities imply that strategic depreciations are sometimes Pareto efficient, and may benefit both the country itself and its importers (though typically not competing exporters).

Next, in section 4, we illustrate that a wide variety of government policies may affect the real exchange rate. The government can affect the real exchange rate by subsidizing the export sector directly, by taxing production inputs at different rates, or by distorting domestic saving decisions. The problem is that many of the policies that affect the real exchange rate are not necessarily targeted at manipulating the real exchange rate. For example, a one-child policy may result in higher domestic savings, and a depreciated real exchange rate; however, the one-child policy was probably not instituted in order to manipulate the exchange rate, but rather in order to stop population growth. The conceptual line between government policies that affect the real exchange rate and real exchange rate _targeting_ is blurry.

The issue is complicated further as there are potentially fundamental sources of real exchange rate misalignment. A real-world economy with frictions typically implies a real exchange rate that is different from its frictionless benchmark. For example, financially underdeveloped economies may naturally have depreciated real exchange rates, both because of the precautionary savings motive and because exporting firms may have better access to credit. A misaligned real exchange rate need not be a product of manipulative or even un-manipulative policy, but rather a byproduct of an environment with frictions.

Finally, in section 5, we turn to the issues of measurement, and illustrate the difficulties in measuring real exchange rate misalignments and assessing their causes. We begin by discussing the three methodologies currently used by the IMF to measure real exchange rate misalignments. The key problem with these measurements is that the “misalignment” is measured as a deviation from some country “norm”, that is calibrated based on other similar countries taking into account country specifics such as government policies and financial development. In other words, 3
<u>Table 3: Baseline estimation results.</u> Baseline IV Alt. 1 Alt. 2 Alt. 3 Entry Cost -0.142*** -0.06 -0.128** — -0.098* [0.055] [0.102] [0.054] [0.051] Export Cost -0.322*** — -0.918*** -0.424*** — -0.280*** [0.092] [0.351] [0.094] [0.087] Entry Procs. -0.394** [0.156] Export Docs. -0.408 [0.248] Distance -0.418*** -0.512*** -0.364*** -0.444*** 0. 455*** [0.088] [0.096] [0.099] [0.078] [0.060] Tariff (EU) -0.639 0.414 0.335 -1.207 -0.217 [1.541] [1.595] [1.514] [1.555] [1.398] Tariff(Own) 0.431 -1.608 0.436 0.424 0.319 [0.418] [2.674] [0.429] [0.312] [0.296] GDP 0.452*** 0.504*** 0.462*** —0.487*** [0.028] [0.025] [0.026] [0.020] GDPPC 0.087 -0.004 0.037 0.022 [0.054] [0.051] [0.067] [0.060] GDP 2000 0.434*** [0.045] GDPPC 2000 -0.086 [0.094] Manuf. % 0.385*** = 0.345*** [0.105] [0.084] Ag. % -0.04 0.01 [0.080] [0.063] GDP Defi. -0.247*** [0.071] Real Int. Rate 0.024 [0.041] Constant 0.466 6.822 8.136 0.674 0.103 [1.213] [4.192] [9.352] [1.175] [1.046] Obs. 11328 11328 11328 10752 9024 Ag. %

1. Estimation is by Poisson, with dependent variable lines_cn2. Independent variables are in logarithms. All models include fixed effects by 2-digit sector. Robust standard errors, adjusted for clustering by exporter, are in square brackets. Statistical significance is indicated by * (10%), ** (5%), and *** (1%).

2. Specification IV uses colonization dummies and distance from the equator as instruments for entry and export costs, and own tariffs. First stage R? is 0.4, 0.3, and 0.3 respectively.

35
# **Analysis by Importers’ Characteristics**

# **Main activity**

The analysis of average dwell time with regard to the shippers’ main activity indicates that manufacturers perform better than traders overall (Figure 12). Moreover, manufacturers have a significant lower average dwell time – one day lower – than the other importers (t(313.826) = 1.679; p = 0.047)<sup>7</sup> . They appear to be more efficient and should be the primary counterparts of Customs/Terminals in contractualization initiatives because they master their logistics (raw materials or intermediary products) and can reach top performance by implementing efficient processes.

# **Figure 12:** **<u>Cargo Dwell Time, by Shippers’ Main Activity</u>**

<!-- Start of picture text --> 20 18.60<br>18<br>16<br>14<br>12<br>10 8.45 8.06 9.59 8.12 8.91 9.17<br>8<br>6<br>4<br>2<br>0<br>Manufacturing Assembling Domestic Transit Small & Other Total<br>Wholesaler Wholesaler Medium<br>Retailer<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

Furthermore, small and medium retailers experience the longest dwell time. They have a significant longer dwell time than the other importers– around 10 days longer (t(9.056) = 1.7991; p = 0.05)). This is not surprising: small retailers generally do not have their own warehouse and probably use the port as a storage facility; they may also experience a slower clearance process than larger shippers<sup>8</sup> . This may be due to informal practices and possible ―negotiations‖ with regard to lowering tariff duties and thus the cost of imports. These findings are consistent with the assumption of the theoretical model (presented in section 2), which posits that companies intentionally leave their cargo in ports since these are cheap storage units.

The analysis by country illustrates that small and medium retailers are more likely to experience long dwell times in Nigeria, Zambia, Uganda, and, to a lesser extent, South Africa, which confirms our assumptions (Figure 13).

The preponderance of trading is self-reinforcing: retailers have longer dwell times, and this makes port dwell time longer for everyone. This constitutes a barrier to assembling industries, which then paves the way for retailers to constitute a large share of port users.

> 7 All t-tests in this chapter are run on groups with unequal variances, thus Satterthwaite's approximation is computed instead of the usual degree of freedom.

> 8 The alternative for them is to rent warehouses spaces from warehouses operators.

24
from the identity _z_<sup>_∗_</sup> = _nh_<sup>_∗_</sup> _e_<sup>_∗_</sup> ) from his/her optimal choice of income and hours worked as well as the labor supply elasticities of income and hours worked with respect to the tax rate.

How can we use labor supply elasticities to infer optimal effort per hour from observable quantities _z_<sup>_∗_</sup> and _h_<sup>_∗_</sup> ? First, note that log( _e_<sup>_∗_</sup> (log( _n_ ) _,_ log( _α_ ))) = log( _z_<sup>_∗_</sup> (log( _n_ ) _,_ log( _α_ ))) _−_ log( _h_<sup>_∗_</sup> (log( _n_ ) _,_ log( _α_ ))) _−_ log( _n_ ). Under the conditions in Proposition 4.2, we can invert the relationship between ( _z_<sup>_∗_</sup> _, h_<sup>_∗_</sup> ) and ( _n, α_ ) so as to write _n_ and _α_ in terms of _z_<sup>_∗_</sup> and _h_<sup>_∗_</sup> . Hence, we can also write _e_<sup>_∗_</sup> as a function of _z_<sup>_∗_</sup> and _h_<sup>_∗_</sup> . We have that: For ease of explanation, let us assume that income effects are negligible. In this case we can show:<sup>23</sup> The first equality in Equation 8 comes from differentiating Equation 7 and the second _∂_ log( _n_ <u>)</u> equality uses the equation for _∂_ log( _h_<sup>_∗_</sup> )<sup>from the inverse Jacobian in Proposition 4.2.Next,</sup> note that _ξz_<sup>_c−ξ_</sup> _h_<sup>_c_isequaltotheelasticityofeffortperhourwithrespecttothetaxrate:</sup> Hence, Equation 8 is intuitive: individuals’ optimal effort per hour changes in proportion to their optimal hours worked in accordance with the ratio of the effort elasticity, _ξe_<sup>_c_=</sup> _∂_ log( _n_ <u>)</u> _ξz_<sup>_c−ξ_</sup> _h_<sup>_c_,tothehourselasticity,</sup><sup>_ξ_</sup> _h_<sup>_c_.Next,weuse</sup> _∂_ log( _z_<sup>_∗_</sup> )<sup>fromtheinverseJacobianin</sup> Proposition 4.2 to show that: Solving the system of partial differential equations given by 8 and 9 allows us to infer optimal effort for any optimal level of income and hours worked. If we again assume all relevant elasticities are constant, we have (for some constant _k_ , which we can normalize to 0 without loss of generality): Hence, by observing both the income and the hours elasticity (with respect to the tax rate), we can infer optimal effort decisions. Once we infer optimal log( _e_<sup>_∗_</sup> ) associated

> 23See Appendix A.9 for a discussion of where this formula comes from and how it changes with income

19
_Social Development_ Two aspects of social development are considered: human development and income inequality. For ranking countries in terms of their achievements on human development, we solely rely on the United Nations' index on human development. This index incorporates life expectancy, adult literacy, educational enrollments and per capita GDP in purchasing power parity terms. Egalitarian nature of the society is captured by an inverse rank on the Gini coefficients estimated by Deininger and Squire (1996). Table 3 shows that fiscal decentralization is positively correlated and statistically significant with both the indices.

# _Institutional Environmentfor Macroeconomic Management_

It is frequently argued that a decentralized public policy environment of the type found in developing countries contributes "to the aggravation of macroeconomic problems" (Tanzi, 1996, p.305). In the following, we reflect upon the available empirical evidence on aspects of institutional framework for monetary and fiscal policies to form a perspective on this issue.

# _Institutional Settingfor Monetary Policy_

Monetary policy is clearly a central function and best entrusted to an independent central bank (Shah, 1994, p.<sup>1</sup> 1). The critical question then is whether or not independence of central bank is compromised under a decentralized fiscal system. One would expect, a priori, that the central bank would have greater independence under a decentralized system since such a system would require clarification of rules under which a central bank operates, its functions and its relationships with various governments. For example, when Brazil in 1988 introduced a decentralized federal constitution, it significantly enhanced the independence of the central bank (Bomfim and Shah, 1994). On the other hand, in centralized countries the role of the central bank is typically shaped and influenced by the Ministry of Finance. In an extreme case, the functions of the central bank of the U.K., the Bank of England, are not defined by law but have developed over time by a tradition fostered by the U.K. Treasury.

For a systematic examination of this question, we relate the evidence presented in Cukierman, Webb and Neyapti (1992) on central bank independence for 80 countries to indices 24
employment allocation by sector in all countries at one point in time. Figure 2 is a stylized representation of this relationship, and we can see that in higher income countries, the share of the services sector is greater by far, than that of industry and agriculture.

As per capita income grows, there are a number of things that induce the change in employment allocation. On the one hand, consumption patterns change in the sense that households can afford more than just basic needs items. The opportunity cost of time spent in the household rises (as individuals recognize that there are employment opportunities in the labor market). This encourages a reorganization of the household economy and an increase in labor force participation, especially among young women. There is also an increasing reliance on the market economy and a surge in the activity of, for example, the transportation sector, food vendors, and repair shops. All of these jobs, along with those in commerce, banking, government, health, education, and entertainment, make up the service economy that absorbs an increasing share of the labor force.

Figure 2: Development shifts workers from agriculture to industry and services <!-- Start of picture text --> 100<br>80<br>. .Share of labor<br>60 force in services<br>. C)1 _ 6 * Share of laborforce in industry<br>40 - Share of labor<br>force in agriculture<br>20<br>n0 O . _ .D .. . .N . . N .<br>t to) U) cD C0 N' OD co) 0)<br>Log GNP per capita 1980 ($US)<br><!-- End of picture text --> Source: World Bank staff estimates; ILO.

As the sectoral distribution of employment changes, the type of employment also changes. From a situation where most workers were self-employed in the agriculture sector, countries experience a steady increase in wage employment as a percentage of total employment. In relative terms, the labor market becomes more important to the household economy in the allocation of time, and trade-offs in that time allocation intensify. Employers must compensate workers in relationship to their opportunity costs if they want to secure a stable and reliable labor force. The changes in organization of production, along with the use of labor-saving equipment, makes possible an increase in labor productivity everywhere.

**11**
O’Higgins <!-- Start of picture text --> IPF Mapping MCE Ranking<br>ID Name Score<br>O'Higgins<br>29 San Francisco 1 (Zonada) 6.2<br>P49<br>100 P44 P45 30 San Francisco 2 (Zonada) 6.2<br>90 P38 31 San Francisco 3 (Zonada) 6.2<br>80 P40 P39 28 Codegua (CFGD) 6.4<br>P48<br>70 P32 P42 P30 P47 37 Manquehue 1 (Zonada) 6.5<br>P33 P41 P28 38 Manquehue 2 (Zonada) 6.5<br>60<br>50 P34 P43 P29 P31 P37 41 Estero Seco (CFGD) 6.7<br>36 Ucúquer (CFRD) 7.6<br>40<br>32 El Maiten 1 (Zonada)* 7.8<br>30 P35<br>33 El Maiten 2 (Zonada)* 7.8<br>20<br>34 El Maiten 3 (Zonada)* 7.8<br>10<br>P36 P46 35 Huehuinco (CFRD)* 7.8<br>0<br>40 Las Palmas 2 (RCC) 8.2<br>42 Cementerio 1 (Hormigón) -<br>FEI<br>43 Cementerio 2 (Hormigón) -<br>Valparaiso<br>IPF Mapping MCE Ranking<br>ID Name Score<br>Valparaiso<br>55 Pullally 3.04<br>100 P59<br>54 Santa Marta 3.26<br>90<br>80 60 Santa Julia 4.48<br>70<br>59 Valle Hermoso* 4.85<br>60 P53<br>56 Cuncumen 1 5.11<br>50 P52<br>40 P61 P60 58 El Zaino 2* 5.18<br>30 P50 P55 P54 52 Chalaco* 5.60<br>P51<br>20 P56 P57 P58 57 Lo Zárate 2 5.62<br>10<br>53 Las Carditas 2* 6.18<br>0<br>50 Vitahue* 6.31<br>FEI<br>51 Paihuen* 6.50<br>SEI<br>0 10 20 30 40 50 60 70 80 90 100<br>SEI<br>0 10 20 30 40 50 60 70 80 90 100<br><!-- End of picture text -->

- Not recommended due to environmental restriction

Note: Some projects that entered the database later do not have a corresponding MCE ranking, such as Project 39, 44-49 in O’ Higgins and Project 61 in Valparaiso 35