rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4670

# **Abstract**

Until the late 1960s, the World Bank presented itself as an institution devoted to making sound and directly productive project loans. Yet, during its very early years, some discussions developed inside the Bank regarding the possibility of issuing different types of loans, namely (i) loans aimed at tackling social issues (“social loans”), and (ii) loans aimed at providing foreign currency to address disequilibria in the balance of payments (“impact loans”). This paper brings together historical analysis and theories of organization development to study the housing issue as a case in point. The analysis reveals that the Bank was unwilling to lend for housing programs not because these were not sound—in fact, they were—but because they were geared toward achieving social welfare objectives and were not directly linked to productive investment projects, such as dams, power stations, and railroads. This early decision had a significant impact on the subsequent development of the Bank’s view of policy-making: it locked the institution into a particular lending pattern, and deprived it of important intellectual resources. It was not until the late 1960s that the Bank began to take social issues into consideration, rather late compared with other multilateral institutions.

This paper—a product of the World Bank Archives and the Development Economics Vice Presidency—is part of a larger effort to use the Bank’s Archives to investigate the links between development economics and operational policy. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Michele.Alacevich@unimi.it _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
often believe that the role of the DB is to finance riskier projects and offer low interest rate financing. But then policy makers need to accept that a high risk - low rate of return model will not be financially sustainable over the long run. If the government wants the DB to address market gaps and thereby take more risk, but it does not want to be continually called upon to bear the cost or subsidize the operations of the DB, the DB must be careful not to under price risk. For first tier DBs, to be able to maintain this delicate balance, they must be professionally managed with strong credit underwriting skills and operate efficiently as commercial enterprises at the risk frontier of the commercial banks. In the case of the BDC, as the bank operates with riskier SMEs that do not have access to private sources, its loan rates are higher than those charged by private commercial banks.

In addition to effective risk-pricing, the provision of technical assistance to borrowers can help managing risks by reducing project risks. For example, the BDC offers in addition to loans, technical assistance to borrowers in the form of management consulting to help strengthen the capacity of its entrepreneurs. As a result, BDC clients who received financing and consulting achieved 60 percent higher revenue growth, employment growth and survival rates than nonBDC clients.<sup>9</sup> Another way in which a first-tier DB can complement and increase private sector participation in the target market is by participating in syndicated loans or club-deals. In some cases, given exposure limits, domestic funds simply do not have the capacity to fund large projects, and the participation of a DB can solve that constraint. Also, private participants may view the participation of the government in the financing may reduce the project political risks. Thus, DBs become partners of the private system to finance priority sectors (such environmental, or infrastructure) through the use of co-financing instruments where risk is studied and shared jointly. Prohibiting DBs from financing more than certain percentage of a project (and in no case more than 50 percent) ensures that the public sector will not compete with the private sector and that the projects undertaken withstand the sustainability test applied by the private sector.

> 9 Full study is available at: www.bdc.ca/smeresearch.

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are members of the same geographic region as the province _i_ , and _GAWFi_ , _t_ for neighbor provinces that belong to a different geographic region.<sup>20</sup>

> Therefore, one province _i_ may have up to four different types of neighbor provinces according to the two criteria.<sup>21</sup> By construction, the weight of the growth rate of one neighbor province _q_ ( _impor_ tan _cei_ , _q_ , _t_ ) is equal to the ratio of its economic size to the total economic size of the group of the neighbor provinces, noted that the sum of such

# **Section 3: Spillover effects and regional growth**

In this section, we examine the spillover effects on regional growth by integrating the "neighbor performance" into the Solow-type growth model<sup>22</sup> using the panel data at provincial level of the period of 1978-1999 from the China Statistical Yearbooks. In order to examine the net effects of economic performance of the neighbors, we control the traditional growth determinants – the initial development level (<sup>_y_</sup> _i_ , _t_ − 1) , investment rate (<sup>_s_</sup> _i_ , _t_<sup>), demographic growth rate (</sup><sup>_n_</sup> _i_ , _t_<sup>). In order to capture the role of geo-economic</sup> position on regional growth, we introduce an indicator "peripheral degree", noted as _DPi_ , _t_ , which equals to weighted average distance of one province to the domestic economic center adjusted by the development level of the infrastructure<sup>23</sup> . The results are

> 20 Here, _M_ stands for the neighbor provinces that are members of the same geographic region of the

> province _i_ in question; while _F_ for the neighbor provinces that are members of a different geographic region.

21 In fact, not each province has all the four different types of neighbor provinces. For example, Shanghai, the richest coastal province, which is surrounding by coastal provinces Jiangsu and Zhejiang, has only one type of neighbor provinces – the coastal neighbors that are poorer than itself ( _GAWCOMPi_ , _t_ ). In other

> words, in the case of Shanghai, the other three indicators _GAWCOMRi_ , _t_ , _GAWCOFRi_ , _t_ and

_GAWCOFPi_ , _t_ have no value. Therefore, the number of different types of neighbor province varies between 1 and 4.

> 22 See, for example, Barro (1991), Barro and Sala-i-Martin (1995).

23 See Annex 2 for the construction of the indicator "peripheral degree", and see Luo (2001; 2004) for further discussions. This indicator measures the market accessibility of the province in question.

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Table 5: Are similar shocks evident in Steel products?

||(1)|(2)|(3)|(4)||(5)|
|---|---|---|---|---|---|---|
|VARIABLES|ln(_SMit_) =|ln(<sup>_SPit_</sup><br>_Pit_ <sup>)</sup><br>+|ln(_Pit_)<br>+|ln(<sup>_SQit_</sup><br>_Qit_ <sup>)</sup>|+|ln(_Qit_)|
|1 Qtr before fle|0.14***|-0.04**|-0.03***|0.28***||-0.06*|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|2 Qtrs before fle|0.18***|-0.04**|-0.00|0.28***||-0.06*|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|3 Qtrs before fle|0.15***|-0.05**|-0.00|0.23***||-0.03|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|4 Qtrs before fle|0.13***|-0.06***|-0.01|0.18***||0.02|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|5 Qtrs before fle|0.16***|-0.04*|-0.01|0.19***||0.02|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|6 Qtrs before fle|0.05|-0.03|-0.02|0.13***||-0.03|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|7 Qtrs before fle|-0.06|0.00|0.00|0.02||-0.09***|
||(0.04)|(0.02)|(0.01)|(0.04)||(0.03)|
|Observations|10294|10294|10294|10294||10294|
|_R_<sup>2</sup>|0.791|0.567|0.942|0.696||0.889|

Estimates include month, year, petition and HS-10 fixed effects Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 them likely candidates for an AD petition, but a product specific negative demand shock appears to be an important factor in the choice of subject products.

# **5.3 Robustness Exercises**

The previous section argued that at least two shocks precede an AD petition: a broad and sustained relative supply shock favoring exports from subject countries, followed by a relatively narrow demand shock that hits imports from all sources. These results were based on pooled data, so it is of interest to examine whether these patterns are evident amongst cases with different characteristics. With this in mind we consider two broad subsamples, one based on product characteristics and the other based on a temporal consideration. In both cases the results presented are based on estimates associated with equation (4).

Consider first a subsample limited to cases involving the steel industry, which is a heavy user of the anti-dumping law. Studies of anti-dumping activity have shown differences between cases 21
Panel A. Predictive Probability Density Function and the Realized Value Line Panel B. Predictive Cumulative Distribution Function and the Realization Cumulative Distribution Function <!-- Start of picture text --> 0.35 1<br>Predictive density 0.9 Predictive CDF<br>0.3 Realized value Realized CDF<br>0.8<br>0.25 0.7<br>0.6<br>0.2<br>0.5<br>0.15<br>0.4<br>0.1 0.3<br>0.2<br>0.05<br>0.1<br>0 0<br>-2 -1 0 1 2 3 4 5 6 7 8 -2 -1 0 1 2 3 4 5 6 7 8<br>Y random variable Y random variable<br>PDF of Y CDF of Y<br><!-- End of picture text --> FIGURE 8. PROBABILITY FUNCTIONS OF AN ISSUED DENSITY FORECAST AND THE REALIZED VALUE On the other hand, the CDF for the value of the realized event-variable is denoted as ss YYaa = . Note that is the indicator function 1 . Under this notation, )≥0} PPaa(ss) PP(YYaa ≤ss) PPaa(ss) {(ss−ssaa the CRPS definition can be stated as presented in (33). Figure 8 shows an example of elicited probability density and cumulative distribution functions for the released predicting variable YY and the realized variable . YYaa ∞ 2 (33) ) = ∫−∞�<sup>PP(ss) −PP</sup> � . CCRRPPSS= CCRRPPSS(PP, ssaa aa<sup>(ss)</sup> mmss The CRPS ranks the distance between the issued event probabilities, and the PP(ss) materialized event probability, . Then, the CRPS measures the difference between the PPaa(ss) predicted and the occurred cumulative distributions. The CRPS best and minimal value of zero is only achieved when , that is, in the case of a perfect categorical PP(ss) = PPaa(ss) (deterministic) foresight. The CRPS has the dimension of the random variable , which is YY entered via the integration over . Besides, the CRPS can be understood as the limit of a mmss ranked probability score (RPS)—Epstein (1969) and Murphy (1971)—with an infinite number of classes and each with zero width.

The orientation of the CRPS is negative; that is, we prefer the scores with the smallest values. The lower the CRPS score, the better the prediction characteristics of the forecaster. Most 44
Figure 9a: Combined wage effects socio-demographic changes, immigration and emigration, 2000 – 2010 (%), for Old Low-Skilled <!-- Start of picture text --> USA CAN AUS GBR DEU AUT CHE FRA BEL NLD LUX ITA ESP PRT GRC DNK SWE NOR FIN CZE HUN SVK POL JPN TUR<br>NZL<br>IRL<br>Socio−demographic Immigration Emigration Combined<br>30%<br>20%<br>10%<br>0%<br>−10%<br>−20%<br>−30%<br><!-- End of picture text --> The figure plots equilibrium wage responses to overall changes in the structure of the labor force Figure 9b: Combined wage effects socio-demographic changes, immigration and emigration, 2000 – 2010 (%), for Young Low-Skilled <!-- Start of picture text --> USA CAN AUS NZL GBR DEU AUT CHE FRA BEL NLD LUX ITA ESP PRT GRC DNK SWE NOR FIN CZE HUN SVK POL JPN TUR<br>IRL<br>Socio−demographic Immigration Emigration Combined<br>30%<br>20%<br>10%<br>0%<br>−10%<br>−20%<br>−30%<br><!-- End of picture text --> The figure plots equilibrium wage responses to overall changes in the structure of the labor force 38
inputs. A deeper reason for the jobless growth in transition economies in general and in Lithuania in particular, is the process of overcoming the legacy of overmanning, inherited from the communist past. Many companies entered the transition with employment above that justified by production requirements. A growing exposure to domestic and international competition coupled the influx of new and more capital intensive technologies has led companies to rationalize employment and shed labor. This process has been spread over time. Although (as we will show later) the destruction of unproductive jobs has been coupled with the creation of new and more productive jobs, job losses have exceeded job gains, leading to a net fall in employment.

**Table 1 Dynamics of GDP, Employment, Productivity and Wages, (1995=100)**

|||||||Average<br>annualrate|
|---|---|---|---|---|---|---|
||1996|1997|1998|1999|2000|ofgrowth<br>(1995-2000),|
|GDPgrowth|_105_|112|118|113|118|3.3|
|Employment growth|101|102|101|100|96|-0.7|
|Productivitygrowth|104|111|117|113|122|4.1|
|Realwagegrowth|103|119|136|143|144|7.6|

Note: Real wage growth is based on gross earnngs.

_Source: Statistics Lithuania, Bank staff calculations._ In the long-run, productivity improvements can be conducive to employment growth as ceteris paribus they imply a decrease in unit labor costs and thus an increase labor demand.' However, this potentially positive effect hinges on the development of wages. If wages grow faster than productivity, unit labor cost increases with detrimental effects on labor demand. That is what is happening in Lithuania. Since 1996 real wages have grown by 7.6 percent per year, outpacing labor productivity growth by a large margin. This implies an increase in labor costs and may have contributed to jobless growth. So far, the benefits of economic growth that has occurred since 1996 have materialized in the form of higher wages rather than higher employment.

> l A relevant measure is so called total factor productivity (tfp), which reflects technological progress, however estimating tfp would go beyond the scope of this paper.

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15 There is another reason that would tend to lead to enrollment ratios being seen as imperfect proxies even for changes in the human capital stock. Note that the stock of human capital is affected by net additions to the labor force, which is the difference between human capital embodied in the labor force entrants (inflows) and the human capital embodied in those who retire from the labor force (outflows). In other words, net human capital investment flows are the proper relevant measure of human capital investment flows. It should be clear that enrollment ratios are gross investment measures and that they proxy only for human capital inflows but not human capital outflows.

In light of the above, current school enrollment ratios are unlikely to have a contemporaneous and stable relationship to the stock of human capital embodied in the current productive labor force of an economy. As such, they would tend to be poor indicators of the human capital stock that exist within an economy.

# **Average Years of Schooling**

The above-mentioned deficiencies render the adult literacy rate and school enrollment ratios as inappropriate proxies for the economically productive human capital stock. As a result, the literature in recent years has turned to using the average years of schooling of the adult population as the most common measurement for the human capital stock<sup>24</sup> . Average years of schooling, or equivalently average educational attainment, is clearly a stock measure and actually reflects the accumulated educational investment embodied in the current labor force. Thus, in accord with the current literature, we use the average years of schooling as our basic indicator for the current productive human capital stock<sup>25</sup> .

There are two main global data sets on educational attainment. The first and probably more widely used data set is that of Barro and Lee (1993, 2000). This data set contains information on educational attainment for persons aged 15 years or older for many countries spanning the period 1960 to 2000 on a 5-year basis. There are 142 countries with at least one observation and 107 countries with complete information. Their data set is based on school attainment figures obtained from UNESCO and they estimate missing values using data on school enrollment rates and the structure of the population by age groups. More specifically, their fill-in procedure uses the gross enrollment rate, adjusted for repeaters. Barro and Lee claim that this measure reflects the inflows of new school graduates to existing educational stock more accurately than the

> 24For example, see Barro and Sala-i-Martin (1995), Barro (1997, 1999), Benhabib and Spiegel (1994), Gundlach (1995), Isham (1995), Krueger and Lindahl (2000), O’Neill (1995) and Temple (1999b).

> 25Note that the measure of the average years of schooling as a proxy for the productive human capital stock is not without its own deficiencies.  It will be seen later that our _actual_ measure of the human capital stock attempts to account for some of these deficiencies.
increases the unit material cost by 3.3 percent and decreases that by 0.8 percent respectively, and both effects do not appear to be statistically significant too. From column (4) it appears that there is correlation between unit material cost, Phase 2 participation and allocated permits, but we are being cautious here on whether these results are causal for reasons explained earlier.

Table 11 Results on Unit Material Cost for Iron and Steel

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Phase 1 Participation|-0.0188|0.0134|-0.00786|0.0335|
||(-1.04)|(1.27)|(-0.53)|(0.54)|
|Phase 2 Participation|0.0192|0.0430***|0.0250|-0.0857|
||(1.07)|(3.37)|(0.83)|(-0.98)|
|Phase 1×Log(Surrendered)||||-0.00125<br>(-0.20)|
|Phase 2×Log(Surrendered)||||-0.0356<br>(-1.37)|
|Phase 1×Log(Allocated)||||-0.00215<br>(-0.35)|
|Phase 2×Log(Allocated)||||0.0438<br>(1.54)|
|Plant Fixed Effects|No|Yes|Yes|Yes|
|Country-by-Year Fixed Effects|No|No|Yes|Yes|
|Observations|950|950|950|950|
|Adjusted_R_<sup>_2_</sup>|0.003|0.033|0.080|0.096|

Note: Dependent variable is the unit material cost, defined as the ratio of total material cost to operating revenue. Standard errors are clustered at the firm level and t statistics are shown in the respective parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

Finally table 12 looks at the effect of EU ETS on firm operating revenue. Both the OLS results and the fixed effects results show a positive and statistically significant relationship between EU ETS and firms’ turnover. These results suggest that Phase 1 and Phase 2 of EU ETS increase the firms’ sales by 48-53% and 24-40% respectively. While it seems unintuitive that a cap-and-trade program (which increases their costs somehow) will increase revenue, this correlation can be explained by some revenue-improving country policies that are correlated with the program timing that also affects the participants. After controlling for time-varying countryspecific factors in column (3), the effects significantly drop and become insignificantly different from zero.

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**Table 2 .  Aspects** **<u>of labor regulation, 2005</u>**

||**Proportion facing**<br>**cost/restriction of**<br>**dismissal**|**Proportion of facing restrictions**<br>**on casual labor**<br>**Proportion facing restrictions**<br>**on hiring temporary workers**|
|---|---|---|
|**Low income states:**|||
|**Low growth**|0.10|0.11<br>0.06|
|**High growth**|0.26|0.20<br>0.13|
|**High income states:**|||
|**Low growth**|0.07|0.06<br>0.05|
|**High growth**|0.11|0.10<br>0.10|
|**Total**|0.13|0.11<br>0.09|
|**obs.**|2267|2271<br>2270|

**Table 3.  Coping with power shortage**

||**Pa**|**nel firms**||**A**|**ll firms**||
|---|---|---|---|---|---|---|
||**2001**|**2004**|**Δ(04-01)**|**2001**|**2004**|**Δ(04-01)**|
|**Proportion owning generator:**|||||||
|**Low income states:**|||||||
|**Low growth**|0.56|0.58|0.02|0.56|0.56|-0.01|
|**High growth**|0.56|0.51|-0.05|0.56|0.39|-0.17|
|**High income states:**|||||||
|**Low growth**|0.44|0.33|-0.11|0.42|0.30|-0.13|
|**High growth**|0.68|0.59|-0.08|0.70|0.59|-0.11|
|**Total**|0.61|0.55|-0.07|0.62|0.52|-0.10|
|**obs.**|1011|1141||1759|2219||
|**Electricty from own generator (%)**|||||||
|**Low income states:**|||||||
|**Low growth**|16.68|21.09|4.41|16.56|26.89|10.32|
|**High growth**|21.57|24.10|2.53|22.50|19.60|-2.90|
|**High income states:**|||||||
|**Low growth**|12.64|19.29|6.64|11.72|22.47|10.75|
|**High growth**|19.81|18.08|-1.73|21.40|19.57|-1.83|
|**Total**|18.23|19.25|1.01|18.97|21.90|2.93|
|**obs.**|1011|608||1759|1126||

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A central aspect in the functioning of these policies refers to their ability to elicit voluntary compliance (World Bank, 2017). This is illustrated, for instance, regarding individuals’ willingness to cooperate fiscally, towards insurance schemes and the provision of public goods. Individuals may choose to ‘exit’ the social contract if the quality of public goods and services received is poor or if coverage is too low (choosing instead to pay for private services or network-based protection). Under – this scenario, workers could view informality more favorably, obtaining flexibility – and perhaps higher earnings without the burden of paying for social insurance that they will not take advantage of. Perry et al. (2007) discuss evidence of informality driven by a voluntary decision to ‘opt out’ of formal work, as a result of a cost-benefit analysis evaluating non-contributory protection programs or informal mechanisms vis-`a-vis formal – often costly – security benefits<sup>6</sup> . Universal coverage schemes based on consumption taxes have been proposed to address high informality, low productivity scenarios, where individuals work without any formal protection. Levy (2008), for instance, suggests converting Mexico’s social security system into one of universal social entitlements, eliminating social security contributions based on wage, and raising value added taxes.

Finally, an important issue to keep in mind as the region moves forward would be to start thinking on how to mitigate the potential effects on job quality of skill-biased technical change. Such a phenomenon of quick technological upgrading (including robotization, digital technologies, etc.) has been impacting parameters of traditional production functions for sectors comprising occupations with high degree of routine

> 6The other line discussed by these authors refers to the case of exclusion, whereby workers are not opting out of formal contracts and would in fact prefer a job in the formal sector.

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