rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Uneven sectoral specialization in GVCs <!-- Start of picture text --> GVC linkages, 2015<br>Low participation<br>Limited commodities<br>High commodities<br>Limited manufacturing<br>Advanced manufacturing<br>and services<br>Innovative activities<br>Data gaps IBRD 44640 | AUGUST 2019<br>Source: WDR 2020 team, based on the GVC taxonomy for 2015 (see box 1.3).<br><!-- End of picture text -->

innovative goods and services. Figures 3 illustrates the extent to which GVC participation has been uneven across countries.

Despite their widespread use in economic research, it is important to emphasize two key limitations of global Input-Output tables. First, because they rely on fairly aggregated Input-Output data, the resulting sectoral disaggregation of GVC flows is pretty coarse, and thus these data sources miss a significant amount of GVC activity occurring within these broadly-defined sectors. For instance, one can compute the origin of “fabricated metal products”in the production of “motor vehicles”in the United States, but where tires, car engines or windshield wipers o ~~riginate fr~~ om cannot be inferred from these datasets. A second ~~key shortcomin~~ g of world Input-Output tables is that, in constructing them, researchers are forced to impose strong assumptions to back out certain bilateral intermediate input trade flows that cannot be readily read from either customs data or national Input-Output tables.

A different, more granular approach to measuring the degree to which production processes are fragmented across countries was first suggested by Yeats (2001) and consists of computing the share of trade flows accounted for by industry categories that can be safely assumed to contain only intermediate inputs (as reflected by the use of the words “Parts of” at the beginning of the product description). Yeats (2001) found that intermediate input categories accounted for about 30% of OECD merchandise exports of machinery and transport equipment in 1995, and that this share had steadily increased from its 26.1% value in 1978. Yeats classification has continued to be refined in recent years based on the Broad Economic Categories (BEC) product classification made available by UNCTAD.<sup>4</sup>

> 4 See https://unstats.un.org/unsd/tradekb/Knowledgebase/50090/Intermediate-Goods-in-Trade-Statistics.

8
# DIAGRAM L4

# MARGINAL REVENUE PRODUCT OF LABOUR SAMPLE VALUES: MIXED ENTERPRISES

<!-- Start of picture text --> I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I<br>' ,,,,,,_,,,.-J-"''''"''''''''" ~~~~~~~~~~~~~00122<br>0.0t22<br>A~~~~~~~~<br>0 1.5 . , 0112<br>p.~~~~~~~~~~~~~~~~~~~~~~~~~0<br>.L o 0.0052<br>N '-/ 0.002<br>0 1~~~~~~~~.0<br>.00,~~~~~~~~~~~~~~~~~~~~~~~~~~008<br>/// ~~~~~~~~~~~~~~~~~~~~~~~0.0032<br>0.0022<br>0 . _ 100 200 300<br>ENTERPRISES ORDERED BY HRP OF LA8LUR<br>'7<br><!-- End of picture text -->
18 investors typically practice high gearing in their investment activities, the risks in the real sectors, and hence to the financial sector, may be relatively large. During the 1980s, these sources of risk appear to have increased in the Indonesian economy, on account of rapid deregulation and growth in both the financial and real sectors, whereas changes in the framework of pnidential regulations have been relatively slow to take place. In the presence of such risks, real domestic interest rates could be expected to be higher than otherwise. One test of this would be the sensitivity of domestic interest rates to the share of private banks' assets in total banking assets in the country, PBA---since arguably, private banks (and their depositors) are exposed to greater risks than in the case of the state-owned banks (because of implicit risk-bearing guarantee, and capacity to do so by the owners, the Government).

32. Testing for the Causes of High Interest Rates in Indonesia. We test for the above hypotheses, tising annual data for the time-period 1983-92 (incorporating estimates for 1992), and within the same basic model presented earlier, but now including the new variables, EDR and PBA. We deviate slightly from the earlier testing framework: (a) instead of testing for the explanations of nominal interest differentials, we directiv test for the determinants of real domestic interest rates; and (b) we also include a test for nominal interest rate determination, since it is the variable on which policy-makers make decisions. The following relationships were estimated by ordinary least squares, with variables as defined earlier: _r = a.CV + z.CAB + y.CDR + x.MPV + n.EDR + m.PBA -- (I0)_ and, _f,,, = h.i""' + z.CAB + y.CDR + x.MPV + n.EDR + ntPBA -- (11)_ 33. The Result. The coefficients of neither the current account balance term, nor the external debt ratio were found to be significant, suggesting that exchange rate or debt repayment risks were evidently not the principal sources of high 'country-risk" in Indonesia. All other variable turned out to be significant, as reported below in Table 3. (Table 3 reports results with the PBA term, but omits the EDR term, which was not significant). The results show that while the central bank discount rate and international interest rates are the more significant factors affecting domestic nominal and real interest rates; risks connected to domestic financial and real sectors (as proxied by the share of private banks in total assets of the banking system) also played
IC to aggregate productivity, average productivity, and allocative efficiency, we find via simulations that the relative 20 percent improvement of infrastructure in Malawi reaches 58 percent, in Eritrea 50 percent, and in Uganda 45 percent (as panel A of figure 5.5 shows). The same holds for average productivity (panel B) where the rankings do not change, and for allocative efficiency (panel C), where, once again, Malawi, Senegal, and Uganda show the largest contributions of the infrastructure group. **Figure 5.6 Infrastructure absolute effects on productivity: Mixed** **_demeaned_ O&P decomposition** <!-- Start of picture text --> A. Aggregate productivity<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>MWI UGA ZMB ETH BEN SEN DZA MLI CMR ERI TZA NER MAR MRT MDG EGY ZAF BFA MUS NAM KEN SWZ BWA<br>Infrastructures Red tape, corruption and crime Finance and corporate governance Quality, innovation and labor skils Other control variables<br>B. Average productivity<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>MWI BEN SEN UGA ETH ZMB DZA ERI MLI CMR MRT BFA NER TZA MA R MDG KEN ZAF MUS EGY SWZ BWA NA M<br>Infrastructures Red tape, corruption and crime Finance and corporate governance Quality, innovation and labor skils Other control variables<br>C. Allocative efficiency<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>MWI SEN NA M DZA UGA TZA MDG BFA MLI ZMB CMR EGY ERI ETH MRT KEN ZAF MUS MAR SWZ BEN NER BWA<br>Inf rastructures Red tape, corruption and crime Finance and corporate governance Quality, innovation and labor skils Other control variables<br><!-- End of picture text --> _Source_ : Authors’ calculations from IC data.

_Note_ : The percentage contribution of the infrastructure group is computed as the sum of the absolute values of the percentage contributions of the individual infrastructure variables, divided by the cumulative sum in absolute terms of the percentage contributions of all the IC and C variables, including infrastructure. The holds for the rest of the IC blocks of variables.

A similar picture is provided by figure 5.6, where, instead of simulations, we consider the relative contributions by groups of variables to average demeaned log productivity and to the demeaned 36
||Table of Contents||
|---|---|---|
|I.<br>Ove|rview|1|
|II.<br>A G|lance at the Data|2|
|A.|Changes in the Chilean Economy Between<br>the Census Years|2|
|B.|Industry-wide<br>Changes in Structure|3|
|C.|Sector-Specific<br>Structural Change|6|
|III.<br>Bet|ter Measures of Efficiency Changes|11|
|A.|Indices of Scale and Technical Efficiency|11|
|B.|Dealing with Imperfect Data|15|
|C.|The Estimated Effect of Trade Reform on<br>Productive Efficiency|17|
|IV.<br>Con|clusion Remarks|23|
|Appendix 1:|Production Technolgoy Estimates|24|
|Bibliograph|y|30|
|Appendix 2:|Data Preparation Procedures|32|
|Bibliograph|y|44|

# List of Tables

|Table|1 Aggregate Industrial Structure. 1967 and 1979|4|
|---|---|---|
|Table|2 Cross-Industry<br>Rank Correlations--Changes in<br>Protection with Changes in Industrial Characteristics|<br>9|
|Table|3 Values and Rank Correlations of Protec, Scale,<br>Effic and Disper|19|
||Figure||
|Figur|e 1 Efficiency Inferences from Technology Estimates|13|

This paper was prepared for the World Bank research project "The Effects of Trade Regimes on Industrial Competition and Efficiency" (RPO 764-46). We thank Raul Galleguillos and Jose Miguel Sanchez for their painstaking work in preparing the data used in this study, and Julie Stanton for very able research assistance. Helpful comments from Bela Balassa, Zvi Griliches, Gene Grossman, Ramon Lopez, Cristian Moran, Dani Rodrik and three anonymous referees were received on an earlier draft.
indicate that in the after 1990 period the impact of growth on headcount poverty would have declined by almost a full 1 percent for every 1 percentage point of per capita growth. In turn, this would imply that in the after 1990 period one would have needed 3.7 times the growth rate observed in the pre 1990 period to achieve the very same rate of poverty reduction. In other words, from a poverty perspective the observed structural change can be extremely important.

It is worth noting that the estimated differences between the scenarios in the preand post-1990 periods tend to increase (in absolute value) as per capita income levels increase (i.e. as _v/z_ increases). Intuitively, in countries with per capita income levels close to the poverty line, changes in inequality will have a low impact on poverty reduction since there is no much to distribute. Thus, from a poverty perspective, the estimated structural change in the growth inequality relationship should have been felt more on middle income countries than in low income countries.

These differences are amplified as we move from the headcount poverty to the poverty gap to the square poverty gap. That is, the differences are amplified as the poverty measure becomes more bottom-sensitive. A reason for this can be found in Kraay (2005) who notices that more bottom-sensitive poverty measures place relatively more weight on changes in relative incomes (i.e. they are relatively more sensitive to changed in inequality). For example, looking at the poverty gap ( _P1_ ) panel and continue to focus on a country with a gini coefficient of .5 and a per capita income to poverty line ratio - _v/z-_ of 3, we find that the rate of poverty reduction would have decline after 1990 by about 1.5 percent. Instead, looking at the squared poverty gap ( _P2_ ) panel we find a reduction of the poverty rate of about 1.6 percent.

# **V. Conclusions**

In this paper we have explored the stability of the growth inequality relationship over time. The paper concludes that while during the 1970s and 1980s the growth process was not accompanied by increases in inequality, during the 1990s a positive and significant correlation between growth and changes in inequality appears in the data. The estimates presented in the paper indicate that a 1 percent growth rates would be associated with increases in the gini coefficient in the .3-.5 percent range. This positive correlation would be hidden when one estimates the correlations without allowing for decadal changes.

When we assess the impact of this structural change on poverty reduction, we find that the associated decline in the pro-poorness of growth is not negligible. For example, our estimates indicate that for a country with a gini coefficient of .5 and a per capita income of about $2200, in the post-1990 period a growth rate of 3.7 percent would have the same poverty impact than a 1 percent in the pre-1990 period.

Clearly, identifying the potential forces at work that may have led to this structural break during the 1990s (e.g. trade liberalization, technological change, crises, etc) should be high on the research and policy agenda.

8
rarely happens. This pattern holds for girls; however, girls are more likely to move from being idle to working while boys are more likely to move from attending school to working. Not surprisingly, child activity is highly associated with household wealth (Table 3). Children in the lowest quartile based on asset index are less likely to be in school (60% for boys and only 29% for girls), and more likely to be working. Similarly, children in the lowest quartile are most likely to be idle, particularly girls. As wealth increases, more children are enrolled in school, and fewer are working or idle. In the following section, the impact of the wheat price changes on school and labor participation is analyzed.

# **5. Regression Results**

# **5.1 Perceived Food Price Shock and Child School Enrollment and Labor**

An understanding of household perceptions of the food price crisis provides insight into its association with child school enrollment and labor. The expectation that households in the same district would have similar experiences with the food price shock proved faulty. The withindistrict standard deviation in the self-reported food shock is 0.39, while the between-district standard deviation is 0.16, which means that most of the variation in self-reported food shock occurred within a district. In other words, household perceptions of the food crisis varied even if they were exposed to similar food price increases.

For example, a poor household might report being more severely affected by the food crisis than a non-poor household. The budget share spent on food in poor households is much higher than in rich households (78 percent for the bottom 20th percentile and 62 percent for the top 20th percentile in PSLM 2007–08); poor households will therefore perceive a greater food shock than rich households. Perceptions of the food crisis are also highly correlated with household behaviors. Households may report being severely affected by the food crisis in order to justify some household behaviors, including putting children to work or taking them out of school.

Table 4 describes how the household-level self-reported food price shock is associated with the probability that a child is enrolled in school, working, or idle using an individual fixed-effect linear probability model. Children ages 5 to 14 are 3.6 percentage points less likely to attend 11
under study were similar between the treatment group and the proposed control group. If the preintervention and “mid-term” (past baseline and probably without implemented intervention) outcome measures were not significantly different between treatment and control schools, there is no compelling reason to believe they would be significantly different in the post-intervention periods had the SBM program not been put in place. However, if we observe changes between pre-intervention and mid-term, we can consider the possibility that the two groups would have diverged with respect to their outcomes even in the absence of the SBM intervention. We can test this assumption for the proposed treatment and control groups by running the following equation on the pre-SBM data (i.e. 2002-2003):

-------------------(eq. 3) Y is the outcome measure including:  school level composite test scores and test scores individually in math, science, and English. _Year_ is a dummy variable that takes value = 1, if year =2003-04 and =0 if year=2002-03 (both pre-intervention and implementation years). SBM is a dichotomous variable that is equal to 1 if the school _s_ received SBM support ( _SBM_ =1). As in eq. (2),  _p_ is intended to capture

> division-specific aggregate fixed effects and  _t_ , _s_ is the error term. We are interested in two issues: first, was there a pre-existing difference between SBM and non-SBM schools before the intervention; and two, did the difference increase over time (between 2002-03 and 2003-04). The coefficient for the interaction term can be interpreted as follows:  if the coefficient  2 =0, then the pre-SBM outcomes for schools that would eventually receive SBM funds are not significantly different from those in the control group (i.e., schools that did not receive SBM funds in 2003).

We run equation (3) separately for years 2002-03 and 2003-04. When year=2002-03 (pre-intervention), we do not have a coefficient for  1 .<sup>Table 2 reports the results from equation (3). For 2002, we report</sup> coefficient  2 which shows the difference between SBM and non-SBM schools at the baseline (2002-03). For 2003-04 (mid-term), we report  2 which shows the increase in test scores of SBM schools compared to non-SBM schools in 2003-04 compared to the baseline (2002-03). For both years Model A is a simple OLS and<sup>Model B includes division fixed effects. For overall test scores, Model B in Table 2 shows that</sup>  2 =2.0, implying that overall test scores for schools that would eventually receive SBM funds in 2003 were about 2 percentage points higher than non-SBM schools in 2002-03. In 2003-04 ( mid-term), the 8
**Figure 1. Relative Importance of the Hybrid Offer Regime** **_Percentage of total issuance value, 2010_**

<!-- Start of picture text --> Malaysia<br>India<br>Brazil<br>EU a<br>Thailand<br>United States<br>Israel<br>Chile<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> _Sources:_ Respective country jurisdictions except for the United States and the EU. United States: Thomson Financial (Thomson One Banker-Deals Module), SIFMA, and World Bank calculations. EU: Luxembourg Stock Exchange, International Capital Market Association (ICMA) consultations, and World Bank calculations.

a. Estimate based on a proxy of offers with a minimum unit denomination of €50,000, one of the most commonly used conditions for hybrid offer exemption in 2010. This threshold was increased to €100,000 in July 2012.

Below we review select country cases in more detail to see if the introduction of the HBOR has had any observable impact on the issuance of corporate bonds. Relevant data were available for only three countries: Brazil, Thailand, and the United States.

It is important to note that conclusions made below are not definitive, given that corporate issuance is influenced by many factors (e.g., level of interest rates, liquidity in the banking sector, or financial crises), for which sufficient data and econometric analysis would be needed; nevertheless, the trends highlighted here provide interesting insights and suggest that the introduction of HBORs likely had a positive impact in increasing the number and/or diversifying the type of issuers coming to the bond market.

# **5.1 The United States**

As shown in Figure 2, issuance through the HBOR (Rule 144A) was on average around 21 percent over the last 15 years; it has reached as high as 28% in 2010.

22
incident for many households in our sample.<sup>13</sup> Standardizing the frequencies by the interval between the initial round and the 2004 follow-up survey, we have the following distributions for the three countries (Table 1).

# Table1 to be inserted

Because the 1998 flood was unusual in its severity and duration, in Bangladesh, instead of using a disaster measure based on frequency of occurrence, we use a flood exposure index that measures the severity of the flood (Del Ninno, et al. 2001). In this measure, exposure was grouped into none, moderately exposed, severely exposed and very-severely exposed. In addition, the Bangladesh data provide some details of the flood impacts such as the depth of water, the number of days covered by water, repair cost and the number of days evacuated from home. The former two measures are objective, but the latter two could be endogenous. Repair cost is an actual expenditure, so this involves household decisions and also depends on their asset holdings. The number of days evacuated is correlated with number of days covered by water, but it also measures the duration of staying safely away from the disaster, so it increases among those who had resources to relocate temporarily away from the flood (e.g., evacuating to other regions). Though these measures principally capture the disaster impacts, we may need to be careful in interpreting the results.

# **5. Empirical Results**

# **_5.1. Flood Impacts on Nutritional Status - Bangladesh_**

From the 1998 flood in Bangladesh, we can investigate the short-run impacts on child weight within a year after the flood. As described in the previous section, the 1998 survey started immediately after the flood, and traced individuals for a year with three rounds of data collection. The analysis uses rounds 1 and 3 to compute changes in weight-for-age and weight-for-height z scores. Flood exposure is measured by water depth, the number of days covered by water, repair costs and the number of days they were evacuated out of their homes (discussed in the previous section). Table 2 reports the estimation results.

# Table 2 to be inserted

Columns 1 to 4 show the impacts on change in the weight-for-age z scores. The estimation controls for union fixed effects (rather than villages) to maintain a reasonable number of observations within the unit. Due to the sampling frame, we have only several households per village, which reduces the number of children below 60 months per village. Both the number of days submerged and repair cost have significant negative effects on weight-for-age z scores.

In Columns 5 to 8, we use the weight-for-height z scores. An increase in repair

> 13Floods are a normal part of the agricultural cycle in Bangladesh. However, the 1998 floods were exceptional both for their severity and duration. Unlike normal floods, which cover large parts of the country for several days or weeks during July and August, the 1998 floods lasted until mid-September in many areas, covering more than two-thirds of the country, causing over 2 million metric tons of rice crop losses (equal to 10.45 percent of target production in 1998/99) (del Ninno et al. 2001).

11
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