rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10: Sector employment shares by agglomeration size distribution**

<!-- Start of picture text --> Secondary sector Secondary sector<br>1970 Tertiary sector 2000 Tertiary sector<br>Core area's secondary industry share Core area's secondary industry share<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Smallest Largest Total Smallest Largest Total<br>City size category City size category<br>Percent Percent<br><!-- End of picture text --> Source: Brazilian Population Census of 1970 and 2000 Overall, the share of tertiary industry has increased rapidly from 54.8 percent in 1970 to 69.4 percent in 2000. The city size decomposition of the tertiary industry shows concentration of service sector employment in bigger cities. As we move up the urban hierarchy, the service industry share in 2000 increases from 62.8 percent among the smallest agglomerations to 71.2 percent among the largest. The manufacturing industry share in non-agricultural employment is highest in the medium size cities (30.2 percent in 2000). It suggests that manufacturing decentralization was relatively more intense from large to medium size cities.

Industrial decomposition within agglomerations shows a pattern of manufacturing suburbanization from the core to the suburb areas. The manufacturing industry share in the core areas, relative to the total city manufacturing industry employment, decreased from 64.2 percent in 1970 to 47.0 percent in 2000 (Table 9). The relative manufacturing employment share of the core areas decreases as a city becomes bigger. Manufacturing suburbanization is more distinct in bigger agglomerations and this process is also observed in the tertiary sector. The suburbanization of service industry shows a similar pattern as those of manufacturing. The service industry has experienced an overall increase in suburbanization over the period, and the suburbanization is 26
- 14 -

A major finding of this chapter is that Bank projects are increasingly dealing with sector-wide ID issues. The foregoing review, however, reveals important internal contradictions between the widening scope of ID in projects and other ID features and practices.

(a) The absence of country specific sector ID strategies is inconsistent with the trend towards widening the scope of ID as there is no map or long-term perspective to guide the design of project ID components.

(b) The uneven quality of institutional analysis and the limited attention to ID issues in economic and sector work go against the requirements of the widening scope of ID in projects.

(c) The continuing focus of ID on project implementation weakens the concern for sustainability in Bank projects and limits the attention given to the design of sectoral ID strategies referred to above.

(d) The more complex sector-wide ID designs and interventions (implied by the widening scope of ID) are being left to Bank sector specialists whose skills and experience are not always adequate to deal with these broader issues. The problems noted above may well have been exacerbated by this mismatch.

# III. PERFORMANCE OF ID COMPONENTS

The evaluation of ID objectives in Bank projects is by no means easy. The diversity of prcjects, sectors and country contexts on the one hand, and the problems of performance measurement and of the choice of evaluation criteria on the other, add to the complexity of this assessment. Above all, there is the problem of data and the limitations of global surveys even if data were available. Given these constraints, our endeavor will be to sift the a'ailable and admittedly partial evidence from Bank performance reviews to discern performance trends and their interpretation by knowledgeable persons. This will be supplemented by a more detailed analysis of the factors underlying the differential ID performance found in Bank projects. The broad performance review will provide a useful setting within which the factors which influence ID performance could be probed in depth and their policy implications identified.

# Patterns of Performance

The Bank's Annual Reviews of Implementation and Supervision (ARIS) FY80-FY88 show that managerial problems are the most common (compared to financial, technical or political) barrier to good performance in Bank projects. These problems in turn are attributed to institutional weaknesses (lack of organizational capacity, staff skills, etc.). Sectors where these problems are relatively more pervasive are agriculture, water supply, population, and urban development. What is even more significant, however, is the ARIS finding that the proportion of projects which face moderate to severe
"sUnit values reflect country-specific f.o.b. export commodity prices. Since unit values are calculated from country-specific trade data they should be a better reflection than world market prices. Export unit values should reflect differences in the seasonality of sales. bilateral contracts and/or other special marketing arrangements, types, grades and quality, and transportation and storage costs. However. export volume data are sometimes of poor quality relative to export earnings data.

'<sup>9</sup> The Manufacturing Unit Value (M.UV) Index is based on the $US unit values of manufactured exports by G-5 countries (France, Germany, Japan, UK. U.S) to developing countries and reflects the purchasing power of exports from developing countries in international markets. See Siegel for the MUV used here.

'Sugar was the commodity whose unit value increased most rapidly during the first period.

21Although sugar price variances are high (see Table 3). the strong negative correlation of its instability with both tobacco and beef prices make it a likely candidate for portfolio variance reduction. See equation 3.

42
Figure 4.2: Correlation Between Income proxy and Behaviors/Consequences, Argentine Youth 2005 <!-- Start of picture text --> 0.90<br>0.85<br>0.80<br>0.75<br>0.70<br>0.65<br>0.60<br>0.55<br>0.50<br>0.45<br>0.40<br>0.35 0.40 0.45 0.50 0.55 0.60 0.65<br>Income<br>Behaviors<br><!-- End of picture text --> _Note_ : Size of bubble indicates cluster size. _Source_ : Own calculations on ESCVJA (four urban areas) Parents’ lack of education is negatively correlated with behaviors or outcomes for their children. Intergenerational effects are important, harming youth if their parents have little or no education (Figure 4.3). The educational background of low skilled parents likely predicts whether families emphasize education and consider it a priority for their children. For example, less educated parents may not motivate their children to study, because they do not value education highly. Likewise, the lack of parents’ schooling may mean they are not able to educate their children about for instance safe sex. Results support these hypotheses, because of the link between parents’ education and outcomes for youth.

Figure 4.3: Correlation Between Parents’ Education and Behaviors/Consequences, Argentine Youth 2005 <!-- Start of picture text --> 0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.40 0.45 0.50 0.55 0.60 0.65 0.70<br>Parent's lack of education<br>Behaviors<br><!-- End of picture text --> _Note_ : Size of bubble indicates cluster size. _Source_ : Own calculations on ESCVJA (four urban areas) 21
20 large parts of their careers—in the US. Most (85) of the top-100 are also men. The US dominance is a little less pronounced in the top-50 (only 62 percent are Americans), but the male dominance is equally pronounced in the top-50 (88 percent of the top-50 are men).

# **Institutions**

Our dataset contains 738 institutions with five or more health economics publications in EconLit. (Recall the author‘s affiliation is at the time the publication was published.) Over half of these (420) have ten or more, and 30 percent have 20 or more.

Table 6 shows the top 100 institutions in terms of the _h_ -index. These 100 institutions account for 62% of health economics publications since the start of the 1980s when author affiliation data started to appear in EconLit records. Also shown in Table 6 are the other indices, the fraction of publications not found in Google Scholar, and the citations of the institution‘s most-cited publication. Harvard emerges the clear winner, beating others on the _h_ -index, the publication count, total citations and the _I_<sup>3</sup> index with  =0.5. Seven institutions come in the top 10 on total publications, total citations, the _h_ -index and _I_<sup>3</sup> with  =0.5, namely Harvard, the World Bank, UC Berkeley, U Chicago, U PA, U MI, and U York. Among the top-10, only MIT is not in the top-20 on all four measures. Eight of those ranked 11-20 are in the top-20 on all four measures, the exceptions being Cornell U, Princeton U and PA State U. Among the 80 institutions ranked 21-100 on the _h_ -index, 61 are in the top-100 on all four indices. Most of the 39 not in the top-100 on all four measures appear in the lower part of the top-100 on the _h_ -index. It is noteworthy that many of the top-100 institutions are not universities.

Table 7 shows the 10 most cited publications of each of the top-10 institutions (based on the _h_ -index, as listed in Table 6). The table suggests some differences in topical specialization across the institutions. This is borne out by Table 8 which shows the distribution by topic of the most-cited 50 publications of the top-5 US institutions and Europe‘s top institution. The (normalized) Herfindahl index shows (in this instance) the degree of concentration of an institution‘s publications on a small number of topics. A value of 0.33 would occur if an institution‘s top-50 publications were evenly split across just three topics. Harvard U is the least specialized—appreciably less specialized than MIT (which has a high degree of specialization in
40 TABLE 7 AVERAQE SHARE OF EXPORTS (AXSHARE) AND IMPORTS (AMSHARE) IN TOTAL OUTPUT OF EXPORTABLE INDUSTRIES ;N _%_ TERMS

|08S<br>YE|AR<br>AXSHARE<br>AM|SHARE|
|---|---|---|
|1<br>$9<br> <br>|76<br>3.9293<br>f9.<br> <br> <br>|7575<br>|
|2<br>19<br>3<br>19|7<br>3.6639<br>19.<br>78<br>3.8270<br>14.|8307<br>4596|
|4<br>19|79<br>3.8087<br>11.|9783|
|5<br>19|80<br>6.3960<br>17.|0996|
|6<br>19|8$ t5.2136<br>18.|5571|
|<br>7<br>19|<br>82<br>23.5270<br>19.|4807|
|a<br>198|3<br>20.7087<br>18.|8979|
|9<br>19|84<br>29.7332<br>22.|2022|
|10<br>19|85<br>42 2294<br>25.|3638|

TABLE a AVERAGE SH4ARE OF EXPORTS (XSHARE) AND IMPORTS _(mSHARE)_ IN TOTAL OUTPUT OF IMPORTABaLE INOUSTRIES

# IN % TERMS

|o8s<br>YE|AR<br>AX|SH|ARE<br>AN|SHARE|
|---|---|---|---|---|
|1<br>19<br>_2_<br>19|76<br>1.<br>7<br>1|8<br>6|771<br>35.<br>79<br>32|5776<br>4762|
|<br> <br>3<br>19<br>_4_<br>19<br>5<br>19|<br>.<br>78<br>1.<br>79<br>1<br>80<br>2.|<br>23<br>.1<br>44|<br>.<br>86<br>24<br>427<br>21<br>16<br>25.|.0558<br>.0782<br>2792|
|6<br>198|t<br>4.4|3|57<br>29.|8413|
|7<br>198|2<br>7.|11|63<br>32|.516t|
|8<br>198|3<br>7.4|5|35<br>31.|2197|
|9<br>198<br>tO<br>i98|4<br>9.5<br>5<br>14.0|3<br>2|45<br>38.<br>71<br>40.|2476<br>8900|

TABLE 9 AVERA&Q SHARE OF EXPORTS (AXSHARE) AND IMPORTS (ANSNARE) 1% TOTAL OUTPUT OF NON-TRADABLE INDUSlRIES IN % TERMS

|OBS<br>YE|AR<br>AXS|HARE<br>AM|SHARE|
|---|---|---|---|
|1<br>s9<br> <br>|76<br>0.955<br> <br>|59<br>**S.S**<br> <br>|**3856**<br>|
|2<br>**¶%**<br>3<br>19|**77**<br>0.6S<br>78<br>0.834|746<br>5.4<br>00<br>3.9|7233<br>5453|
|4<br>19|79<br>1.00|965<br>**4.9**|**1855**|
|**5**<br>**19**|**80**<br>1.64|123<br>**4.8**|**1153**|
|a<br>19|8$ 4.039|64<br>**3.4**|**6921**|
|<br>7<br>19|<br>82<br>4.78|960<br>3.2|143$|
|a<br>i98|3<br>4.420|91<br>3.7|8402|
|a<br>19|84<br>7.54|774<br>6.3|4838|
|1o<br>19|85<br>9.79|160<br>6.6|8571|
# Beef **Supply** ComDonent

- TCOWKE - Cows & heifers that have calved (cow inventory) in the U.S., 1,000 head

- PRDBEEF - Commercial production of beef, million pounds COF - Cattle on feed in 13-states, 1,000 head

- CATPL - Cattle placed on feed in 13-states, 1,000 head

# Hog SUDDly Component

BRHOGKE - Breeding hog inventory for 10-states, 1,000 head

- PRDPORK - Commere.ial production of pork, million pounds PIGC - Pig crop for 10-states, 1,000 head

# Poultry Supplv Component

- PRDBR - Total production of young chicken, million pounds CPL - Pullet chicks placed in broiler hatchery supply flocks in thousands

- BRCH - Broiler-type chicks hatched, millions

# Meat Demand ComDonent

- RPBEEF - Average retail price of choice beef in cents per pound deflated by the GNP price deflator

- RPPORK - Average retail price pork in cents per pound deflated by the GNP price deflator

- RPBR - Average retail price in 4-regions of broilers in cents per pound deflated by the GNP price deflator

- PCDBEEF - Per capita disappearance of carcass weight of beef in pounds

- PCDPORK - Per capita disappearance of carcass weight of pork in pounds PCDBR - Per capita civilian disappearance of young chickens in pounds MBEEF - U.S. net imports of beef, million pounds MPORK - U.S. net imports of pork, million pounds XBR - U.S. net exports of poultry, million pounds

# Exchange Rate Component

- EXR - Trade weighted Exchange rate index in dollars per unit of foreign currency, 1972 - 1.00

24
9 nominal wage in each sector that were defined as gross remuneration in local currency. In some regressions the nominal interest rate (i[) was introduced to capture the influence of financial costs on price-setting decisions. Finally, the price of foreign goods (expressed in local currency) was defined by the multilateral real exchange rate based on Argentina's most important trading partners.

We used the Two-Stage-Least-Squares (TSLS) to account for the simultaneity problem between prices and current demand (the list of instrumental variables is presented at the bottom of every Table). Overall, the results appear satisfactory as evidenced by the R<sup>2</sup> and the Durbin-Watson (DW) statistics (see Tables 2-5).

# _Industry_

As expected, the industrial inflation rate is correlated positively, but not perfectly, to the variations in wages and in foreign prices (Table 2). The estimnated impact associated with the nominal interest rate is also positive, suggesting that industrial firms partially absorbed their higher debt-service through higher prices. An alternative explanation is that many industrial firms are liquidity-constrained, and that an increase in interest rates forces them to generate internal funds through an increase in their prices (see Chevalier and Scharfstein (1995)). **'°** The most interesting aspect of the empirical findings is, however, the direction of the effects of changes in excess demand on the price behavior: the inflation rate responds negatively to a transitory excess demand (yt) but positively to a permanent expected demand (xi). These results are consistent with a collusive market behavior rather than with the customer market model, and they are robust with alternative demand indicators, including GDP growth (AGDP,) demand on its lagged value and on (detrended) real GDP: y, = clyt,,+c2gdp, together with an AR(l) process for real GDP: gdp,= c3gdpt-1. Hence x,= y,/(I- dc,) + dc2c3gdpt/(1- **c0)(I-** 6C3) '0 These authors have shown some empirical evidence in this direction for the US industrial sector.
Rose, Adam, Oladosu, Gbadebo, and Liao, Shu‐Yi. 2007a. “Business Interruption Impacts of a Terrorist Attack on the Electric Power System of Los Angeles: Customer Resilience to a Total Blackout.” _Risk Analysis_ 27(3): 13–31.

Rose, Adam, Stevens, Brandt, and Davis, Gregg. 1989. “Who Gains and Who Loses from Natural Resource Policy: Distributional Information and the Public Participation Process.” _Resources Policy_ 15(4): 282–291.

Rose, Adam, Oladosu, Gbadebo, Lee, Bumsoo, and Beeler Asay, Garrett. 2009. “The Economic Impacts of the 2001 Terrorist Attacks on the World Trade Center: A Computable General Equilibrium Analysis.” _Peace Economics, Peace Science, and Public Policy_ 15(2): Article 6.

Rose, Adam, Porter, Keith, Tierney, Kathleen, et al. 2007b. “Benefit‐Cost Analysis of FEMA Hazard Mitigation Grant.” _Natural Hazards Review_ 8(4): 97–111.

Sheffi, Yossi. 2005. _The Resilient Enterprise_ . Cambridge, MA: MIT Press.

Surminski, Swenja. 2010. “Adapting to the extreme weather impacts of climate change—how can the insurance industry help?” _Climate Wise._ Swiss Re. 2011. _Closing the Financial Gap: New Partnerships between the Public and Private Sectors to Finance Disaster Risks_ . Zürich, Switzerland.

Swiss Re. 2012. _Microinsurance—Risk Protection for 4 Billion People_ . Zürich, Switzerland.

U.S. Global Change Research Program (USGCRP). 2014. _National Climate Assessment._ Washington, DC. http://nca2014.globalchange.gov/report.

Vorhies, Francis. 2012. “The Economics of Investing in Disaster Risk Reduction.” Working paper based on a review of the current literature commissioned by the Secretariat to the UN International Strategy for Disaster Reduction (UNISDR), Geneva.

Weimer, David L. and Vining, Aidan. 2005. _Policy Analysis: Concepts and Practice_ (4th ed.). Upper Saddle River, NJ: Pearson.

Wein, Anne, and Rose, Adam. 2011. "Economic Resilience.” _Earthquake Spectra: Special Issue on the ShakeOut Earthquake Scenario_ 27(2): 559‐73.

31
firms. Weak control of corruption hence makes it easier for informal firms to inflict an unfair cost advantage over formal firms consistent with the parasite view.” The discussion above suggests that the impact of informal competition on the formal firms is magnified when the business environment is less conducive (more costly or less beneficial) to operating in the formal compared to the informal sector. As Distinguin et al. (2016) point it, the cost of operating in the formal sector is higher and benefits lower when there is higher corruption in the country, business regulations are more burdensome, tax rates (on formal businesses) are higher and the rule of law (enforcement) is weaker. These predictions are specific to functioning of the informal sector and therefore we can expect them to hold if our result for the relationship between informal competition and R&D activity is truly causal. However, if the result suffers from reverse causality problem or informal competition is a mere proxy for other correlated determinants of R&D activity (omitted variable bias problem), there is no reason for the observed correlation between R&D and informal competition to vary with corruption, business regulations, rule of law, etc. as predicted above. This constitutes our first endogeneity test.

We implement this test by using interaction terms between our informal competition variable and the following business environment variables: Control of Corruption variable from the Worldwide Governance Indicators, World Bank; Rule of Law variable from the Worldwide Governance Indicators, World Bank; Profit tax rate taken from Doing Business project, World Bank; Freedom from Business Regulations taken form the Heritage Foundation’s Economic Freedom of the World database; and Distance to Frontier (DTF) measure of regulatory burden on the private firms due to World Bank’s Doing Business project.<sup>11</sup> Note that except for the profit tax

> 11 The DTF measure is based on 8 sub-components for which data are available for the countries and years in our baseline sample. We focus on 5 of the 8 sub-components that are more directly related to the regulatory environment relevant for the formal-informal interface. See Table A1 in the online Appendix for more details.

24
32 WHEN IS THE GOVERNMENT _TRANSFER_ MULTIPLIER LARGE?

Taylor rule coefficients, price stickiness and depreciation rates are estimated by Iacoviello (2005). In the full model, steady state government purchases are set at 20 per cent of GDP ( _GSS_ = 0 _._ 2) as is common in the literature, funded by a lump sum tax on each household in proportion to labour income (though any new fiscal stimulus is still funded by taxes on Ricardian HHs). Government transfers as part of fiscal stimulus ( _Trt_ ) are zero in steady state, though the HtM HH does receive steady state transfers equal to a share (1 _− α_ ) of total firm profits and capital income (after depreciation), such that the two households consume a fractions _α_ and 1 _− α_ of total consumption. We solve the full model numerically using Dynare.

4.3. **Quantitative Results when the central bank follows a Taylor rule.** In this section we calculate the targeted transfer and purchase multipliers in normal times when the central bank follows a Taylor rule. The white region of Figure 4.2 Panel B shows that the transfer multiplier in the full model (when policy follows a Taylor rule) is larger than one when fiscal policy is not very persistent or the share of HtM HH is not too small, which is consistent with the results in the simple model. The transfer multiplier is rarely larger than — the purchase multiplier except for once-off stimulus with very high shares of HtM HHs (Figure 4.2 Panel A). In part, this reflects the fact that the purchase multiplier itself is large for temporary stimulus and moderate shares of HtM HHs.

The first row of Table 2 (Columns A and B) presents the targeted transfer and purchase multipliers (respectively) when the central bank follows a Taylor Rule. When fiscal policy — — is once-off like the Bush 2001 tax rebates both transfer and purchase multipliers are slightly below 1. With persistence _ρ_ = 0 _._ 9 (similar to the ARRA) the transfer multiplier is around 0.25 and the purchase multiplier around 0.4. Impulse response functions with a Taylor rule or 2 years ZLB are presented in Online Appendix 10.

4.4. **Zero Lower Bound (ZLB) in the full model.** The Federal Reserve maintained nominal interest rates at 0-0.25 per cent from December 2008 to December 2015, a period of