rafmacalaba/datause-encoder-data huggingface dataset:
## Table 6

# Decooposition of Change Over Time in **the** Fraction of Wage Workers Among Female Employed Workers, Within **and** Between sectors, by Region

|Class of<br>|Sectors<br>|Average<br>|Total<br>|Sample<br>|Percent of|Chanae due|to:e|
|---|---|---|---|---|---|---|---|
|Countries|Analyzed|Fraction of<br>Wage Earners<br>(in<br>percent)|Change<br>Per<br>Year|Size<br>(#of<br>Countries)|Intrasector<br>Change|Intersector<br>Change|<br>Residual<br>Interaction|
||Agriculture<br>Nonagriclu|lture'||||||
|World (Entire Sampl|e)|62.7|1.51|61|82|17|2|
|High Income<br>||79.9|.79|22|39|58|3|
|Latin America<br>||65.9|.23|17|-28|107|20|
|Africa<br>||30.6|.87|6|57|66|-22|
|East Asia<br>||29.4|.61|6|77|17|5|
|South & West Asia||36.0|2.75|10|97|2|1|
||All 8 Sec|torab||||||
|World (Entire Sampl|e)|62.7-|1.51|61|82|17|2|
|High Income<br>||79.9<br>|.79|22|39|59|2|
|Latin America<br>||65.9|.23|17|-2|87|14|
|Africa<br>||30.6<br>|.87|6|55|57|-12|
|East Asia<br>||29.4|.61|6|82|14|4|
|South & West Asia||36.0<br>|2.75|10|96|3|1|

## "Agriculture includes forestry and fishing.

bNonagriculture divided into the following ISIC categories: Mining; manufacturing; electricity, gas, and utilities; construction; wholesale and retail trade; transportation; business and personal services.

cPercent of changes due to the three components may not precisely sum to one because of rounding. The change in workers in wage employments is summed across countries before total changes in the share of wage earners are calculated and decomposed for the sample and for the regional aggregations of countries.

See Appendix Table A-1 for composition of sample.
**Figure 1. Two-stage stratification sampling**

<!-- Start of picture text --> N = 561,624<br>n* = 1,277<br>Second-stage<br>21 large states First-stage<br>stratification by Random draws<br>with high-quality stratification by<br>state-level without Sample<br>expenditure administrative<br>household replacement<br>information category<br>expenditure<br><!-- End of picture text --> In the first stage we relied on a disproportionate sampling strategy. The 561,624 administrative units comprise 3,852 statutory urban places, 4,645 places that are urban in terms of the census, and 553,127 villages. If a proportionate sampling strategy had been adopted, the share of statutory and census urban places in the sample would have been a mere 2 percent. But villages are likely to have rural characteristics despite the limitations of administrative definitions, while misclassification is more likely for statutory and census urban places. It therefore made sense to overrepresent these two groups.

For each administrative category stratum, we chose a sample size sufficiently large to ensure the accuracy of subsequent analyses. There is a well-known trade-off in this respect. A small sample could fail to generate enough information, whereas a large sample would be costly to implement. Building on the survey design literature, we rely on the Central Limit Theorem to determine the sample size of each stratum (appendix 1).

In the second stage, we divided each of the seven strata into three substrata defined by average consumption per capita at the state level. Consumption data were from the household consumer expenditure module of the 68<sup>th</sup> round of National Sample Survey of India (NSSO 2012). Monthly household expenditure was divided by household size to estimate consumption per capita. To account for the spatial variation in prices, the result was deflated by a year-specific price index that differs across states and between rural and urban areas. We ranked states on the basis of their average consumption per capita and divided them into three groups of equal size: richer, intermediate, and poorer. The sample for each administrative stratum was split evenly across these three groups of states. The sampling process is completed by conducting random draws without replacement for each of the 21 substrata.

# _The resulting sample of places_

The sample drawn through this process comprises 1,277 places, of which 405 are statutorily urban and 557 are urban in terms of the census; the remaining 315 places are villages (table 1). For the full sample we achieved a relative standard error of 8 percent, which is below the 10 percent threshold required for a survey design to be considered good (Knottnerus 2003; UN 2005).

Recognizing that the Central Limit Theorem may not apply for a finite sample, we used bootstrapping to generate an approximation of the distribution through a Monte Carlo simulation (Cameron and Trivedi 2005). The analysis confirmed that the sample was sufficiently large for each administrative stratum. Standard errors were below 3 percent for six administrative strata, and around 5 percent for industrial towns.

7
Crozet, M and E Milet (2015), “Should Everybody Be in Services? The Effect of Servitization on Manufacturing Firm Performance”, GSEM working paper series 15-10-2.

Cusumano, M. A., S. J. Kahl, and F. F. Suarez (2015). Services, Industry Evolution, and the Competitive Strategies of Product Firms. Strategic Management Journal 36 (4), 559-575.

Dachs, B., Biege, S., Borowiecki, M., Lay, G., Jäger, A., & Schartinger, D. (2012). The Servitization of European Manufacturing Industries (MPRA Paper No. 38995). Vienna, Austria: AIT Austrian Institute of Technology.

Fang, E.; Palmatier, R. and Steenkamp, J. (2008) “Effect of service transition strategies on firm value”, Journal of Marketing, 72, pp. 1-14.

Federico, S., & Tosti, E. (2012). Exporters and importers of services: firm-level evidence on Italy: Bank of Italy, Economic Research and International Relations Area.

Gebauer, H. and E. Fleisch (2007). An Investigation of the Relationship Between Behavioral Processes, Motivation, Investments in the Service Business and Service Revenue. Industrial Marketing Management 36 (3), 337-348.

Gebauer, H., Fleisch, E. and Friedli, T. (2005) “Overcoming the service paradox in manufacturing companies”, European Management Journal 23(1): 14-26.

Khandelwal, A. (2010). The Long and Short (of) Quality Ladders. The Review of Economic Studies, 77(4), 14501476.

Lodefalk, M. (2013). Servicification of Manufacturing - Evidence from Sweden. International Journal of Economics and Business Research 6 (1), 87-113.

Lodefalk, M (2015), "Servicification of Manufacturing Firms Makes Divides in Trade Policy-Making Antiquated", Örebro University, Working Paper 2015:1.

Malleret, V. (2006), "Value creation through service offers", European Management Journal, Vol. 24 No.1, pp.106‐ 116.

Mathieu, V. (2001). Service strategies within the manufacturing sector: benefits, costs and partnership. International Journal of Service Industry Management 12 (5), 451-475.

Mayer, Thierry, Marc J. Melitz, and Gianmarco I. P. Ottaviano. 2014. "Market Size, Competition, and the Product Mix of Exporters." American Economic Review, 104 (2): 495-536.

Miroudot, S., Sauvage, J., & Shepherd, B. (2013). Measuring the cost of international trade in services. World Trade Review, 12(4): 719-735 National Board of Trade. (2012). Everybody is in Services - The Impact of Servicification in Manufacturing on Trade and Trade Policy. Stockholm, Sweden: National Board of Trade.

Neely, A.D. (2009) “Exploring the Financial Consequences of the Servitization of Manufacturing”, Operations Management Research, 2, 1, pp. 103-118.

Neely, A. (2011). The Servitization of Manufacturing: Further Evidence, Paper presented at the 18th European Operations Management Association Conference, Cambridge.

Oliva, R. and R. Kallenberg (2003). Managing the Transition from Products to Services. International Journal of Service Industry Management 14 (2), 160-172.

19
12 not had been developed or introduced in the absence of patent protection.

Mansfield's conclusions received important support from a comprehensive study undertaken by Levin, et. al (1987). This study examined

# Table 2 : PERCENT OF INVENTIONS THAT WOULD NOT HAVE BEEN DEVELOPED OR INTRODUCED IN THE ABSENCE OF PATENT PROTECTION a/

||Percent that would<br>not have been<br>introduced|Percent that would<br>not have been<br>developed|
|---|---|---|
|Pharmaceuticals|65|60|
|Chemicals|30|38|
|Petroleum|18|25|
|Machinery|15|17|
|Fabricated Metal Products|<br>12|12|
|Primary metals|8|1|
|Electrical equipment|4|11|
|Instruments|1|1|
|Office equipment|0|0|
|Motor vehicles|0|0|
|Rubber|0|0|
|Textiles|0|0|

a=/ Some inventions that were developed in this time period (1981-83) were not introduced then, and some inventions that were introduced then were not developed then. Thus, the left-hand column of the table refers to somewhat different inventions than does the right-hand column.

# Source : Mansfield (1986)

the relative importance of different ways of protecting the inventor's returns on his inventions. The authors prepared a comprehensive questionnaire and interviewed research managers from 130 U.S. industries. One set of questions included the views of these managers regarding the importance of alternative means of appropriating the returns from the innovations made by their enterprises -- patents to prevent duplication, patents to secure royalties, industrial
It is precisely this contrast between peasants in Africa and Latin America that Che must have found unexpected, and perplexing, that also informs our view on income inequality in Africa. Had European colonists managed to create extensive plantations along the west coast of Africa,<sup>3</sup> African and Latin American land ownership structures would have been more similar, and broadly the same level of inequality would be expected. Similarly, we are not particularly puzzled by very the high levels of inequality that obtain in Zimbabwe and South Africa where Europeans did manage to create a strong presence, a two-tier society and where independence or abolishment of the apartheid took much longer than elsewhere in Africa. What is doubly puzzling (in view of the Kuznets hypothesis, _and_ broad ownership of land) is high levels of inequality in the rest of sub-Saharan Africa.

The hypothesis that we want to test in this paper is that high inequality in Africa is principally a political phenomenon.<sup>4</sup> Historically, African societies have been hierarchical with a large power vested in top leaders. Prevalence of slavery, conducted both by Muslim traders and later by European colonists, rested on active collaboration of African chiefs who often sold their own subjects into slavery as a way of acquiring money and power (see e.g. Bairoch, 1997, vol. 2). This very fact denotes a very hierarchical social organization. Hierarchical structure was reinforced by European colonialism. European colonists became the new rulers, whose affinitive links to the bulk of the population were all but inexistent.<sup>5</sup> The population was again seen as a perfect

> 3 However, the effects of this failure were not benign. It only added a further stimulus to the slave trade as Europeans needed slaves to work on the plantations in the Americas. As Behrendt, Eltis and Richardson (2001, p. 474) write, “European weakness [inability to establish plantations on the continent] in the face of African political and military realities, coupled with..Europeans’ taste for plantation produce, created the labour shortage that the slave trade filled.”

4 Gelb (2001) writes that there is a consensus that principal causes of Africa’s economic decline are political. Nkurunziza and Ngaruko (2002) in a detailed study of Burundi’s growth 1960-2000 come to the same conclusion and write (page 1): “The usual economic factors explaining growth are endogeneous to political decisions, suggesting that it is politics not economics that explains the dismal performance.” The origin of the view that it is the politics that is behind the African “dummy variable” (significantly worse growth performance compared to the rest of the world) goes back to the World Bank Berg Report (1981), and Bates (1981) (see Arrighi, 2001).

> 5 As Hobson (1965 [1902]) put it in his classic _Imperialism_ , “The normal state of such a country [in Africa] is one in which the most fertile lands and the mineral resources are owned by white aliens and worked by natives under their direction, primarily for their gain: they do not identify themselves with the interest of

3
19 Table 3: CORRELATION TABLE _,T MI QM M2 DC w_ **_rer_** _Tvar Tsd Tsk var sd sk wvar wsd wsk_ T 1.00 **_Ml_** 0.84 1.00 _QM_ 0.77 0.54 1.00 _M2_ 0.85 0.72 0.97 1.00 _DC_ 0.63 0.53 0.63 0.67 1.00 w 0.92 0.75 0.83 0.89 0.68 1.00 _rer_ -0.25 0.05 -0.58 -0.48 -0.23 -0.51 1.00 _Tvar_ 0.66 0.42 0.40 0.45 0.28 0.68 -0.51 1.00 _Tsd_ 0.82 0.59 0.54 0.60 0.46 0.80 -0.40 0.96 1.00 **_Tsk_** 0.17 0.02 0.11 0.09 0.26 0.11 -0.03 0.11 0.15 1.00 **_var_** 0.82 0.63 0.44 0.53 0.38 0.75 -0.30 0.94 0.98 0.14 1.00 _sd_ 0.79 0.61 0.42 0.50 0.31 0.73 -0.31 0.94 0.96 0.10 0.99 1.00 _sk_ 0.11 0.09 0.08 0.10 0.29 0.13 -0.08 0.10 0.12 0.86 0.13 0.10 1.00 **_wvar_** 0.76 0.52 0.57 0.61 0.38 0.80 -0.57 0.98 0.97 0.10 0.94 0.94 0.09 1.00 **_wsd_** 0.82 0.64 0.44 0.53 0.38 0.75 -0.30 0.94 0.98 0.14 1.00 0.99 0.13 0.94 1.00 **_wsk_** 0.15 0.08 0.13 0.12 0.09 0.25 -0.20 0.17 0.14 0.53 _0.15_ 0.14 0.47 0.18 0.15 1.00 where _MI_ - the sum of currency outside banks and demand deposits other than those of the central government _QM-_ quasi-money, the sum of time, savings, and foreign currency deposits<sup>other than those of the central</sup> government; M2 - the sum of MI and QM ; _DC_ - domestic credit includes net claims on general government, nonfinancial public enterprises and public sector ; _reer = 7qNT - rT_ real exchange rate _source: author's calculations using GUS and IFS data_ multicollinearity would increase standard errors of the estimates and render the t-statistics invalid.
# **1.5 Numbers affected**

# **Many more individuals are affected by floods to a greater or lesser degree than are**

**deemed to be killed by them** . According to EM-DAT, on average over the past three decades more than 100 million people each year have been affected by floods. This is reason enough for governments to take action towards reducing these statistics. The numbers affected has grown since 1950 from around 4 million a year to the present level, which represents more than 1% of the global population. Figure 7 shows the numbers and 10 year moving median. However, it is difficult to determine whether the number affected by events is still on an upward trajectory, has peaked or has stabilized.

# **Figure 7 Number affected by flooding**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>number affected (millions) 10 year moving median<br><!-- End of picture text -->

# **1.6 Are damages – economic, financial and insured losses – increasing?**

# **On the whole, the developing nations have insurance coverage lower than 25% of**

**total losses** . In evaluating the monetary impact of flooding there are two main perspectives typically adopted: that of economic losses, which attempts to measure the impact on society of the actual damage and disruption; and that of financial, or insured losses, which covers the cost of the replacement of damaged items and associated expenditures. These types of losses can differ significantly, depending on the items included and the scale of the economic entity considered (Barthel and Neumayer 2010). For example, from Munich Re records (Kron 2011) the difference between actual and insured losses is clear. Insured losses represent between less than 1% to over 70% of actual losses (see Table 1) depending largely on country and insurance coverage rates which also vary greatly (Gaschen et al. 1998). For example, in 2010 the Pakistan flood with an estimated $9.5bn in economic loss resulted in very low insurance claims of $100m as it occurred in very low income areas of a low income country where insurance coverage is almost negligible (Munich Re 2011). In contrast, the recent Queensland floods were among the most expensive

10
# **Building up International Reserves**<sup>15</sup>

Despite the proliferation of greater exchange rate flexibility for developing countries noted above, the ratio of international reserves to GDP has increased substantially, as shown in Figure 2. At the end of 1999, reserves were about 6 percent of global GDP, 3.5 times what they were at the end of 1960, and 50 percent higher than in 1990. Practically all the increase in reserve holdings has been in developing countries, mostly in East Asia (Flood and Marion 2002), with the question of whether reserve holdings were excessive being raised (Edison (2003)). Reserve accumulation continued in the 2000s, with emerging markets increasing the ratio of international reserves to GDP from single-digit percentage levels in the 1980s to 15 to 30 percent of GDP for most, with some countries exceeding 50 percent (China; Hong Kong SAR, China; and Singapore in 2007)—something unprecedented.

Table 1: Evolution of reserve adequacy measures

|Period|Evolving adequacy measure|Influential contributors|
|---|---|---|
|1950s-1980|Months of imports; dealing with trade volatility|Triffin, Heller|
|Late 1990s-<br>early 2000s|Short term external debt/IR, dealing with sudden<br>stop of capital flows|Guidotti-Greenspan|
|2000s|M2/IR, dealing with sudden capital flight from<br>domestic assets|Calvo<br>Obstfeld, Shambaugh &<br>Taylor|
|2000s|Hybrid; increasing with external debt/GDP,<br>M2/GDP, country risk, and foreigners‘<br>shareholdings.|Wijnholds and Kapteyn<br>Aizenman, Lee and Rhee|
|2000s|Provided a calibration cum estimation that allows<br>estimating ―optimal IR‖ as a function of the costs<br>and probability of sudden stops|Garcia and Soto<br>Jeanne & Ranciere<br>Jeanne|

This section focuses on the evolution of the underlying motives for IR accumulation by developing countries. Table 1shows the evolution of ̳reserve adequacy ratios‘ overtime. Under BW I, IR served mostly as a buffer to deal with trade uncertainty. Reserve adequacy was measured by months of imports with a rule of thumb considering four months of imports reasonable. This approach was suitable in a world with limited financial integration, with trade openness being the main channel for external shocks (Fischer 2001). Absent sufficient reserves,

> 15 This section draws on Aizenman (2011).

11
**Table 16: Trade Creation and Diversion for Alternative FTA Membership (Including Trade-Productivity Dynamic Linkages)**

|||Billion US$||Percen|tage change fr|om base|
|---|---|---|---|---|---|---|
||Trade<br>expansion|Trade<br>creation|Trade<br>diversion|Trade<br>expansion|Trade<br>creation|Trade<br>diversion|
|**Full Asian FTA**<br>|||||||
|United States|16.18|34.76|-18.59|2.81|21.26|-4.50|
|Japan|28.66|33.37|-4.71|7.04|14.43|-2.68|
|China|8.94|13.76|-4.82|6.30|17.92|-7.41|
|ASEAN 4|15.24|6.48|8.76|11.09|6.99|19.63|
|Asian NIEs|16.42|16.70|-0.28|6.42|10.71|-0.28|
|_Total,_**_FTA_**_members_|85.43|105.07|-19.63||||
|European Union|-0.27|-7.38|7.12|-0.04|-3.47|1.37|
|**VTAExcluding China**|||||||
|<br>United States|14.69|36.71|-22.02|2.55|25.85|-5.07|
|Japan|25.04|30.11|-5.07|6.15|15.52|-2.38|
|ASEAN 4|13.63|6.17|7.45|9.92|7.35|13.95|
|Asian NIEs|13.31|13.89|-0.58|5.20|11.36|-0.43|
|_Total,_**_FTA_**_members_|66.66|86.88|-20.22||||
|China|0.15|-2.45|2.60|0.10|-3.20|4.00|
|European Union|-0.19|-6.73|6.54|-0.03|-3.53|1.21|
|**FIA Excluding ASEAN 4**|||||||
|United States|15.56|32.89|-17.33|2.70|22.48|-4.03|
|Japan|25.85|27.85|-2.00|6.35|13.84|-0.97|
|China|8.18|12.68|-4.50|5.77|17.91|-6.34|
|Asian NIEs|14.53|12.01|2.52|5.68|9.34|1.98|
|_Total,_**_PTA_**_members_|64.12|85.43|-21.31||||
|ASEAN 4|0.13|-2.74|2.86|0.09|-2.95|6.42|
|European Union|-0.16|-6.52|6.36|-0.02|-3.33|1.19|
|**FTA Excluding Unkted States**|||||||
|Japan|12.76|13.64|-0.87|3.13|10.64|-0.31|
|China|7.53|15.62|-8.09|5.31|39.99|-7.88|
|ASEAN 4|12.43|7.38|5.05|9.04|11.18|7.07|
|Asian NIEs|10.02|15.56|-5.54|3.92|16.39|-3.45|
|_Total,_**_FTA_**_members_|42.74|52.20|-9.46||||
|United States|-0.36|-4.32|3.96|-0.06|-2.64|0.96|
|European Union|-0.20|-2.16|1.96|-0.03|-1.96|0.31|

Notes: _Trade creation_ is the increase in exports to _members of the FTA;_ the trade creation figure for the European Union indicates the change in EU exports to FrA members. _Trade diversion_ is the increase in exports to _non-FTA members,_ comprised of EU and rest of world.

_Lewis, Robinson, and Wang_ 48
**Notes:** Robust standard errors in parentheses (clustered on country pairs). *** p<0.01, ** p<0.05, * p<0.1.  The data cover 1995-2018. Estimation in all columns include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).  Estimation in all columns also include controls for the regional trade agreement, tariff preference margins, and the seven “deep” clauses shown in Table 4.  Estimation in column 3 also includes the two subcategories for category 1 shown in Table 1.

pg. 39
deficit bias precisely because they accentuate the procyclicality of fiscal policies. They are just not good and useful rules.

On the other hand, a rule that attempts to support counter cyclical fiscal policies but is not designed to achieve long term debt sustainability will<sup>also be</sup> unsustainable and non credible. It would not serve in the end any purpose.

A well designed rule must, as a consequence, attempt both to facilitate the operation of automatic stabilizers (or even permit a limited active counter cyclical fiscal policy) and avoid a deficit bias. This will by necessity make the rule somewhat more complex, but realistic and eventually useful.

In what follows I will review some LAC attempts with fiscal rules that have tried either to cope with pro ciclicality or with the deficit bias or both,<sup>and</sup> attempt to draw some practical conclusions.

# 6. **THE** EXPERIENCE **WITH FISCAL RULES IN LAC**

# _A. The Experience with COMMODITY STABILIZATION FUNDS_

The Annex to this paper presents an analysis of the Chilean Copper Stabilization Fund, the Colombian Coffee and Oil Stabilization Funds and the proposed Oil Savings and Stabilization Funds in Ecuador. From this review we can draw some general conclusions.

Stabilization Funds are easier to institute "before" the fact (when prices are low or before expected increases in quantities take place) and, in the case of shared revenues, when they treat all beneficiaries (central/federal governments and subnationals) in a symmetric way. This is highlighted by the experience of the Colombian Oil Stabilization Fund and was a major political consideration in the design of the Ecuadorian Oil Savings and Stabilization Funds.

They may indeed play a useful role to insure some savings from fiscal revenues associated with commodity export booms, specially where there are automatic saving rules and the accumulated net surplus remains "out of sight and out of reach" of the normal discretionary budget process. This is clear in both the cases of the Chilean Copper Stabilization Fund and the Colombian Coffee<sup>Fund</sup> and Oil Stabilization Fund.

However, in so far as they operate over a limited portion of current revenues, they cannot by themselves assure aggregate expenditure restraint during booms in economic activity (as happened in Colombia during the 90s or recently in Venezuela with the Fund for Macroeconomic Stabilization -FAEP-). In such cases one may end with some savings in the Funds but at the same time with an unsustainable deterioration of the "non-oil" budget. To avoid such a problem one would need a complementary rule to require that "non-oil" deficits be limited to 9
58

**Figure 4: Plotted Quadratic Returns to Education (Wage Earners)**

<!-- Start of picture text --> 25 -<br>20<br>15 z<br>5 e 1>~<br>20 I-- I -- I ----- - T 1 -- - - - - - r-- -- - - - r<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17<br><!-- End of picture text --> Years of Schooling <!-- Start of picture text -->
-i-  1976  1981  . 1985  -+-  19961<br><!-- End of picture text -->