rafmacalaba/datause-encoder-data huggingface dataset:
23 urban households were regrouped in one stratum per division. Yet it is feasible to retrieve approximate expansion factors by stratum for previous years using information on the number of household living in the various areas in the 1981 and 1991 censii, and using geometric projections for computing rates of growth in the number of households between these two years. We conducted this exercise, which yielded the expansion factors in Table A2 for the survey years 1983-84 to 1991-92. Note also that the definition of urban areas in the HES does not match that of the 1991 census (this was taken into account in computing the expansion factors in Table A2). In 1991-92 for example, the HES counts as rural 12 of 107 municipalities reported by the 1991 census as urban, as well as all 415 thana headquarters and nonmunicipal towns also reported as urban by the census. Therefore, the urban population share in the HES is lower than that in the Census (in 1991-92, the urban share was 16.5 percent according to the HES, versus about 20 percent according to the 1991 census). We used BES shares for the macro simulations. A second comparability issue between the 1995-96 LES and previous surveys relates to the collection method for the food diaries recording consumption expenditures. In 1995-96, the households kept their food diary for 7 days (for a few households, the number of days is lower, but this information is available in the data, so that adjustments can be made). Accordingly, the total monthly food expenditure was computed as the total expenditure recorded in the diary times 30.42/7 (with 30.42 days also being used to estimate the monthly food poverty lines in the cost of basic needs method). In previous years however, the households kept their diary for 15 days. The issue relates to the quality of the recall. It could be conjectured that households keep better track of their food expenditures over a 7 days than over a 15 days period. Then the monthly food expenditure totals for previous years would be under-estimated as compared to the totals computed for 1995-96. As discussed, poverty decreased sharply in 1995-96. It could well be that part of this decrease is due to the difference in collection method for the food diaries.

A third comparability issue has to do with standard errors. For the 1995-96 HES, we have information on both stratification and clustering, so that appropriate standard errors can be computed.
- 38 -

# The Robinson **plan** (1988) 44_

This plan is more ambitious than its predecessors. It combines debt reduction with voluntary new lending. The plan's central structure is a new international debt agency, the Institute of International Debt and Development, (I2D2). I2D2 would be a joint venture of the World Bank and IMF, and rely heavily on their staff. Sponsoring governments would provide its equity capital.

I2D2 would negotiate case-by-case agreements with debtor countries. If the latter agreed to participate, they would have to commit themselves to growth-oriented policies. All participations by sponsoring and debtor governments would be completely voluntary.

With creditor banks I2D2 would negotiate the repurchase of debt at a discount, in exchange for its own obligations. These would be of two kinds: floating-rate perpetuals, and participating preferred stock. The purchase discount would be determined in light of the probability of debt service by the debtor, of the secondary-market price of the debt involved, and of concessions that are deemed approp-iate to make debt service more sustainable. In return for that discount I2D2 would grant the debtors "meaningful concessions" during the adjustment period.

12D2 would also reserve the rig t to subordinate the debt so acquired to all future debt of the same borrower, to pave the way for new voluntary lending to that country. However, both the original concessions and the subordination could be suspended by I2D2 if the debtor(s) were to deviate from agreed adjustment plans or to exceed targets for new senior lending. Given the subordination feature, and to avoid free-rider problems, all the commercial debt of a given country would either have to be acquired by I2D2, or treated comparably.

In Robinson's estimate, up to $250 billion in commercial debt of the Baker 17 would be eligible for repurchase. Assuming the total amount were repurchased at 60 percent of par, 12D2 would have to issue a maximum of $150 billion in its own obligations, respectively as to $125 billion in perpetuals and $25 billion in preferred stock. Assuming a 10:1 gearing ratio, I2D2 would require $12.5 billion in equity participations by sponsoring governments, either paid up, or callable. Such capital would be kept as reserve by 12D2.

One weakness of this proposal is that if I2D2 subordinates the debt it has acquired from banks, its risk exposure could be considerable. On the other hand, that subordination would be a "carrot" for new lending. As another carrot to promote the enforcability of this essentially voluntary 431 The New York Times38, 8/9/1987.

44/ James D.Robinson III: (i)"Weaving a Debt Net", The International Economy, May/June 1988, pp. 50-55, and (ii) The Amex Bank Review, Special Papers, No 13, March 1988, "A Comprehensive Agenda for LDC Debt and World Trade
Table 1b: Female employment in manufacturing and services establishments

||2001|2006<br>Manufacturing|2010|2001|2006<br>Services|2010|
|---|---|---|---|---|---|---|
||||A. Estimated|employment|||
|**Total employment**|40,678,774|40,299,063|43,161,148|26,064,374|32,607,152|34,091,395|
|Male employees|26,727,119|24,605,496|26,391,181|21,469,096|23,063,789|24,795,723|
|Female employees|11,297,579|12,311,462|12,145,754|4,595,279|9,543,363|9,295,672|
|**Organized sector**|6,716,239|7,469,639|9,593,975|3,179,267|6,339,472|4,964,826|
|Male employees|3,410,628|3,360,105|4,036,127|2,072,754|2,874,900|2,710,789|
|Female employees|651,535|727,428|933,635|1,106,513|3,464,572|2,254,036|
|**Unorganized sector**|33,962,535|32,829,425|33,567,173|22,885,107|26,267,679|29,126,569|
|Male employees|23,316,491|21,245,391|22,355,054|19,396,342|20,188,889|22,084,934|
|Female employees|10,646,044|11,584,034|11,212,119|3,488,766|6,078,790|7,041,636|
|_Female-owned establishments_|5,551,358|7,552,761|7,852,968|1,716,817|1,966,594|1,987,362|
|Male employees|688,936|635,602|514,930|426,001|402,020|254,455|
|Female employees|4,862,423|6,917,159|7,338,039|1,290,816|1,564,574|1,732,907|
|_Male-owned establishments_|26,563,558|23,249,453|23,906,992|19,738,576|20,433,653|22,417,701|
|Male employees|21,114,096|18,988,163|20,529,257|17,804,897|18,415,198|20,703,546|
|Female employees|5,449,463|4,261,290|3,377,735|1,933,679|2,018,455|1,714,155|
||||B. Shares in|employment|||
|**Total employment**|||||||
|Male employees|0.703|0.667|0.685|0.824|0.707|0.727|
|Female employees|0.297|0.333|0.315|0.176|0.293|0.273|
|**Organized sector**|||||||
|Male employees|0.840|0.822|0.812|0.652|0.453|0.546|
|Female employees|0.160|0.178|0.188|0.348|0.547|0.454|
|**Unorganized sector**|||||||
|Male employees|0.687|0.647|0.666|0.848|0.769|0.758|
|Female employees|0.313|0.353|0.334|0.152|0.231|0.242|
|_Female-owned establishments_|||||||
|Male employees|0.124|0.084|0.066|0.248|0.204|0.128|
|Female employees|0.876|0.916|0.934|0.752|0.796|0.872|
|_Male-owned establishments_|||||||
|Male employees|0.795|0.817|0.859|0.902|0.901|0.924|
|Female employees|0.205|0.183|0.141|0.098|0.099|0.076|

Notes: See Table 1a. This table shows the number of employees in the unorganized sector to be greater than the combined number of employees in female-owned and maleowned establishments, because the unorganized sector total includes establishments for which the gender of the owner is unknown. The table also shows Total employment and Organized employment to be greater than combined male and female employment in Manufacturing, reflecting discrepancies in the raw ASI data. Employee gender is known for only ~55% of the employees in the organized manufacturing sector, so the employment shares in Panel B are calculated over only these known figures.
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18
8 2007). In Warsaw, Poland, proceeds from municipal land auctions fell from 329.6 million PLN in 2007 to 1.2 million through the first 10 months of 2009 (Kaganova 2010).

## **Box 3: Volatility of Land-Asset Revenues and Capital Spending**

**_Mumbai, India_** has adopted a long-term urban development plan. The anchor infrastructure projects in this plan are a new metro rail system and a 23-km bridge over open water connecting the island city of Mumbai to the mainland peninsula to the east, where a concentration of industrial Special Economic Zones is being constructed. The most recent financing plans for these projects called for both to be financed primarily from the proceeds of land sales received by the Mumbai Metropolitan Regional Development Authority (MMRDA). The vast majority of MMRDA’s land sale revenue comes from sale of land at its Bandra-Kurla financial complex. The table below shows how steeply demand and land prices have fluctuated at Bandra-Kurla. As a result, no private bidders responded in January 2009 to the government’s tender for bids to build the trans-harbor bridge.

|**_Land Sales by MMRDA at Bandra-Kurla Complex_**|
|---|
|**Year and Use**<br>**Price per Square Meter**|
|1993<br>Rs.30,000|
|1995 (Diamond Bourse)<br>Rs.42,500|
|1998-2000<br>Sales suspended because of Asian<br>financial crisis|
|Late 2000 (Citibank)<br>Rs.86,000|
|January 2006 (convention center)<br>Rs.153,000|
|November 2007 (commercial complex)<br>Rs.504,000|
|Late 2008<br>Failed auction<br>Sales suspended because of the<br>global financial crisis|
|Source: Peterson (2009).|

# **_Land as Collateral for Subnational Borrowing_**

During the high-growth period of urban development, publicly owned land often has been used as collateral for borrowing to finance subnational public investment. The expectation that land values will increase with urban growth has made land an attractive asset for loan collateral, both for public borrowers and private lenders. However, this practice magnifies the risks related to borrowing, as land values decline in periods of economic stress, when it is most difficult to finance loan repayments. In many cases, not only subnational governments but also their enterprises have borrowed using property under their control as collateral. There is a potential for heightened systemic risk when the entire subnational sector relies heavily on land and land values to provide security for subnational borrowing. Box 4 illustrates the historical use of land-based municipal borrowing to finance the re-building of Paris in the 19<sup>th</sup> century, and the risks that eventually triggered a financial crisis that impacted the national finance system. It also summarizes China’s use of subnational borrowing against anticipated land-value gains to finance major urban highway projects—a practice that the national government now has prohibited by regulation in order to limit subnational fiscal risks.
distance, common borders or landlockedness, which are either bilateral or country-specific, and time-invariant.

# **_Econometric methods_**

If no correlation exists between the explanatory variables and the specific bilateral effects, the Generalized Least Square estimation (GLS) provides consistent estimates of the coefficients. However, variables like GDP or infrastructure may be correlated with bilateralspecific effects. The Hausman test allows us to control for the presence of any correlation between explanatory variables and bilateral-specific effects.<sup>15</sup> The usual way to deal with this issue is to consider an estimation of instrumental variables, as proposed by Hausman and Taylor (1981). The Hausman and Taylor (HT) estimator is based upon an instrumental variable estimator which uses the between and within variation of the strictly exogenous variables, as well as the within variation of the variables correlated with the bilateral-specific effects.

# **_Data_**

The model is estimated with data on 167 countries (see Appendix A.5 for a list of the countries in the sample) over the 1992-2004 period, with trade data from UN COMTRADE (total bilateral imports in current dollars). Data sources for the explanatory variables as well as the data transformations are presented in Appendix A.7. Once the missing values have been removed,<sup>16</sup> 185,967 observations for 23,470 country pairs remain.

# **_Results_**

Results are reported in Table 1. The results from the error-component model (GLS) are reported in the first column. The fit is good (R<sup>2</sup> =0.66), given that the impact of random specific effects are not in the R<sup>2</sup> but are part of the residuals. However, the Hausman test, based on differences between within and GLS estimators, reveals a χ218 = 875.72, which is highly significant (1%). Hence, this test rejects the null hypothesis according to which there

> 15 In order to verify H0: when bilateral effects are not correlated with explanatory variables, Hausman (1978) suggests a statistic test based on: [ β GLS - β W] . [var( β GLS) - var( β W)]<sup>-1</sup> . [ β GLS - β W]’. Under the null H0, this test statistic is distributed as a Chi-square ( χ<sup>2</sup> ) with K degrees of freedom (K is the dimension of the vector β W in the within regression, constant excluded).

> 16 Countries which do not declare their imports from a partner or which do not import from this partner are identified in the same way, i.e. with a missing value. Hence, our data are not censored at zero.

16
followed by the vulnerable classes and lastly by the middle classes. The considerable and statistically different differences between the vulnerable class and the middle class emphasize the importance of making the distinction of vulnerable households from the middle class.

**Table 5: Select statistically significant mean comparisons**<sup>**/a**</sup>

_Percentage of households_

||**Poor**<br>|**Vulnerable**|**Middle**|
|---|---|---|---|
|||**Chile**||
|Dirt floor|10.7|6.4|3.2|
|Head is farmer|40.3|23.4|12.9|
|Rural|33.8|22.7|13.5|
|||**Mexico**||
|Dirt floor|24.8|10|3.5|
|Head is farmer|48.4|19.5|10.3|
|Rural|76.8|48.5|31.5|
|||**Peru**||
|Dirt floor|69.4|34.2|17.4|
|Head is farmer|71.6|31.5|16.6|
|Rural|77.5|44.8|26.8|

_Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases. /a Full table with t-tests in annex; table A5.

# **6. The size of the middle class over the last two decades**

Applying our absolute definition to cross-sectional surveys, the results show that the percentage of middle class households has increased significantly from 28.3 to 52.8 percent in Chile, from 24.9 to 42 percent in Mexico, and from 23 to 39.4 percent in Peru over the analyzed period (see figure 5). It is important to notice that the growth of the middle class has been sustained in Chile over the complete time span, while in Mexico and Peru this occurred only after the economic downturns of 1994/95 in Mexico, and the downturns of 1998/99 and 2001 in Peru.

**Figure 5: Size of the middle class in Chile, Mexico and Peru**

_Percentage of households_ <!-- Start of picture text --> 52.8<br><!-- End of picture text --> <!-- Start of picture text --> 39.4<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases.

- 14 -
Figure 1: Poland’s trade openness and inward FDI from Germany, 1995-2013 <!-- Start of picture text --> 1994: Association<br>agreement<br>1995 1996 1997 1998<br><!-- End of picture text --> <!-- Start of picture text --> 2001: All<br>tariffs<br>removed<br>1999 2000 2001<br><!-- End of picture text --> <!-- Start of picture text --> 2004<br><!-- End of picture text --> Notes: The grey line depicts the evolution of Poland’s degree of trade openness relative to Germany (defined as the sum of bilateral exports and imports with Germany over GDP). The dashed line depicts the share of Poland in Germany’s total outward foreign direct investment in the years 1995 to 2012. The sources of the data are UN COMTRADE and the World Development Indicators of the World Bank for trade openness, and OECD for FDI position.

Figure 2: Event study: the effect of German acquisition on firm-level employment: common treatment <!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>Coefficient<br>-.1<br>-.2<br>-.3<br>-.4<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The plot is created by regressing the log of employment on a full set of event time indicators interacted with the a dummy variable indicating whether a firm was acquired by a German shareholder after 2004 and controlling for firm and year fixed effects (equation (1)). The omitted baseline is the last pre-treatment year, 2003. The vertical bars indicates 95% confidence interval. Results include only firms established before 2004.

24
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