rafmacalaba/datause-encoder-data huggingface dataset:
# V. THE DEMAND FOR MONEY AND THE LIMITS TO THE INFLATION TAX

Households have a oure transactions demand for real cash balances of local currency, which we can expect to be positively related to real income and negatively related to the cost of holding those balances, that is, the expected rate of inflation. In this module, we assume that the expected rate of inflation equals its realized value in the current period.

To know the demand for real cash balances is critically important to estimate the effect on inflation of deficit-induced money creation, as well as to estimate the limits of this means of financing.

Given the level and volatility of the Argentine rates of inflation, as well as the unending series of more or less radical changes in monetary policy, one would not expect to find stable demand-for-money parameters for extended periods of time. This means that we should expect to see the revenue maximizing rate of inflation, and the maximum revenue itself, changing over time. For this reason, we have chosen here to estimate de demand for real cash balances using two different approaches: (1) Using a sample of monthly data for the period January 1984June 1988. This period covers from the return to democracy to the onset of the Primavera Stabilization Plan, which ended in the first hyperinflationary episode of 1989. The most important monetary event of this period was the Austral Stabilization plan, launched in June 1985.

(2) Using long term series of annual data (1960-1988) allowing for a time dependent dummy variable to capture the possibility of a structural change taking place. We found that an additive dummy variable (D77) taking the value of one for 1977 onwards (and zero otherwise) captures best the structural change that took place as a consequence of the financial liberalization of 1977.

The estimation on the basis of monthly data precludes the use of income series to estimate a velocity function. Consequently, we estimate a demand for real cash balances with the inflation rate as the only explanatory variable in our regressions. To avoid the simultaneous determinatiop problem (between current period real cash balances and current perird inflation), we have used a Two Stage Least Squares estimation procedure using lagged inflation up to three per ods as instruments. TSLS is also used in the estimation using annual data.

31
**Table** **<u>8: Determinants of time allocation by generation II</u>**

||**Producti**<br>**%**|**ve work**<br>|**House**<br>**%**|**work**<br>|**L**|**eisure**<br>**%**|
|---|---|---|---|---|---|---|
|**Panel A: Female spouse**|||||||
|Female spouse’s father died after 1994 or alive|0.019**|0.017*|-0.012|-0.011|-0.007|-0.006|
||(0.004)|(0.005)|(0.005)|(0.005)|(0.008)|(0.009)|
|Education gap female-male||<br>0.003*||<br>-0.000||<br>-0.002***|
|||(0.001)||(0.001)||(0.000)|
|Share of female spouse’s gifts at marriage||<br>0.031||<br>-0.046*||<br>0.014|
|||(0.079)||(0.012)||(0.067)|
|Reform*Female spouse’s father died after 1994 or|-0.041***|<br>-0.039**|0.008|<br>0.008|0.033*|<br>0.032*|
|alive|||||||
||(0.004)|(0.005)|(0.005)|(0.005)|(0.008)|(0.009)|
|Reform*Education gap female-male||<br>-0.001||<br>-0.002||<br>0.002***|
|||(0.001)||(0.001)||(0.000)|
|Reform*Share of female spouse’s gifts at marriage||<br>-0.003<br>(0.079)||<br>0.000<br>(0.012)||<br>0.007<br>(0.067)|
|Observations|1,095|<br>1,095|1,095|<br>1,095|1,095|<br>1,095|
|R-squared|0.538|0.542|0.567|0.571|0.572|0.576|
|**Panel B: Male head**|||||||
|Female spouse’s father died after 1994 or alive|-0.000|-0.000|-0.004***|-0.004***|0.005|0.006|
||(0.015)|(0.016)|(0.000)|(0.000)|(0.017)|(0.018)|
|Education gap female-male||0.001||0.001||-0.001|
|||(0.001)||(0.001)||(0.001)|
|Share of female spouse’s gifts at marriage||<br>-0.013||<br>0.003||<br>0.011|
|||(0.020)||(0.028)||(0.007)|
|Reform*Female spouse’s father died after 1994 or|-0.034|<br>-0.034|0.011***|<br>0.011***|0.024|<br>0.023|
|alive|||||||
||(0.015)|(0.016)|(0.000)|(0.000)|(0.017)|(0.018)|
|Reform*Education gap female-male||<br>-0.003*||<br>-0.001||<br>0.004|
|||(0.001)||(0.001)||(0.001)|
|Reform*Share of female spouse’s gifts at marriage||<br>-0.009||<br>-0.031||<br>0.046**|
|||(0.020)||(0.028)||(0.007)|
|Observations|1,092|1,092|1,092|1,092|1,092|1,092|
|R-squared|0.538|0.540|0.454|0.457|0.555|0.559|

_Note:_ All regressions include village fixed effects as well gender and state specific year of birth fixed effects. Figures in brackets are robust standard errors clustered by state. * significant at 10%; ** significant at 5%; *** significant at 1%.

23
# **1. Introduction**

The international financial integration of the East Asia and Pacific (EAP) region has captured significant attention in recent years. Part of this interest comes from the fact that EAP has been the region with the highest GDP growth in the world in the past decades, accounting for 29 percent of the world GDP in 2015, up from about 21 percent in 1990. As it captured a larger share of the world’s income, became more developed, and deregulated its financial markets, EAP has been expected to play an increasingly larger role in international financial transactions along with the _North_ (G7 members, excluding Japan, and 15 other Western European economies). But EAP is not alone. The increasing participation of EAP in the global economy is part of a broader trend that includes the _South_ (non-EAP and non-North economies). Since the early 1990s, both EAP and the South have been growing faster than the North and, by now, they account for an important fraction of the world’s economic and financial activity (Hanson, 2012; World Bank, 2011, 2013, 2017; UNCTAD, 2013, 2015; United Nations, 2014; WTO, 2014; de la Torre et al., 2015; Broner et al., 2016).

As part of the interest in EAP’s role in the world economy, one strand of the literature has concentrated on documenting the international financial integration patterns of the region. A natural way to do it is by analyzing the geographical composition of its investments, which requires crossborder bilateral data.<sup>1</sup> Several papers focus on the comparison of EAP’s global and regional financial integration using gravity models (Eichengreen and Park, 2005a; Lee, 2008; Kim et al., 2008; GarciaHerrero et al., 2009; Borensztein and Loungani, 2011; Pongsaparn and Unteroberdoerster, 2011; Lee et al., 2013; Ananchotikul et al., 2015). This literature typically finds that EAP economies maintain stronger financial links with the rest of the world than with other economies within the EAP region. Some of these studies also benchmark EAP’s intra-regional financial integration with that of Europe

> 1 A number of papers have studied EAP’s financial integration process using other alternative measures, such as prices (Eichengreen and Park, 2005b; Chi et al., 2006; Yu et al., 2010; Hinojales and Park, 2011; Boubakri and Guillaumin, 2015), correlation between domestic savings and investment rates (Guillaumin, 2009), and the degree of consumption risk-sharing (Kim et al., 2006; Ng and Yarcia, 2014).

1
**Figure 4: Location of commercial maize farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands**

<!-- Start of picture text --> Legend<br>Kebele boundary 2004<br>Yield (q/ha) by quintile<br>1 - 5<br>6 - 10<br>11 - 13<br>14 - 18<br>19 - 34<br>Region boundary<br>Buffer around Maize farm (04)<br>Distance (km)<br>25<br>50<br>100<br>150<br><!-- End of picture text --> 29
# **1. Introduction**

This paper presents estimates of the relationship between the share of income held by the middle class and economic growth of ASEAN economies. As such, the paper is part of a literature in macroeconomics that examines the relationship between income inequality and aggregate output of countries; see Galor (2011) for a review. At least since Kuznet (1955), it has been noted that the relationship between income inequality and GDP per capita may be non-linear. Galor and Zeira (1993) provide a model that generates such an inverted U-shaped relationship between income inequality and GDP per capita. In their model, a bi-directional causality between income distribution and GDP per capita emerges: (i) an increase in GDP per capita reduces income inequality; (ii) the effect of an increase in income inequality on GDP per capita is positive for relatively poor countries and negative for relatively rich countries. In the Galor and Zeira (1993) model, the mechanism that generates the non-linear effect of income inequality on GDP per capita is human capital accumulation: With credit market imperfections and fixed costs of human capital accumulation, an increase in income inequality increases investment in human capital in relatively poor countries; but the opposite is the case in relatively rich countries.

The first part of this paper presents effects that economic growth during 1970-2010 had on changes of ASEAN countries' shares of income accruing to the third and fourth quintiles. Brueckner et al. (2015) used international oil price shocks and trade-weighted world income of countries as instruments to estimate the effect that exogenous variation in GDP per capita has on various measures of countries' income inequality. Using these estimates and data on changes of ASEAN countries' GDP per capita between 1970 and 2010, this paper computes predicted effects that GDP per capita growth during 1970-2010 had on changes in the income share accruing to the middle class of ASEAN countries. The main finding is that the middle class gained substantially from economic growth over the past four decades: the share of income accruing to the 3<sup>rd</sup> (4<sup>th</sup> ) quintile increased for the average ASEAN country by around 6 (8) percentage points due to growth in 2
Accordingly, this paper builds on the work of the forthcoming World Bank report on jobs and zooms into the patterns of job creation across surviving firms in the EU11 economies. The analysis utilizes the Amadeus database (provided by Bureau van Dijk) which contains comparable and comprehensive balance sheet and profit and loss account data for the EU11 countries for a time period spanning from 2002 to 2009.<sup>4</sup> This data set is augmented with data from the World Bank's Business Environment and Enterprise Performance Survey (BEEPS) which collects information on the business environment in which the firms operate. In the EU11 economies, these surveys have been conducted in the years 2002, 2005, 2007 and 2009, allowing the analysis of dynamics of job creation at the firm level in the boom years prior to the global financial crisis as well as during the “bust”. The next section presents stylized facts on job creation in the EU11 economies, while Section 3 offers a structural analysis of job creation patterns across and within-industries. The comparison of employment growth dynamics across industries allows for examining whether the EU11 economies have been successful in shifting employment to more productive sectors, while the within-industry analysis provides evidence on which firms have been most crucial for overall job creation. Section 4 re-examines this latter issue by investigating the key-industry and firm-specific characteristics of high-growth firms. Section 5 offers policy conclusions.

# Stylized Facts

**Many jobs created in EU11 during 2002-2008 were lost during the 2008 global financial crisis.** Among the surveyed firms, the number of employees in firms that were active in 2002 increased by approximately 76 percent (Figure 1). For all countries the minimum increase in the number of workers amounted to approximately 50 percent. Lithuania, Poland, Bulgaria, Slovenia and Slovakia were able to more than double the number of employed workers. However, the global financial crisis induced an overall job loss in 2009, nearly halving the cumulative gains of the previous period (2002-2008) among the surveyed firms. In the EU11 countries, half the jobs created during 2002-2008 were lost in 2009.

**While jobs were lost throughout the EU11 region, the magnitude of job destruction varied among the countries** (Figure 1) **.** In Lithuania and Poland, the number of workers employed in firms that already existed in 2002 was about threefold in 2008. The crisis, however, reversed these dynamics in 2009 to 174 percent in Lithuania and 131 in Poland of the respective 2002 level. In Slovakia and Latvia, despite notable gains in employment prior to 2008, the overall number of employees in established firms was at or below the 2002 level among the surveyed firms.

> 4 A detailed data description for the Amadeus database is provided in Udomsaph (2013). One should note the large variation in the coverage and representativeness across the EU11 countries in the Amadeus data base, and hence the quality of data across the EU11 countries. The results presented here pertain solely to the surveyed firms in the data base.

3
regional boundaries that can fuel sovereignty demands. In a homogenous country such as Japan, regions’ ethnic compositions are roughly the same with a single group dominant in all regions. Demand for sovereignty does not arise in this setup. The same is true if there are ethnically heterogeneous regions in an equally ethnically heterogeneous country (the United States offers examples of states with no substantial difference between regional and country-wide level of ethnic heterogeneity). What matters in both cases, is that regional distinctiveness is small. Demand for sovereignty arises when regional distinctiveness is high, as for example when a fairly ethnically homogeneous region is different from the rest of the country. _Prima facie_ support of this hypothesis is found in state efforts in multi-ethnic countries to dilute the ethnic homogeneity of regions by gerrymandering internal borders.<sup>27</sup> Thus, we hypothesize that: _H2: Regions that are ethnically different should demand more sovereignty._

# **_Inequality_**

The distribution of resources among groups in any state is a source of conflict. A common claim is that a high degree of economic inequality leads to crime and violence. The usual measure of inequality is the Gini coefficient, measuring inter-personal income inequality at the country level. In this study, we use, for the first time, a measure of inter-personal income inequality at the regional level. This information is derived from household survey data and allows us to compute inter-personal inequality for almost 900 regions from 48 countries, for one year in the period from 1998-2003.

High within-regional income inequality ( _Gi_ ) may be considered in two different ways.

First, it may hamper efforts to collectively organize a regional demand for sovereignty because nation (see Darden 2010). It follows that ethnic distinctiveness need not be a precondition for nationalism.

27 Dilution can also occur through assimilation (as in Greece’s strategy toward Slavic-speaking groups in the post-war period) or through forced population movements (Stalin’s deportations of Chechens). It follows that internal administrative borders are endogenous to the risk of regional challenges to the center. This does not invalidate our empirical analysis, since our results are conditional on a given set of internal borders.

11
As explained, CHS estimate purchased intangible investment directly whenever possible: that is, from the expenditure data of the industries purchasing the asset in question. When such direct data are not available, their strategy is to estimate purchased intangible investment using the revenue of the industries that produce the intangible investment in question. The availability of direct expenditure data is poorer in the case of Brazil, and as a result, we have to use the indirect approach – the revenue of the producing industry— more frequently. This is the most common difference between our sources and those in CHS. For example, we find no surveys in Brazil that estimate the amount spent by industries on computer software and databases, and have to resort to using the Net Operating Income (NOI) of computer software and database services industries from the Annual Survey on Services.

In cases where CHS use crude approximations to estimate something not captured in surveys or national accounts, we replicate their approximation using the closest Brazilian counterpart. Arguably, there are cases in which the CHS approximation could be considered unrealistic for Brazil. To give an example, assuming that as much as 20 percent of intermediate expenses in financial services industry go towards new product development could be too optimistic. But in the absence of any Brazil-specific or other developing country literature that could guide us towards a better approximation, we have chosen to use the number suggested to CHS by the US literature; this has the additional advantage of methodological comparability.

Most of the key surveys for our study are quite new to Brazil. For example PINTEC, our main source for R&D data, has been conducted in 2000, 2003, 2005 and 2008. This restricts us to estimating intangibles during 2000-2008, a much shorter time span than that examined in studies of intangible investment in developed countries, some of which go back to the 1970s. Future intangible asset studies on developing countries should expect a similar disadvantage in historical data availability.

Finally, while most existing studies of intangibles present only national aggregates, we present intangible investment estimates disaggregated to the industry level. This is a simple corollary of the CHS approach, since they take firm level spending as their starting point. For those intangibles for which we use firm level surveys, this is easily done by aggregating the firm level data to the industry level; the surveys are designed to be representative at the industry level. In cases where we use aggregate national accounts data, we estimate the amounts purchased by different industries by using the national accounts input 11
# **_CONTENTS_**

|1.<br>Introduction|1|
|---|---|
|1.1.<br>Motivation and objectives of the study|1|
|<br>1.2.<br>The context of urban poverty in ECA: the socialist legacy|1|
|1.3.<br>A framework for viewing urban poverty|4|
|2.<br>Measurement and Data Issues|5|
|2.1.<br>Data sources|5|
|2.2.<br>Selected indicators of income and non-income dimensions of well-being|6|
|2.3.<br>Measurement issues regarding infrastructure and urban poverty in household surveys|7|
|_2.3.1._<br>_Urban poverty may not be properly represented in sample surveys_|_7_|
|_2.3.2._<br>_Poverty indicators are not necessarily comparable between urban and rural areas_|_8_|
|_2.3.3._<br>_Poor coverage of infrastructure and energy in multi-topic questionnaires_|_9_|
|3.<br>Economic Overview|11|
|3.1.<br>Uneven economic recovery across the region|11|
|3.2.<br>Large disparities in national income and national income poverty|13|
|3.3.<br>Migration and urban change|16|
|4.<br>Revisiting the Extent and Nature of Urban Poverty|18|
|4.1.<br>Income poverty|18|
|_4.1.1._<br>_Comparisons of income poverty_|_19_|
|_4.1.2._<br>_Inequality by settlement area_|_23_|
|_4.1.3._<br>_Employment and labor force participation_|_24_|
|_4.1.4._<br>_Other sources of income and transfers_|_27_|
|_4.1.5._<br>_Household expenditure patterns_|_28_|
|4.2.<br>Non-income dimensions of poverty: access to infrastructure, energy and housing|28|
|_4.2.1._<br>_Infrastructure and energy services remain widely available but no-longer reliable_|_29_|
|_4.2.2._<br>_Households pay little for infrastructure and energy services and housing_|_32_|
|_4.2.3._<br>_The incidence of poor sanitary and environmental conditions is high_|_35_|
|_4.2.4._<br>_The links between access to infrastructure and energy services and income poverty_|_36_|
|_4.2.5._<br>_Housing context_|_41_|
|_4.2.6._<br>_Housing affordability_|_45_|
|_4.2.7._<br>_Low mobility rates_|_47_|
|_4.2.8._<br>_Implications for the urban poor_|_49_|
|4.3.<br>Human capital|54|
|4.4.<br>Social Capital, empowerment and security|56|
|5.<br>Conclusion|57|
|5.1.<br>Summary of results|58|
|<br>5.2.<br>Issues for policy makers|59|
|<br>_5.2.1._<br>_Strategic questions_|_59_|
|<br>_5.2.2._<br>_Sectoral issues_|_60_|
|_5.2.3._<br>_Questions related to data and methodology_|_61_|
|_5.2.4._<br>_Implications for future empirical research_|_61_|
**Table 2: Proportion of Children at Work and/or School, by Sex and Area**

|7-Gl<br>-/) '|-<br>-Boy%) ;|<br>Gd%|B-y./I|
|---|---|---|---|
|Work Only<br>1|2|5|12|
|WorkandSchool<br>5|10|18|36|
|SchoolOnly<br>89|83|66|45|
|Neitherworknorattendschool<br>5|5|11|7|
|100|100|100|100|

Source: Calculations based on PNAD, 1996

# **Intensity of Work**

16. Intensity of work is indicated by hours worked per week or per day. Although the survey found no child working over 20 hours a day, about 5 percent of boys and 6 percent of the girls worked exceedingly long hours, that is, between 10 and 20 hours (see Table 3). Differences between the intensity of work carried out by boys and girls were not dramatic. It is surprising to find, however, that although a greater number and a higher proportion of children work in rural areas, rural children are more likely to work fewer hours than their urban counterparts.

**Table** 3: Proportion of Working Children by Hours Worked Per Day

|**Hours**of<br>**r**<br>.,<br>Urb|ah|---Ru|iiral---|||
|---|---|---|---|---|---|
|Perday.<br>-<br>- Girl-/)-<br>_-_|Boys(4o)|idiO)|B.(Gis)|Boy|(°;o)-|
|0<h<=5<br>47|50|62|56|55|53|
|5< h<=10<br>45|44|34|40|39|42|
|10< h<=20<br>8|6|4|4|6|5|
|h >20<br>0|0|0|0|0|0|
|100|100|100|100|100|100|

Source: Calculadons based on PNAD, 1996 6
WTO (World Trade Organization). 2013. _Fourth Global Review of Aid for Trade 2013: Connecting to Value Chains._ Geneva: World Trade Organization.

# **Appendix A.  Conditioning Variables Definition**

# **Table A1.  Firm Covariates Whose- Pre-Treatment Values are Used in the Synthetic Control Method Estimation**

|EU share|share of firm's total value of imports in a month accounted for by imports from<br>the EU 27 countries.|
|---|---|
|Special customs clearance|share of firm's total value of imports in a month accounted for by declarations<br>that benefit from entering Serbia under a special customs clearance regime<br>other than the in-house clearance.|
|**Share of imports of:**|**share of firm's total value of imports in a month accounted for by:**|
|Animal<br>and<br>vegetable<br>products and foodstuffs|HS 10-digit products belonging to HS2 chapters 01-24|
|Minerals|HS 10-digit products belonging to HS2 chapters 25-27|
|Chemicals,<br>plastics<br>and<br>rubbers|HS 10-digit products belonging to HS2 chapters 28-40|
|Raw hides, skins, leathers,<br>furs, textiles and footwear|HS 10-digit products belonging to HS2 chapters 41-43 and 50-67|
|Wood and paper products|HS 10-digit products belonging to HS2 chapters 44-49|
|Stone and glass|HS 10-digit products belonging to HS2 chapters 68-71|
|Metals|HS 10-digit products belonging to HS2 chapters 72-83|
|Machines and electrical|HS 10-digit products belonging to HS2 chapters 84-85|
|Transportation|HS 10-digit products belonging to HS2 chapters 86-89|
|Miscellaneous and services|HS 10-digit products belonging to HS2 chapters 90-99|

47