rafmacalaba/datause-encoder-data huggingface dataset:
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The surge in the weight of C&SL‐intensive products indicates the growth in exports of machinery as well as participation of domestic firms in global division of labor based on production fragmentation or global sharing. The latter, also referred to as producer‐driven networks, are dominant in electronic and automotive sectors. Domestic firms become then parts of producer‐driven networks characterized by common ownership of vertically‐integrated stages of production managed by large MNCs. This is one of the critical links between changes in factor intensities and FDI. Having had examined developments in producer‐driven network trade over 1996‐2005, the authors of the World Bank study concluded that only “... BiH witnessed a jump ... in its network trade share...” and other SEE economies “... are underperformers...” (Kathuria 2008, p. 42). This should by no means suggest that BiH has been a ‘super‐ performer,’ as there are doubts as to its sustainability (Kathuria 2008, p. 44).

Indeed, there is a reason to believe that BiH firms are not firmly entrenched into MNC‐driven production and distribution networks. We can use the ratio of the FDI stock to the value of C&SL intensive exports as a proxy of the extent of foreign ownership in manufacturing. In the absence of consistent data on the distribution of FDI stocks in SEE‐economies, this is a very approximate measure. A high value of this ratio may suggest that firms exporting C&SL intensive products are foreign owned and operate in global MNC‐driven production networks. Low values of this ratio may point to much larger presence of local capital and limited ownership by foreign MNCs. BiH ratio of 4.0, although on a par with that for Serbia and Montenegro and higher than in Macedonia, is relatively low indicating relatively strong local ownership.

# **B. Mode of entry: implications of dominance of joint ventures**

Low ratio of FDI stock to C&SL exports combined with evidence from interviews points to joint ventures rather than subsidiaries as the dominant mode of entry of FDI into BiH. BiH firms have been successful in marketing their machinery products in world markets.<sup>8</sup> So have they been in integrating into global automotive networks of production and distribution. The level of integration is similar to that of Central European automotive powerhouses—Czech Republic and Slovakia. Parts import intensity, defined as percent of parts imports in total network exports, rather dramatically declined from 384 in 2002 to 78 percent in 2006 (Table 13). This suggests a very significant increase in domestic processing and assembly operations of automotive products. For comparative purposes, note that parts import

> 8 The share of machinery in EU imports from BiH increased from 11 percent in 2003 to 19 percent in 2007 (as reported by the EU to the UN COMTRADE database).
floor’ proposals) cannot avoid dealing with such trade-offs without running the risk of placing social protection on an unsutainable path.<sup>37</sup> Improving individual program efficiency, rationalizing duplicative programs and prioritizing the use of fiscal resources on the basis of both equity and efficiency considerations are the core of efforts to strengthen social protection systems.

In many countries, however, current levels of social protection spending are insufficient (even under the most optimistic assumptions regarding targeting efficiency) to enable a significant impact on poverty, let alone broader equity objectives. Increasing budgets for social protection programs can, in many of those countries, conceivably be done by shifting resources away from often large, badly targeted and otherwise distortive generalized subsidies. A prime example are fossil fuel subsidies. A recent IMF study<sup>38</sup> documents the extent of such subsidies. Even in Sub-Saharan Africa (which accounts for only about 4 percent of global energy subsidies), subsidies amount to 11⁄2 percent of regional GDP or 51⁄2 percent of total government revenues, with electricity subsidies accounting for over 70 percent. It is hard not to see the potential for subsidy reform enabling the necessary increase in social protection.

Of course, country conditions will vary and the right (and politically feasible) response will most likely differ across countries. However, the evidence reviewed in this paper strongly suggests that social protection cannot and should not remain as the silent partner to the new set of development goals the international community is currently debating.

> 37 In this paper we have not delve into the intricacies of how to design social protection programs.  Considerations such as the need to set payment levels to both ensure adequacy and avoid savings and labor market distortions, or the need to define graduation policies that avoid perverse incentives remain at the core of the policy discussions. 38 IMF (2013) http://www.imf.org/external/np/pp/eng/2013/012813.pdf

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Figure 6: Average Firm Size under the Doing Business Indicator’s Measures of Barriers to Entry <!-- Start of picture text --> 0.2 0.4 0.6 0.8 1.0 1.2 1.4<br>Data_country/Data USA<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>Distorted/Undistorted<br>0.4<br>0.2<br><!-- End of picture text --> Note: The figure plots the average firm size in each country’s stationary equilibrium under distortion pairs � _τi_<sup>_E,DBI_</sup> _, γi_ �. _τi_<sup>_E,DBI_</sup> stands for the entry barriers from the World Bank’s Doing Business Indicator, and _γi_ denotes the productivity elasticity estimate of idiosyncratic distortions. Units in the vertical axis are reported as ratios between the distorted average firm size relative to the one in the undistorted allocation. The horizontal axis reports the average firm size in each country relative to the US.

# **5.5.1 Validation for the Endogenous Productivity Distribution**

One important channel through which distortions translate into changes in the economy’s average firm size is the innovation decisions of firms and the associated stationary distribution of productivity. Biases in the properties of these distributions will transmit directly into the average firm size and therefore on the estimate of the entry barrier.

We seek reassurance in the model’s prediction for the productivity distribution by exploring two sources of validation: the life cycle dynamics of firms and the share of firms at the top of the productivity distribution. Hsieh and Klenow [2014] show salient differences in the life cycle of firms between the US, India, and Mexico, which can be used to gauge the plausibility of the firms’ innovation decisions in response to the mix of distortions in the economy. The share of firms with employment levels above a certain size in turn provides information about a nontargeted moment of the size distribution against which predictions of the model can be compared.

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where _φi_ is the cognitive ability of the child, and we assume that _δ_ 0 _, δ_ 1<sup>_j, δ_</sup> 3<sup>_j>_0and</sup> _δ_ 2<sup>_j, δ_</sup> 4<sup>_j, δ_</sup> 5<sup>_j≥_0.The last inequalities are weak to allow for the possibility that over the relevant</sup> range the education production function is approximately linear in financial investment. This specification allows for a flexible effect of a child’s ability on the educational outcome; a higher ability is complementary to financial investment we have _ω_ 1 _, ω_ 2 _>_ 0, increasing the linear coefficient by a multiplier (1 + _ω_ 1 _φi_ ) and also lowering the quadratic coefficient by (1 _− ω_ 2 _φi_ ).<sup>13</sup> The specification adopted by Becker et al. (2015) is nested in this formulation: they assume that _α_ 2 = _ω_ 2 = 0. The direct effect of parental education can be either concave or linear which represents nonfinancial aspects of parental influences on children’s education such as home tutoring and role model effect.<sup>14</sup> The intercept term ( _δ_ 0) captures the common factors irrespective of location and thus is not indexed by _j_ .

There are two major sources of differences in rural vs. urban areas: (1) the returns to education ( _R_<sup>_pj_</sup> in equation (1) above) may be different because of differences in occupational and economic structure. The rural areas are engaged predominantly in agricultural activities, although the share of non-farm activities has increased substantially in many countries including Indonesia in the last few decades. The existing evidence suggests that returns to education in agriculture is low, especially beyond secondary schooling (Kurosaki and Khan (2006)), Phillips (1994).<sup>15</sup> While returns to education in nonfarm occupations in villages are usually higher, they may still be lower than the returns to education in urban areas specializing in modern manufacturing and services activities. (2) The quality of schooling is likely to be different across rural vs. urban locations. The rural areas might be primarily served by government schools and (some) low-quality private schools (Febriana et al. (2018)). The costs incurred by the parents are much less for the public schools compared to the private schools, and, as a result, the role parent’s income can play may be more limited in this case.<sup>16</sup> The high-cost private schooling is likely to be much

> 13In this formulation, substitutability implies that _ω_ 1 _, ω_ 2 _<_ 0.

> 14Becker et al. (2015) are agnostic about whether the direct impact is concave or convex. We believe in the context of a developing country, it is perhaps more likely that it is concave.

> 15Based on a meta analysis, Phillips (1994) reports an yearly rate of return of 1.60 percent. Kurosaki and Khan (2006) find that returns fall sharply after primary schooling in rural Pakistan.

> 16According to the compulsory schooling law in Indonesia, schooling must be provided by government free of costs to every citizen. However, the compulsory education law allowed “voluntary contributions” by

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# **5. Conclusions**

We have studied a new time series of survey-based poverty measures for urban and rural India spanning 50 years, including 15 years after economic reforms started in earnest in the early 1990s. While progress against poverty has been highly uneven over time, we find a long-run trend decline in all three of the poverty measures we have used. Exponential (proportionate) trends are higher for the poverty gap and squared poverty gap indices than the headcount index, reflecting gains to those living well below the poverty line. Both urban and rural poverty measures have shown a trend decline; rural poverty measures have historically been higher than for urban areas, though the urban and rural poverty measures have been converging over time, and in recent years the squared poverty gap index for urban India has started to exceed that for rural India.

Progress against poverty has been maintained in the post-reform period. Indeed, we find a higher proportionate rate of progress against poverty after 1991, although the difference in trend rates of change between the two periods is only statistically significant for the headcount index. The linear trend—the annual percentage point reduction in the poverty measures—has remained about the same in the post-reform period. We also find that the responsiveness of poverty to growth in the survey mean—the growth elasticity of poverty reduction—has generally remained the same between the two periods; only for the headcount index do we find a significant increase in the absolute growth elasticity in the post-reform period. When we use growth as measured in the national accounts there are signs that the post-reform growth process has become less propoor in the sense of attaining a lower proportionate rate of poverty reduction from a given rate of growth. Overall, while the higher rate of growth in the post-reform period has come with a higher proportionate rate of progress against poverty, we do not see in these data a robust case for saying that the growth elasticity of poverty reduction has risen since the reforms began.

Recognizing that the fortunes of the poor in each of the urban and rural sectors are linked in various ways—through trade, migration, and transfers—we have also revisited our earlier (pre-reform) findings on the relative importance of growth in the two sectors to poverty reduction in both sectors and nationally. Like our 1996 study, we find that that the pattern of growth matters for poverty reduction. But we find that the post-reform period has seen a striking change in the relative importance of urban versus rural economic growth. Our 1996 study found 24
Frankel (2009), it became harder to sustain the orthodox view according to which “central banks should essentially pay no attention to asset prices, the exchange rate, or export prices, except to the extent that they are harbingers of inflation.”

# _1.2 Asset Price Booms and Busts as a Missing Dimension_

The blueprint of basic principles for a monetary policy framework outlined above did not give due attention to how financial markets and their channels of interconnectivity are relevant for macroeconomic stability. It had been long held that asymmetric information and market failures played a significant role in financial systems and in business cycles. Nonetheless, the mainstream view remained that markets and private institutions could self-adjust in an efficient way and manage their own market and liquidity risks properly. Micro-regulation and supervision of individual entities would sufficiently discipline the behavior of private agents.

Even when the frequent appearance of bubbles started to be acknowledged, the belief was that attempts to detect and prick them at an early stage would be impossible to accomplish and potentially harmful. If necessary, resorting to interest rate cuts to safeguard the economy after bubble bursts would be the optimum procedure, conditional on subsequent impact on inflation and output gap (Bernanke and Gertler, 2000).

In fact, the issue was the object of an intensive debate for sometime before the crisis – the so-called “lean versus clean debate” (Mishkin, 2009). While many argued in favor of monetary policy “leaning against the wind” from financial developments, the prevalent opinion was that difficulties in detecting bubbles would outweigh the advantages of doing so. Furthermore, monetary policy tools would be too a blunt way to curb the rise of bubbles, as correspondingly sharp interest rate hikes would have harmful unintended consequences on output growth and volatility. The best approach would then be to have monetary policy react only if and when “mopping up” or “cleaning up” the financial mess after bubble bursts was necessary.

As the evidence on the significant presence of real-estate and stock-market asset price busts over the past 40 years became clear – see e.g. IMF (2009) – the pendulum swung toward arguments in favor of some “leaning against the wind”. The experience with widespread busting of both house and stock price bubbles beginning in 2007 is indeed singular in the last 40 years (Figure 1). However, one can observe not only the frequency of previous episodes, but also that those “asset price busts are relatively evenly distributed before and after 1985 – a year that broadly marks the beginning of the Great Moderation” (p.95).

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**Figure 5: Retail LPG prices in March 2015** (US$/kg)

<!-- Start of picture text --> 4.00<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>Iran, Islamic Rep. of Venezuela, RB Egypt, Arab Rep. of Ecuador Algeria Equatorial Guinea Argentina Tunisia Bolivia Morocco Angola China Indonesia India Niger Malaysia Cameroon Ghana Bangladesh Gabon Thailand Côte d'Ivoire El Salvador Jordan Mexico Sri Lanka Peru Nepal Guatemala Brazil Philippines United States Uruguay Cape Verde Vietnam Senegal Pakistan Chile Fiji South Africa Ethiopia Kenya Uganda Malawi<br><!-- End of picture text --> _Sources_ : Government websites and announcements, local news articles _. Notes_ : Prices are for LPG sold in 10–15‐kg cylinders, except 3‐kg cylinders in Indonesia and Morocco which are commonly used cylinder sizes for subsidized LPG for household use, 6‐kg cylinders in Cape Verde and Senegal, and 18‐kg cylinders in Malabo in Equatorial Guinea. The price of LPG shown for Argentina is the subsidized price for the poor. The price for China is the price for LPG sold in 15‐kg cylinders for use by households in Beijing, with a limit on purchase at this price.

The caveat in the previous paragraph notwithstanding, the differences in end‐user prices for the same fuel are far greater than what can be explained by differences in taxes and costs alone. The low prices arise because of price controls in many countries. There is little consistent pattern by country: pricing policy is not only country‐specific but also fuel‐specific. Only a few countries subsidize every fuel, and they are all major oil exporters. It is more common for a country to have deregulated prices for a few fuels (such as jet fuel) or consumers (mining companies, for example), cost‐reflective controlled prices for a few others, and an ad‐hoc pricing policy for one or two fuels (say household LPG) carrying subsidies. Box 1 provides some considerations for and observations on liquid fuel prices and pricing.

# **Box 1: Pricing of liquid fuels**

The long‐term goal in most countries with price controls is deregulation of liquid fuel prices in a competitive market with effective enforcement of sound technical, environmental, health, and safety regulations. In such markets, efficient operators would flourish and, importantly, efficiency gains would be passed onto consumers in the form of lower prices. One exception is pipeline transportation of liquid fuels, which is a natural monopoly and requires economic regulation.

Government regulation and enforcement are critical to ensure that consumers benefit from the lowest prices <u>possible enabled by competition, while adequate standards are maintained. Short‐selling (rigging pumps and</u>
it builds on an earlier report describing the characteristics of poor households in Vietnam (Poverty Working Group, 1999).

Second, it examines the spatial distribution of poverty in Vietnam using the 1998 VLSS and a 3 percent sample of the 1999 Population and Housing Census. This analysis represents an improvement on the earlier Vietnam study in several respects: a) the data are more recent, an important consideration in a rapidly growing country such as Vietnam, b) the analysis covers both urban and rural areas, providing a broader view of poverty in Vietnam, and c) we calculate the standard error of the poverty headcount. The standard errors are based on the methods suggested by Hentschel _et al._ (2000), with extensions to incorporate the sampling error associated with the fact that we are using a 3% sample of the Population Census rather than the full Census.

Third, this study examines the efficacy of Vietnam's existing geographically targeted antipoverty programs and investigates the potential for improving the targeting of the poor by using the type of additional household level variables that could be collected in a "quick-and-dirty" enumeration of households.

# **1.3 Organization of paper**

Section 2 describes the data and methods used to generate poverty maps for Vietnam from household survey data and census data. Section 3 describes the results of the regression analysis. Although these are an input in the poverty mapping procedure, they also yield insights on the factors associated with poverty and how they vary between urban and rural areas. Section 4 presents the provincial estimates of urban and rural poverty in Vietnam, along with the standard errors of these estimates. Section 5 examines the efficacy of Vietnam's poor and disadvantaged communes program and investigates whether use of additional household variables might improve poverty targeting. Finally, Section 6 summarizes the results, discusses some of their policy implications, and suggests areas for future research.

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Notes: Entries are expressed in percentage points. The fitted rate of decline in CM is expressed as a deviation from the sample mean. CM stands for child mortality. Only the statistically significant regressors are used.

If we slit the non-high-income OECD sample into quintiles of rate of decline in child mortality, it appears that four oil-rich countries (Algeria, Azerbaijan, the Islamic Republic of Iran, and Nigeria) are in the top quintile and three (the República Bolivariana de Venezuela, United Arab Emirates, and Republic of Congo) in the bottom quintile. These differences in rates of decline in child mortality illustrate the extent to which some oil-rich countries performed better than others. These differences are the results of conditional convergence, undernourishment, public spending on health and, to some extent, economic growth and population density.

Second, undernourishment or caloric sufficiency turns out to have played a surprisingly important role. In several countries, namely, the United Arab Emirates, Malaysia, Kazakhstan, the Islamic Republic of Iran, Mexico, and Algeria, the high degree of caloric sufficiency (low undernourishment) played the most significant role in reducing child mortality. In another two countries, namely, Angola and Republic of Congo, the relatively low degree of caloric sufficiency played the most significant role in preventing the reduction of child mortality.

Third, high levels of public spending on health were the most important determinant in Kuwait, the United Arab Emirates, Trinidad & Tobago, the República Bolivariana de Venezuela, Malaysia, and Algeria. In Indonesia, Nigeria, and the Republic of Congo, by contrast, public spending was so low that it became the most important factor in retarding the decline of child mortality. The above suggests that different factors contribute to different degrees in the fitted rate of decline in the child mortality rate (as a deviation from the sample mean) for the country in question.

**Table 7: Contributions to gap between actual and predicted change in school enrollment**

|Country|Fitted rate of<br>growth in CM|Initial school<br>enrollment|Ln of Education<br>spendingPC,PPP|Undernourishment|GDP Per Capita<br>growth|
|---|---|---|---|---|---|
|Nigeria|3.202|2.979|-0.259|0.489|-0.008|
|Angola|2.066|3.173|0.021|-1.106|-0.021|
|Algeria|0.845|-0.044|0.290|0.612|-0.012|
|Indonesia|0.188|0.143|-0.151|0.175|0.021|
|United Arab<br>Emirates|-0.553|-1.581|0.358|0.716|-0.046|
|Mexico|-0.561|-1.443|0.201|0.690|-0.010|
|Malaysia|-0.562|-1.615|0.319|0.715|0.019|
|Iran, Islamic<br>Rep.|-0.632|-1.484|0.184|0.683|-0.014|
|Venezuela, RB|-1.306|-1.980|0.308|0.386|-0.020|

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