rafmacalaba/datause-encoder-data huggingface dataset:
with   1/ _b_ and    / _b_ . Rearranging terms and taking logs on both sides yields the following equation: This growth curve assumes an increasing and then decreasing adjustment speed of attainments with an inflection point at _t_     _b_ , the mean of the underlying (symmetric) logistic distribution function. _MDGt_ will be 0.5 at the inflection point: half-way achievement. We now add a country-fixed effect _uc_ and a random error _ε_ , which yields estimation equation (11).

We estimate this equation using Ordinary Least Squares (with country-fixed effects) on data on net primary enrollment for 1991, 1999 (or 2000), and 2006 (or 2007) from the MDG monitoring website run by the United Nations. The sample only includes developing countries, here roughly defined as low- and middle-income countries with a GDP per capita of less than US$10,000 in 1990 (in current USD). The inclusion of a country-fixed effect allows countries to start at different points in time. We then repeat this exercise for (under-five) child mortality using data from the same source, but for 1990, 1995, 2000, and 2006. Note that we need to normalize child mortality by setting a maximum initial level, which we choose to be 300. The results of estimating equation (11) for both net primary enrollment and under-five child mortality are reported in the Appendix.

The empirical results for these two social sector targets are remarkable. Overall, there is quite some similarity, across countries as well as across sectors. On an unrestricted sample, the parameter _β_ is 0.040 for primary enrollment and 0.042 for under-five child mortality. Figure 2 illustrates the implications of these parameter values in terms of MDG progress rates. Note that the constant (plus the country-specific fixed effect) puts each country at a different (starting) point on the time axis t. The black line in the graph on the left-hand side shows that the average developing country would need, for example, approximately 25 years to increase enrollment from 40 percent (a rate common in poor Sahel countries in the early 1990s, for example) to 65 percent. Similarly, bringing child mortality from 250 to less than 100, that is, a two-thirds reduction, would take more than 50 years. These results are in line with the 10
# **TABLES**

Table 6. Population, by Comparison Groups

|||Host<br>Community|Settled Migrant HHs<br>(>4 years in Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009|Near the Syrian Border|13,645,091|32,177|1,368|
|Population|Rest of the Country|55,441,281|956,218|27,345|
|2013|Near the Syrian Border|14,442,691|18,099|29,846|
|Population|Rest of the Country|57,129,706|952,052|50,989|
|2014|Near the Syrian Border|9,942,906*|28,272*|99,243*|
|Population|Rest ofthe Country|45,700,589*|1,004,532*|210,658*|

_Notes_ : *The population in 2014 is only of the population aged 15+. In 2014, the population weights exclude individuals under 15 due to changes in the LFS _. Source_ : LFS 2009-2014. World Bank author’s calculations.

Table 7. Population Shares, by Comparison Group

||||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|---|
|2009|Population|Near the Syrian Border|19.8%|3.3%|4.8%|
|Share||Rest of the Country|80.2%|96.7%|95.2%|
|2013|Population|Near the Syrian Border|20.2%|1.9%|36.9%|
|Share||Rest of the Country|79.8%|98.1%|63.1%|
|2014|Population|Near the Syrian Border|17.9%|2.7%|32.0%|
|Share||Rest of the Country|82.1%|97.3%|68.0%|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _Source_ : LFS 2009-2014. World Bank author’s calculations.

Table 8. Households, by Comparison Group

|||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009|Near the Syrian Border|3,046,843|9,744|399|
|Population|Rest of the Country|15,623,820|315,158|10,433|
|2013|Near the Syrian Border|3,384,455|6,439|5,064|
|Population|Rest of the Country|16,947,974|349,414|14,678|
|2014|Near the Syrian Border|3,176,654|10,917|18,575|
|Population|Rest of the Country|17,047,500|417,846|64,973|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _._ However, this should not affect estimates of the number of households.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

20
WPS 3371

# **ON THE MEASUREMENT OF MARKET-ORIENTED REFORMS**<sup>*****</sup>

**Norman V. Loayza** World Bank

**Raimundo Soto**

Catholic University of Chile

## December 2003

## **Abstract**

This paper presents policy- and outcome-based ways of measuring the progress of market-oriented reforms in both traditional areas of first-generation reform and the areas of institutional reform that have been emphasized lately. These policy areas are the domestic financial system; international financial markets; international trade; the labor market; the tax system; public infrastructure and public firms; the legal and regulatory framework; and governance. For each of them, first, we discuss the general principles underlying market-oriented reform; second, we present various indicators of the policy stance in the area in question; and third, we present various outcome indicators of the policy stance.

> * Patricia Macchi provided excellent research assistance.  We are grateful to José Fanelli, Gary McMahon, and Klaus Schmidt-Hebbel for valuable comments and suggestions. The usual disclaimer applies.

1
||(1)<br>Crop<br>income|(2)<br>Livestock<br>income|(3)<br>Agricultural<br>wage<br>income|(4)<br>Non-<br>agricultural<br>wage income|(5)<br>Non-farm<br>self-<br>employment<br>income|(6)<br>Consumption<br>(2006, 2010)|
|---|---|---|---|---|---|---|
|Log of rainfall (percent of<br>needs measured by|2.417***|1.533|-6.419***|-4.366***|-3.335***|0.405**|
|WRSI)|(0.506)|(1.177)|(1.017)|(0.946)|(1.015)|(0.190)|
|Log of maize price|0.715***|0.437|-1.418***|-0.281|-0.435|-0.00504|
||(0.113)|(0.377)|(0.519)|(0.466)|(0.504)|(0.122)|
|Log of beans price|1.247***|2.019***|0.0956|4.078***|1.678**|1.049***|
||(0.214)|(0.722)|(0.752)|(0.542)|(0.701)|(0.140)|
|Log of number of fatalities|-0.187***|-0.287|0.690***|0.455***|0.103|-0.00918|
||(0.0662)|(0.177)|(0.185)|(0.170)|(0.184)|(0.0163)|
|Constant|-11.48***|-16.37**|35.56***|-8.024|7.701|1.782|
||(2.818)|(8.120)|(8.563)|(6.843)|(8.198)|(1.463)|
|Observations|3,334|3,359|3,102|3,102|3,102|1,502|
|Number of HHID|966|966|964|964|964|927|

Source: Staff calculations using UNPS 2006-2012. Notes: Household fixed effects estimation with robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

**Table 11: Impact of weather,** **<u>prices and peace on weight for age and weight for height</u>**

||(1)<br>Weight for<br>age Z-score|(2)<br>Weight for<br>height Z-<br>score|(3)<br>Weight for<br>age Z-<br>score|(4)<br>Weight for<br>height Z-<br>score|
|---|---|---|---|---|
|Log of rainfall (percent of needs measured by|||||
|WRSI)|0.364**|0.397|0.586***|0.512|
||(0.158)|(0.381)|(0.223)|(0.669)|
|Log of beans price|0.194|0.704|0.364|1.213**|
||(0.284)|(0.434)|(0.404)|(0.609)|
|Log of number of fatalities|0.0259|-0.0230|0.0536|0.0330|
||(0.0503)|(0.0738)|(0.0638)|(0.0885)|
|Constant|-3.798*|-6.413*|-6.009**|-10.35*|
||(2.059)|(3.801)|(2.914)|(6.139)|
|Observations|1,658|1,643|803|801|
|Number of HHID|957|953|465|465|
|Bottom 40percent|No|No|Yes|Yes|

Source: Staff calculations using UNPS 2010-2012. Notes: Household fixed effects estimation with robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. In each case the dependent variables is averaged across all children below 5 years old in the household.

Using equation 3, we explore if households with a higher level of human capital and access to financial instruments-such as having a savings account or a loan- are better equipped to smooth the impact of climate shocks and price declines. We also test the effectiveness of some the economic institutions, 24
enterprises need also to be reformed.<sup>1</sup> Anderson (1999) argues that after the successful accession to the WTO, and given that some appropriate measures are taken, a number of broad-brush effects can be anticipated, including economic growth, expansion of agriculture and export-oriented light manufacturing, enhanced food security, more equitable income distribution, and increased government revenue.

However, the higher economic growth induced by further liberalization does not automatically imply reductions in poverty or inequality. Jensen and Tarp (2005), for example, predict that poverty will rise following a revenue-neutral lowering of trade taxes. Niimi et al. (2004) show that the employment in garment and textiles industries has been adversely affected in the 1990s by trade policies. Liu (2001) analyzes poverty and inequality of Vietnam using the Vietnam Living Standards Surveys (VLSS) 1992–93 and 1997–98. While Vietnam achieved a very rapid poverty reduction before the US bilateral trade agreement or WTO accessions, rural areas have lagged behind urban areas and overall inequality has increased slightly. Decomposition of inequality measures shows that urban-rural and regional differences have been the major source of rising national inequality over time.

Indeed, not everyone in Vietnam has benefited from the broad improvement in living standards, as indicated by results such as Litchfield and Justino (2004). Using the VLSS datasets, their regression model of the change in consumption suggests that there are large differences in household performance in different regions. Glewwe et al. (2002) also reported similar findings using the VLSS datasets.

One of the factors that significantly affected the probability of escaping poverty during the 1990s was location. Urban households, as well as households in the Red River Delta and the South East, had a higher probability of escaping poverty.

> 1 See Thanh (2005) for further discussion on the process and progress of Vietnam’s efforts to become a WTO member.

6
eciaries have not been able to produce internationally competitive ICT products. Regarding the Lei do Bem, the evidence suggests that the program had a positive but modest impact on innovation. For instance, Devereux and Guceri (2015) nd that the average realized R&D intensity is relatively low. They argue that this is because the legislation design favors incumbent, older and larger rms and it does not reach most small or new companies. This is due to the fact that Lei do Bem excludes rms that declare income tax returns based on their presumed prot, which includes most of the young rms.<sup>22</sup> To the extent that Lei do Bem favors incumbent rms, it may have contributed to slow down the reallocation process from low-growth incumbents to high-growth young rms. A recent paper by De Souza (2021) studies a Brazilian innovation program implemented in 2001 that taxed the leasing of international technology to subsidize national innovation. The Brazilian Technology Substitution Program (TSP) created a 10 percent marginal tax rate on the payments of any international intellectual property. The revenue raised by the tax was used to subsidize innovation projects by rms in targeted sectors. The author uses a novel rm-level dataset with information on international technology leasing, patent applications, and employment of Brazilian rms and calibrates a model to analyze the eects of the innovation policy. He shows that the program led rms to replace technology licensed from developed countries with in-house innovations, which led to a decline in employment and an increase in the share of low-skilled workers. He nds that rms more exposed to the TSP program had an increase of 4.47 percentage points in the probability of having at least one patent, in comparison to the control rms, while drastically reducing the probability of leasing technology from developed countries.

Another important source of eciency gains at the level of the rm is through an increase in management quality.<sup>23</sup> Bank (2017b) provides evidence on the average management quality of rms in Brazil relative to some peers (Mexico, Chile, Turkey and Argentina) and to higherincome OECD countries.<sup>24</sup> It shows that average management quality of Brazilian rms is lower than most comparator countries. Moreover, management quality dispersion of rms in Brazil is higher than in other countries, with a much larger share (18 percent) of rms being poorly-managed. This contrasts with 2 percent of rms in the U.S., 6 percent in China, and 11 percent in Mexico.

This evidence is consistent with the stylized facts previously discussed according to which Brazil has a larger productivity dispersion and a higher proportion of low-productivity rms than other emerging economies. The existence of a large segment of poorly-run rms suggests

> 22See Klenow and Li (2020) for evidence on the importance of young rms for innovation.

> 23See Bloom et al. (2013).

> 24There is available data for many countries, including Brazil, in the World Management Survey.

21
can be attributed to redistribution without growth. The last three columns of Table 9 show the decomposition of the change of the middle-class share (Δ share) into the growth effect and the redistribution effect over different sub-periods, using the initial year of each sub-period as the reference year.

**Table 9: Evolution of the Kazakh Middle Class: Size, Shares of Total Income, 2003–15, Percent**

|**Year**|**Middle**<br>**class**<br>**(share %)**|**Share of**<br>**Total**<br>**Income**|**Δ share**<br>**middle**<br>**class**|**Growth**<br>**effect**|**Redistribution**<br>**Effect**|
|---|---|---|---|---|---|
|2003|3.7|12.1||||
|2006|15.4|30.9|11.7|12.8|-1.1|
|2010|31.2|50.3|15.8|14.0|1.8|
|2013|43.5|61.7|12.3|12.0|0.3|
|2014|44.2|62.1|0.7|1.1|-0.4|
|2015|44.4|62.7|0.2|-0.2|0.4|

Overall, the growth component played the prominent role in increasing the share of the middle class, especially in the first decade of the 2000s. The redistribution component marginally contributed to increase the middle class between 2006 and 2013, but it had a negative effect between 2003 and 2006 and in 2013-2014. Redistribution was responsible for the slight increase of the middle group in the last years (2014-2015) that would otherwise have reduced due to the growth effect.

# **7. Conclusions**

What constitutes the middle class is hotly debated; different concepts result in different conclusions about the size of the middle class and its evolution. Taking an income-based approach, in this paper we propose a way to estimate absolute thresholds for identifying the middle class in Kazakhstan. Instead of taking boundaries from recent studies for other countries, we formulated a model-based approach to estimating country-specific boundaries. The approach relies on how citizens identified their own status in response to a module of the 2013 HBS questionnaire.

Our main findings are these:

- Taking an absolute approach, for 2013 we estimated absolute boundaries of

23
# **Prices, Engel Curves, and Time-Space Deflation:**

# **Impacts on Poverty and Inequality in Vietnam**

_John Gibson, Trinh Le, and Bonggeun Kim_ JEL Codes: D12, E31, O15 Keywords: Deflator bias, Engel curves, inequality, poverty, prices John Gibson (corresponding author) is a professor of economics at University of Waikato, Hamilton, New Zealand; his email address is jkgibson@waikato.ac.nz. Trinh Le is a research fellow at Motu Economic and Public Policy Research, Wellington, New Zealand; her email address is Trinh.Le@motu.org.nz. Bonggeun Kim is a professor of economics at Seoul National University, Seoul, Korea; his email address is bgkim07@snu.ac.kr. The authors are grateful for assistance from Valerie Kozel, Ian Hinsdale, Nguyen Tam Giang, Tinh Doan, and Geua Boe-Gibson, along with many staff from the General Statistics Office. Helpful comments were received from the editor and three anonymous referees and from seminar audiences at Monash and Otago. All remaining errors are those of the authors.
Y3 (left hand side plots). We see that the treatment effects are not only zero on average, but also cannot be statistically distinguished from zero at any part of the test score distribution. On the righthand side, we present the corresponding first-stage quantile plots, which show the number of years that a student at each quantile of the test-score distribution spent with a certified teacher in a treatment and control school. The figure makes clear that students at every percentile of the testscore distribution after Y2 and Y3 experienced a significant increase in their exposure to a certified teacher, but that nevertheless there was no impact on learning outcomes.

One possible concern in interpreting our school-level ITT estimates is that the estimated zero effects could reflect a combination of positive effects on students of target teachers (who may be motivated to increase effort by the pay raise) and negative effects on students taught by "non-target" teachers (especially those who were not eligible for certification), who may have reduced effort in response to the perceived "unfairness" of not receiving the certification allowance.<sup>26</sup> We test for this possibility by decomposing the impact on mean test-scores shown in Table V into test-score impacts on students taught by target teachers and those taught by non-target teachers (across treatment and control schools). We present the results in Table VI, for both Y2 and Y3.

For the Y2 data, we consider whether a student was taught by a target teacher in Y2 (since none of the target teachers would have been paid the certification allowance in Y1), and test separately for treatment effects on students taught by target and non-target teachers (Table VI – column 1). We see that there is no effect on test scores of students in treatment schools taught by either type of teacher relative to the control schools (point estimates are zero), and cannot reject equality of test scores of students taught by target and non-target teachers in treatment schools.<sup>27</sup> For the Y3 data, we consider the four possible combinations of teacher type that a student could have had in Y2 and Y3 (target – target; target – non-target; non-target – target; and non-target – non-target) and again find no significant different in test-score outcomes across these categories between treatment and control schools. When we focus on the most extreme comparison – students taught by a target teacher in both Y2 and Y3, compared with those taught by a non-target teacher in

> 26 As described earlier, this was unlikely because the experiment did not change any of the certification norms in the law, and thus there is no reason for non-eligible teachers to feel such resentment. But we still test for this possibility. 27 The table separately reports outcomes for the small fraction of students (around 5% of observations) for whom we are not able to verify the target status of their teacher (reported as “no match between student and teacher”).

21
6 Although Lao PDR has witnessed rapid economic growth and poverty reduction over the past 15 years, the incidence of poverty remains high in remote rural areas, in the uplands, and among ethnic minorities. Vulnerability to poverty is also a significant concern. For example, in the nationally representative Lao Expenditure and Consumption Survey, a large proportion of village leaders identified natural shocks, particularly insect infestations and animal disease, as constraints on income growth. A growing share of villages also report lack of jobs as a constraint. Similar findings are reported from a vulnerability assessment in three provinces (Committee for Planning and Cooperation and UNCRD 2002), with low income, shortage of capital or credit, lack of land or poor soil quality, natural disasters, pests and livestock disease, lack of employment opportunities, and illness as important sources of vulnerability. Qualitative poverty assessments (Asian Development Bank/National Statistics Center 2001; Asian Development Bank/National Statistics Center 2006) paint a similar picture. They highlight the importance of natural resources to household livelihoods, and the risks associated with loss of land and agriculture related shocks, but also illustrate the widespread and sometimes devastating effect of illness, injury and deaths on household welfare.

In the Lao context, risks (agriculture, health, employment, prices, etc.) translate into significant vulnerabilities due to the limited access to formal insurance institutions and safety nets. For example, while there are donor supported programs for providing food relief in the event of disasters, there are no programs to systematically support households during the agricultural low‐season or in the event of crop failure or livestock disease. Similarly, poor and near‐poor households affected by food price increases, reductions in wages or remittances, or unemployment are also largely uncovered. Hence, when shocks materialize, households tend to rely on precautionary savings and informal networks to manage risks.
# **TABLE OF CONTENTS**

|1. INTRODUCTION ...............................................|1|
|---|---|
|2. NATIONAL CONSUMER SURVEY ...............................................|3|
|3. THE MEASUREMENT OF WELFARE AND POVERTY .........................|......................<br>4|
|_3.1 Household Expenditure ..............................................._|_4_|
|_3.2 Price Index .._|_5_|
|_3.3 Poverty Lines in Nigeria ..............................................._|_6_|
|_3.4 Basic Needs Approach to Poverty ................................................_|_7_|
|_3.5 Poverty Line ..............................................._|_8_|
|_3.6 Poverty Indices ..............................................._|_9_|
|_3.7 Macro-economic Background .............................................._|_11_|
|4. EVOLUTION OF POVERTY IN NIGERIA ..............................................|I 1|
|_4. 1 The Evolution of Mean Expenditure in Nigeria ........................................_|_......_<br>_11_|
|_4.2 Evoluition of Regional Poverty in Nigeria .............................................._|_17_|
|_4.3 Evolution of Poverty and Household Composition ..................................._|_..........._<br>_25_|
|_4.3 Poverty and Employment ...._<br>_.........................................._|_30_|
|_4.3 Decomposition of Changes in Poverty .............................................._|_35_|
|_4.5 Implications for Targeting ..............................................._|_39_|
|_4.6 Sensitivity Analysis of Poverty .............................................._|_42_|
|_4. 7 Dominance Test of Poverty .............................................._|_49_|
|_5._EXPENDITURE PATTERNS OF THE POOR .............................................|.<br>46|
|6. CONCLUDING REMARKS||
|_6.1. Analytic Methodology .............................................._|_50_|
|_6.2. Beyond the Poverty Profile .............................................._|_50_|