rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2 Impacts on Firm Growth**

Better business practices are strongly correlated with greater firm performance in both the crosssection and over time (McKenzie and Woodruff 2017). We would therefore expect improvements in practices to start leading to firm growth for the insourcing, outsourcing and consulting treatments. However, the heterogeneity among firms in our sample, coupled with a smaller than anticipated sample size, makes measuring impacts on firm growth more challenging. Our preanalysis plan specified that our primary measures would be the IHS transformations of firm sales and profits for two reasons. The first is an economic theory rationale: we believe the treatments are more likely to have a constant proportional treatment effect than a constant level treatment effect. That is, better business practices are more likely to help firms of different initial sizes grow by the same proportion, than by the same absolute amount. Second, these transformations help improve statistical power, by reducing the influence of large outliers. Table 5 shows the estimated treatment effects on firm sales, profits and employment for all four treatments.

<u>Outsourcing. In the first follow-up survey, outsourcing has a positive and significant impact on</u> yearly sales. This treatment also has a positive, but statistically non-significant, effect on the other measures of firm growth. The magnitudes of these impacts increase in the second year, and are statistically significant at the 5 percent level for outsourcing on four out of the five sales and profits measures. This includes our overall Sales/Profit Index: outsourcing increases sales and profits by 0.23 standard deviations.<sup>10</sup> Outsourcing also has a positive impact on firm employment, which is significant at the 10 percent level when using the IHS measure.

<u>Insourcing. The impacts are less clear for the insourcing treatment in both survey rounds. The point</u> estimates are all positive, but none are statistically significant from zero – although, at the same time, we can not reject that they are equivalent in size to those of outsourcing. The impact on the overall Sales/Profit Index is 0.12 standard deviations at the second follow-up.

<u>Consulting.</u> This treatment has impacts that mostly lie between insourcing and outsourcing in magnitude. There was a significant effect on monthly sales, yearly profits, and the IHS measure of

> 10 Even after accounting for multiple hypothesis testing across outcomes and survey rounds, outsourcing has sharpened q-values below 0.10 for impacts on yearly sales in both rounds, and on monthly sales, yearly profits, and the profits and sales index in round 2 (Appendix Table 9.1).

21
yield in all types of crops at varying degrees, ranging from 0 to 30 percent, depending on whether the crop is rainfed or irrigated, and the region it is produced in.

Under scenario 3.a, we first notice that overall production volume in irrigated crops such as vegetables, other grains (rice, etc.), wheat, oil seeds, fruits, and other crops category rise at varying rates. The highest increase is seen in the other grains category by 34 percent, followed by vegetables with 30 percent. Irrigated wheat activity, on the other hand, increases by 9.63 percent overall. We must mention that the crops named above are modeled as growing on both irrigated and rainfed land. While we observe production volume increases in crops which have activity in both irrigated and rainfed land, the production quantity in crops such as cotton (raw), sugar beet and potatoes, which are only grown on irrigated land, all decrease significantly (Table 4.14.).

**Table 4.14. Change in production of irrigated activities**

||**Base**<br>**Level**|**%**<br>|**Change **<br>||
|---|---|---|---|---|
||<br>**(Tons)**|**Sc. 3.a**|**Sc. 3.b**|**Sc. 3.c**|
|**Wheat**|7,877|9.63|-4.41|-8.07|
|**Maize**|1,355|-1.81|-14.89|-16.02|
|**Other grains**|304|33.63|17.10|7.02|
|**Sugar beet**|12,622|-8.84|-13.71|-13.76|
|**Cotton (raw)**|2,295|-15.99|-23.52|-14.34|
|**Oil seeds**|976|19.76|5.11|-5.16|
|**Potatoes**|5,300|-5.54|-7.74|-7.90|
|**Vegetables**|9,827|29.77|14.50|6.66|
|**Fruits**|6,562|13.56|1.60|-3.56|
|**Other crops **|1,203|35.19|18.69|8.59|

_Source: Results from TACOGEM-W._ As a result of the fall in the yields of crops on rainfed land, and as production shifts to irrigated land, the shadow value of irrigation water is expected to rise due to increased competition for water. Particularly, the shadow value (or price) of irrigation water in the Central region increases by 27 percent, in the East region by 26 percent, in the LSCB region by 10.46 percent, and in the Southeast region by a moderate 3.34 percent (Table 4.15). At the base run, 36 percent of all wheat produced in Turkey is produced in the Central region, making up the highest share among all regions. In the Central region, 36 percent of total agricultural land is devoted to wheat activity. Furthermore, wheat is an irrigated crop; 41 percent of all wheat is produced on irrigable land, overall. In that sense, recognizing the significance of wheat production in the 54
-25-

# BOX6 TYPICAL PROJECTS IN A REHABILITATION PLAN

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# **3. What explains change in inequality?**

Explaining the increase of inequality during the transition has to rely on very little theory. The reason is to some extent obvious, namely that the transition from Communism to capitalism took place quickly and unexpectedly, so no a priori theory was developed. After the beginning of transition, and faced with the often dramatic increases in inequality, several papers tried to formalize the factors and mechanisms associated with the increase. They were reviewed in Section 1. Milanovic (1999) saw increased inequality arising from the transfer of labor force from an egalitarian public sector to a much more in inegalitarian private sector. In his view, the structural, or rather, ownership, transformation was the primary force behind increased inequality. Similarly, Ivaschenko (2002) linked privatization and structural change expressed as “deindustrializaton” to increased inequality. But, as discussed in the review of the existing literature, other factors were, in a heuristic fashion, also associated with changes in inequality. Noticing smaller increases in Central Europeans countries whose welfare systems “survived” the first wave of transition much better than those of the former Soviet republics. Keene and Prasad (2002) argued that maintaining social expenditures provided a strong cushion against runaway inequality. In an early article on the political economy of reforms, Hellman (1988) linked high levels of inequality to non-completed reforms. In his simple cross-section, both the more advanced reformers in Central Europe and non-reformers in Central Asia had lower levels of inequality than reformers that stopped “halfway” like Russia and Ukraine. Hellman ascribed these developments to the entrenched role of the new oligarchic elites.

Following on some of these insights, we estimate a country fixed-effect model where inequality is associated with the growth rate of the economy (measured by the annual GDI increase), inflation rate (measured by the annual increase in the consumer price index), intensity of structural reforms (measured to be the unweighted average value of nine EBRD reform indices), government spending as a share of GDI, and level of country’s democracy (as measured by the Polity database). In addition, we control for 12
5 discriminating between foreign and domestic producers, or not). In practice, NTMs include policies such as an import quota to shield domestic producers from foreign competition, an export tax to lower the price of an essential good such as staple food, or a domestic regulation to lower the health risks associated with the use of certain chemicals in production.<sup>2</sup> These measures have in common the fact that they all are not tariffs and have an impact on trade or the potential to do so. But they are at least as distinct from each other as they are collectively different from a tariff.<sup>3</sup> A natural question is why the concept of NTMs is used in policy-making circles and in the economic literature and why it is useful to lump together this set of widely heterogeneous measures. As Staiger (2011) points out, the key insight is that import tariffs historically had a special place in the multilateral trading system. In the GATT/WTO system, governments negotiate trade concessions through reciprocal reductions in tariff bindings. The effective granting of these concessions is contingent on the overall policy stance of a country: Market access by foreign producers will eventually depend also on what home governments do with all other measures that can impact trade flows (i.e., what governments do with NTMs). As a result, tariff and non-tariff measures are differentially regulated in the GATT/WTO system. A first issue that we will address below is whether and under what conditions this differential regulation has an economic rationale.

As NTMs are such a broad category, there can be multiple ways to divide them. UNCTAD, the UN agency that collects data on NTMs, currently classifies non-tariff measures into 16 chapters divided in two broad categories: import and export measures (UNCTAD, 2013). Import measures are all those requirements that are imposed by importing countries. These measures are divided into technical measures, which include chapters A (Sanitary and Phytosanitary Standards or SPS), B (Technical Barriers to Trade or TBT) and C (Pre-Shipment Inspections), and non-technical measures that encompass chapters D (Contingent Trade Protection) to O (Rules of Origin). Export measures, which are all requirements imposed only by the exporting country, comprise chapter P.

> 2 Some non-tariff measures are covered elsewhere in this Handbook. Border NTMs also include contingent measures (also called temporary trade barriers) such as safeguard provisions and other escape clauses (studied in Chapter 10 of this Handbook), anti-dumping and countervailing duties (Chapters 11 and 12, respectively), and export subsidies (Chapter 12). Similarly, behind-the-border measures also encompass domestic subsidies, which are just a form of negative taxation (Chapter 12).

> 3 The definition of NTMs can be extended beyond government policies to include private standards, such as quality requirements set by downstream producers for their foreign providers. The focus of this paper is on government policies only; see WTO (2012) for a discussion on private standards and their impact on international trade.
Countdown to 2015 countries: an analysis of national surveys.” _Lancet_ 380 (9848): 1149–56.

- Victora C.G., J.P. Vaughan, F.C. Barros, A.C. Silva and E. Tomasi. 2000. “Explaining trends in inequities: evidence from Brazilian child health studies.” _Lancet_ 356(9235): 1093-98.

- Wagstaff, A.  M. Bilger, Z. Sajaia and  M. Lokshin. 2011. _Health Equity and Financial Protection : Streamlined Analysis with ADePT Software_ . Washington D.C.: World Bank.  https://openknowledge.worldbank.org/handle/10986/2306 License: CC BY 3.0 IGO.

- Wagstaff A, E. van Doorslaer and N. Watanabe. 2001.  “On decomposing the causes of health sector inequalities, with an application to undernutrition inequalities in Viet Nam.” _Policy Research Working Paper_ No. 2714. Washington D.C.: World Bank.

- Wagstaff, A. 2002. “Poverty and health sector inequalities.” _Bulletin of the World Health Organization_ , 80 (2).

- Wilunda, C., G. Putoto, F. Manenti, M. Castiglioni, G. Azzimonti, W.  Edessa, and B. Criel. 2013.  “Measuring equity in utilization of emergency obstetric care at Wolisso Hospital in Oromiya, Ethiopia: a cross sectional study.” _International Journal for Equity in Health,_ (12) 27.

- WHO Multicentre Growth Reference Study Group. 2006. _WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development_ . Geneva: World Health Organization . <u>http://www.who.int/childgrowth/publications/en/.</u>

- World Bank. 2012. Health Equity and Financial Protection Datasheet - Ethiopia. Washington, D.C.: World Bank.

- Yesuf, E. A. and R. Calderon-Margalit. 2013. “Disparities in the use of antenatal care service in Ethiopia over a period of fifteen years.” _BMC Pregnancy and Childbirth_ , (13) 131.

41
# _V.1 Income models_

We begin with constructing regression models for (log) income. Also here, we estimate two models for each of the eight regions, a large and a small model specification. The results from the large and small models are very similar. However, the large models produce lower standard errors of estimates. Thus, in this paper we present the estimation results from the large models.

Tables A.2 to A.9 in the Appendix present the GLS estimates for the large models. It shows that all model coefficients make economic sense (have expected signs).

# _V.2 Poverty and inequality estimates_

# _Poverty estimates_

Table 2 reports the estimates of the rural poverty incidence for all eight regions. The estimates are all very close to the estimates based on the 2006 VHLSS. The poorest region is North West with a poverty rate of just below 50 percent. With a poverty rate of around 8 percent, North East South is among the least poor regions.

**Table 2 The income poverty incidences of regions**

|Region|VHLSS 2006<br>Sm|all area estimation|
|---|---|---|
|Red River Delta|15.5|15.3|
||[1.1]|[0.7]|
|North East|22.0|24.4|
||[1.6]|[1.4]|
|North West|48.8|49.2|
||[3.6]|[2.5]|
|North Central Coast|28.2|26.7|
||[1.9]|[1.1]|
|South Central Coast|20.3|18.7|
||[1.9]|[1.4]|
|Central Highlands|24.4|25.4|
||[3.0]|[1.2]|
|North East South|7.7|8.6|
||[1.2]|[1.2]|
|Mekong River Delta|11.5|11.0|
||[0.9]|[1.0]|
||_Standard error in the brackets_||

_Source: Authors’ estimation from VHLSS 2006 and ARFC 2006_ 17
Policy Research Working Paper 10880

# **Abstract**

Advances in the scale and sophistication of individual income data compiled across high-income countries have enabled researchers to measure inequality and intergenerational mobility at a highly granular level and over long periods of time. The edited volume _Measuring Distribution and Mobility of Income and Wealth_ (Chetty et al (2022), comprising 23 empirically rich studies, provides a comprehensive stocktaking of the recent empirical evidence. This paper reviews the volume’s key findings obtained for high-income countries and raises several questions that extend to low-income countries. What stands out is that income and wealth inequality are increasing, while intergenerational mobility is declining, across most high-income countries. This begs the following questions. Is the Kuznets curve due for a revision? How can countries achieve (and maintain) higher levels of socioeconomic mobility as they develop? To what extent do inequality and mobility trajectories unfold in different ways for countries at varying levels of economic development? How and why do certain subgroups within societies consistently remain at the bottom of the income distribution? Finally, what policy measures that redress inequality and enhance mobility are likely to be both more effective and politically supportable?

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rvanderweide@worldbank.org and mwoolcock@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<u>Table 9: Modified Decomposition Results</u>

|Exp|lained (%)|Unexplained (%)|
|---|---|---|
|PISA Math 2000-2006|||
|Overall|68.7|31.3|
|Schools|71.0||
|Family|28.9||
|Student|0.2||
|PISA Reading 2000-2006|||
|Overall|66.1|33.9|
|Schools|83.6||
|Family|16.6||
|Student|0.2||

# **8. Conclusions**

The vocationalization of the secondary school curricula has been advocated for many decades. The call for technical and vocational schooling used to be a standard recommendation promoted by international organizations and followed by several countries. Unfortunately, the fervor for this approach has run ahead of substantial evidence on the impact of this policy. This paper contributes towards modestly filling that void by studying the effects of a change in curricular structure on educational quality.

The Polish education reform program generated an exogenous variation in vocational school attendance at the secondary school level across time that provided us the opportunity to assess the impact on test scores. Our identification strategy used the fact that likely vocational graduates did not have that option in PISA 2003, thus providing a comparison group for our empirical approach, propensity score matching and differences-in-differences estimation.

Our results suggest that vocationalization, on average, reduces test scores by a full standard deviation. Clearly, given the importance of the reform program, other factors played a role in the increase in Poland’s scores in PISA. Nevertheless, the delayed vocationalization played a major role. The pathway, we argue, is through increased hours of math instruction, possibly more exposure to testing, and increased motivation on the part of students and teachers.

We substantiated our findings by taking advantage of the application of PISA to 16 and 17 year olds in PISA. We find that once vocational school options are available again, by the time students are 16, then test scores decline for those students who enter the vocational track. While this goes a long way towards proving our initial findings, it also serves as a caution to policymakers about the effectiveness of vocational schooling – when that schooling is not designed to improve math and reading skills, which we show such students can learn when they are given the opportunity, and which have become the real vocational skills in the world of work today. To increase test scores, it helps to have students study the subjects of the tests. We conclude that much of the test score increase in Poland in recent years has to do with the delayed vocationalization of the secondary school curriculum.

26
evaluation of a teacher training scheme has also documented heterogeneous impacts on the teachers' own English test score, where only teachers with university degree beneted well from in-service training (Zhang et al., 2013). On the other hand, providing classroom-library materials could change the resources available in treated schools ( ∆ _Rc,s_ ). This is particularly the case in a resource-poor setting, such as Mongolia, where essential teaching aids such as textbooks and workbooks were lacking.

In the context of the current study, the interventions that could improve education productivity have been randomly assigned with the intention to increase test scores in the treatment group: in-service teacher training to improve teacher quality and/or classroom libraries to ease resource scarcity. A control group of students, on the other hand, have not been exposed to any treatment until the experiment was nalized. Analysis of the baseline survey data conrms that the `initial randomization' was properly done: there were no systematic dierences between test scores (and other covariates) of students in the treatment and control groups.<sup>5</sup>

# 3.2 Identication strategy and empirical approach

The treatment eects from the above three interventions are estimated as follows: (1) Training and Books: To identify the causal impact of providing books and in-service training as complementary education inputs, students in treatment group one ( _T_ 1 ) and control group that has not received any treatment ( _C_ 2 ) were matched, and mean achievement-gaps between students in these groups provided the estimated impact of the two interventions. (2) Extra Training: Identication of the impact of extra inservice teacher training, on top of books, is based on the comparison of the dierences in student achievement between (matched) treatment one ( _T_ 1 ), which received training and books, and treatment two ( _T_ 2 ), which received books (but not teacher training). It is important to note that this identication of the contribution of teacher training likely includes the returns from the joint provision of training and books as well as the contribution of each input plus any complementarity between them. In the context of rural Mongolia, where education inputs were lacking prior to READ interventions, it is most likely that education production function to exhibit increasing return for

> 5The `initial randomization' refers to the initial randomization undertaken before the control group was divided into two, following change in policy regarding the interventions.

11
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.2 Trends in infrastructure endowments by African subregion, 1960–2000**

|||1960|1970|1980|1990|2000<br>a|Average<br>annual<br>growth<br>b<br>(percent)|
|---|---|---|---|---|---|---|---|
||Central Africa|1|2|5|8|9|6|
|Density of paved road network<br>|EAC|3|6|12|13|8|3|
|(km/1,000 km<br>2)|ECOWAS|11|14|21|24|38|3|
||SADC|4|9|46|53|92|8|
||Central Africa|19|75|156|161|159|6|
|Density of paved road network|EAC|46|106|135|148|95|2|
|<br>(km/1,000 arable km<br>2)|ECOWAS|122|161|213|237|305|2|
||SADC|64|123|1,715|2,015|3,649|11|
||Central Africa|57|60|82|94|101|1|
|Density of total road network<br>|EAC|56|77|82|94|105|2|
|<br>(km/1,000 km<br>2)|ECOWAS|84|93|109|121|144|1|
||SADC|81|90|140|154|211|2|
||Central Africa|2,101|1,720|1,776|1,677|1,587|–1|
|Density of total road network<br>|EAC|1,053|1,201|1,073|1,124|1,297|1|
|(km/1,000 arable km<br>2)|ECOWAS|1,137|1,259|1,391|1,319|1,300|0.3|
||SADC|1,588|2,058|4,002|5,045|6,174|4|
||Central Africa|—|1|2|4|7|5|
|Density of fixed-line telephones|EAC|—|2|3|4|6|4|
|(subscribers per 1,000 people)|ECOWAS|—|2|3|5|17|8|
||SADC|—|10|18|32|56|6|
||Central Africa|—|—|49|57|48|–0.1|
|Electrical generating capacity|EAC|—|—|21|20|23|0.4|
|<br>(MW per 1 million people)|ECOWAS|—|—|38|34|31|–1|
||SADC|—|—|177|204|185|0.2|
||Central Africa|—|—|—|46|56|1|
|Water|EAC|—|—|—|42|64|2|
|(% of households with access)|ECOWAS|—|—|—|51|63|1|
||SADC|—|—|—|60|73|1|
||Central Africa|—|—|—|22|27|0.5|
|Sanitation|EAC|—|—|—|44|45|0.1|
|(% of households with access)|ECOWAS|—|—|—|28|35|1|
||SADC|—|—|—|37|42|0.4|

_Sources_ : As for table 2.1.

a. Water and sanitation data are from 2002.

b. Calculated over entire period available for each sector.

- = data not available.

21