rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 14. Labor productivity, TFP, and investment**

_Slowing capital accumulation and total factor productivity growth have lowered EMDE income catch‐up and labor productivity growth. Labor productivity growth has slowed in EMDEs since the crisis, most markedly in economies with relatively low investment growth._

## **A. Catch‐up to U.S. per capita incomes**

<!-- Start of picture text --> Number of years 1993-2008<br>120 2003-08<br>2013-15<br>100<br>80<br>60<br>40<br>20<br>0<br>EMDE EMDE EMDE<br>commodity commodity<br>exporters importers<br><!-- End of picture text -->

## **C. Changes in TFP and investment growth: 2010‐15**

<!-- Start of picture text --> Percentage points<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-15 -10 -5 0 5 10<br>Change in TFP growth<br>Change in investment growth<br><!-- End of picture text -->

## **B.  Average TFP growth**

<!-- Start of picture text --> Percent Commodity importers<br>4 Commodity exporters<br>3 Dashed lines: Long-term<br>average<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**D. Differential between EMDEs and AEs in per capita investment and GDP growth**

## **E. Labor productivity growth in EMDEs**

<!-- Start of picture text --> Percent Percent<br>Investment growth<br>12 6<br>Labor productivity growth (RHS)<br>10 5<br>8 4<br>6 3<br>4 2<br>2 1<br>0 0<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **F. Labor productivity growth in EMDEs with high and low investment growth, 2010‐15**

<!-- Start of picture text --> Percent<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>Low investment High investment<br>growth growth<br><!-- End of picture text --> Sources: Haver Analytics, International Labor Organization, International Monetary Fund, Penn World Table, World Bank. A. Number of years needed to catch‐up with 2015 real per capita GDP level in the United States, assuming average growth rates over each period denoted for each group. B. Unweighted averages. TFP calculated as residual from the growth‐accounting framework in Didier et al. (2015). Dashed lines indicate long‐term average for 1990‐2008 for each respective group. C. Correlation of change in investment growth from 2010‐15 with change in TFP growth over the same period. Red dotted line denotes the linear regression line. Includes 40 EMDEs. D. Weighted averages. Difference between EMDEs and AEs. The shaded areas are global recessions and downturns. E. Weighted averages. Labor productivity is defined as real output per person engaged. F. “Low” and “High” indicate annual growth rates in real investment in the bottom and top one‐third of the distribution, respectively. Difference in medians between “high” and “low” subsamples is significant at the five percent level. Group medians for 123 EMDEs during 2010‐15.

36
# Quantifying Vulnerability to Poverty in Uganda*

JEL Classification: Q54, C63, R11, R5, I3 Key words: Uganda, vulnerability, poverty, risk Authors: Aziz Atamanov*, Chris Ndatira Mukiza** and Vincent Fred Ssennono**. Corresponding author is Aziz Atamanov (aatamanov@worldbank.org).

Acknowledgments: The authors are thankful to Emmanuel Skoufias for peer-reviewing the paper. We would like to thank Jia Gao, Moritz Meyer and Cara Ann Myers for useful comments and suggestions how to improve the paper. We are thankful for overall guidance to Mukami Kariuki, Pierella Paci and Franklin Mutahakana.

* World Bank, ** Uganda Bureau of Statistics.
causal mechanism, and merely controlling for the pre-reform youth share would not solve this problem.

As we believe that the ITT approach is the more conservative of the two, we apply this measure of treatment intensity in our main model. However, we also estimate a model using actual youth shares during the reform period as our treatment variable. This alternate approach is partly motivated by the fact that our firm population differs significantly in its properties from that used by Skedinger (2013), where the firm population examined consisted primarily of larger firms. As our ITT measure only correlates moderately with actual firm youth shares during the reform period for smaller firms (corr=0.46 for small firms, corr=0.90 for large firms) there is a risk that the reduced precision of our treatment measure impacts the results. It is hence useful to know to which degree the results of the two treatment measurements differ.

# _Choice of controls_

As our random effects model will take into account between-firm variation there is a need to explicitly include differing firm characteristics in our model. We have settled on the following control variables, using Skedinger (2013) as a starting point and adding a pre-reform firm size variable on account of our more heterogeneous firm population:

- The pre-reform share of young employees.

- Pre-reform firm size.

- Pre-reform average educational level of firm employees (a measure of human capital intensity).

- Sectoral dummies according to the SNI 2002 sectoral classification system.

- Regional dummies for Sweden ́s 21 major regions (“län”).

- Year dummies for each year.

- A dummy for the reform period.

This gives us the following main panel model specification, with � denoting the vector of control and dummy variables and ��� is our firm profitability measure at time t for firm _j_ :

# ��� ������������������������� ����������������∗��������������

As well as the following alternate model specification using actual youth shares as a treatment variable: 8
# TABLE C.2: Estimation of rent, ANTANANARIVO

||Simple||Job densit|y|Interactio|n|Tenure||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Directly affected by food|-0.137||-0.140||||-0.152*||
||(0.107)||(0.107)||||(0.088)||
|Flood risk perception||-0.043||-0.044||||-0.059|
|||(0.064)||(0.064)||||(0.058)|
|Job density|||0.164***|0.164***|0.183***|0.100|0.172***|0.172***|
||||(0.057)|(0.057)|(0.061)|(0.067)|(0.056)|(0.056)|
|Number of rooms|0.639***|0.639***|0.640***|0.640***|0.636***|0.640***|0.497***|0.497***|
||(0.054)|(0.055)|(0.052)|(0.052)|(0.052)|(0.051)|(0.055)|(0.054)|
|Number of rooms squared|-0.039***|-0.039***|-0.038***|-0.038***|-0.038***|-0.038***|-0.032***|-0.032***|
||(0.007)|(0.007)|(0.007)|(0.006)|(0.007)|(0.006)|(0.005)|(0.005)|
|Unimproved house|-0.103|-0.100|-0.117|-0.114|-0.120|-0.105|-0.069|-0.066|
||(0.078)|(0.078)|(0.077)|(0.077)|(0.076)|(0.075)|(0.072)|(0.073)|
|Unimproved roof material|0.000|-0.001|0.002|0.001|-0.007|0.009|0.094|0.095|
||(0.088)|(0.087)|(0.088)|(0.087)|(0.089)|(0.087)|(0.088)|(0.086)|
|Unimproved wall material|-0.519***|-0.520***|-0.536***|-0.537***|-0.548***|-0.520***|-0.398***|-0.401***|
||(0.152)|(0.148)|(0.146)|(0.142)|(0.145)|(0.133)|(0.138)|(0.134)|
|Unimproved foor material|0.012|0.008|-0.006|-0.009|0.002|-0.015|-0.033|-0.039|
||(0.079)|(0.079)|(0.081)|(0.081)|(0.079)|(0.080)|(0.064)|(0.064)|
|Unimproved drinking water source|-0.625***|-0.629***|-0.556***|-0.561***|-0.558***|-0.570***|-0.582***|-0.588***|
||(0.084)|(0.085)|(0.085)|(0.085)|(0.086)|(0.084)|(0.072)|(0.073)|
|Unimproved toilet facility|-0.057|-0.056|-0.078|-0.077|-0.085|-0.061|-0.068|-0.066|
||(0.090)|(0.092)|(0.094)|(0.095)|(0.094)|(0.093)|(0.083)|(0.085)|
|Not collected solid waste|-0.154**|-0.166***|-0.139**|-0.152**|-0.132**|-0.148**|-0.132**|-0.147***|
||(0.061)|(0.059)|(0.061)|(0.059)|(0.063)|(0.059)|(0.055)|(0.055)|

_Continued on next page_
# **4.1.1 Results from the 110-country panel**

The estimated coefficients of ln _GDP_ and  ln _GDP_  2 are positive and negative, respectively, suggesting the presence of the EKC curve. Note that the EKC is a phenomenon of the long run [39, 40], further suggesting that the countries with different income levels should follow a long-term strategy that achieves the targeted balance between reduced CO2 emissions and high economic growth.

The results further indicate that, for the whole sample of 110 countries, the estimated coefficients of ln _EC_ , ln _ECS_ , ln _EI_ , ln _P C G_ , and ln _P_ are positive and significant at a 1% significance level. Similar to the decomposition analysis presented in Section 3 above (see also Figs. B1 & B2 in Appendix B), the econometric analysis suggests that the change in emission increased with per capita GDP (i.e., economic growth) and population growth. These two factors are the primary drivers of the increase of CO2 between 1980 and 2015, while changes in fuel mix (i.e., increasing share of clean fuels in total primary energy supply) and improving energy efficiency (i.e., decreasing energy intensity of GDP) slowed this increase in growth of CO2 during the same period.

# **4.1.2 Differences in impacts across income levels**

The theoretical EKC is an inverted U-curve, with the coefficients of ln _GDP_ and  ln _GDP_  2 positive and negative, respectively. However, in the results in columns 2-5, only for HI and UMI countries the estimated coefficients of ln _GDP_ and  ln _GDP_  2 are positive, and negative respectively. For HI and UMI countries, the estimated turning points are $42,152 and $13,391, respectively. In contrast, we cannot identify the turning point of the EKC hypothesis for LMI and LI countries. The large gap between the current economic level in LMI and LI countries ($2,030 and $599 in 2015, respectively; see Fig. 7) and the turning point of EKC may result in the data failing to identify the peak in CO2 emissions.

17
|**_Table 71': Co_**<br>|**_ntribuition r_**<br>|**_ates nee_**<br>|**_ded to g_**|**_enerate_**|**_a repl_**|**_acemen_**|**_t rate o_**|**_~ 33% o_**|**_f avera_**|**_ge male_**|**_adut e_**|**_arnings_**|**_-_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|X Z|ZI<br>**I**|T<br>|**dY**<br>|**O%/1**||dY=|%||dY=|15%||dY<br>|___2|
|Contributio|n ge|is8|2125||18|21|251|8s211||25|18|I21|25|
|Reali=2%|____|||||||||||||
|Retire<br>at<br>55|80|i5|<br>16|<br>18|<br>18|<br>194|21|1t)|20|<br>22|<br>21|<br>22|24|
||81|16|<br>17|<br>19|<br>18|<br>194|22|20|<br>21|<br>23|<br>21|<br>23|25|
||83|17|<br>18|<br>20|<br>19)|201|23|21|<br>22|<br>24|<br>23|<br>24|26|
||85|18|<br>19|21|<br>20|I21|24|<br>22|**I23**|25|**I24**|25|27|
|Retire<br>at60|80|1<br>1|1<br>1|13|<br>13|<br>14|<br>15|<br>14|115|**lb **|**I15**|16|<br>_17_|
|___________|81|11|12|13|13|14|15|15|lb|<br>17|16|17|18|
||83|12|13|14|<br>14|<br>i5|<br>16|<br>16|<br>16|118|17|118|19|
|___________|85|I12|13|15|15|16|17|17|17|19|18|19|20|
|Retire<br>at<br>65|83|7|8|9|9|10|<br>10|<br>10|<br>11|<br>11|11|<br>12|12|
|___________|81|8|8|9|10|10|11|11|11|12|12|12|13|
||83|8|9|10|10|11|12|<br>12|<br>12|_113_|13|_113_|1|
||85|1<br>9|10|11|11|112|13|<br>_13_|<br>_13_|<br>14|14|<br>14|15|
|Retire<br>at<br>70|80|5|5|5|6|6|7|9|10|<br>10|8|8|_8_|
|___________|81|5|5|6|6|_7_|7|10|10|11|8|8|9|
|___________|83|6|6|7|7|8|8|10|11|112|9|10|10|
|_________|85|6|7|1<br>7|8|8|9|11|12|<br>13|10|<br>11|1<br>1|
|Real i=3%|_____|||||||||||||
|Retire<br>at 5Sf|80|11|12|14|14|15|16|<br>15|<br>16|<br>18|16|<br>17|19|
|____________81||12|_13_|151|<br>14|15|17|15|16|18|16|17|19?|
||83|12|_13_|is|15|161|18|16|17|19|17|<br>18|20|
||85|_13_|14|16|15|16|18|<br>17|18|20|18|<br>19|21|
|Retire<br>at60|_80_|8|**9)**|**10**|**10**|**10**|12|<br>11|<br>11|<br>13|<br>12|<br>12|14|
|___________|81|8|9|10|10|I11|12|11|12|_13_|12|13|14|
|___________|83|9|9|11|II|1 1|_13_|12|13|14|13|14|15|
|___________|85|9|10|12|12|12|14|**13**|13|15|14|15|16|
|Retire<br>at65|80|6|6|17|7|7|81|<br>8|8|9|9|9|10|
|______|~81|6|6|7|7|8|8|8|8|9|9|9|1|
|___________|83|6|7|7|8|8|9|9|**9)**|**10**|**10**|**10**|**I11**|
|___________|85|_7_|7|9)|8|9|10|9<br>|I10|11|10|I11|12|
|Retire<br>at<br>70|80|4|41|<br>4|5|5|5|5|5|6|6|6|6|
|______|_~81_|4|4|4|5|5|6|5|6|6|6|6|7|
|______|~83|4<br>|4<br>|5<br>|5<br>|6<br>|6|6|6|_7_|7|7|8|
||185|5|5|56|6617||||7<br>1|||||

34
Among all countries, the median country size in terms of land area is 100,00 sq. km; in terms of population – 4.151 million inhabitants; and in terms of labor force it is about 3 million. We use population as our benchmark variable for size, while land area and labor force are used in the robustness evaluations. Note that LAC countries are quite spread out along the size distributions, with a slight overrepresentation in the left tail of the distribution. This is driven by the small Caribbean economies.

We consider two key thresholds to separate countries into small and not small. First, we define a country to be small if its population is below the threshold of 1.5 million people. This threshold is common in the literature and has been used by the IMF (Small States Report, 2000; Macroeconomic Issues in Small States, 2013, etc.) and the World Bank to define small states. We contrast the characteristics of these countries with those above the threshold of 1.5 million, to which we refer as “large” countries. With this threshold we have 51 countries in the small group and 141 countries in the large group.

In the robustness exercises we also define smallness based on labor force using a threshold of 1 million people; or on land area with a threshold of 20,000 sq. km. In both cases 41 countries fall into the small group. We also consider alternative population thresholds of 1 million and 2 million to define small countries since these have also been used in the existing literature. For instance, Easterly and Kraay (2000) adopt a threshold of 1 million population, while Favaro (2008) used a higher threshold of 2 million people. In these cases, the small group contains 44 countries with 1 million threshold, and 57 countries with 2 million threshold. These results are reported in Appendix B.

Second, we define a country to be small if its average size (measured by population, land area or labor force) is below the median. A large country is the one whose size (measured by population, land area or labor force) is above or equal to the median. This threshold gives us a more conservative measure of smallness.

To understand how country size varies between developed and developing countries, we re-plot the size distributions above, but now conditioning on the developmental status. Specifically, we combine countries classified by the World Bank as low income and middle income countries into a “developing” countries group, while high income OECD and non-OECD countries are combined into a “developed” countries group. Figure 2 plots these conditional distributions.

<u>Figure 2. Distributions of country size by development level</u> 13
While section 2 emphasized the fact that the observable error terms come as a result of the underlying structural shocks, it is now of interest to see how this unobservables forces could be empirically deduced from the former observable forecast errors. It follows from the previous discussion that in the open-economy setting under study we have not only one, but two simultaneous monetary policy shocks. One of them operates in the foreign exchange market, where the central bank also reacts to the demand for domestic and foreign currency. The other one can be found by appropriately filtering the onestep-ahead forecast error for the change in the central bank’s net bonds holdings from the result of the external sector and the policy reaction to the public and commercial bank’s liquidity demand shocks.<sup>8</sup> From (20) it is easy to see that: Unsurprisingly, under the currency board example ( _φca_ = _φka_ = 1) neither the current account nor the capital account forecast errors play any role whatsoever when deducing the monetary policy shock on the external sector. In this case, this policy shock becomes _v_ ∆ _nfa_ = _ρuir_ + ( _β − δ_ ) _uer_ . On the other hand, to the extent that the monetary authority reacts to contemporaneous shocks to its balance sheet (i.e., _θ_ ∆ _j_ for some _j_ = _cp, li, nfa_ ), to obtain the local-currency policy shock it is necessary to consider the news provided by additional forecast errors other than _u_ ∆ _mp_ itself.

# **4 Measuring monetary policy in Argentina**

To evaluate its performance, this section applies the methodology developed in section 3 to the case of Argentina. The first subsection presents the variables included in the model and the specification to be used. The second subsection presents and discusses the estimation results.

## **4.1 Data and specification**

Estimation is conducted employing monthly data from 2003:4 through 2008:9.<sup>9</sup> Based on the discussion of the previous sections, the variables in the VAR are constructed according to the following description. All stocks are as of the end of the corresponding period.

> 8Note that while for the U.S. monetary policy is often associated with innovations in the monetary target (i.e. the Fed Funds rate), Schabert [35] also proposes to identify monetary policy via changes in open market operations.

> 9Argentina abandoned its currency-board regime on January 6, 2002. I leave 2002 and the first months of 2003 out of the sample because of the turmoil affecting the financial system during that period.

10
issues for mashup indices; practices for other composite indices are often less than ideal in these respects. However, by their very nature—as composite indices for which virtually everything is up for grabs—these concerns loom especially large for mashup indices.

**What is being measured and why?** The fact that the target concept is unobserved does not mean we cannot define it and postulate what properties we would like its measure to have. Understanding the purpose of the index can also inform choices about its calibration.

In practice we are often left wondering what the concept is that the index is trying to measure and why. For example, what exactly does it mean to be the ―best country‖ in Newsweek‘s rankings (which turns out to be Finland). (I guess I should be pleased to see my country, Australia, coming in at number 4, but I have little idea what that means.) The rationale for the choices made in the Newsweek index is far from clear, not least because one is unsure what exactly the index is trying to measure.<sup>7</sup> Some mashup indices have been motivated by claimed inadequacies in more standard development indices. The construction of a number of the mashup indices of development has been motivated by the argument that GDP is not a sufficient statistic for human welfare—that it does not reflect well the concerns about income distribution, sustainability and human development that matter to welfare. To my eyes this is a straw man, and it has been so for a long time. Soon after the HDI first appeared, motivated by these inadequacies of GDP, Srinivasan (1994, p.238) wrote: ―In fact, income was never ... the sole measure of development, not only in the minds of economists but, more importantly, among policy makers.‖ In poverty measurement, a similar straw man is the view that mainstream development thinking has been concerned solely with ―income-poverty,‖ ignoring other dimensions of welfare. For example, in Alkire and Santos (2010b), the authors of the MPI counterpoint their measure with the World Bank‘s ―$1 a day‖ poverty measures, which use household consumption of commodities per

> 7   Why, for example, does ―economic dynamism‖ matter independently of the standard of living in the Newsweek index? The way we normally think about this, it is not economic growth _per se_ that helps deliver human welfare but the realized level of living. But maybe there is some other concept of what it means to be the ―best country‖ that motivated this choice such as the possibility of being the best country at some time in the future.  There are also some puzzles in the choices made for filling in missing data; for example, for some unexplained reason a ―Global Peace Index‖ was used for the Gini index of inequality when the latter was missing. Greater conceptual clarity might also help guide such choices.

8
significant (χ<sup>2</sup> ( 1)=4.89; p=0.027). On the other hand, the effect of male and female networks on male migration is indistinguishable (χ<sup>2</sup> ( 1)=0.74; p=0.389).

The effects of community migration networks also differ by gender, though differently. Male community networks strongly increase male migration while female community networks reduce male migration, and the difference between the effects of male and female networks is significant (p=0.000). In contrast, male community migration networks are associated with increased female migration while the effect of female community networks is insignificant, as is the test of the difference between male and female networks. (χ<sup>2</sup> ( 1)=0.09; p=0.767).

As hypothesized, the effect of networks, whether gendered or not, weaken over time. Looking at the bottom panel of Table 5, with the epoch interaction term, shows that male network effects before 1996 are associated with a 56 percent increase in the hazard odds of male ever migration, whereas in later years the effect declines by a third. For female migration, female family networks are associated with a 120 percent increase in the odds of female first-migration, but this effect again declines by one third in the latter period. This shift in the family network effect is gender specific – i.e. there is no significant decline in the effect of female networks on male migration or of male networks on female migration. The effects of male and female community networks also vary across the two periods. Male community networks increase the odds of firstmigration among men by 281 percent in the first period but the odds are reduced by 59 percent in later years (this shift is only marginally significant). The odds for the effect of female community networks on female migration is extremely large – indicating that most female migrants arrived from a few select municipalities in the first period as well as due to the tendency of odds ratios to rise (or decline) rapidly when approaching limits.

# **7. Discussion and Conclusion**

Albania, perhaps more than any other nation, offers a unique perspective on the entire international migration process for a nation -- from a point where migration was legally forbidden until a time when migration became a central demographic and social process with over one-half of households reporting family members with migration experience (Carletto, Davis et al. 2006). This context provides an exceptional setting in which to investigate the relationship between gender and migration. While female roles and life course expectations are 18