rafmacalaba/datause-encoder-data huggingface dataset:
Interestingly, the “between” and “within” elasticities estimated in panel A are both around 0.6 on the intensive margin. These coefficients suggest that a 10% growth in labor productivity for an exporter-industry is associated with a 6% growth in exports. The estimates in panel B are lower at 0.2–0.4, but they remain economically and statistically important. These elasticities are somewhat lower than the unit elasticity often found in this literature with OLS estimation techniques and cross-sectional data. There are many empirical reasons why this might be true, with greater measurement error for productivity estimates outside of OECD sources certainly being among them. An elasticity greater than or equal to one is also the baseline for the Ricardian theory presented earlier. The IV estimates reported below are greater than one and have a comparable level on some dimensions to those estimated with OECD countries. The next subsection continues with extensions for these OLS estimates to provide a foundation for the IV results.

# _Extended OLS Results_

Table 2 provides robustness checks on the first-differenced estimates, which are the focus of the remainder of this study. The first column repeats the core results from column 2 of table 1. The next two columns show robustness to dropping Brazil and China. Brazil, of all included countries, displays the most outlier behavior with respect to its productivity growth rates, likely due to definitional changes, but Brazil’s exclusion does not affect the results. The results are also similar when excluding China, which experienced substantial growth during the sample period. It is generally worth noting that the 1980–1999 period predates the very rapid take-off of Chinese manufacturing exports after 2000 (Autor et al. 2013). Unreported tests consider other candidates like Mexico, Germany, and Japan, and these tests, too, find the results very stable to the sample composition, reflective in large part of the underlying exporter-importer-year fixed effects.

Column 4 shows the results when excluding industry 383 (Machinery, electrical). The coefficient estimates are reduced in size by about 30% from column 1, but they remain quite strong and well-measured overall. The exclusion of industry 383 has the largest impact on the results of the 26 industries in the sample, which is why it is reported. This importance is not very surprising given the very rapid development of technology in this sector, its substantial diffusion around the world, and its associated trade. On this
18 Baltic republics, the actual shares of services in consumption are typically low r than average. Among the republics with lower income levels, some have very high shares -- Tadjikistan, Armenia, and Turkemenistan - - and some have extremely low shares: - Usbekistan, Azerbaijian, Georgia, and Moldova. The republics clearly do not conform to the international patterns estimated by H&S. More study of the data and the sources of these differences is needed.

Actual data for Russia and other Soviet republics on the share of household consumption in GDP show the familiar decline with increasing incomes, reflecting the fact that, at higher incomes, societies can afford to devote more resources to investment and growth. The decrease in the consumption ratio partly offsets the tendency for service shares in consumption to increase as income rises, with the result that the share of services in GDP increases only moderately with incomes. Also, the typical Soviet consumption share is between 50-55 percent of GDP, while the typical share for lower-income European countries is above 60 percent. Thus, the low share of actual services in GDP reflects not only a low share of services in consumption but also a low share of consumption in GDP.

# D. Services as a Major Source of Growth

In Section A, we set forth a view that services, and in particular business and consumer services, could be a major source of growth in Russia and other - former Soviet states in the coming years. This view was based on two phenomenathe existence of a large gap in service activity, which will be closed with the transition to a market economy, and the existence of externalities arising from the contribution made by services to productivity increases in the goods sector. Two additonal types of externalities would appear to be particularly applicable to economies in transition. One is the potential contribution to consumer
<!-- Start of picture text --> Fig 3: Simple Average MFN duties in MENA<br>35 (%)<br>30.1<br>30<br>28.6<br>27.3<br>25<br>20 18.7 19.1 19.6 19.9<br>17.0<br>15<br>13.2<br>10<br>7.7<br>6.0<br>5.4<br>5 4.2<br>3.6<br>0<br>KWT QAT LBN SDA OMN JDN LBY ALG MENA SYR EGY Iran, Tunisia Morocco<br>Islamic<br>Rep.<br><!-- End of picture text -->

# **Rigidities in Factor Markets**

Recent studies in both Morocco and Tunisia point to the existence of significant rigidities in factor markets. The recent World Bank Development Policy Review on Tunisia recommends that for Tunisia to maximize the employment gains of its international integration, labor market regulations and institutions need to be flexible so that employers and workers can adjust to changes in business conditions. While acknowledging recent efforts to introduce flexibility in hiring, rigidities in the labor market persist due to: restrictions on the use of temporary help agency workers and expatriate employees; heavy bureaucratic processes to be followed before dismissals; and generous severance payments. Similar rigidities also exist in the Morocco labor market. The recent Morocco Investment Climate Assessment (ICA) reports that a third of the enterprises surveyed expressed difficulties in laying-off workers that were no longer needed due to the high costs involved. Some 25% of enterprises surveyed indicated that if restrictions on worker lay-offs were lifted they would downsize their labor force, whereas some 11% indicated that they would increase their employment. Furthermore, evidence from the World 6
workers is similar to that of native workers, although wage levels are much lower. In addition, informality is associated to lower wages and, in turn, informality is strongly influenced by legal status.

# **5 Occupational Analysis**

## **5.1 Occupations in Ecuador**

Education levels may lack comparability across Ecuador and Venezuela, and this could distort the wage gaps by education level reported in the previous section (as discussed in footnote 13). For instance, if tertiary education has deteriorated in Venezuela to the point that the quality of a college education is substantially lower than in Ecuador, the large migrant-native wage gaps for college graduates reported earlier would not reflect productivity gaps and, hence, a waste of productive skills.

To address this concern we use data on workers’ occupations in Ecuador. More specifically, we rely on the survey question that informs us about the type of employer and classifies workers into the following categories: private-sector employee, self-employed, government employee, day laborer ( _jornalero or peón_ ), manager, domestic employee and outsourced employees ( _empleado terciarizado_ ).<sup>16</sup> The results are collected in Table 8. The top panel reports the occupational distribution for Ecuadoran natives. According to the data, an estimated 2.7 million individuals are private-sector employees and a similar number are self-employed. The highest average hourly wage is $5.59 for government employees, followed by $3.67 for managers. The middle panel reports the analogous information but in reference to Venezuelan migrants. We estimate that about 102,000 Venezuelans in Ecuador are private-sector employees, about 66,000 are self-employed and close to 14,000 are day laborers.

The bottom panel compares the within-occupation characteristics of Venezuelan migrants to native workers by computing ratios for all variables included in the table. The main takeaways are the following. Relative to their population density, Venezuelan migrants are over-represented in three occupations: outsourced workers, domestic employees and private-sector employees. Except for private-sector employment, these occupations are characterized by low wage levels (on the basis of the data for Ecuadoran

> 16The EPEC survey also contains a more traditional occupation variable. However, the variable is too detailed and cannot be aggregated as it lacks numerically coded categories. For example, the top 10 occupations only account for 4% of the workers in the survey.

13
# **Appendix Table 1 Contemporaneous Herding Statistics**

This table presents the average Lakonishok et al. (1992) herding statistic calculated over all assets and by asset class. The herding statistic is calculated using the overall portfolio probability of buying an asset at any point in time. Column (1) presents the results considering all assets, column (2) considers assets traded by more than one PFA, and column (3) considers assets traded by more than half of the PFAs in operation at any point in time. T-tests are two-tailed. One asterisk indicates statistical significance at the ten-percent level and two asterisks indicate statistical significance at the five-percent level. The standard error from the significance t-test is presented in parenthesis. Numbers represent percentages (results are multiplied by 100). The dashes in column (3) indicate asset classes that are not traded by more than half of PFAs in operation.

|||<br> <br>HerdingStatistic|
|---|---|---|
||All Assets|Assets Traded by More<br>ThanOnePFA<br>Assets Traded by More<br>Than Halfof PFAs|
||(1)|(2)<br>(3)|
|**All Asset Classes**|4.32**|8.28**<br>10.48**|
||(0.02)|(0.05)<br>(0.11)|
|**Domestic Assets**|||
|Former Pension System Bonds|0.69**|-13.13**<br>4.99**|
||(0.03)|(0.10)<br>(0.59)|
|Corporate Bonds|4.52**|9.40**<br>8.97**|
||(0.26)|(0.69)<br>(0.58)|
|Financial Institutions|5.81**|12.48**<br>10.59**|
||(0.07)|(0.24)<br>(1.03)|
|Government Papers|2.76**|2.61**<br>5.91**|
||(0.08)|(0.19)<br>(0.57)|
|Investment and Mutual Funds|7.18**|18.41**<br>5.01**|
||(0.72)|(1.83)<br>(0.74)|
|Equity|4.98**|5.57**<br>4.45**|
||(0.21)|(0.29)<br>(0.29)|
|Mortgage Bonds|7.63**|15.12**<br>14.75**|
||(0.04)|(0.05)<br>(0.13)|
|**Foreign Assets**|||
|Fixed Income|3.16**|6.16**<br>26.50**|
||(0.22)|(1.13)<br>(4.87)|
|Investment and Mutual Funds|6.24**|8.32**<br>4.06**|
||(0.15)|(0.27)<br>(0.37)|
|Equity|4.30**|4.8*<br>-|
||(0.27)|(2.58)<br>-|
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE looks daunting relative to the available resource envelope. As noted above, the funding gap includes only public interest infrastructure. It is assumed that infrastructure relating to the development of mining and natural resource concessions will be fully funded by the concessionaires.

**Table 16. Funding gaps by sector**

## <u>US$ millions</u>

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|_Basic scenario_||||||
|Spending needs|(45)|(251)|(198)|(122)|(615)|
|Existing spending|19|23|39|8|90|
|Efficiency gains|0|5|9|4|8|
|Funding gap|(25)|(224)|(149)|(110)|(508)|
|_Pragmatic scenario_||||||
|Spending needs|(45)|(122)|(100)|(77)|(344)|
|Existing spending|19|23|39|8|90|
|Efficiency gains|0|5|9|4|8|
|Funding gap|(26)|(94)|(52)|(65)|(246)|

Source: Derived from Foster and Briceño-Garmendia 2009. — = data not available.

# What else can be done?

Liberia’s funding gap is very large relative to the size of its economy. Nevertheless, there are a number of options for making it more manageable, including raising additional finance, taking additional cost-cutting measures, and lengthening the period of time allotted for meeting the illustrative infrastructure targets. In any case, given the magnitude of the gap, difficult decisions will have to be taken regarding the prioritization of the different investments.

In addition to any measures that the public sector can take to close the funding gap, the private sector can also contribute _._ Liberia has already established a strong track record on foreign direct investment, and has in recent years captured 2 percent of GDP as private investment for (ICT) infrastructure, which is a strong performance relative to African peers (figure 19). Although not all components of the required infrastructure platform are suitable for private finance (particularly roads, water, and sanitation), other components may be (for example, ICT, power generation, and ports). Nevertheless, due to significant country risk, the costs associated with private investment in infrastructure may be relatively high. Private investors will have to carefully consider whether Liberia provides the best value for their money. Risk mitigation instruments available from international financial institutions may be relevant in this context.

33
has plunged the pension system back into deficit. It incurred a deficit equivalent to 2.1 percent of GDP in 2010 (table 4).

# **Table 4: Revenues and Expenditures of the State Pension Fund (percent of GDP); 2001-2011**

||2001|2002|2003|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|---|---|---|
|Revenues|6.3|6.2|6.2|6.1|6.1|5.7|5.9|6.1|6.9|6.2|
|Expenditures|7.2|7.0|6.5|6.8|6.3|5.4|5.7|6.6|8.1|8.3|
|Balance|-0.9|-0.8|-0.4|-0.7|-0.2|0.3|0.2|-0.5|-1.2|-2.1|

_Revenues exclude state subsidy Source: National House of Public Pensions (2011), “Social Insurance Budget”, Bucharest http://www.cnpas.org/portal/media-type/html/language/ro/country/null/user/anon/page/buget_ A new pension law was enacted in December 2010 to improve the financial viability of the public pension system. The new law included the following provisions: a phased increase of the retirement ages of women to 63 years; a phased switch to the price indexation of pension benefits (from wage indexation); increased penalties for early retirement; the incorporation into the public pension scheme of the special sectors pensions schemes (for defense, interior, intelligence, etc.)<sup>11</sup> ; the integration of new categories of contributors (e.g. the self-employed) within the public pensions system; the introduction of a maximum ceiling for the contribution base; and tightened eligibility criteria for disability benefits.

The new pension law is projected to reduce public pension expenditures substantially over the long term. By 2025, pension expenditures based on the pension legislation in force immediately prior to the enactment of the new law, were projected to reach 10.5 percent of GDP, with the

> 11 The state pension system included, alongside the ordinary public pensions, separate schemes for specific categories of state employees; the army, the police, the secret services, civil aviation and support staff, Parliament, diplomats, magistrates and support staff, etc. These special pension schemes provided much more generous benefits than the ordinary public pensions, with replacement rates of 80-100% of the last 1-6 month salary earnings. In 2010, about 200,000 people received special pensions at a total cost to the budget of 1 percent of GDP. The special pension schemes are analysed in Dragota and Miricescu (2010).

20
# **VI.   APPENDIX**

## **Figure VI-1: Spectral Density Function of Real Credit**

## **A- Oil exporter**

<!-- Start of picture text --> Bahrain: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Kuwait: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Iran: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Oman: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text -->
Table D.1: Key indicators by regions

|#|Code|Region|Revenue ratio (%)<br>2000-2008|2009-2016|
|---|---|---|---|---|
|1|ECA|<br>Europe and Central Asia|34.48|36.52|
|2|LAC|Latin America and Caribbean|22.01|24.01|
|3<br>|MENA<br>|Middle East and North Africa<br>|36.97<br>|33.72<br>|
|4|SEAP|South, East Asia and Pacific|17.72|19.76|
|5|SSA|Sub-Saharan Africa|22.27|22.49|
||||Tax (%)<br>||
||||2000-2008|2009-2016|
|1<br>|ECA<br>|Europe and Central Asia<br>|28.07<br>|24.39<br>|
|2<br>|LAC<br>|Latin America and Caribbean<br>|24.39<br>|20.16<br>|
|3|MENA|Middle East and North Africa|8.11|8.50|
|4|SEAP|South, East Asia and Pacific|13.98|17.27|
|5|SSA|Sub-Saharan Africa|11.12|12.56|
||||Credit ratio (%)||
||||2000-2008|2009-2016|
|1|ECA|Europe and Central Asia|28.96|47.09|
|2|LAC|Latin America and Caribbean|32.50|38.75|
|3|MENA|Middle East and North Africa|43.98|56.29|
|4|SEAP|South, East Asia and Pacific|48.30|66.43|
|5|SSA|Sub-Saharan Africa|16.70|23.15|
||||Political corruption (0-1)||
||||2000-2008|2009-2016|
|1|ECA|Europe and Central Asia|0.46|0.48|
|2|LAC|Latin America and Caribbean|0.48|0.51|
|3|MENA|Middle East and North Africa|0.40|0.46|
|4|SEAP|South, East Asia and Pacific|0.35|0.36|
|5|SSA|Sub-Saharan Africa|0.33|0.35|
||||Public service corruption (0-1)||
||||2000-2008|2009-2016|
|1|ECA|Europe and Central Asia<br>|0.48|0.49|
|2|LAC|Latin America and Caribbean|0.52|0.54|
|3|MENA|Middle East and North Africa|0.38|0.42|
|4|SEAP|South, East Asia and Pacific|0.38|0.38|
|5|SSA|Sub-Saharan Africa|0.30|0.33|
||||Law and order (1-6)||
||||2000-2008|2009-2016|
|1|ECA|Europe and Central Asia|4.13|3.92|
|2|LAC|Latin America and Caribbean|2.69|2.40|
|3|MENA|Middle East and North Africa|4.23|4.19|
|4|SEAP|South, East Asia and Pacific|3.43|3.43|
|5|SSA|Sub-Saharan Africa|2.96|2.90|
||||Resource distribution (0-1)||
||||<br>2000-2008|2009-2016|
|1|ECA|Europe and Central Asia|0.77|0.75|
|2<br>|LAC<br>|Latin America and Caribbean<br>|0.57<br>|0.59<br>|
|3|MENA|Middle East and North Africa|0.49|0.52|
|4|SEAP|South, East Asia and Pacific|0.55|0.54|
|5|SSA|Sub-Saharan Africa|0.51|0.53|
||||Ethnic and religious stability (0-6)||
||||<br>2000-2008|2009-2016|
|1|ECA|Europe and Central Asia|1.02|0.99|
|2|LAC|Latin America and Caribbean|4.96|5.04|
|3|MENA|Middle East and North Africa|3.79|3.97|
|4|SEAP|South, East Asia and Pacific|3.75|3.46|
|5|SSA|Sub-Saharan Africa|3.61|3.67|

Notes: Average levels are computed for the subperiods 2000-2008; 2009-2016.

49
The backcasting exercise relies on two particularly strong assumptions.

First, the analysis assumes that inflation is fully captured by the GDP deflator and does not account for differential effects of inflation on the poor. Accelerating inflation in Nigeria in recent years has been driven disproportionately by food prices, and even poor households – many of whom are concentrated in subsistence agriculture – purchase food, so this could have uneven effects on consumption and hence alter the progress of poverty reduction (Joseph-Raji et al., 2021). Second, it assumes that there are no switches between sectors over time. While this is an important assumption, evidence on Nigeria’s labor market shows that structural transformation was slow over this period (Jenq et al., 2021) suggesting that this may not be too big of a concern for the purposes of this analysis.

While we do not address these limitations directly in the backcasting exercise, the fact that the survey-tosurvey imputations produce such similar results offsets some of these concerns. Distributional effects of macroeconomic growth are likely to be picked up – at least partly – by the non-monetary indicators collected in different waves of the GHS, which are used to impute household consumption. Similarly, some of the variables included in the imputation model – such as frequency of consumption of certain food items – would be affected by inflation and reflect the larger effect of changes to food prices among poorer households.

The main drawback of the survey-to-survey imputation exercise is the difference in data collection schedules between the GHS and NLSS. The GHS data are only available during the post-planting and postharvest seasons, which might bias our results if the indicators used in the consumption model vary significantly throughout the year and are hence different from those collected over a 12-month period in the 2018/19 NLSS. This may explain the difference between the imputed 2018/19 GHS estimate and the 2018/19 NLSS estimate. Yet, if the GHS systematically overestimate poverty rates – as also seems to be the case when comparing estimates in other waves to the backcasted series – this would suggest an even lower “true” poverty rate in 2010/11, even lower poverty reduction in the decade to 2019, and an even larger difference with the 2009/10 HNLSS poverty estimate.

# _<u>7.3. Sense-checking with other surveys</u>_

The evidence of a slowdown in improvements to household welfare emanating from the backcasts and the survey-to-survey imputations is in line with other survey data collected in Nigeria in the decade prior to the COVID-19 crisis. For example, using data from the Demographic Health Survey (DHS) data set, we look at trends in non-monetary indicators that are highly correlated with monetary welfare over the period 2003-2018. Specifically, Figure _8_ in the Appendix shows the trends in access to electricity, improved 23
institutions would have to pay for capital in the open market. This choice exaggerates measures of profit and artificially shrinks measures of subsidy (Cull et al. 2009a). As the price that deposittaking institutions typically pay savers for capital, the deposit rate is a justifiable measure of capital costs in some contexts. Most microfinance institutions, however, do not count deposits as their most important source of capital. Moreover, the measure does not account for the transaction costs of servicing deposit accounts. Measures of trends are less clearly affected by this bias, and Cull et al (2009a) show that the main conclusions here would be even stronger were the bias corrected.

Another issue is with the Financial Self-Sufficiency Ratio (FSS), our preferred metric for an institution’s profitability. FSS incorporates figures adjusted to account for different kinds of subsidy to approximate an institution’s returns in the absence of subsidized funding. However, FSS analysis captures a “snapshot” of current conditions at a given moment. It cannot indicate an institution’s flexibility, or what strategies its management would pursue if access to subsidized funding dried up. Ultimately, operating on commercial terms is tied to the ability to shift strategies as required. If the need arose, could the institution re-allocate funds and find ways to operate more efficiently? The FSS ratio provides only a rough guide to this question. In this sense, the ratio usefully reveals circumstances at a given slice of time, but it gives only a limited perspective on what might be possible without subsidy.

It is also important to keep in mind that these studies are based on cross-country data, which allows us to draw conclusions about the microfinance landscape but which may offer an imperfect guide to possibilities in specific countries.

Finally, the papers map correlations and patterns in the data. For the most part we are not making strong causal claims (the exception is in our work on regulation, and even there we tread lightly). Most progress in establishing causal relationships has been achieved in micro studies of the sort reviewed in Karlan and Morduch (2009), and World Bank (2008). Still, since many policy questions concern the working of institutions, analyzing institution-level data is critical.

# **3. Contracts**

5