rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 60.0 80%<br>68%<br>50.0 70% 64%<br>58% 60%<br>40.0 60% world avg.<br>30.0 50% 42% 40%<br>40%<br>20.0<br>30%<br>10.0<br>20%<br>‐<br>10%<br>0%<br>Low & middle income High income AFR EAP ECA LAC MENA SAR<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> Source: World Development Indicators For most countries, particularly middle and high income countries, the large majority of export growth takes place at the intensive margin – i.e. by selling more of the same products to the same markets (Brenton & Newfarmer, 2009). This deepening of trade relationships is supported by increasing specialization, which may be across products or within products. Within‐product specialization can be observed through levels of intra‐industry trade, which may derive from specialization in stages of production as well as from specialization at different levels of the quality ladder. The former is the source of the trade in components – or intermediate inputs – that characterize global production networks. The latter is the source of “cross‐hauling”, or the two‐way trade in similar end products that allows for the intense two‐way trade within high income countries in areas like automobiles (e.g. Fiats to Germany and BMWs to Italy), clothing (e.g. Zara to Sweden and H&M to Spain), and commonplace food items like yoghurt, juice, and ice cream.

At the intensive margin, a critical indicator of competitiveness is the survival rate of export flows. Recent research (Besedes and Prusa, 2006; Brenton, Pierola, & von Uexkull, 2009; Lederman, 2009) also demonstrates that exporting is an activity with very low survival rates for new entrants. This is particularly the case for low income countries, where a host of constraints – including exchange rate volatility, and search and information costs – can conspire to fatally undermine export flows in these vital early years. Thus, beyond the discovery stage of the export cycle, competitiveness challenges for export survival at the intensive margin remain significant.

# <u>Diversification</u>

Export diversity – both in terms of products and markets – is strongly associated with economic growth (Hesse, 2009; Lederman & Maloney, 2009), particularly for developing countries<sup>5</sup> . This positive link between diversity and long‐run growth accrues from reduced volatility in output that would otherwise result from the impact of external shocks on concentrated export basket (Haddad, Lim, and Saborowski, 2010), as well as from the increased potential for generating spillovers. Although much of the focus on diversification in low income countries tends to focus on concerns over a “natural resources curse”, there is increasing evidence that it is not natural resources per se that is the problem but rather concentration of exports (Lederman & Maloney, 2007).

> 5 In contrast, after reaching a certain income level, advanced countries tend to re‐specialize by expanding exports at the intensive margin (Cadot, Carrere & Strauss‐Kahn, 2007)

5
Policy Research Working Paper 10989

# **Abstract**

Economic growth is often associated with welfare gains through job creation. However, the number and quality of new job opportunities created in a growing economy vary across countries and sectors, due in great part to changes in labor productivity. This paper provides estimates of country and sector-specific GDP-employment elasticities based on data from the past two decades, including an evaluation of the predictive power among alternative methodological approaches. The results show that employment elasticities of growth vary significantly across countries and sectors, but are in most cases below 1.0, implying that employment grows less than GDP due to increasing productivity. Across sectors, agriculture has mostly lower elasticity values, becoming negative for more than one-third of developing countries. In addition, increases in labor productivity are associated with reductions in informal employment. These empirical results are in line with the implications of a theoretical model about the relationship between GDP growth, job creation, and labor productivity in economies with varying levels of productivity and informality.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group 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 SHovhannisyan@ifc.org and CMondragonvelez@ifc.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
# Disability pensions

After the old-age pensions, the disability pensions represent the second most important item of the social insurance pensions in Moldova. The total number of beneficiaries in 2017 was around 128,000, in a steady decline compared to previous years (133,700 in 2015 and 132,900 in 2016). The decline can be explained by two factors: 1) the health conditions reevaluation policy initiated by the Government with the aim of stopping the practice of unwarranted attribution of the disability pension to persons not meeting the medical conditions; and 2) increase in the minimal contribution period.

The average pension in 2017 was 1,149 MDL, which is almost 13% more than previous year in nominal terms. The increase has been due to indexation of the pension, valorization of the average insured income and application of the new formula for pension calculation. In 2017 there have been two rounds of valorization: starting with April 1<sup>st</sup> 2017, the pensions established in the period 2001-2008 for 15,632 beneficiaries increased by almost 19% to reach 1,432 MDL, while starting with November 1<sup>st</sup> 2017, the pensions established in 2009-2011 for 9,049 beneficiaries increased almost 38% to reach almost 1,419 MDL.

The disability is established by the National Council for Determining Disability and Work Capacity and can be of severe degree, accentuated degree and medium degree. According to the legal provisions, the minimal amount of the pension established for severe disability should be 75% of the minimum old-age pension; the minimal amount is 70% for accentuated disability and 50% for the medium degree disability. The law requires minimal contribution period for the person to be entitled to disability pension, as shown in the Table A6. In case of a disability caused by a work accident or professional disease, the pension is established with no regard to the contribution period.

**Table A6. Minimal contribution period for attribution of disability** **<u>pension</u>**

|**Age when the disability was established**|**Contributionperiod(years)**|
|---|---|
|**Until 23years**|2|
|**23-29years**|4|
|**29-33years**|7|
|**33-37years**|10|
|**37-41years**|13|
|**Above 41years**|15|

_Source: Law nr.156 of 14/10/1998 on public pensions system._ The amount of the pension is established according to the following formulas:

- Pension for severe disability:

- Pension for accentuated disability:

- Pension for medium disability:

where:

- P – amount of the pension;

- Vav – valorized insured monthly income, but not more than double amount of the average wage per economy for the year preceding the year of establishing disability;

- Tt – total contribution period;

- Tmax – maximal potential contribution period from the year of 18 until the retirement age, but not more than 40 years.

41
-47 -

finance consunption when output is again low.

The tables reveal several relationships: How much debt can be sustained depends positively on the variability of output and the discount factor, and negatively on the interest rate. Moreover, the effect of output variability is dramatic: An order of magnitude more debt relative to average income can be sustained when output fluctuations are 20 per cent rather than 10 per cent. Also notable is the sensitivity of the maximum debt level to the interest rate.

Finally, note that increases in P offset by reductions in R that maintain R. constant increase the maximum sustainable debt level. An interpretation is that high frequencv fluctuations (say, over the season) allow for more debt to be accumulated than low frequency fluctuations (say over the business cycle).

The analysis is simple. For example, there are no allowances for uncertainty, growth, and investment. Intro-4ng these factors will not<sup>4</sup> affect the basic point of the exercise, that maintaining access to credit markets is a sufficient reason to service debt.

In fact, domestic investment opportunities provide an additional reason to want to maintain access to credit markets. The expected return on investment opportunities may also fluctuate. If a country must finance its own investment, taking advantage of investment opportunities may then force consumption to fluctuate. Access to international capital markets allo4s it to vary its investment level in response to changes in domestic opportunities without completely offsetting movements in consumption.

Annual data on net resource transfers presentLd in the Appendix indicate that, during the 1970s and 1980s, the direction of flow of funds between debtor countries and international capital markets changed on average ar:-nd 5
hypothesis.<sup>4</sup> The reminder of the paper is organized as follows. Section 2 provides the background on Nepal's civil conict, major events that helped shape the war, and the physical and economic costs of the war. Conict intensity from the INSEC's database and individual data from the 2016 NDHS are discussed in section 3. Section 4 presents empirical strategies for evaluating both the rst and second-generation impacts. Empirical results along with identication validation and potential mechanisms for intergenerational transmission are presented in section 5 and concluding remarks are presented in section 6.

# 2 Background

Nepal is a landlocked country between India and China. Because of its highly mountainous and rugged terrain and lack of adequate infrastructure and economic development, most parts of the country remain remote, and access to basic services remains unattainable to many. With twothirds of its 30 million inhabitants (estimated as of July 2017) relying on agriculture and a quarter living under the poverty line, Nepal is one of the least developed nations in the world (CIA World Factbook, 2019) and is among the lowest in health, sanitation, primary education and electricity in South Asia.

Figure A.1 shows the administrative divisions of Nepal before the implementation of a new constitution in 2015. The country was divided into ve geographically homogeneous development regions, which were further divided to form 75 districts. Districts were further divided into rural (village development committee, VDC) and urban (municipalities) areas, which were the lowest level of administrative units. At the time of the 2011 population census, Nepal consisted of 3,914 VDCs and 58 municipalities (Central Bureau of Statistics, 2012). I calculate conict intensity at the level of these 3,972 local administrative areas.<sup>5</sup>

> 4Along these lines, Nepal et al. (2018) nd that Nepal's civil conict increased women's desired and actual fertility during the conict by 22 percent.

> 5For convenience, throughout the paper I refer to these local administrative units as villages although some are technically urban municipalities.

4
<!-- Start of picture text --> 60<br>50<br>wm 40<br>N<br>=<br>oO<br>® 30<br>oD)<br>Oo<br>—<br>$<br>< 20<br>10<br>0<br>% a © % % ><br>SF S S 3 3 Sv<br>cS x iS v s v is v is v iS v<br>Fiscal year<br><!-- End of picture text -->
37

**Table 1. Number of Commercial Banks and Measures of Concentration**

|Number of Banks (Excluding development and|**1980**|**1983**|**1986**|**1989**|**1991**|
|---|---|---|---|---|---|
|investment banks)|36|39|49|53|55|
|-State Owned|8|9|8|8|8|
|-Private|24|19|24|24|26|
|-Foreign|4|11|17|21|21|
|Number of Branches|5894|6221|6337|6579|6460|
|Concentration Measures**(m**percent)||||||
|CR3D|53|57|57|45|40|
|CR5D|69|71|68|61|_55_|
|CR8D|82|83|86|85|82|
|CRIOD|88|88|86|85|82|
|CR3L|56|53|55|49|44|
|CR5L|70|65|69|60|56|
|CR8L|82|78|81|74|71|
|CRIOL|87|83|85|80|78|
|CR3A|50|51|48|45|40|
|CR5A|64|63|62|58|54|
|CR8A|76|76|76|73|69|
|CR1OA|83|81|81|79|77|
|CR3SD|_55_|61|63|50|42|
|CR5SD|72|74|76|66|58|
|CR8SD|83|84|86|81|75|
|CR1OSD|90|90|92|89|83|
|CR3NSD|55|60|68|65|62|
|CR5NSD|70|72|79|75|74|
|CR8NSD|84|85|92|87|87|
|CRIONSD|89|90|98|93|94|

Sources: The Banks Association of Turkey, The Central Bank and own calculations.

Variables: CRD = Deposit Concentration (3, 5, 8, 10 banks), CRL = Loan Concentration (3, 5, 8, 10 banks); CRA = Asset Concentration (3, 5, 8, 10 banks); CRSD = Savings Deposit Level Concentration (3, 5, 8, 10 banks); CRNSD Savings Deposit Number Concentration (3, 5, 8, 10 banks).
_analysis of the 2000, 2005 and 2011 Demographic and Health Surveys_ . DHS Further analysis Reports No. 83. Calverton MD: ICF International.

World Bank Group. (2015), Ethiopia Poverty Assessment 2014. Washington, DC.

https://openknowledge.worldbank.org/handle/10986/21323 UNICEF (2013), _Female Genital Mutilation/Cutting: A statistical overview and exploration of_ _the dynamics of change_ , New York: United Nations Children’s Fund.

29
TABLE II

# Balance Tests on Teacher-Level Variables

||(1)|(2)<br>All teachers|(3)|(4)|(5)<br>Target teachers on|(6)<br>ly|
|---|---|---|---|---|---|---|
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Raw (fraction correct) test score|0.56|0.56|0.00|0.55|0.56|-0.01|
||[0.16]|[0.16]|(0.01)|[0.17]|[0.17]|(0.01)|
|Eligible but not certifed at baseline (i.e., Target)|0.56|0.57|-0.01|1.00|1.00|-0.00|
||[0.50]|[0.50]|(0.02)|[0.00]|[0.00]|_d.n.a._|
|Already certifed at baseline|0.19|0.18|0.02|0.00|0.00|0.00|
||[0.39]|[0.38]|(0.01)|[0.00]|[0.00]|_d.n.a._|
|Not eligible for certifcation at baseline|0.25|0.25|-0.00|0.00|0.00|0.00|
||[0.43]|[0.43]|(0.01)|[0.00]|[0.00]|_d.n.a._|
|Bachelor’s degree|0.62|0.59|0.04<sup>_∗∗∗_</sup>|0.69|0.65|0.06<sup>_∗∗∗_</sup>|
||[0.49]|[0.49]|(0.02)|[0.46]|[0.48]|(0.02)|
|High rank (rank IV) in civil service|0.41|0.44|-0.03|0.48|0.51|-0.04<sup>_∗∗_</sup>|
||[0.49]|[0.50]|(0.02)|[0.50]|[0.50]|(0.02)|
|Certifed and paid the certifcation allowance|0.11|0.12|-0.01|0.00|0.00|0.00|
||[0.32]|[0.33]|(0.01)|[0.00]|[0.00]|_d.n.a._|
|Base pay (in mil. IDR)|1.87|1.92|-0.05|2.02|2.07|-0.07<sup>_∗∗_</sup>|
||[0.83]|[0.80]|(0.03)|[0.73]|[0.69]|(0.03)|
|Other allowances (in mil. IDR)|0.53|0.54|-0.02|0.55|0.59|-0.04<sup>_∗∗∗_</sup>|
||[0.34]|[0.33]|(0.01)|[0.31]|[0.31]|(0.01)|
|Certifcation pay (in mil. IDR)|0.21|0.22|-0.01|0.00|0.00|0.00|
||[0.59]|[0.60]|(0.02)|[0.00]|[0.00]|_d.n.a._|
|Second job|0.34|0.34|-0.00|0.33|0.35|-0.02|
||[0.47]|[0.47]|(0.02)|[0.47]|[0.48]|(0.02)|
|Hours worked on second job (last week)|3.50|3.40|0.12|3.18|3.40|-0.19|
||[8.04]|[7.69]|(0.29)|[6.99]|[7.48]|(0.34)|
|Started or completed the certifcation process|0.61|0.29|0.33<sup>_∗∗∗_</sup>|0.73|0.18|0.54<sup>_∗∗∗_</sup>|
||[0.49]|[0.45]|(0.02)|[0.45]|[0.39]|(0.03)|

_∗ ∗∗ ∗∗∗ Notes: p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Table compares average values between treatment and control groups for all teachers (left panel) and target teachers (right panel). Estimates are based on a regression model that includes district-triplet fixed effects, which are the strata used for randomization. Within-group standard deviations are reported in brackets in columns (1), (2), (4) and (5). School-level clustered standard errors of the estimated mean differences between treatment and control are reported in parenthes in columns (3) and (6).
**Figure 2: Overview of regional effects**

<!-- Start of picture text --> 0.80<br>SSA<br>EAP SSA<br>0.60 SSA<br>EAP EAP SSA SA<br>SA<br>0.40 SA SA SSA EAP<br>SA EAP<br>0.20 MENA LAC EAP ECA MENA MENA<br>0.00 LAC HI MENA LACHI ECAHI LAC ECAHI MENAHI LACECAHI HI MENAEAPHI LACECA HI ECAHI<br>-0.20 ECA ECA MENA ECA MENA LAC ECA EAP<br>MENA LAC<br>-0.40 SSA SSA SA SA LAC SA SSA MENAEAP LAC SA SA<br>-0.60 EAP SSA<br>SSA<br>-0.80<br>Bank Credit / Bank Net Interest ATMs / Stock Mrkt Listed Firms / Turnover Private Public Bonds Institutional<br>GDP Deposits / Margin / 100,000 Cap / GDP Mill people Ratio Bonds / GDP / GDP Investors<br>GDP Assets people<br><!-- End of picture text -->

# **e. Principal components analysis**

Factor analysis was used to help order and sort out the indicator sets for institutions and markets (but only for equities).<sup>34</sup> For each sector a first analysis was conducted with all the pre-selected indicators for the sector. A second analysis was then run with the more limited number of indicators that have broader country coverage. Results appear in Table 8 for institutions, and Table 9 for equities. A final analysis combines key banking and equity indicators (Table 10). Highlights are as follows:

- Starting with Table 8, three clearly distinct dimensions appear, ranked by order of importance: i) size; ii) efficiency; and iii) reach.<sup>35</sup> As regards size, total assets have the highest loading, credit the lowest, which is not surprising (some banking systems may be large but have a hard time to channel resources into loans).

- As regards efficiency, net interest margin has the highest loading while the spread has the lowest. Furthermore, the latter has a very high uniqueness factor, suggesting that spreads may be reflecting an altogether different dimension. Notice also that while size and efficiency exhibit clearly negative crosscorrelations, the size indicators do not show up in the reach dimension, suggesting that reach and size are largely orthogonal to each other.

> 34 Underlying dimensions are constructed by a weighting of indicators, the indicator that is more associated with the dimension receiving a higher weight (so-called factor loading). Conversely, every indicator can be thought of as being a weighted sum of the dimensions or factors. The factor loadings, are computed using the squared multiple correlations as estimates of the communality, the part of the variance of the indicator that is captured by the factors. To arrive at factor loadings that are more intuitively interpretable, we use the varimax technique which maximizes the squared loadings for each factor.

> 35 The columns are place in the order of decreasing variability in the indicators. Hence, Size contains more information than Efficiency which contains more information than Reach.

26
caused rapid rises in food prices (e.g., currency crises, etc.), in particular, have played a key role in pushing the country to a near famine in 2018 and again beginning in 2020 (e.g., OCHA 2019; Favari et al. 2021; OCHA 2021).

# **Section 3. Data**

Since the conflict escalated in March 2015, traditional data collection in the country has been significantly limited. In parts of the country, permission from authorities is needed for any data collection, survey teams are unable to perform longer and more detailed surveys that are needed for traditional welfare metrics and national accounts information, and there are widespread reports of interference in sampling and household responses to try to influence the outcome of data collection (e.g., OCHA 2021; Tandon and Vishwanath 2021; Favari et al. 2022). These hurdles, in addition to an already difficult security situation, make it impossible to rely on traditional data collection.

Given the substantial constraints and the need for high frequency data, we use the most complete household survey that captures displacement status- the mobile Vulnerability and Assessment Mapping Survey (mVAM) conducted by the WFP. The monthly survey began in August 2015, just months after the escalation of violence and the initial surge in forced displacement. Importantly, the data provide an opportunity to analyze household decisions during an active conflict.

The mVAM is a monthly survey of approximately 2400 households used to assess the food security situation in the Republic of Yemen. The survey is stratified by governorate/capital city, where potential respondents continue to be contacted until a sufficient number of completed responses have been met in each governorate based on its share of the total population (aside from the island of Soccatra).<sup>7</sup> The survey is a rolling panel, where the call center initially surveys 2400 households reached via random digit dialing (RDD). In each subsequent month, 80 percent of the sample is composed of households that answered a previous survey round, and 20 percent of the sample is composed of newly reached households via RDD.<sup>8</sup>

> 7The Republic of Yemen has 333 districts that fall within 20 governorates and one municipality (capital city).

> 8The WFP is unable to observe whether a phone number exists in the random digit dialing procedure,

8
**Table S4. Average incidence of engaging in safe practices, by survey round**

||Hand<br>washed|Malawi<br>Avoided<br>physical<br>greetings|Avoided<br>crowds|Hand<br>washed|Uganda<br>Avoided<br>physical<br>greetings|Avoided<br>crowds|
|---|---|---|---|---|---|---|
|Round 1|0.908|0.730|0.609|0.969|0.962|0.909|
||(0.013)|(0.019)|(0.020)|(0.005)|(0.006)|(0.010)|
|Round 2|0.817|0.740|0.346|0.805|0.855|0.768|
||(0.017)|(0.019)|(0.021)|(0.013)|(0.011)|(0.013)|
|Observations|3375|3375|3375|4376|4376|4376|

_Note_ : Each column reports the estimated population-weighted mean number of people living in households that report changing their behavior. Standard errors are in parenthesis.

31