rafmacalaba/datause-encoder-data huggingface dataset:
recessions. Differences in the relative strength of country youth labor markets can explain observed differences across countries in the average age when the youth leave home (Card and Lemieux 2000). In Germany and Spain, the likelihood of leaving home increases significantly with youth employment status and labor earnings (Blanco and Kluve 2002).

As previously noted, much of the literature on the consequences of youth unemployment is drawn from the experiences of advanced economies. The literature has been constrained in large part by the lack of appropriate (panel) data in transition economies. There is one important exception: Audas and others (2005) follow some 3000 young workers in Hungary over the 1994 to 1998 period. Not surprisingly they find a strong state-dependence in labor market status; that is, the labor market status the previous month is a strong predictor of labor market status the following month. They also find that employment in summer is negatively related to the probability of unemployment. In addition, they provide evidence that academic excellence confers an enduring advantage to young workers.

This paper contributes to the nascent literature on youth unemployment in transition economies by focusing on Bosnia and Herzegovina (BiH), where relatively little is known about the labor market, in general, and youth unemployment, in particular. Relatively few relevant empirical studies exist: these include the first labor market study (World Bank 2002); a study of the gender dimensions of the labor market, (Lokshin and Mroz 2003); the labor market chapter of the World Bank’s _Country Economic Memorandum_<sup>6</sup> ; and a recent note on poverty and the labor market (Tiongson and Yemtsov 2006).

An important recent contribution to the literature on youth unemployment is the update of the 2002 labor market study, _Labor Market Update: The Role of Industrial Relations_ (World Bank 2006). The study notes the “exceptionally high” youth unemployment, their difficulty in obtaining formal sector employment, and the rapid increase in the average age of formal sector workers. It proposes a separate youth minimum wage to facilitate their employment in the formal sector. In addition, the study observes that youth unemployment decreases with education, but enrollment rates are low at higher levels of education, much lower than comparable European countries.

We build on these recent findings to examine more in depth the labor market disadvantage of the youth. In the case of Bosnia and Herzegovina, the difficult transition period is made doubly challenging by the post-conflict environment. Thus, while relatively little is known about them, the longer-term consequences of unemployment and inactivity may have an impact not only on individuals’ future outcomes in the labor market but also on the longer-term stability of the region. Finally, Bosnia and Herzegovina has a rare, but little used, panel data set, one of the few panel datasets in the region. The availability of this dataset allows us to study systematically youth unemployment and its consequences over a four year period.

> 6  See World Bank (2005).

6
<!-- Start of picture text --> a<br>=<br>fal}<br>=oc tin<br>ea&vu<br>3SGI<br>an<br>E=Sin<br>o g<br>z<br>g<br>2 co<br>a<br>&<br>t-1 t t+1 t+2 t+3 t+4 t+5 +6 t+7 t+8 t+9<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> a<br>=<br>fa)<br>ro}xCo in<br>ea<br>Ssc<br>z-<br>Ea4<br>=Sain<br>g<br>Eea<br>26<br>2<br>a,3<br>£ 1"<br>t-1 t t+ t2 3 td t5 t6 +7 t8 t+<br>Horizon, ht (years)<br><!-- End of picture text --> <!-- Start of picture text --> oO<br>=<br>fal}<br>=°<br>&Cavu<br>3GIS<br>a<br>—TM=a<br>ogz<br>g°<br>2<br>a<br>&<br>t-1 t t+1 t+2 t+3 t+4 t+5 +6 t+7 t+8 t+9<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> =<br>an<br>xio}o)<br>&<br>&c<br>z7<br>Ea4<br>=a,<br>g So<br>aEe<br>Z<br>2<br>3a,<br>£ 7<br>tl t t+1 t+2 +3. t4 t+5 +6 7 t8 to<br>Horizon, h (years)<br><!-- End of picture text -->
**Appendix table A2: Covariates contribution to variance explained in percent**

||**N**<br>**variables**|**Pooled**|**Oblas**<br>**t**|**Rayon**|**Old**<br>**rayon**|**Commu**<br>**nity**|**Village**|**Cad.**<br>**zone**|**Cad.**<br>**block**|
|---|---|---|---|---|---|---|---|---|---|
|All variables|30|21.5%|18.8%|16.3%|15.1%|14.3%|12.5%|11.9%|10.8%|
|Area, HA|2|1.2%|1.6%|1.7%|1.7%|1.5%|1.2%|1.0%|1.1%|
|War dummy|1|0.0%|0.2%|0.2%|0.2%|0.2%|0.3%|0.3%|0.3%|
|Year dummy|3|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Missing/below NMV dummy|2|5.6%|6.1%|6.3%|6.0%|6.3%|5.5%|5.6%|5.4%|
|Transaction details|3|0.1%|0.2%|0.2%|0.2%|0.1%|0.1%|0.1%|0.1%|
|Settlements proximity|1|0.3%|0.3%|0.4%|0.2%|0.1%|0.1%|0.0%|0.0%|
|Roads and storage|7|1.2%|0.6%|0.4%|0.3%|0.2%|0.3%|0.2%|0.1%|
|Land use, lag 1 year|4|2.5%|1.7%|1.7%|1.6%|1.5%|1.4%|1.2%|0.9%|
|Soil features|7|4.6%|3.2%|2.0%|1.6%|1.2%|1.1%|0.9%|0.6%|

32
|If there were a disagreement between you and [spouse] on whether or not<br>to sell or lease this parcel, whose opinion do you think would most likely<br>prevail?|Husband, Wife|
|---|---|
|Think about major decisions your household has recently made regarding<br>your land. Do household members talk about the decision together<br>before it is made?|No, the issue was not talked<br>about before the decision is<br>made; Yes, household members<br>may talk about it, but the<br>discussion rarely changes the<br>decision;<br>Yes,<br>household<br>members talked a lot about it,<br>and the discussion can change<br>the decision that is made|
|Which household members are usually involved in the discussion?<br>SELECT ALL THAT APPLY|Husband; Wife; Son; Daughter;<br>Father of title owner; Mother of<br>title owner; Other household<br>member; Other non-household<br>member|
|Who usually brings up the idea and starts the discussion?|Husband; Wife; Husband and<br>wife bring up ideas and start the<br>discussion<br>about<br>equally;<br>Another household member|

# **Appendix Table A3: List of decision and process variables compared in Model 1 Decision-making variables:**

Who generally makes major decisions on this parcel? Who would make the decision to sell or lease this parcel?

# **Process-related variables:**

Conversation usually happens before making decisions about the land<sup>21</sup> Husband is present in conversations about major decisions about the land Wife is present in conversations about major decisions about the land Who usually brings up ideas and starts the discussion about major decisions about the land?

# **Appendix Table A4: List of specific and general decision-making variables compared in Model 2**

# **Specific decisions:**

Who would make the decision to sell or lease this parcel? Who generally makes Buying and selling agricultural inputs such as fertilizer? decisions about... Choosing seeds for food and cash crops? Growing crops for sale and household consumption? Choosing where and how to sell crops? Buying or renting farm equipment? Hiring and paying laborers? Major household expenditures? Off-farm self-employment, such as running a sari-sari store or buy and sell? Paid employment such as wage and salary labor? Rearing livestock? Motorbike 21 This question had three possible answer options (see Appendix 1), but the two affirmative options were collapsed into one given the subjective nature of the differences between them.

33
# **Micro determinants of consumption, poverty, growth, and inequality in Bangladesh**

Quentin T. Wodonl Key words: growth, poverty, inequality, Bangladesh JEL classification: D63, 132, 040 Correspondence: qwodon@worldbank.org 'World Bank (LCSPR). This research was completed as a background paper for the povertv assessment of Bangladesh at The World Bank. The research was made possible through a long term collaborative effort between the Bangladesh Bureau of Statistics and the World Bank. I am especially grateful to Martin Ravallion and Shekhar Shah for discussion on this topic.
EXECUTIVE SUMMARY The rural nonfarm economy accounts for 20 percent<sup>of full-the employment In</sup> Indla's rural economy and for 30 percent of rural<sup>heome. Expanding the definition of</sup> rural to Include rAral _tmns_ up to 1930,000<sup>h size, these shares Increase to 25</sup> percent and 35 percent, respectively. Nontarm activlties are espeially huportant to the poor; landless laborers and mall farmers tyolcally<sup>obtain half or moreofther</sup> Income from nonagricultural actIvitIes. Women are also<sup>active participants, particularly</sup> In food procossig and household marutacturing<sup>activities. Sew In thIs context, It Is</sup> clear that the rural nonfarm econouy will<sup>play a key role In determining future</sup> prospects for employment growth and poverty<sup>allevlatW In India.</sup> II. Dominating the rural nonfarm economy are commerce,<sup>service and small-scale</sup> manufacturing activitlis that cater largely to agrcultural<sup>atid rural consumer demands.</sup> As such, tveir fate Is Intimately linked to agricultural<sup>performance. This can be seen</sup> In the shifting structural composition of the<sup>Indian economy over the.</sup> The nonfarm sharos of national empoyment and Income remahid<sup>ahlost constant for many decades,</sup> but showed a sizeabos Increase In the 1970s.<sup>This chango coincided wlth a perlod of</sup> rapid agricultural growth associated with the<sup>green revolution.</sup> Ill. The relationship can also be seen by comparing<sup>states of varying agricultural</sup> performance. High performance states, such<sup>as PunJab and Haryana, have a greater</sup> density of rural nonfarm activity, a greater density<sup>of rural towns, and proportlonally</sup> more commerce, service and factory manufacturing<sup>than states wlth poorer records of</sup> agricultural productivlty and growth.

Iv. More formally, the relationship between agricultural<sup>growth and growth in the</sup> rural nonfarm economy can be analyzed wlth the aid<sup>of models. Two approaches are</sup> used in thl paper. The first Is an econometric<sup>analysis of cross-sectiona! state- and</sup> district-level data. The second Is based on a<sup>sem-Input- output model fitted to a</sup>

- I -
Figure 5.1: INDIA, evaluation of TFP measures under different imputation methods

# I. Kernel<sup>1</sup> estimates of TFP densities

|0<br>.5<br>1<br>1.5<br>Density of TFP||||
|---|---|---|---|
|-2|-1|0<br>1<br>2<br>log-TFP|3|
|II.|Table of de<br>|scriptive statistics of TFP measures<br> <br> <br> <br>Complete case<br>ICA method<br>Random ICA<br>ICA on inputs<br>EM alg. [1]<br>EM alg. [2]<br>EM alg [3]||
||**# Obs**|**Mean**<br>**Std. Dev.**<br>**Min**|**Max**|
|Complete case|4327|1.15<br>0.68<br>-12.51|12.08|
|ICA meth.|5915|1.17<br>0.98<br>-12.53|12.19|
|Random ICA|5915|1.10<br>1.19<br>-12.51|12.15|
|ICAon inputs|5821|1.16<br>0.95<br>-12.55|12.25|
|Em alg [1]|6848|0.83<br>0.90<br>-12.96|12.09|
|Emalg [2]|5731|1.13<br>0.71<br>-12.66|12.44|
|Em alg [3]|5731|1.13<br>0.71<br>-12.67|12.43|

III. Correlation matrix between TFP measures

||Complete<br>case|ICA meth.|Random ICA|ICA on<br>inputs|Em alg [1]|<br>Em alg [2]<br>Em alg [3]|
|---|---|---|---|---|---|---|
|Complete case|1.000||||||
|ICA meth.|0.999|1.000|||||
|Random ICA|1.000|0.999|1.000||||
|ICA on inputs|0.998|1.000|0.998|1.000|||
|<br>Em alg [1]|0.993|0.995|0.994|0.996|1.000||
|Em alg [2]|0.990|0.991|0.993|0.993|0.997|1.000|
|Em alg [3]|0.990|0.991|0.993|0.993|0.997|1.000<br>1.000|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. Complete case: TFP measure from the sample without replacement of missing values; likewise, input-output elasticities are obtained from estimating equation (1) in the complete case (see I-O elasticities in Table 9.1). ICA method: TFP measure with inputs and output replaced by ICA method and input-output elasticities from Table 8.1. Random ICA: TFP measure with inputs and output replaced by random ICA method and input output elasticities from Table 9.1.

ICA on inputs: TFP measure when only inputs are imputed by the ICA method (not sales), the I-O elasticities and semielasticities used are in Table 9.1.

Em alg. [1]: TFP measure obtained under imputation of inputs and output by the EM algorithm described in section 5.1.1. Likewise, the I-O elasticities are in Table 9.1.

Em alg. [2]: In this case the EM algorithm used is that described in section 5.1.2. The I-O elasticities are in Table 9.1. Em alg. [3]: The description of the EM algorithm is in section 5.1.3. The I-O elasticities are in Table 9.1. Source: Authors’ estimations with ICSs.

83
- [18] Eaton, J, S. Kortum, B. Neiman and J. Romalis (2011), “Trade and the Global Recession”, NBER Working Paper 1666

- [19] Friedman, M. (1968), “The role of monetary policy,”American Economic Review, vol. 58(1), pages 1—17.

- [20] Galí, J. and L. Gambetti (2009), “On the Sources of the Great Moderation,”American Economic Journal: Macroeconomics, vol. 1(1), pages 26—57.

- [21] Galí, J. and L. Gambetti (2014), “The Effects of Monetary Policy on Stock Market Bubbles: Some Evidence,” American Economic Journal: Macroeconomics, forthcoming.

- [22] Harding, D. and A. Pagan (2002), “Dissecting the cycle: a methodological investigation,”Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365—381.

- [23] Holden, S. and F. Wulfsberg (2008), “Downward Nominal Wage Rigidity in the OECD,”The B.E. Journal of Macroeconomics, vol. 8(1), pages 1—50.

- [24] Li, N. (2011), “Cyclical Wage Movements in Emerging Markets Compared to Developed Economies: the Role of Interest Rates,”Review of Economic Dynamics, vol. 14(4), pages 686—704.

- [25] Messina, J., C. Strozzi and J. Turunen (2009), “Real wages over the business cycle: OECD evidence from the time and frequency domains,”Journal of Economic Dynamics and Control, vol. 33(6), pages 1183—1200.

- [26] Messina, J. and A. Sanz-de-Galdeano (2014), “Wage Rigidity and Disinflation in Emerging Countries,” American Economic Journal: Macroeconomics, vol. 6(1), pages 102—133.

- [27] Neftçi, S. N., (1978) “A time series analysis of the real wage-employment relationship,”The Journal of Political Economy, vol. 86, no. 1, pp. 281—291

- [28] Primiceri, G.E. (2005), “Time Varying Structural Vector Autoregressions and Monetary Policy,”Review of Economic Studies, vol. 72, pages 821—852.

23
5 new planting, replanting has declined botia absolutely and relatively since 1981.

Table 1 shows RPBAH, NPBAH and their respective growth rates for the period 1967-1985. It is clear that new planting and replanting are rather similar in their varlability. Figure 5 shows how the ratio of replanting to new planting has varied over the period. **From** 1968 to 1976 new planting rose, though not steadily, relative to replanting. However, between 1976 and 1984 this ter jency was reversed and replanting was almost 40% of new planting in 194; since 1984 replanting has once again sharply declined relative to new planting.

Age Distribution of Trees :The effect of replanting and new planting undertaken in the last two decades has been a major rejuvenation in the age structure of the cocoa tree stock. Table 2, derived from Knight (1976), shows the estimated age distribution of the Bahia tree stock between 1936 and 1966. Accordingly, nearly 60% of the tree stock in 1966 was more than 40 years old. No estimates are available of the age distribution In the recent years. As a rough index or the average age one **may** use the ratio of the surviving pre-1966 planted area to total planted area [AGEINDEX]. This is shown in Figure 6. As a consequence of the vigorous new planting and replanting program, AGEINDEX has dropped nearly 60% in just over a decade. Productivity Using information on the age distribution of trees planted since 1966, together with estimated age ,ield profile data for new plantings, the contribution of new plantJng to total production was separated. By subtraction the contribution of old area can be derived, and
# Figure 3. Waves of debt, 1970-2020

# A. Change in total debt

# B. Annual average change in total debt

<!-- Start of picture text --> Percentage points of GDP Percentage points of GDP<br>90 9<br>60 6<br>30 3<br>0 0<br>1970-89 1990-2001 2002-09 2010-20 1970-89 1990-2001 2002-09 2010-20<br>-30 -3<br>World Advanced EMDEs EMDEs World Advanced EMDEs EMDEs<br>economies excl. China economies excl. China<br>C. Government debt in selected regions D. Share of EMDEs in rapid debt<br>accumulation episodes<br>Percent of GDP Percent of EMDEs Private debt only<br>100 100 Government and private debt<br>EAP ECA LAC SSA<br>Government debt only<br>75 75<br>50 50<br>25 25<br>0 0<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Sources: Kose et al. (2021); World Bank.

A.B First wave covered the period 1970-1989; second wave from 1990-2001; third wave from 2002-2009; and fourth wave from 2010 onwards. Data are available for up to 191 countries, including 39 advanced economies and 152 EMDEs.

A. Change in total debt-to-GDP ratios from the start to the end of each wave.

B. Rate of change calculated as total increase in debt-to-GDP ratios over the duration of a wave, divided by the number of years in a wave.

C. Averages computed with current U.S. dollar GDP as weight. Line for ECA starts in 1995 due to smaller sample size prior to that year.

D. Share of EMDEs which are in rapid debt accumulation episodes, as discussed in Kose et al. (2021).

23
in migration (Lucas, 2001). The focus on distance as proxy for migration deterrent in this study, is further motivated by the work of Ingelaere et al. (2018), who conduct detailed life history interviews with 75 respondents sampled from the Kagera Health and Development Survey (KHDS), the same survey underlying the analysis in this paper. They show how respondents themselves, when narrating their life histories, talked about distance as an impediment to moving: it raises the cost of moving, it complicates returning home to visit or when things go wrong at destination, it makes it difficult to maintain ties, can erode informal property rights and typically comes with higher cultural barriers to moving.

# **3.2 Econometric specification**

If we are to understand destination choice among migrants then it is as important to know where the migrant moved to as it is to know which potential destinations the migrant did _not_ migrate to. Within the census we can identify 78 locations that were destinations for KHDS migrants, representing either urban or rural locations in 57 districts. Our analysis makes the assumption that these 78 locations are the potential destinations for our sample of migrants. They revealed themselves as practically feasible destinations for at least one of our migrants.

Each of the migrants in our sample has chosen to move to one potential destination; and has therefore also chosen _not_ to move to the 77 other potential destinations. In order to understand that choice better, we create a dyadic data set that contains 78 observations for each migrant _i_ ; one observation for each potential destination _d_ . Our dependent variable _Yid_ is a dummy equal to one if _i_ was found in location _d_ during the last survey round and zero otherwise. Our analysis will consist of studying the correlates of _Yid_ . This kind of dyadic analysis has been used before to understand destination choice by Fafchamps and Shilpi (2013). We follow these authors in analyzing only those who have actually migrated in order to not confound the destination choice with the migration choice. We also analyze separately those who have moved to rural areas and those who have moved to urban areas.

Destination choice _Yid_ will depend on the observed and unobserved characteristics of the individual (including those related to household and community circumstances), the desti10
<!-- Start of picture text --> a<br>3° H :<br>2 1 i H ' H<br>5“<br>23 - iH 'H HH<br>7 : TMi §e =<br>EA LAC MENA SA | SA SA "SA OSSA<br>hygiene sanitation water quality multiple<br>GGG Standard Error EG) pcorr<br><!-- End of picture text -->

<!-- Start of picture text --> © ' :<br>S= H i<br>34 i-: mm OS:<br>v7 — to<br>5<br>z i; 7 = i-<br>° = - S———-<br>°<br>SA SSA EA LAC SSA LAC MENA SSA<br>sanitation water supply multiple<br>[EG Standard Error EE peor<br><!-- End of picture text --> <!-- Start of picture text --> 2 ‘ ' $<br>Be : ' :<br>Ps H ' :<br>&<br>=go> ' ' H<br><<br>oa ' ' '<br>5 : ey '<br>2 ; ’ '<br>c= bs . ’<br>S| — ¢ : H<br>a3 ant‘' ,(] roaiio: iH: ]<br>| ~; aM 0 -~_ bls,<br>EA LAC SA SSA WENA SA LAC SA_ SSA<br>hygiene — water quality water supply multiple<br>GE) Standard Error = EE) pon<br><!-- End of picture text -->

<!-- Start of picture text --> 2 4<br>@ H<br>Sa :<br>en<br>= '<br><7] —<br>2<br>5 '<br>2 _ \<br>= '<br>ogo5 — —-}'i —<br>*<br>East Asia MENA Sub-Saharan Africa<br>hygiene multiple<br>(EG Standard Error ~=EE) por<br><!-- End of picture text --> <!-- Start of picture text --> c;HH<br>2' ' ‘<br>s : : :<br>52 ' ' '<br>ne ry ‘ '<br>at . H : '<br>3 ri P<br>20 H H '<br>3 : r en a =<br>iS5 wx us F: a: il: -<br>Bo] ei - 73 et i i<br>a Pa dt<br>A ASA BH UAC MEMS SHA OA LA SA SSA OBA LAC MENASA SSA<br>hygiene sanitation —vaterqualty voter supply mutiple<br>GE StandardError EE) pon<br><!-- End of picture text -->
countries such as Brazil, Mexico, China and Russia and there were only a few documents on the subject from the Middle East North Africa or South Asia. It is unclear whether the lack of literature in these regions is due to lack of importance of SPSPs or a result of low priority accorded by sector practitioners.

# **3.2  Summary of Literature Review:  Electricity SPSPs**

For electricity, literature from 32 countries with SPSPs was reviewed, with a focus on dealers of solar panels and other household electricity generating equipment as well as networked services.<sup>9</sup> The level of SPSP activity is high in 15 of these countries, primarily in rural areas and small settlements. Data on micro-power systems show that the total number of such systems in developing countries has more than doubled over the past decade. This gradual expansion took place over a period in which public investment in power was declining and private funding for large-scale investments was inconsistent. An estimated 10-50 million clients are served by the 7,000 SPSPs currently operating in the electricity sector<sup>10</sup> . About 85 percent of these are in Asia. In Africa, the Middle East and Latin America the number of SPSPs identified in each region was less than 500. In Latin America and the Caribbean, SPSP activity, particularly in large countries such as Brazil, was focused largely in remote regions of the country and provided only a small fraction of electricity service. In Africa, SPSP activity was largely limited by access to financial markets, high transaction costs, and the monopolistic rights granted to national utilities. That said, a few post-conflict countries such as Mozambique demonstrated a notable incidence of SPSPs. The maps in Annex 1 shows countries which were found to have significant levels of SPSP activity in water supply and electricity according to available literature.

> 9 For the purposes of this study, literature on battery recharging businesses, categorized as “point source” systems in the typology, was not reviewed.  This level of service is not considered comparable to delivered electricity or that available from diesel and photo-voltaic home-based systems.

> 10 As in the water supply sector overall numbers of small-scale providers, including community and public systems would be much higher.  In China alone there could be 40,000 such systems, of which SPSPs comprise an estimated 1,000.

19