rafmacalaba/datause-encoder-data huggingface dataset:
The term te requires some explanation. When **the** central bani makes transactions **ac** more than one exchange rate, there will **generally** be a difference betw en the central bank's valu ation of foroLgn exchange acquired through lntervention and the amount of domeste curreney actually used in the _Latervention._ **We** denote by 9 the excess of the fore r over the latter. In period 1, for example, **9** is given by (10) * ( a * )IL*J - 2 -

**We** asoum in equation (9) that both * and Literest on the centrol bank's foreign exchange holding are transferred directly to the governmnt account and therefore reduee domsatLc credit requirements one-for-one - In real terus, and using the shorthand v(t) v(ut/(l+st)yt), the finane constraLit reads

(1l , - t , -t a [e- t.Ct (I-v(t))Ytl + r (bG, ftl _bG_ t b The terminal constraints facing the government are bg, f2 t 0. ImposLng these with equallty, and eliminating pt from (8) and (11), we have the following consolLdated government budget constraints for periods 1 and 2: (12a) - 8 1 -l s 1 [c-(lv(l))My 1 [a - g° I (f 1 + b - (<sup>+r</sup> 1 1<sup>)(fo</sup> + bo) 8
# Annexes

## Annex 1

### **Synthetic panel methodology**

**Economic mobility has gained relevance in academic and wider policy discussion over the last years.** Despite significant reductions of economic poverty during the early 2000s in most countries in Europe and Central Asia, few studies currently exist on this process. Did poverty rates decrease as poverty reduction was a one-way street with households predominantly escaping poverty? Or is there significant churning with large numbers of households falling back into poverty offsetting transitions out of poverty? From a policy perspective it makes a large difference if a country which reduced poverty by 5 percent experiences either of these scenarios since each requires very different policy responses. Here, estimates of economic mobility allow for a better design of policies that can support economic development in a sustainable and inclusive way.

**Data requirements to analyze patterns of economic mobility are not trivial and require the use of innovative methodologies.** A proper study requires household-level information for one and the same household for at least two periods, not only for income or consumption, but also for other variables that can affect changes in income or consumption. Unfortunately, the availability of panel surveys that contain this type of information is quite limited, and even when existent, many times they suffer from high attrition rates and relatively short survey periods.

**The synthetic panel methodology overcomes these shortcomings and builds on an imputation methodology to construct synthetic panel data with predicted consumption using two different rounds of cross sections (Figure A1).** This way a new data set is created which provides information on consumption for one and the same household in two different time periods. The approach relies on time-invariant individual and household characteristics. Consumption in each period is modeled as the sum of two components: a first one associated with time-invariant characteristics, and a second one capturing non-observable factors. To create the predicted consumption in the second round ( _Ĉ2_ ) for households whose consumption was only observed in the first round ( _C1_ ), we generate a new component based on how their time- invariant characteristics are associated with consumption, but in the second round ( _F2(X1)_ ). Adding up this new component to the non- observable factors ( _ê2_ ) we obtain the predicted consumption in round two. With these two consumption measures ( _C1 and Ĉ2_ ) we construct transition matrices to analyze patterns of economic mobility between two rounds.

**Depending on the assumptions made about the non- observable characteristics, the method generates a high and a low mobility scenario.** For the low mobility scenario, non- observable characteristics do not change in time (such that the correlation of ê2 and e1 is 1), whereas for the high mobility scenario they change between rounds (i.e., the correlation of ê2 and e1 is 0). More

43
_<mark>Consumption Expenditures.</mark>_ <mark>Ruiz, I., & Vargas-Silva, C. (2013). The economics of forced migration.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>49</mark>_ <mark>(6), 772-784.</mark> <mark>Rutstein, S. O. (2015). Steps to constructing the new DHS Wealth Index.</mark> _<mark>Rockville, MD: ICF International</mark>_ <mark>.</mark> <mark>Taylor, J. E., Filipski, M. J., Alloush, M., Gupta, A., Valdes, R. I. R., & Gonzalez-Estrada, E. (2016). Economic impact of refugees.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>,</mark> _<mark>113</mark>_ <mark>(27), 7449-7453.</mark> <mark>UNHCR. (2018).</mark> _<mark>UNHCR Global Trends: Forced Displacement in 2018.</mark>_ <mark>Geneva: UNHCR.</mark> <mark>Verme, P., & Schuettler, K. (2021). The impact of forced displacement on host communities: A review of the empirical literature in economics.</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 102606.</mark> World Bank Group, & UNHCR. _(2015). Forced Displacement and Mixed Migration in the Horn of Africa._ World Bank. (2018). _Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle_ . Washington, DC: World Bank.

<mark>Zetter, R., & Ruaudel, H. (2016).</mark> _<mark>Refugees’ right to work and access to labor markets–An assessment.</mark>_ <mark>KNOMAD Working Paper.</mark> Figure 1: Number of refugees hosted in Ethiopia 35
# **Appendix A: Calculating Earnings**

Self-employment income was calculated in the weekly survey by summing the responses to the questions “How much of that money was for work you did over the past 7 days?” (the follow-up to the question “How much money have you received over the past 7 days?”) and “How much more do/did you expect to receive for the work you did over the past week? Please do not include money that you received over the past week.” Reported costs were then subtracted from these revenues, using the answer to the question “How much were the costs for only the past 7 days (if the costs are for a machine for example, only include the cost of using the machine for a week)”, which was the follow-up to the question “Did you have any costs for goods or equipment that were needed for your work over the past week?”.

Wage employment income was calculated by multiplying days of work by the respondent’s usual daily wage rate, as calculated from the baseline reports of their usual weekly pay “On average, how much do you earn (or, if in kind, what is the value of what you earn) in your primary job, in a week?” divided by their usual days of work, given by their response to the question “How many days do you work in your primary job during a normal week?” unless the respondent answered yes to the question “Since the last interview, did the way that you calculate the amount of work you get paid for change?” If so, they were asked for the earnings in this new job in a normal week, and this rate was multiplied by the number of days worked that week to the calculate earnings. Note that this calculation means that workers who are paid less frequently than on a weekly basis – 73 percent of wage workers – are assigned the fraction of their weekly pay that corresponds to the number of hours worked per week.

38
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35
# **Appendix S1. Alternative price indices**

Food prices can be measured in different ways. The analysis in this paper uses the consumer food price index relative to the total price index to measure changes in real food prices. Alternative aggregated price indices include the FAO producer price index for all agricultural commodities or all cereals, or the agricultural GDP deflator. All three indices uses production data to weight the indices, and all are deflated using the total GDP deflator to capture real price changes. All are also available for a reasonably large array of countries, although the FAO producer price indices are only available after 1990, and are not available for all countries. These series are also widely regarded as being prone to substantial measurement error, and the same is likely true of the agricultural GDP deflator, since national accounting systems are quite poor in many African countries in particular. Finally, international price indices were also available to us. We constructed country-specific indices of international prices based on weighting prices for nine food staples by domestic consumption patterns, as measured by the share of calories obtained from each of these food items. Since international prices are exogenous, we use this price series as an instrument in most of our regressions.

Table A1 shows a correlation matrix of these various price indices, all measured in real terms using either the total CPI or the total GDP deflator to convert the nominal indices into real price indices. Strikingly, the correlation between the domestic price indicators is often quite weak. The correlation between the real consumer food price index and the agricultural GDP deflator is moderately strong (0.47), but the FAO agricultural and cereal price indices are quite weakly correlated with both consumer prices and the agricultural GDP deflator, suggesting these FAO series may include substantial measurement error. Perhaps most pertinent – at least in terms of our instrumentation strategy – is that it is the consumer food price index which shares the strongest correlation with the two international prices indices (0.31-0.34). This suggests that consumer food price indices are quite sensitive to international prices relative to the other indices.

23
# **Table 7**

Dependent Variable: Female Employment

|VARIABLES|(1)<br>OLS-FE|(2)<br>OLS-FE|(3)<br>IV-FE|(4)<br>IV-FE|(5)<br>IV-FE|(6)<br>IV-FE|(7)<br>IV-FE|(8)<br>IV-FE|(9)<br>IV-FE|(10)<br>IV-FE|
|---|---|---|---|---|---|---|---|---|---|---|
|POST|-0.01|0.18*|0.76|0.76|0.12|0.57**|4.27|0.63|0.12|0.57**|
||(0.13)|(0.10)|(2.47)|(0.67)|(0.26)|(0.23)|(5.67)|(0.92)|(0.26)|(0.23)|
|Time|0.11**|-0.02|0.08|-0.09|0.15**|-0.07*|-0.29|-0.08|0.15**|-0.07*|
||(0.05)|(0.02)|(0.26)|(0.09)|(0.06)|(0.04)|(0.59)|(0.11)|(0.06)|(0.04)|
|Timesq|-0.00|0.00*|0.00|0.01|-0.00|0.01**|0.02|0.01|-0.00|0.01**|
||(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|(0.00)|(0.03)|(0.01)|(0.01)|(0.00)|
|WorkingAgePop||0.00***<br>(0.00)||0.00***<br>(0.00)||0.00***<br>(0.00)||0.00***<br>(0.00)||0.00***<br>(0.00)|
|rGDP||-0.00||0.00||0.00||0.00||0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|LFPRate||0.04***||0.07*||0.07**||0.07*||0.07**|
|||(0.01)||(0.04)||(0.04)||(0.04)||(0.04)|
|Observations|800|785|575|575|575|575|575|575|575|575|
|Number of Countries|70|70|60|60|60|60|60|60|60|60|
|R-squared|0.29|0.82|||||||||
|Partial R-squared|||0.01|0.01|0.29|0.29|0.00|0.01|0.29|0.29|
|Hansen J p-value|||0.46|0.42|0.69|0.56|0.51|0.28|0.42|0.44|
|Kleibergen-Paap F<br>statistic|||1.09|1.10|30.88|36.78|0.52|0.62|32.62|42.95|

Note: Robust and clustered standard errors at the country level are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. POST is instrumented with the debt-to-GDP ratio, terms of trade, and five-year change in democracy score in columns (3) and (4). In columns (5) and (6) the instruments also include lagged POST. POST is instrumented with external debt-to-GDP ratio interacted with the change in world interest rates, terms of trade, and five-year change in democracy score in columns (7) and (8). In columns (9) and (10) the instruments also include lagged POST.

33
productivity in recent decades (International Food Policy Research Institute (IFPRI); 2012; Jin, Huang, Hu and Rozelle, 2002). For instance, a study by the IFPRI (2012) documents that more than one-third of the increase in global public agricultural R&D spending between 2000 and 2008 was attributable to Chin <mark>a.</mark><sup>21</sup> <mark>However, there have been some concerns expressed about measurement problems with China’s livestock production, an issue that is discussed in more detail in the Appendix.</mark>

# **3.4. Supply and Demand Balance for Food**

Figures 10a-c compare how the historical patterns of CE production and consumption differ depending on land endowments, translating the differences in endowments into differences in the income response curves based on the parameter results reported in Table 3 (regression (2)). Figure 10a plots actual CE production and consumption points for China for the period 19802009 along with estimated global CE production and consumption trend curves. The global production schedule is evaluated at China’s land endowment (0.21 hectare per person on average). Figure 10a demonstrates the differing growth patterns of CE production and consumption. At early stages of development, demand for food tends to grow faster than production, widening the gap between supply and demand. As incomes grow, the growth of consumption will slow down relative to growth of production and the gap will begin to close.

Figure 10a demonstrates that, at the onset of the reform, China’s CE food production and consumption grew together, albeit from a very low level, at a much faster rate than the global trend, most likely reflecting the impacts of institutional reforms (Rozelle and Swinnen, 2004). China’s production fell slightly below its consumption from around 2000, even though China’s

> 21 During the period 2000-2008, global public agricultural R & D spending increased by $5.6 billion from $26.1 to $31.7 billion in 2005 PPP prices. 38 percent of the increase ($2.1 billion) was accounted for by China (IFPRI, 2012).

21
Table 10: Trade, Legal Origins <u>and Property Rights, 5-year averages</u> Dep. Var.: log of average trade flow

||(1)<br>OLS|(2)<br>IV|
|---|---|---|
|Years in Average|19|95-1999|
|Different Legal Origin|-0.30|-0.39|
||(-6.42)|(-7.27)|
|Protection of Property<br>|0.29|0.58|
|Rights|(16.20)|(8.29)|
|log distance|-1.27|-1.30|
||(-45.90)|(-44.91)|
|log product of GDP|1.04|0.94|
||(92.83)|(37.87)|
|log product of land area|-0.11|-0.05|
||(-12.24)|(-2.84)|
|no. of landlocked|-0.43|-0.49|
||(-10.72)|(-11.70)|
|no. of islands|-0.22|-0.38|
||(-4.67)|(-6.40)|
|common border|1.14|1.18|
||(7.81)|(7.79)|
|common language|0.34|0.18|
||(5.86)|(2.54)|
|currency union|1.32|1.42|
||(5.38)|(5.50)|
|Observations|8632|8632|
|R^2|0.72|0.71|
|First stage R^2||0.43|
|% increase in trade due<br>to 1s.d. institution|54%|139%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant is not reported. Each column reports a regression performed on one cross-section of 5-year averages. The averages are taken over all _available_ years in the 5-year sub-sample, and the procedure takes into account missing year observations. IV estimators use the sum of absolute latitude as an instrument for the sum of property rights for both countries in a trading pair. The sum of property rights ranges from 0 to 12 with mean 4.8 and standard deviation 1.5, in 1995-99. The percent increase in trade due to 1s.d. institution (property rights) is calculated as follows: exp(coef*sd)-1.

45
**Table 8. Labor Transitions and Income Growth, Linear Model with Individual Random Effects, RLMS 1994-2015**

|**Occupation category**|**Variable**||**Pe**|**riod**||
|---|---|---|---|---|---|
|<br>**_Panel A_**||**1994-1998**|**1998-2004**|**2004-2009**|**2009-2015**|
||Transition to category|0.090**|0.091***|0.100***|0.053***|
|**Fllti lt**||_(0.04)_|_(0.02)_|_(0.02)_|_(0.01)_|
|**u-me empoymen**|No transition within category|0.073***|0.096***|0.096***|0.067***|
|||_(0.03)_|_(0.02)_|_(0.01)_|_(0.01)_|
||Transition to upper category|0.008|0.126***|0.102***|0.064***|
|**Ud kill bilit**||_(0.05)_|_(0.03)_|_(0.02)_|_(0.01)_|
|**pwar ss moy**|No transition within category|-0.061*|0.066***|0.048***|0.037***|
|||_(0.03)_|_(0.02)_|_(0.01)_|_(0.01)_|
||Transition to category||0.214***|0.010|0.064***|
||||_(0.07)_|_(0.03)_|_(0.02)_|
|**Formal sector**|No transition within category||0.131***|0.020|0.093***|
||||_(0.04)_|_(0.02)_|_(0.01)_|
||Transition to category|||-0.086***|-0.045***|
|||||_(0.02)_|_(0.01)_|
|**Public sector**|No transition within category|||-0.080***|-0.052***|
|||||_(0.01)_|_(0.01)_|
|**_Panel B_**||**1994-2004**|**2004-2015**|**1994-2015**||
||Transition to category|0.089***|0.072***|0.081***||
|||_(0.02)_|_(0.01)_|_(0.01)_||
|**Full-time employment**|No transition within category|0.086***|0.078***|0.083***||
|||_(0.02)_|_(0.01)_|_(0.01)_||
||Transition to upper category|0.078***|0.073***|0.081***||
|**Ud kill bili**||_(0.02)_|_(0.01)_|_(0.01)_||
|**pwar ss moty**|No transition within category|0.019|0.043***|0.041***||
|||_(0.02)_|_(0.01)_|_(0.01)_||
||Transition to category||0.043***|0.067***||
||||_(0.02)_|_(0.02)_||
|**Formal sector**|No transition within category||0.069***|0.078***||
||||_(0.01)_|_(0.01)_||
||Transition to category||-0.056***|||
||||_(0.01)_|||
|**Public sector**|No transition within category||-0.049***|||
||||_(0.01)_|||

**Note** : *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in parentheses. The estimation sample is restricted to individuals who are 18 years old and older. The dependent variable is log of household income per capita in year _t_ . Incomes are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. All control variables are measured in the reference year _t-1_ except for the occupation transition variables, which are the changes between year _t-1_ and year _t_ .

41
**Table 3: Estimated benefit withdrawal rates using OLS**

||Whole sample|Participants only|
|---|---|---|
|Simple OLS regression|-0.065***|-0.065***|
||(0.012)|(0.023)|
|With year effects|-0.072***|-0.067***|
||(0.014)|(0.025)|
|With both year and household|-0.031***|-0.049***|
|fixed effects|(0.008)|(0.021)|

<u>Note: Regression coefficient of DB receipts on income net of DB. Standard errors in parentheses. ***: Significant at</u> < 1%. “Participants” are defined as those who participated at least once.

**Table 4: Instrumental variables regressions using lagged exogenous income sources as IVs**

||Whole<br>|sample<br>|Participa<br>|nts only<br>|
|---|---|---|---|---|
|Income net of DB|-0.1205***|-0.1179***|-0.1446***|-0.1377***|
||(0.009)|(0.009)|(0.020)|(0.019)|
|Lagged income net|n.a.|-0.0022|n.a.|-0.0047***|
|of DB||(0.010)||(0.001)|
|No. obs.|3935|3935|2245|2245|
|F|178.91|95.69|55.73|84.46|
|(prob.)|(0.000)|(0.000)|(0.000)|(0.000)|

<u>Note: BWR estimated by regressing DB payments on income net of DB using lagged formal salary and lagged</u> property income as the IVs. Robust standard errors in parentheses.***: Significant at < 1%.

23