rafmacalaba/datause-encoder-data huggingface dataset:
_Reduced growth of earnings was the main culprit for the slowdown in the wage bill._<sup>**11**</sup> Real earnings growth began to decline in the first quarter of 2008, as increases in food and fuel prices led to accelerating inflation. The sharp decline in GDP growth in the fourth quarter of 2008 was mirrored by a relatively large decline in nominal earnings growth: nearly three quarters of the 8.7 percentage point slowdown in the wage bill, among countries for which data is available, came from the reduction in earnings growth **,** which fell 6.5 percentage points. This is largely consistent with the evidence from past crises, as pointed out in the introduction (For more details see footnote 1). The simultaneous drop in inflation of 2.8 percentage points, due to easing demand for commodities, prevented an even steeper drop in the wage bill and led to a small **increase in th** _e_ rate of real wage growth of 0.7 percentage points (Figure 2).

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>Hours growth<br>CPI growth<br>-5<br>Real earnings growth<br>Nominal earnings growth<br>-10<br>Percentage<br><!-- End of picture text -->

**Figure 2: For 8 countries - Hours pulled down earnings, while prices mitigated the impact**

_Hours fell considerably in several countries._ The slowdown in hours tracks closely the reduction in earnings growth (See Figure 2).<sup>12</sup> On average, in the 14 countries for which data are available, growth in hours worked fell by a striking 7 percentage points but the drop was highly concentrated in only half of these countries (Figure 3). The decline in hours worked not only accounted for the entire decline in earnings but also allowed for a slight increase in real hourly wages (0.7 percentage points) (Figure 4). However, the extent to which the reduction in hours was an active policy response rather than simply an adjustment mechanism is unclear. For example, an ILO survey of 54 countries (10 LICs, 10 lower middle income, 17 upper middle income and 17 HICs) shows that reductions in hours worked was a common response to the downturn in countries as different as China, Vietnam, the Philippines, Indonesia, Mexico, Argentina, Colombia and Jordan (ILO, 2009).

> 11 Table A1 in the appendix shows that these conclusions hold when examining pre and post-crisis average growth in a slightly larger sample of 28 countries that report quarterly, semi-annual, or annual data.

> 12Figure 2, however, underestimates the extent of the fall in hours, because it only reflects the 8 countries that report hours quarterly

5
complexity, and hence specialist knowledge, associated with an ever-increasing scope of publicly-financed decision-making, has the effect of increasing the prominence of NEDDs relative to EDDs. Formally, as the size of the public sector increases:

> where  denotes a large public sector and _l s_ denotes a small public sector. Given that increments in government size are associated with increased complexity, and hence relatively more policy-making power for NEDDs, it follows that:

(1.19) That is, the EDD/NEDD ratio in large states is less than the EDD/NEDD ratio in small states. If the model is correct, it follows that countries with smaller public sectors and hence a higher EDD/NEDD ratio will experience rent extraction. As Figure 1.01 below shows, high-income democracies with relatively small public sectors appear to experience less rent extraction<sup>19</sup> vis-à-vis high-income democracies with larger public sectors. For ease of interpretation, the x-axis measures the value of the private sector, i.e. the relative size of the private sector vis-à-vis the size of the public sector (as a percentage of GDP). Essentially, this relative measure of the size of government is calculated by subtracting 100 from the size of public sector expenditure (measured by the size of public expenditures as a percentage of GDP). A country’s relative size of the ‘private sector’ will obviously be smaller as the size of the public sector is larger, as 100 minus an ever-larger number yields an ever-smaller outcome<sup>20</sup> . Therefore the measure generates a simple cardinal measure of public sector size that is easy to interpret and easy to convert into a fraction (simply dividing the final score by 100 – critical when combining the different measures of limited government into an index; see below)<sup>21</sup> .

> 19 Measured by the standard error weighted averaged Corruption Perceptions Index (CPI) score of countries between 19952000 (higher scores denote less rent extraction).

> 20 Obviously, as this measure is an inversion of public sector size it is perfectly and negatively correlated (-1.00) with the size of the public sector as a percentage of GDP.

> 21 As in all the countries in the sample, the level of government expenditure is not equal to zero (or less) and is not greater than 100. The level of government expenditure is the inverse of the size of government as a percentage of GDP.

13
|**TABLE 10: SATISFA**<br>**INTERVIEWS†**<br>Panel A|**CTION WITH**<br>(1)<br>Overall<br>satisfaction|**ANTENATAL**<br>(2)<br>Reasonable<br>registration|**CARE CON**<br>(3)<br>Reasonable<br>lab fees|**SULTATIONS**<br>(4)<br>Reasonable<br>medicine|**REPORTED**<br>(5)<br>No<br>additional|**DURING F**<br>(6)<br>Clean<br>health|**ACILITY EXIT**<br>(7)<br>Reasonable<br>wait time|
|---|---|---|---|---|---|---|---|
||score|fees||fees|payment|facility||
|Post indicator|0.006|0.055|0.074|0.088|0.010|-0.045|-0.009|
||[0.034]|[0.097]|[0.075]|[0.090]|[0.060]|[0.075]|[0.079]|
|PBF/Post interact|0.086*|0.037|0.154|0.190|-0.043|0.241**|0.161|
||[0.048]|[0.128]|[0.113]|[0.134]|[0.078]|[0.111]|[0.115]|
|Control<br>1/Post|0.051|-0.051|-0.051|-0.069|0.067|0.228**|0.014|
|interact|[0.044]|[0.142]|[0.129]|[0.121]|[0.092]|[0.106]|[0.129]|
|Control<br>2/Post|-0.019|-0.085|-0.027|-0.127|-0.020|0.002|-0.055|
|interact|[0.049]|[0.127]|[0.127]|[0.118]|[0.087]|[0.111]|[0.134]|
|p-value PBF vs. C1|0.419|0.523|0.129|0.044|0.230|0.903|0.269|
|<br>p-value PBF vs. C2|0.036|0.309|0.185|0.015|0.776|0.040|0.127|
|<br>p-value PBF vs. C3|0.077|0.774|0.176|0.158|0.586|0.032|0.163|
|Baseline mean C3|0.853|0.804|0.782|0.754|0.885|0 .787|0 .738|
|N|730|669|665|689|723|730|727|
|Panel B|(8)|(9)|(10)|(11)|(12)|(13)||
||Enough<br>privacy<br>during visit|Adequate<br>hours|Courteous<br>health staff|Good<br>health<br>worker<br>communic|Sufficient<br>visit time<br>with health<br>worker|Easy to<br>get<br>prescribed<br>medicines||
|||||ation||||
|Post indicator|-0.025|-0.026|-0.041|-0.031|0.055|0.018||
||[0.061]|[0.041]|[0.036]|[0.024]|[0.052]|[0.068]||
|PBF/Post interact|0.042|0.154**|0.037|0.106**|-0.045|0.000||
||[0.086]|[0.071]|[0.063]|[0.050]|[0.074]|[0.079]||
|Control<br>1/Post|0.149|0.032|0.070|0.039|0.139*|0.041||
|interact|[0.093]|[0.055]|[0.053]|[0.052]|[0.080]|[0.085]||
|Control<br>2/Post|-0.010|-0.079|0.019|0.101|0.030|-0.040||
|interact|[0.098]|[0.062]|[0.051]|[0.077]|[0.101]|[0.075]||
|p-value PBF vs. C1|<br>0.254|<br>0.073|<br>0.599|<br>0.308|<br>0.026|<br>0.488||
|<br>p-value PBF vs. C2|0.605|0.002|0.783|0.961|0.443|0.402||
|<br>p-value PBF vs. C3|0.629|0.033|0.556|0.038|0.544|0.997||
|<br>Baseline mean C3|0 .902|0.900|0 .967|0 .951|0 .852|0 .883||
|N<br>* = p < 0.10, ** p < 0.05,<br>†Results from differenc|728<br>*** p< 0.01<br>e-in-difference|724<br>s regression m|730<br>odels examini|725<br>ng the effect of|728<br>PBF on satis|716<br>faction with|antenatal care|

<mark>† Results from difference-in-differences regression models examining the effect of PBF on satisfaction with antenatal care components reported by patients during facility exit interviews</mark> . <mark>Regression models adjusted for individual (age, literacy, marital status, education level) and facility-level control variables (type of health facility public/private/religious, urban/rural). Standard errors were clustered at the health facility level.</mark> 43
- 25 -

Table **4:** Projections of Mexican Sugar Production, Consumption, Trade a d **World** Prices, 1985-2004 (Unchanged Policy Scenario)

|Year|Production|Consumption|Not Exports|Year-End<br>Stocks|<br>World Price|World Price<br>Sim. i a/|**Cane**Price|
|---|---|---|---|---|---|---|---|
||-<br>----|--<br>(illion|tons)-------|----------|------(1984 U|S¢/lb)------|(1180 pesos/ton)|
|1985|3.45|3.31|0.726|0.76|7.9|6.7|448|
|1986|3,75|3,44|0.248|0.83|6.6|5.1|483|
|1967|3.98|3.63|0.394|0.88|9,6|7.4|435|
|196|4.29|3.67|0.551|0.94|10.7|12.6|330|
|1989|4.00|3.80|0.26|0.88|15.1|14.4|350|
|990|3.94|3.94|0.016|0,87|15.5|18.6|393|
|1991|3.98|4.08|-0.108|0.88|12.8|1o.7|393|
|1992|4.01|4.23|-0.223|0.88|15.7|17.5|393|
|1993|4.25|4.38|-0.185|0.94|26.7|17.9|393|
|1994|4.63|4.54|-0.001|1.02|24.7|26.8|393|
|1995|4.70|4.71|-0.029|1.03|15.4|25.5|393|
|1996|4.77|4.88|-0.128|1.05|12.3|9.7|393|
|1997|4.88|5.07|-0,209|1.07|13.3|9.8|393|
|1998|4.98|5.25|-0,292|1.10|15.8|16.4|393|
|**1999**|**5.09**|5.45|-0.382|1.12|16.3|21.0|393|
|_2000_|5.19|5,65|-0.481|1,14|15,6|25.5|393|
|2(01|5.07|5,86|-0.761|1.12|20.8|24,2|393|
|_2002_|5.40|6.08|-0.753|1.19|23.7|22,6|393|
|2003|5.50|6,31|-0.83|1.21|19.9|30.6|393|
|2004|5.60|6.55|-0.964|1.23|17.3|25,4|393|
|Mean o|f 1200 observ|dtions|||15.8|||
|Coeffi|cient of Vaci|dtion|||0.45|||
|Range|Min-Max o! 60|simulations|||3.1-50.1|||
|Sof O|bservationsih|ithe Range|||8.8-21.8x67|||

- a/ Simulation I wd'. the first ot ilie 60 stochastic simulations and was included to illustrate a moro realistic variability of prices than is evident in the averages of all 60 simulations given in the preceding columns.
poor in a country such as China come from household surveys carried out by the statistical bureau, surveys that aim to measure what households actually consume. Most of the extreme poor in the world are peasants, and they subsist to a large extent on their own agricultural output. To look only at what money income they have would not be very relevant, since the extreme poor have only limited involvement in the money economy. Thus, what Chinese poverty measures try to do is ask households what they actually consume, and attach a value to this based on prices of different commodities. So, a poverty line is meant to capture a certain real level of consumption. Estimating the extent of poverty is obviously subject to error, but in many countries the measures are good enough to pick up large trends. In discussing poverty it is important to be clear what poverty line one is talking about. In global discussions one often sees reference to international poverty lines of either $1 per day or $2 per day, calculated at purchasing power parity. For discussions of global poverty we need to choose a common line to apply to all countries.

Chen and Ravallion (2004) have used household survey data to estimate the number of poor worldwide based on the $1 and $2 poverty lines, back to 1981. They find that the incidence of extreme poverty (consuming less than $1 per day) has basically been cut in half in 20 years, from 40.3 percent of developing world population in 1981 to 21.3 percent in 2001. It is interesting that the decline in $2 per day poverty incidence was not as great, from 66.4 percent to 52.9 over this period. In 1981 extreme poverty was concentrated in East and South Asia, and these are the regions that have grown especially well, dramatically reducing extreme poverty.

17
|TABLE6. Decompos|ition of the Public|-Private Wage|GapBased on|our Simulati|ons|
|---|---|---|---|---|---|
||1993|1998|2002|2006|Change<br>1900–2000s|
|**Real Gap**|0.21|0.21|0.48|0.53|0.30|
|**Simulation 1**||||||
|Price effect|−0.36|−0.23|0.11|0.18|0.44|
|Selection|0.56|0.44|0.37|0.35|−0.14|
|**Simulation 2**||||||
|Price effect|−0.08|−0.01|−0.06|0.03|0.03|
|Selection|0.28|0.22|0.54|0.51|0.27|
|**Average 1–2**||||||
|Price effect|−0.22|−0.12|0.02|0.10|0.23|
|Selection|0.42|0.33|0.45|0.43|0.07|

_Source:_ Author's calculations based on VLSS and VHLSS data.

Compared with the Oaxaca-Blinder method, our model offers a much more comprehensive view of selection on workers' characteristics. Hence, our model strengthens the case against explaining the widening of the public-private sector gap only by selection. The fact that α is significantly inferior to one in the first panel suggests that neither a simple wage equation model nor a fixed effect model (which, de facto, assumes constant returns to the unobserved characteristics across the sectors) could adequately describe the differences in the returns on workers' skills between the public and private sectors.

# **EXPLAINING THE CHANGES IN THE OVERALL LABOR MARKET EARNINGS INEQUALITY**

In this section, we turn to the entire distribution of earnings and its evolution over time. As we saw in section 1, between 1993 and 2006, the overall inequality falls despite the rising public-private gap because the within-sector inequality decreases sharply in both the public and the private sectors. This stylized fact stands in stark contrast with the other experiences of transition into a market economy, such as the Polish case studied in Keane and Prasad (2006).

25
# **6. Final remarks**

Our analysis shows how using a gender lens that differentiates between types of households and considers economies of scale can enrich the understanding of poverty in situations of forced displacement. In the case of Syrian refugees, specific household structures, particularly those that result from disruptions caused by displacement, are more prone to experiencing poverty.

However, while we have described how patterns of poverty have changed over time, lack of data on critical variables, most importantly the amount of social assistance received by households and labor force participation, employment and earnings data limits our analysis. While some of the changes in poverty over time experienced by male and female PA households are consistent with some of the changes in the circumstances of Syrian refugees, specifically their improved access to labor markets and better access to services, it is not possible to understand the extent to which these and potentially other factors have made a difference to their risk of poverty.

Overall, our results show that the share of female PA households has increased substantially from from 26 to 38 percent of all Syrian refugee households between 2013 and 2018. There are differences between the characteristics of male and female PA households in terms of education, marital status, and family types. While male PAs are likely to be married, the vast majority of female PAs are either single, divorced or widowed or married but separated from their spouse. However, by 2018 the share of married female PAs living with their spouse has increased from 10 percent in 2013 to 18 percent, suggesting some men who had stayed behind in Syria left to rejoin their families in Jordan in this time period.

We use a relative poverty line and examine changes in the composition of the types of households that lie in the bottom 40 percent of the distribution between 2013-14 and 2017-18. We show that assessment of the evolution of poverty over time is highly sensitive to assumptions made about economies of scale. Assuming a moderate amount of economies of scale, we find that female PA households have fared worse than male PA households between 2013-14 and 2017-18 and are disproportionately represented in the bottom 40 percent of the distribution. Furthermore, female PA households have lower per capita expenditures than male PA households across the whole consumption distribution. Female PA households have on average 22 percent lower consumption 29
i.e., most notably, the budget deficit, the principal amortization payments, and the accumulation of financial assets. The debt strategy affects the fiscal and debt performance of a country to the extent that those instruments have different financing terms and cost-risk characteristics. Analytically, the strategy outlines the _composition_ of the new borrowings in terms of representative, stylized debt instruments, and over a certain projection horizon. The MTDS Report explored four options for the period 2012-15, described below, and concluded that the current debt strategy should be maintained. The salient features of these strategies are as follows: Strategy S1 (baseline): external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and zero _net_ domestic borrowing, i.e., new T-bills are issued just to roll over maturing domestic government securities.

Strategy S2: external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and zero _gross_ domestic borrowing, i.e., no new T-bills are issued.

Strategy S3: external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and non-zero _net_ domestic borrowing at short maturities, i.e., new T-bills and a small amount of two-year T-bonds are issued to roll over maturing domestic government securities and raise additional funding.

Strategy S4: external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and non-zero _net_ domestic borrowing at slightly longer maturities, i.e., new T-bills, two-year T-bonds, and three-year T-bonds are issued in volumes higher than those in strategy S3, with the view of further developing the domestic government securities market.

**Alternative debt strategies are assessed in the DSA** . This study uses the instruments already identified in the MTDS Report—reported in Table D1—and extends the projection horizon to 10 years. A continuation of the current debt strategy—i.e., S1—is the assumption underpinning all the scenarios— including the baseline outlook—presented in Section B and C. Other options, however, are worth exploring in the DSA in order to inform decisions on which policies are preferable to pursue. The options S2, S3, and S4 proposed in the MTDS Report are re-assessed, together with two _optimal_ strategies— denoted S5 and S6—whose nature is explained below.

**Optimal debt strategies are benchmarks useful to assess the performance of debt outcomes** . The debt strategies envisaged in the MTDS Report incorporate a specific approach in terms of objectives and cost-risk preferences. Nevertheless, by construction, these policies are neither _efficient_ nor _optimal_ . A portfolio is _efficient_ if it has the lowest possible cost for a given level of risk. In turn, an _optimal_ portfolio is an _efficient_ portfolio that best matches the policy makers’ attitudes towards risk. Optimal portfolios

21
people living in extreme poverty. Because this estimate is much greater than the estimate of this study which predicts an increase of extreme poverty by 44 million people as a result of the June– December 2010 food price crisis, in this section we explain the differences between these two estimates by analyzing the differences in magnitudes of the price changes as well as their composition.

Global food prices reached very similar historical highs during the period of January 2005– March 2008 considered in Ivanic and Martin (2008), and the more recent period of June 2010– December 2010 analyzed here, but the relative changes during these periods were quite different: the increase in food prices over the three years leading up 2008 represented a considerably greater relative price change from the historically low food prices observed prior to 2005 while the food price increase in 2010 occurred when the overall food prices were already at double their historical levels (Figure 3). Using the available values of the World Bank's Food Price Index during these periods, we estimate that the 2008 food price increase raised average global food prices by 118 percent, while the food price increase of 2010 raised food prices by 37 percent.

Most of the difference between the poverty changes reported in Ivanic and Martin (2008) and in this study appear to be due largely to the differing scale of the global price shocks over these two time spans. However, the poverty change of 44 million for 2008 is somewhat higher than would have predicted by applying the 2008 elasticity of the percentage increase in poverty from a change in food prices. In order to examine the possible positive or negative poverty bias of the more recent food price increase relative to the price shock observed in 2008, in Figure 3 we calculate an alternative food price index weighted by individual commodities' marginal impacts on global poverty. Using the values of this index, we find that the change in the povertyweighted food price increase was 46 percent in the period June–December 2010 as compared with 103 percent in the period January 2005–March 2008. These values are very close to the poverty changes reported by Ivanic and Martin (2008) for the food price crisis of 2006–8 and the poverty changes calculated in this study.

13
109 Budget D0l cIt(-)/GDP CFA Countries <!-- Start of picture text --> BWugt DeIIht (/00P<br>O 2<br>-4<br>4 ' ~~~I*4S<br>1 0<br>. 1 T07 so so 00 1I 02<br>Year<br>-holk^ +Aoguale<br><!-- End of picture text --> Gross Domestic Investment/GDP CFA Countries <!-- Start of picture text --> mrem Deetiat Inv*unt<br>10~~~~~~~~~~~~~~~~~~~~<br>%6 6<br>a 67 go S 00 S91 92<br>Year<br>PrefoetI.ns +'Actual*<br><!-- End of picture text -->

# Real Export of GNFS Growth Rate CFA Countries

<!-- Start of picture text --> Exzp.N ONfO Gmwt Rob<br>6 0 7 l0 Is 90 91 92<br>Y er<br>Pteooleimn. +Aotule<br><!-- End of picture text --> <!-- Start of picture text --> Current Account/GDP<br>CFA Countries<br>ClmtWi Mc@nmvODP<br>.4<br>.<br>11<br>.14<br>@0 07 so s0 90 91 e2<br>Year<br><!-- End of picture text --> Pmje.tI.ne +Aotmle *Aotual TOT adjualad TOT a Terms o Trade

# Domestic Savings/GDP CFA Countries

<!-- Start of picture text --> De-d- bevIng.IWDP<br>so. S7 es 89 00 01 92<br>Y"r<br>-ProjetIons +Aotulae<br><!-- End of picture text -->

# Real GDP Growth Rate CFA Countries

<!-- Start of picture text --> GDP &ewth Rob*<br>4<br>3<br>2*<br>06 0? 0 so 00 91 92<br>Year<br>'Pr.jetluoe +AlIue<br><!-- End of picture text -->