rafmacalaba/datause-encoder-data huggingface dataset:
taking Bhutan’s 2022 population as the base, this corresponds to about 45,100 people.<sup>7</sup> On the other hand, the probabilities reported in the off-diagonal refer to what we might call “mobile” patterns: people who experienced different poverty statuses in the two periods, either because they became poor or because they escaped poverty. Although the two trajectories are opposite in terms of social desirability, their combined probability provides an aggregate measure of mobility: overall, fewer than one in six people in Bhutan, or 15.7 percent, crossed the poverty line in either direction between 2017 and 2022, which indicates that overall poverty mobility was relatively low. However, most of it was upward mobility: only 3 percent of the population ended up falling into poverty over the period considered.

**Table 5. Poverty dynamics based on synthetic panel data, joint probabilities (percent)**

||Poor 2022|Non-poor 2022|
|---|---|---|
|Poor 2017|5.77***|12.71***|
|se|(0.47)|(0.60)|
|Non-poor 2017|3.00***|78.52***|
|se|(0.47)|(0.66)|

Note: *** p<.01, ** p<.05, * p<.1. Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

Table 6 offers a different set of measures of poverty mobility: conditional probabilities, _P_ ( _yi_ 2 ~ _z_ 2 | _yi_ 1 ~ _z_ 1 ), represent the likelihood of an individual’s status in the second period, _given_ their status in the first period. The first line of Table 6 refers exclusively to people who were classified poor in 2017: out of this group, how many remained poor in 2022, and how many managed to escape? A similar interpretation can be applied to the second row, which focuses on people who were not poor in 2017: out of this (much larger) group, how many fell into poverty in 2022, and how many remained above the poverty line? Clearly, this implies that the row-wise sum of probabilities in Table 6 is 100.

The takeaways from Table 6 go along with those discussed for Table 5 – after all, the trajectories underlying the measures presented are the same – but they provide a different point of view, one that allows for a sharper focus on the poor. In fact, while the share of chronically poor individuals is relatively low as a proportion of the country’s population,

> 7 National population from the World Bank World Development Indicators.

13
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|**Table 2. Participation in market and**|**non-market land t**<br>|**ransactions betw**<br>|**een 1981 and 1**<br>|**999**<br>||
|---|---|---|---|---|---|
||**Total **|**North **|**West**|**East**|**South **|
|||**M**|**arket transactio**|**ns**||
|**Purchased land**<br>||||||
|Population share|15.04|6.36|11.60|19.04|24.95|
|Land share|9.25|3.11|7.61|19.95|17.60|
|**Sold land**|7.74|1.40|8.11|12.62|10.30|
|Population share|15.04|6.36|11.60|19.04|24.95|
|Land share|5.04|1.33|5.99|10.18|5.36|
|||**Non**|**-market transac**|**tion**||
|Inherited in land||||||
|Population share (1982)|4.72|3.64|6.34|2.57|4.78|
|Population share (1999)|10.00|7.20|12.58|5.96|11.36|
|Land share|7.17|5.05|6.94|6.41|10.78|

_Source:_ Own computation from NCAER ARIS/REDS surveys 20
6.2% appears reasonable, consistent with the average of private capital depreciation rates for low‐ and middle‐income countries used in the IMF FAD Investment and Capital Stock Database 2017.<sup>14</sup> In panel B of Table 1, we calibrate the initial capital‐to‐output ratio, K�/Y� at 2.560 assuming steady‐state properties<sup>15</sup> and based on 30‐year averages of the investment‐to‐GDP share (22%), GDP growth (4.1%),<sup>16</sup> and aggregate depreciation rate (4.5% in PWT 8.1). Taking this approach at least provides us with some basis of setting an initial K�/Y� that is in between the PWT values for 2010 (2.384 based on PWT 9 and 2.632 based on PWT 8.1) especially since the PWT 9 value puts Syria on the border of the 75<sup>th</sup> percentile of lowest K/Y ratios, and is below the respective averages of LMI countries, low‐income countries as well as MENA countries.<sup>17</sup> Using the IMF FAD Investment and Capital Stock Database 2017 to calculate the shares of public and private capital shows that the share of public capital �K<sup>G</sup> /K� is relatively high in Syria, accounting for 0.610 of total capital. This is higher than the average for LMI countries of 0.399, but closer to the average for oil‐based economies in the Middle‐East and North Africa (MENA) region, 0.506. Still, Syria appears to have the highest ratio among oil‐based economies that have fuel exports accounting for less than 60% of exports (for which, the average is 0.397).<sup>18</sup> Taking all this into account, we use a “mid‐ point” public capital share of 0.450 for our simulations.

# _2.2.2  Central projections_

The central projected paths of key growth drivers over 2011‐2030 (panel C, Table 1) are based on:

- (i) average I/Y over 2001‐2007 of 21.5% (from WDI), and public investment share of total investment of 40%;

- (ii) average growth rates over 2001‐2010 for TFP of1.4% (according to The Conference Board 2018 and our own calculations following Kim and Loayza 2018) and human capital of 1.0% (following PWT 9);

- (iii) United Nations (2011) projections for growth rates of total population of 1.6% and of the working‐age population share of 0.6% (as discussed in Section 1.1); and

- (iv) zero growth in the LFPR (i.e. the 2010 participation rate level is maintained) given some signs of stabilization (and possible increase) from the 2013 ILO‐modelled estimations and projections.

> 14 4.25% for low‐income countries and 8.30% for middle‐income countries as of 2015.

> 15 In steady‐state/equilibrium, output grows at the same rate as capital stock, which allows us to write,

> � � � � ~~�~~ � ~~�~~ /�g� � δ � where g� is average output growth and δ the depreciation rate.

> 16 Investment‐to‐GDP and GDP growth are averages of WDI and WEO data (1981‐2007 and 1981‐2010 respectively).

> 17 Our calibrated value is also closer to what the calculated K/Y would be based on MFMod data (2.498).

> 18 Based on data from the IMF database for K�/K in 2010, and World Bank income and region classifications for the same year. Oil‐based economies are defined as those with fuel exports at least 30% of total merchandise exports (using WDI data).

9
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Rutherford, T. (1999). “Applied General Equilibrium Modeling with MPSGE as a GAMS Subsystem: An Overview of the Modeling Framework and Syntax.” _Computational Economics_ , 14: 1-46.

Simpson, M. (1994). “The Distributive Effects of Inflation Stabilization Policies in Brazil: A CGE Modeling Approach.” _PhD Dissertation_ . University of Illinois.

Timilsina, G. and Shrestha R. (2007). “Alternative Tax Instruments For CO2 Emission Reduction and Effects of Revenue Recycling Schemes.” _Energy Studies Review_ , 15(1): 19-48.

Timilsina, G. (2009). “Carbon Tax Under the Clean Development Mechanism: A Unique Approach for Reducing Greenhouse Gas Emissions in Developing Countries.” _Climate Policy_ , 9(2): 139-154.

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Van Heerden, J., Gerlagh, R., Blignaut, J., and Horridge, M. (2006). “Searching for Triple Dividends in South Africa: Fighting CO2 Pollution and Poverty while Promoting Growth.” _Energy Journal_ , 27(2): 113-142.

Welsch, H. (1998). “Coal subsidization and Nuclear Phase-Out in a General Equilibrium Model for Germany.” _Energy Economics_ , 20: 203-222.

The World Bank (2010). _2010 The Little Data Book_ . Washington, DC.

World Resource Institute (2010). _Brazil's Global Warming Agenda_ . Washington, DC. (http://www.wri.org/stories/2010/03/brazils-global-warming-agenda) 29
Table A9: Exogeneity of treatment assignment

|_Dep. Var. =1 if Treated._||||||
|---|---|---|---|---|---|
|**Panel A: Controls**|_Coef._|_Std. Err._||||
|Female|0.003|(0.013)||||
|Age|-0.002|(0.004)||||
|Currently attending school|0.019|(0.022)||||
|Years of education|0.000|(0.008)||||
|English Spoken|0.034|(0.038)||||
|Single|-0.005|(0.022)||||
|Does not live with the family|-0.038|(0.032)||||
|Household size|-0.005|(0.004)||||
|Wealth index|-0.013|(0.027)||||
|Home owner|-0.089**|(0.039)||||
|Father obtained education higher then sec.|-0.048***|(0.017)||||
|Mother obtained education higher then sec.|-0.019|(0.021)||||
|Muslim|0.008|(0.032)||||
|Yoruba Native|0.012|(0.052)||||
|Constant|0.784***|(0.172)||||
|Observations|5,166|||||
|R-squared|0.021|||||
|P-val F-test of joint signifcance|0.147|||||
|**Panel B: Outcomes**|(1)|(2)|(3)|(4)|(5)|
|HIV knowledge|0.002|||||
||(0.001)|||||
|HIV attitudes||-0.000||||
|||(0.003)||||
|HIV testing|||0.001|||
||||(0.002)|||
|Attitudes towards risky sexual behavior||||-0.000||
|||||(0.002)||
|Risky sexual behavior (for sexually active)|||||0.002<br>(0.002)|
|Constant|0.820***|0.782***|0.796***|0.784***|0.745***|
||(0.168)|(0.171)|(0.168)|(0.171)|(0.176)|
|Observations|5,166|5,166|5,166|5,166|3,246|
|R-squared|0.022|0.021|0.021|0.021|0.025|
|Controls <sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|
|P-val F-test of joint signifcance|0.156|0.159|0.188|0.142|0.304|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. The dependent variable takes value one if the individual has been assigned to treatment. (a) Controls in each regression of panel B include: female, age, currently attending school, years of education, english spoken, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language.

xvi
insignificant in both employment growth (L-growth) and labor productivity growth (LP-growth) regressions (Table 6).

**Table 6. How the BE Effects Differ by City Size**

||**Lgr**<br>|**ow**|**LPg**<br>|**row**|
|---|---|---|---|---|
||**coef**|**se**|**coef**|**se**|
|In Big City|**0.017***|0.007|**0.019**|0.015|
|Corruption Obstacles|**-0.015***|0.006|**0.023**|0.014|
|Overdraft Facility|**0.015***|0.006|**0.031***|0.014|
|Trade Credit|**-0.008**|0.009|**0.045****|0.016|
|Inf Competition|**-0.012***|0.006|**0.002**|0.012|
|Skilled Labor Obstacle|**0.000**|0.006|**-0.001**|0.013|
|Labor Regulation Obstacle|**0.002**|0.009|**0.021**|0.017|
|Labor Regulation Obstacles * In Big City|**-0.001**|0.009|**-0.047****|0.017|
|Tax Rate Obstacle|**-0.005**|0.007|**0.004**|0.016|
|Land Access Obstacles|**-0.005**|0.007|**-0.006**|0.015|
|Outage|**0.003**|0.007|**-0.009**|0.014|
|Web Intensity|**0.019****|0.006|**0.029***|0.013|
|Security Costs|**-0.105***|0.053|**-0.049**|0.115|
|Capacity Agglomeration|**0.047***|0.019|**0.032**|0.028|
|Capacity Agglomeration * In Big City|**-0.034**|0.019|**-0.023**|0.035|
|Other Controls as in Reyes et al. (2017)|Y|es|Y|es|
|Observations|52,|614|40,|507|
|Adjusted R squared|0.1|23|0.2|87|

Interestingly, most BE variables do not have different effects in big versus non-big cities, which may point at the fact that many business environment constraints are still due to national level restrictions, such as tax or trade regulations. However, labor inflexibility seems to have more harmful association with labor productivity growth in big cities. Only in big cities, increasing Labor Regulation Obstacles by one standard deviation (0.2) is associated with a drop in labor productivity growth by 0.5 percentage points. This makes sense since in large cities the benefits of a flexible labor market should be larger due to better opportunities for labor-employer matching, and labor inflexibility is likely to cause more harm in terms of productivity growth. In contrast, the benefits of capacity agglomeration, while present in both big and non-big cities, are more pronounced in non-big firms. Increasing capacity agglomeration by one standard deviation 16
Figure 8: Price and Quantity Changes for 64 Groups <!-- Start of picture text --> 1992-2002 1993-1995<br>-.4 -.2 0 .2 .4 -.6 -.4 -.2 -5.55e-17 .2<br>S_ch_02_92 S_ch_95_93<br>W_ch_02_92 Fitted values W_ch_95_93 Fitted values<br>1996-1998 1999-2001<br>-.6 -.4 -.2 -5.55e-17 .2 .4 -.4 -.2 -5.55e-17 .2<br>S_ch_98_96 S_ch_99_01<br>W_ch_98_96 Fitted values W_ch_99_01 Fitted values<br>.6 .3<br>.4 .2<br>.2 .1<br>0<br>-5.55e-17<br>W_ch_02_92/Fitted values -.2 W_ch_95_93/Fitted values -.1<br>-.4 -.2<br>.5 .4<br>.2<br>0<br>-5.55e-17<br>-.2<br>-.5<br>W_ch_98_96/Fitted values W_ch_99_01/Fitted values<br>-.4<br>-1 -.6<br><!-- End of picture text --> Therefore, a tentative conclusion is that there could have been increased demand for skills over the entire decade. However, the models do not perform as well as in the case of the United States (Katz and Murphy 1992) or Austria (Fersterer and Winter-Ebmer 2003), so we cannot outright reject stable factor demand for the period 1992 to 2002 in Argentina, although it seems that the rising supply in more educated workers is being compensated by a rising demand for skills.

19
-11-

terms.<sup>2</sup> (See Chart 3) The appreciation of the exchange rate not only eased the servicing of foreign official debt, but the slower crawl of the exchange rate also implicitly served as a nominal anchor and helped to control inflation in an environment of deteriorating fiscal deficits.

Against this background, the move to fully liberalize the capital account started in 1989 and through a series of decrees Turkey accepted IMF's Article VIII in 1990. This marked the completion of the external financial liberalization process, which was initiated in 1984 when Turkish residents were allowed to hold foreign exchange denominated deposit accounts. While the goal of capital account liberalization was put forward as further integration with international capital markets, and in particular the European Union, Celasun and Arslan (1996) suggest that easing of the financial constraint on surging public expenditures was en important consideration underlying this decision.

Events in 1989 seem to confirm this view. In that year, chronic inflation became the major issue on the policy agenda. Aiming to limit the monetization of fiscal deficits, the Central Bank and Treasury came to an agreement to constrain the Central Bank financing to 15% of total budgetary appropriations. With the share of net external financing by the public sector also being rather limited, domestic borrowing became the main source of financing the deficits. Yet, with the exchange rate following a path of real appreciation, lending by the domestic banks to the public sector was based on a rapid build up of short term foreign debt. Ekinci (1996) notes that, with the Central Bank creating reserve money mainly against foreign reserve accumulation, amd external borrowing being delegated to domestic financial institutions, short term capital inflows became the ultimate financing source of fiscal deficits.

# **11I.2 The Determinants of Capital Flows:** Some **Econometric** Evidence

To capture the main determinants of net capital inflows, we follow the literature and regress capital flows on a constant, the uncovered interest differential between Turkish three month T-bills and the TL equivalent of three-month LIBOR rate, and the growth rate of real GDP or the industiial production index. Estimation was done for total, portfolio and short term capital flows, using monthly data for 1990-1997 as well as foreign direct investment using quarterly data for the period 1990-19973. The results are presented in Table 8.

2 No objective behind the move to a (managed) float from a real exchange rate targeting rule was ever officially announced. With increasing capital inflows, the feasibility of real exchange raLte depreciation had decreased substantially and the Central Bank seemed not to have another option but allow for some real appreciation.

3 Since portfolio flows are mainly composed of debt instruments placed by Turkish residents in foreign markets, foreign interest rates are likely to be most relevant measure of opportunity costs of these flows. Therefore we broke down the uncovered differential into the continued
already been documented in the 1991 Census of Rwanda where it was found that the age at marriage for Hutu women was 22.6 and for Tutsi women 25.6<sup>2</sup> We will further argue that the only reason that women who married later have been more likely to die in the genocide is because they were Tutsi. In other words, our assumption is that the genocide made more victims among people who married later, not because they were targeted as such, but because they were Tutsi - who happen to marry later.

We therefore use in the 2000 DHS age at marriage as a proxy for being Tutsi. Once again, we do not have the age at marriage of the individuals whose mortality we are investigating, but we are using the age at marriage of their respondent sister as the proxy. Table 3 implements this strategy. Like table 1, it contains three panels for the three different measures of education: primary education dummy, secondary education dummy and years of education. Column 1 repeats the specifications of table 1 but adds age at marriage as a proxy for being Tutsi. This regression limits the sample to siblings of married women. However, since for a given age, being married might be correlated with being a Tutsi, we want to avoid this truncation of the sample. Column 2 therefore introduces, for the full sample, an interaction between being never married and age at marriage as the proxy for being Tutsi.

We find that being never married and age at marriage are positively correlated with dying in 1994 (significant at the 10 percent level), which suggests that this is a good proxy. The coefficients on urban residence remain positive and significant, even after using the proxy in the three panels. The same is generally true for the coefficients on

> 2 Recensement Général de la Population et de l'Habitat au 15 Aout 1991, Résultats Définitifs, Service National de Recensement, Ministère du Plan, Kigali, 1994, p. 186

14