rafmacalaba/datause-encoder-data huggingface dataset:
# **_Income distribution_**

_LYl_ `ly(l)` Aggregate net labor remuneration _KYkt_ `ky(kt)` Aggregate after-tax capital income _TRkE_ , _kt_ `ktre(kt)` Capital income transferred to enterprises _CYe_ `cy(e)` Corporate income _Sec_ `savc(e)` Corporate retained earnings _TRcH_ , _e_ `ctrh(e)` Corporate earnings transferred to households _YDh_ `yd(h)` Disposable income net of taxes and transfers _TRhH_ `htr(h)` Aggregate transfers by households _TRhW_ `htrw(h)` Household transfers abroad **_Domestic demand variables_** _XAk_ , _h_ `xa(k,h)` Household demand for goods and services _Shh_ `savh(h)` Household savings _CPIh_ `cpi(h)` Household-specific consumer price index _PAck_ , _h_ `pac(k,h)` Consumer prices _XAf k_ , _f_ `xaf(k,f)` Other domestic final demand for goods and services _PF f_ `pf(f)` Other domestic final demand price deflator _YFf_ `yf(f)` Other domestic final demand aggregate expenditure level **_Trade variables_** _XDkd_ , _a_ `xdd(k,a)` Domestic demand for domestic production _XMTk_ , _a_ `xmt(k,a)` Domestic demand for aggregate imports _PAk_ , _a_ `pa(k,a)` Price of Armington good _PM tr_ , _k_ , _r_ `pm(tr,k,r)` Domestic tariff-inclusive price of imports by region of origin _XM tr_ , _k_ , _r_ `xm(tr,k,r)` Import demand by region of origin and tariff regime _PMTk_ , _a_ `pmt(k,a)` Price of imports by Armington agent _PEk_ , _r_ `pe(k,r)` Producer price of exports by region of destination _XDks_ `xds(k)` Domestic output sold domestically _XETk_ `xet(k)` Aggregate export supply _X k_ `x(k)` Aggregate output _XEk_ , _r_ `xe(k,r)` Export supply by region of destination _PETk_ `pet(k)` Price of aggregate exports _EDk_ , _r_ `ed(k,r)` Demand for exports by region of destination **_Goods price equilibrium_** _PDkk_ `pd(k)` Price of domestic goods sold domestically _WPEkk_ , _r_ `wpe(k,r)` World price of exports by region of destination

# **_Domestic demand variables_**

# **_Trade variables_**

# **_Goods price equilibrium_**

_PDkk_ `pd(k)` _WPEkk_ , _r_ `wpe(k,r)`

# **_Macro variables_**

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the number of observations in the treatment and control groups during the baseline survey. It covered 172 schools, 311 classes, 308 teachers and 5,322 students (see Table 1). The follow-up survey covered all students and teachers who were surveyed in the baseline, but also included additional teachers and students. The cause of imbalance and how this additional observation is leveraged to address the imbalances is discussed under the `identication strategy' subsection.

Table 1: Treatment assignment, timeframe and number of schools and students in each arm

||Treatment 1|Treatment 2|Control 1|Control 2|
|---|---|---|---|---|
|April-May 2007||Baseline|||
|May-2007|Books & Training|Books|-|-|
|Oct-2007|-|-|Books|-|
|October 2007-March 2008|-|-|Training|-|
|Number of Schools and Students|||||
|Schools|50|41|26|20|
|Students|946|784|505|377|
|Apr-2008||Endline|||
|May-2008|-|-|-|Books|
|May-Sept 2008|-|-|-|Training|
|Number of Schools and Students|||||
|Schools|48|41|49|34|
|Students|1665|1432|1326|899|

# 2.3 Instrument

The data collection required a signicant number of survey sta. For the baseline survey, 32 people were deployed. Each survey team included three people (a team leader and two enumerators), who spent a full day in each school implementing the survey instrument (MEC and LRCM, 2008). The survey sta used measures to ensure that assessment items were appropriately translated, used transparently documented assessment procedures, including quality control procedures, and availed procedures to ensure that assessments were implemented in a standardized manner across all participating schools.

The survey instrument encompasses two sets of questionnaires: the rst regarding 7
Owing to the complex data structure produced by the PIRLS survey design and the multilevel nature of the explanatory variables, the error term ε of the regression has a non-trivial structure. Although we include a considerable amount of school-related variables, we cannot be sure that there are no omitted variables at the school level. Given the possible interdependence of students within the same school, the use of class- and school-level variables and the fact that schools were the primary sampling unit (PSU) in PIRLS, there may be unobservable correlation among the error terms ε _is_ at the school level (cf. Moulton 1986). We correct for potential correlations of the error terms by imposing an adequate structure on the covariance matrix. Thus, we suppose the error term to have the following structure: where _ηs_ is the school-level element of the error term and _υi_ is the student-specific element.

We use clustering-robust linear regressions (CRLR) to estimate standard errors that recognize this clustering of the student-level data within schools. The CRLR method relaxes the standard independence assumption and requires only that the observations be independent across the PSUs, i.e. across schools. By allowing any given amount of correlation within the PSUs, CRLR estimates appropriate standard errors when many observations share the same value on some but not all independent variables (cf. Deaton 1997). To avoid inefficiency due to heteroscedasticity, CRLR imposes a clustered covariance structure on the covariance matrix, allowing within-school correlations of the error term. Thus, the form of the covariance matrix _V_ is with ∑ _i_ ( _i_ =1,..., _l_ ) representing the covariance matrices of the residuals of least-square regressions within each school cluster (PSU). Observations of two different PSUs are thus assumed to be independent and uncorrelated, leading to the block-diagonal matrix _V_ with PSUs as diagonal elements. This method yields consistent and efficient estimates, enabling valid statistical inferences of the obtained estimation results (cf. White 1984).

Finally, the stratification of the PIRLS sampling design within each country produces varying sampling probabilities for different students. To obtain representative estimates from 14
24. An ordered probit model was used to analyze respondents’ rating of how serious they consider the issue of climate change to be. The results of the order probit regressions confirm differences across various population sub-groups discussed above are indeed statistically significant.

||`Frequency`|`Percent`|
|---|---|---|
|**Income level**|||
|`Poorest one-third`|`167`|`33.40`|
|`Middle`|`167`|`33.40`|
|`Richest one-third`|`166`|`33.20`|
|**Region:**|||
|`Dushanbe`|`50`|`10.00`|
|`GBAO`|`20`|`4.00`|
|`Soghd`|`150`|`30.00`|
|`Khatlon`|`170`|`34.00`|
|`RSS`|`110`|`22.00`|
|**Sale/barter of farm products is the**|**main source of livelihoo**|**d of the household**|
|`No`|`436`|`87.20`|
|`Yes`|`64`|`12.80`|
|**Awareness of main consequences**|**of climate change**||
|`Not at all informed`|`44`|`9.22`|
|`Not very well informed`|`162`|`33.96`|
|`Fairly well informed`|`227`|`47.59`|
|`Very well informed`|`44`|`9.22`|

# **Climate-related Hazards during the Past 10 Years**

25. Sixty-two percent of respondents in Tajikistan reported having encountered extreme hot/cold seasons that were harmful for health during the past 10 years; 42 percent experienced unpredictable and extreme weather that harmed agricultural production, while 40 percent reported having suffered a decline in fresh water availability for drinking (Figure 8).

**Figure 8: Respondents affected by climate-related hazards during past 10 years**

<!-- Start of picture text --> 62<br>Extreme hot/cold seasons that were harmful for health<br>23<br>Unpredictable and extreme weather that harmed 42<br>agricultural production 9<br>40<br>Decline in fresh water availability for drinking<br>12 Tajikistan<br>36 Kazakhstan<br>Decline in fresh water availability for irrigation<br>10<br>23<br>Natural disasters (e.g. floods, fires, mudslides, etc.)<br>1<br>0 20 40 60 80<br>Percent of respondents<br><!-- End of picture text -->

**Source:** Authors calculations based on P-LITS 2 data.

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# **_6.2 Exports to New Destinations and Contribution to Export Growth of Incumbent Exporters_**

To characterize further entrepreneurship along the new destination dimension in the LAC region, Figure 9 shows the average size of new destinations relative to incumbent destinations in terms of export value for the incumbent exporters in each country during 2005-2007 and 20082009. Exports to new destinations were generally small, ranging from less than 5% of the exports to incumbent destinations in Chile to 12% of the exports to incumbent destinations in Ecuador. As in the case of exports of new products, we can only speculate that the differences between Chile and Ecuador were due to the former country’s longer time under an open trade regime, which has perhaps fostered outward-oriented firms with longer exporting experience. Panel B shows that during the crisis the size of exports to new destinations by incumbent exporters relative to exports to incumbent destinations increased as an average in the LAC region and in most individual countries.

**Figure 9: Relative Size of Exports to New Destinations for Incumbent Exporters Panel A. Averages in 2005-2007 Panel B. Averages in 2008-2009**

<!-- Start of picture text --> .15<br>.28<br>.13<br>.11 .11 .11<br>.10<br>.08 .08<br>.12 .13 .07 .07 .06 .07 .07<br>.09 .08 .10 .09 .07 .07 .04 .03<br>.05 .05 .04<br>.03 .03<br>A.Natural Resource B.Simple Processing C.Broad Manuf. D.Other A.Natural Resource B.Simple Processing C.Manuf. D.Other<br>LAC countries Other countries LAC countries Other countries<br>.3 .15<br>.2 .1<br>.1 .05<br>0 0<br>Size of New Destin. Rel. to Incumbent Destin.<br>Size of New Destin. Rel. to Incumbent Destin.<br>Ecuador Peru South Africa Costa Rica Chile Bangladesh Nicaragua Guatemala El Salvador Dominican Rep Cambodia Egypt Mexico LAC countries Ecuador Peru South Africa Colombia Costa Rica Chile Guatemala Bangladesh Nicaragua El Salvador Dominican Rep Cambodia Egypt Mexico LAC countries<br><!-- End of picture text --> Notes: in Panel A the average for Ecuador covers 2006-2007. Colombia does not enter in Panel A due to lack of data. Brazil does not enter in the figure due to lack of access to the exporter-level customs data.

Finally, we examine the contribution of new destinations to short-run export growth of incumbent exporters in LAC and comparator countries. Figure 10 shows the average annual growth rate in total exports of incumbent exporters for each country in 2005-2007 in Panel A and 2008-2009 in Panel B. The average contribution of the three terms in the incumbent exporters’ export growth decomposition in Eq. (3) -- for new, incumbent, and exiting destinations -- is presented for each country for 2005-2007 in Panel C and for 2008-2009 in Panel D. During the steady growth period, incumbent destinations accounted for the largest share of the growth of 30
decade but declined, though less sharply, in the second half. Third, as with North America and Western Europe, intra-regional trade has grown prorortionately over the years.

In 1980, East Asia exportc J 30% of its gces to itself, 26% to North America, 19% to Europe, and 18% to Latin America, Africa, Middle East, and South Asia (Table 3). In 1985, the region's exports to North America rose to 38% and those to itself fell to 25%. Between 1985 and 1990, there was a rapid shift in East Asia's exports away from other partners to itself. By 1990, the region was the largest recipient of its own goo's. It exported approximately 32 % of the goods to itself, 32 % to North America and 21 % to Europe. The combined share of Latin America, Africa, Middle East, and South Asia fell to a mere 8%.

1.2 Major Players in the Region

In economic terms, there are ten major players in East Asia: the ASEAN 4 consisting of Indonesia, Malaysia, Philippines, and Thailand; the Newly Industrialized Economies (NIEs) comprising Hong Kong, Republic of Korea, Singapore, and Taiwan (China); and China and Japan. F 'veral features of the countries are worth noting.

(i) Per-Capita Incomes. Growth. ad Centrality of Japan. The countries in the region are at very different levels of development. With a per-capita income of $23,800 in 1990, Japan is the richest country in the region (table 2). Per-capita incomes in the rema±ing countries range from a low $327 in China to $11 700 in Singapore.<sup>4</sup> ASEAN 4 enjoy significantly lower per-capita incomes than the NIEs. Within ASEAN 4, Malaysia and Thailand are richer than 4Per-capita incomes change dramatically if they are calculated at the purchasin- power parity (PPP) rather than the nominal exchange rate. According to the recent IMF calculations, they rise almost by a factor of four in the case of China. In the case of Japan, they fall by a factor of 0.7.

_5_
Diaz-Cayeros, Alberto, Beatriz Magaloni, and Alexander Ruiz-Euler. “Traditional Governance, Citizen Engagement, and Local Public Goods: Evidence from Mexico,” _World Development_ (January 2014): 53.

Duflo, Esther, Pascaline Dupas, and Michael Kremer. “School Governance, Teacher Incentives, and Pupil-Teacher Ratios: Experimental Evidence from Kenyan Primary Schools,” National Bureau of Economic Research Working Paper 17939, Cambridge, MA (2012).

Escaleras, Monica, Shu Lin and Charles Register. "Freedom of information acts and public sector corruption," _Public Choice_ , Springer, vol. 145(3) (2010).

Ferraz, Claudio and Frederico Finan. "Exposing Corrupt Politicians: The Effects Of Brazil’s Publicly Released Audits On Electoral Outcomes." _The Quarterly Journal of Economics_ 123 (2) (2008): 703-745.

Fox, J. “Social Accountability: What Does the Evidence Really Say?” GPSA Working Paper 1 (September 2014).

———. “The Uncertain Relationship between Transparency and Accountability,” _Development in Practice_ ([August 2007] 17: 405).

Fujiwara, Thomas. “Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil.” _Econometrica_ 83 (2) (2015): 423–64.

Gaventa, J., and R. McGee. “The Impact of Transparency and Accountability Initiatives.” _Development Policy Review_ 31 (2013): s3–s28.

Goldfrank, B. “Lessons from Latin American Experience in Participatory Budgeting,” Presentation at the Latin American Studies Association Meeting, San Juan, Puerto Rico (March 2006).

Gonçalves, Sonia. “The Effects of Participatory Budgeting on Municipal Expenditures and Infant Mortality in Brazil,” World Development (2013).

Grandvoinnet, Helene, Ghazia Aslam, and Shomikho Raha. _Opening the Black Box: The Contextual Drivers of Social Accountability: New Frontiers of Social Policy_ , World Bank Group: Washington, DC (2015).

Grant, Ruth & Robert Keohane. “Accountability and Abuses of Power in World Politics.” _American Political Science Review_ 99(1) (2005).

Gruening, G. “Origin and Theoretical Basis of New Public Management,” _International Public Management Journal_ 4 (2001).

Gurin, J. “Open Data Now: The Secret to Hot Startups,” Smart Investing, Savvy Marketing, and Fast Innovation (2014).

Herzog, Timothy, and Jeanne Holm. “Financing Investments in Data Literacy and Use: Issues Paper for Data Revolution Consultation,” World Bank and NASA (January 15, 2015).

Hogge, Becky. “OGD Study: Transparency and Accountability Initiative,” Open Society Foundation (2010).

42
**Figure 3 Excess Savings** Volatile vs. Non-volatile firms

<!-- Start of picture text --> 10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>Decile 1 Decile 2 Decile 3 Decile 8 Decile 9 Decile 10<br>Average Excess Savings<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Figure 3 shows the evolution of Excess Savings for firms facing a more volatile environment (decile 8, 9, and 10) and those facing a less volatile environment (decile 1, 2, and 3). Firms were separated into deciles according to the value of the variation coefficient of Tobin's Q, and then the average Excess Savings was computed for each year by decile.
of the impact of price changes on consumption, the devaluation had relatively little differential impact on the consumption basket of the poor relative to the nonpoor. The devaluation increases the share of tradables among both the poor and the nonpoor. The poor show a slightly larger increase bringing their share of consumption of tradables closer to the nonpoor. Thus, far more important to outcomes for the poor is what happened to their real income. That of course depends on what the particular source of income was, as the devaluation was accompanied by changes in tax policy which varied by crop and also by changes in the terms of trade.

**Table 14 Mean Share of Tradable Household Expenditure and Food Expenditure**

||19<br>|93<br>|19<br>|95<br>|
|---|---|---|---|---|
|_Region_<br>|_Share of_<br>_tradable_<br>_expenditure_|_Share of tradable_<br>_food_|_Share of_<br>_tradable_<br>_expenditure_|_Share of_<br>_tradable food_|
|Abidjan<br>|||||
|Poor<br>|0.354<br>|0.469|0.384|0.491|
|Non-poor|0.332|0.441|0.352|0.486|
|Other Cities<br>|||||
|Poor|0.355|0.418|0.408|0.446|
|Non-poor<br>|0.361|0.426|0.399|0.474|
|East Forest|||||
|Poor<br>|0.273|0.238|0.332|0.307|
|Non-poor|0.304|0.292|0.336|0.312|
|West Forest|||||
|Poor|0.334|0.368|0.403|0.408|
|Non-poor<br>|0.337|0.333|0.399|0.424|
|Savannah|||||
|Poor|0.255|0.244|0.301|0.301|
|Non-poor|0.259|0.244|0.287|0.279|
|Cote d'Ivoire<br>|||||
|Poor<br>|0.303<br>|0.311|0.358|0.367|
|Non-poor|0.328|0.365|0.355|0.404|

# **Sources of income and the impact on poverty**

In theory, one would expect that real product wage of tradables to be bid up relative to nontradables to bring about a reallocation of labor from nontradables to tradables. **1**<sup>**7**</sup> Depending on the share of tradables goods in the consumption basket, and the size of the nominal wage increase, wage laborers who consume relatively few tradables could still benefit from the fall in the product wage. While in theory wages should equalize across sectors, in practice there may be some segmentation between the urban and rural labor market, or even within the rural labor market. In the short run real consumption wages in the tradable sector are likely to be bid up to attract labor. Thus, wage earners in the tradable sectors would have been the most likely to have benefited from the devaluation. Whether they benefited, however, depends on the size of the wage increase and the share of tradables in their consumption basket.

> 17 See World Bank (1990) for a discussion of the impact of devaluation on wage earners and farm households.

16
the most trade restrictive regions in the world (World Bank 2003), this places domestic producers (including exporters) in Morocco and Tunisia at a competitive disadvantage in terms of accessing cheap inputs, as well as raising the prices of imported final goods to consumers with its concomitant effect on their welfare. However, the existence of export processing zones as well as a network of preferential trade agreements (including with the EU) have reduced the actual tariffs faced by some producers. Indeed, preferential access to the EU market has accounted for the large share of trade that both Morocco and Tunisia have with the EU (over 60%). Nonetheless, it would appear that there is considerable room for further liberalization of the MFN tariff regime of both Morocco and Tunisia, especially so as both countries are soon to fulfil their liberalization obligations under the free trade agreement with the EU.

**Fig 1: Tunisia: simple average MFN tariffs**

<!-- Start of picture text --> 34<br>33<br>32<br>31<br>30<br>33.9<br>29<br>28<br>29.5 29.3<br>27 28.9 29.0 28.6<br>28.3<br>26<br>25<br>1990 1992 1995 1998 2002 2003 2004<br><!-- End of picture text --> 4
References Anand S and Bärnighausen T. (2012) Health workers at the core of the health system: framework and research issues. Health Policy; 105:185‐91. <u>http://dx.doi.org/10.1016/j.healthpol.2011.10.012 PMID:22154420</u>

- Andalon, M., and Fields, G. (2013), ‘A Different View of the Crisis of Health Care Professionals in Africa: A Labor Market Approach’, in Soucat et al. (eds), The Labor Market for Health Workers in Africa: A New Look at the Crisis, Directions in Development, Washington, DC, World Bank Group.

- Araujo E, Evans T and Maeda A (2016).  “Using Economic Analysis in Health Workforce Policy‐ Making”.  Oxford Review of Economic Policy, 32:1, pp. 41–63. <u>http://oxrep.oxfordjournals.org/content/32/1/41.full.pdf?keytype=ref&ijkey=7U Vq9oXTft4TgYk</u>

- Basu, K., & Gupta, A. (2004). [A physician demand and supply forecast model for Nova Scotia]. _Cahiers de sociologie et de démographie médicales_ , _45_ (2‐3), 255‐285.

- Bruckner TA, Scheffler RM, Shen G, et al. The mental health workforce gap in low‐ and middle‐ income countries: a needs‐based approach. Bull World Health Organ 2011; 89: 184–94.

- Buchan J, Wismar M, Glinos IA, and Bremner J. (eds) (2014), Health Professional Mobility in a Changing Europe: New Dynamics, Mobile Individuals and Diverse Responses, Publication of the European Observatory on Health Systems and Policies, Geneva, World Health Organization.

- Campbell J, Dussault G, Buchan J, et al. A Universal Truth: No Health Without a Workforce. Geneva, Switzerland: Global Health Workforce Alliance and World Health Organization, 2013

- http://www.who.int/workforcealliance/knowledge/resources/hrhreport2013/en / (accessed Feb 1, 2015).

- Chen LC, Evans TG, Anand S, Boufford JI, Brown H, Chowdhury M, Cueto M, Dare L, Dussault G, Elzinga G, Fee E, Habte D, Hanvoravongchai P, Jacobs M, Kurowski C, Michael S, Pablos‐Mendez A, Sewankambo N, Solimano G, Stilwell B, de Waal A, Wibulpolprasert S (2004). “Human Resources for Health: Overcoming the Crisis”, The Lancet, 364, 1984–90.

- Cooper RA, Getzen TE, Laud P (2003). Economic Expansion Is a Major Determinant of Physician Supply and Utilization. Health Serv Res; 38: 675–96.

- Cooper RA, Getzen TE, McKee HJ, Laud P (2002). Economic And Demographic Trends Signal An Impending Physician Shortage. Health Aff (Millwood); 21: 140–54.

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