rafmacalaba/datause-encoder-data huggingface dataset:
- 48 -

**Table 25**

**TURKEY Net Foreign Asset Position of the Central Bank** (In million of U.S. dollars)

||1990|1991|1992|1993|1994|1995|1996|1997|
|---|---|---|---|---|---|---|---|---|
|**NFA**|**-449**|**-87**|**1,601**|**1,142**|**-659**|**2,008**|**5,894**|**8,557**|
|**Net international reserves**|**7,296**|**6,400**|**7,607**|**7,700**|**8,232**|**13,067**|**16,950**|**18,917**|
|Gold|1,467|1,492|1,494|1,488|1,420|1,383|1,383|1,124|
|Foreign bank notes|222|366|429|626|422|796|292|363|
|Correspondent accounts|5,683|4,547|5,687|5,587|6,740|11,595|15,980|18,056|
|Intemational reserve liabilities|76|4|2|1|350|706|706|626|
|**Other foreign liabilities (net)**|**7,744**|**6,487**|**6,007**|**6,558**|**8,891**|**11,059**|**11,056**|**10,360**|
|Non-resident FCDs|7,534|6,432|6,368|6,975|9,290|11,558|11,949|11,360|
|Medium-term loans (net) I/|-1,196|-1,106|-1,093|-1,039|-1,073|-1,110|-1,167|-1,218|
|Others (net)|1,407|1,160|731|622|674|611|274|217|

Source: Statistics Department of the Central Bank of Turkey.

1/ Mainly foreign credit extended.

**Open Foreign Exchange Positions and Maturity Mismatches** Largely as a result of chronically high inflation and asset substitution, the government's overwhelming borrowing presence in Turkey's financial markets, and due to the unique set of tax and regulatory incentives affecting banks and their customers (e.g. the blanket deposit guarantee provided by the government), many Turkish banks have turned to arbitrage activities to generate a substantial portion of their profits. In particular, many private-sector banks run large currency and interest rate risk positions than would be considered prudent in most countries. A number of banks run open foreign exchange positions that exceed the already generous regulatory limit of 50% of capital.<sup>2 0</sup> Such behavior is also a reflection of moral hazard as a result of the existence of extensive govemment guarantee on deposits and regulatory forbearance. The fact that the regulatory authorities allowed non-compliance without imposing serious sanctions encouraged the banks to continue taking large foreign exchange risks.

The Turkish banking sector was hard hit during the 1994 currency crisis. At year-end 1993, the total open position of the banking sector was US$4.6 billion, or 68% of total equity. The risks of such a capital structure became apparent only a few weeks later, when the lira collapsed, losing half of its value in the first quarter of 1994. This currency crisis led to large foreign exchange losses and higher loan defaults at most banks. Net income for the sector fell from US$1.9 billion in 1993 to US$0.9 billion in 1994. Moreover, a number of 20 This limit is now lowered to 30% of capital as a result of the agreement with the IMF on a Staff Monitored Program.
Bryson, A., R. Dorsett, and S. Purdon. 2002. The Use of Propensity Score Matching in the Evaluation of Labour Market Policies," Working Paper No. 4, Department for Work and Pensions.

Buckland, Peter. 2005. _Reshaping the future: Education and post-conflict reconstruction_ . Washington: World Bank.

Caliendo, Marco & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," _Journal of Economic Surveys_ , Wiley Blackwell, vol. 22(1), pages 31-72, 02 Emsley, R. A. 2007. Statistical Models of Selection and Causation. PhD thesis, University of Manchester, UK.

ENV-2008, "Enquête sur le Niveau de Vie des Ménages de Côte d'Ivoire," National Statistical Institute and Ministry of Planning and Development of Côte d'Ivoire.

Heckman, J., H. Ichimura, and P. Todd. 1997. Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," _Review of Economic Studies_ , 64, 605-654.

Lai, Brian, and Clayton Thyne. 2007. The effect of civil war on education, 1980-97. _Journal of Peace Research_ 44, no. 3 (May): 277-292.

Merrouche, Ouarda. 2006. The human capital cost of landmine contamination in Cambodia. _Households in Conflict Network_ , no. 25. Working Paper (December).

Robins, J. M. 2000. Robust estimation in sequentially ignorable missing data and causal inference models. In _Proceedings of the American Statistical Association Section on Bayesian Statistical Science 1999_ , 6–10. Alexandria, VA: American Statistical Association.

Rosenbaum, P. R., and D. B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. _Biometrika_ 70: 41–55.

Shemyakina, Olga. 2006. _The effect of armed conflict on accumulation of schooling: Results from Tajikistan_ . HiCN Working Paper 12. Falmer: University of Sussex, November.

Sany, J., 2010, "USIP Special Report," United States Institute of Peace. Available on: http://www.usip.org/files/resources/SR235Sany_final_lowres-1.pdf UCDP/PRIO, 2009. _UCDP/PRIO Armed Conflict Dataset Codebook Version 4-2009._ Uppsala and Oslo: Uppsala Conflict Data Program (UCDP) and International Peace Research Institute (PRIO). Available on: http://www.prio.no/CSCW/Datasets/Armed-Conflict/Battle-Deaths/ 27
33

|Skinner (1987)|YPC|-.658|I1.27|OIL,COUPS,lpyI<br>I<br>ToT,laxesof<br>varioustypes|1965-82|31 African|Population<br>Growth|
|---|---|---|---|---|---|---|---|
|Otani & Villa<br>Nueva (1990)|Growlh of per<br>capita real GNP|-1.13|1.62|S/GDP, EdExp/GOV,<br>RIR,_XIGDP_|1970-R5|17 low income<br>LDC's|Population<br>Growth|
||(IMF4)|_<br>-1.32|1.78|||15 middle<br>income LDC's||
|||-.66|.74|||23 upper<br>income LDC's||
|Grier& Tallock|YPC|-.66|1.47|yO, GOV, SIGDP,<br>|1961-1980 using|43 SS African|Mean<br>|
|(1989)|(SIt, PWT4)|-.339|1.04|INP, STDINP, OPEC,<br>LID|five year averages|24 Latin Am.|'Population<br>CGrowth|
|||-.746|.91|||22 Asia & Far<br>East||
|Kormendi &<br>Meguire (1985)|YPC<br>iMP|-.42<br>-.37|2.47<br>2.17|yO, Sy, STDMS, MS,<br>XIGDP,_GOV,_INP|1950-77|47 countriesDC<br>& LDC|<br>Population<br>Growth<br>_<br>_<br>_<br>_<br>_<br>_|

Notes: 1) Dependent variables, YPC: growth rate of GDP per capila. Sources: PWTXX<sup>indicates various versions of tie Penn World Tables, WB is World Bank national</sup> accounts, wlile IMP indicates IMP national accounts dtla.

2) Control variables: _YO,_ initial Income; I/GDP, ralio of invesiment to GDP; PRI (SUC),<sup>primary (or secondary) enrollment rale; COV, ralin government spending to GDP;</sup> SOC, dumiimy for socialist economilies; RHVC, revolutionis & coups, AP, duimmy for sub<sup>saliaran Africa; LAM,dumnmy</sup> for Latin America: XIGI)P, ratio exports to GDP. GDC, growilt of domestic credit; STDDC, standard devialion or domestic credit; CVL,<sup>civil umrest; SCHOOL, weighted average primary and secondary enrollment; LRP. log</sup> deviation of output per worker from US levels- LF/POP, ratio of labor force aged populalion<sup>to tolal population: DENSITY, ralio of land area to population.</sup><sup>_SIGDP._domestic</sup> savings ratio; GOVC/QDP, ratio of government consumption spending to GDP, ASSN,<sup>Assassinations, IPPDEV, devialion of investlmient</sup> prices froin expected level based on income per capita; EdBxp/GOV, fraction of govt. expentliture on education; MS. growdli<sup>of money supply; STDMS, standard deviaiion of money supply;X/GDP,ratio of</sup> exports to GDP; RIR, real interest rate; Ipvt, private investment; Ipub, public investment; ToT,<sup>terms of trade shocks; LEX, life expectancy at birth; INP. inflation; STDINF,</sup> standard deviation of innlation: OPEC, dummy for OPEC; LID, dummy for lack of civil<sup>liberties.</sup> 3) Net fertility is ferlility adjusted for infant mortality.

4) All coefficienis reported in original sources are transformed so tlat it indicates the<sup>effect of an increase of one perceniage point in population growilt on the growlh of output</sup> per person per annum (i.e., if tlie coefficient is w38, a 1% increase in population growth<sup>reduces output per person by .38% of percentage points).</sup>
> <sup>32</sup>

the monogamous clustering rule (one party/one group). In two of these, clustering is significantly associated with partisanship; in four, gift-giving is significant.

For most countries and most clustering rules, therefore, political parties do not make credible commitments to serve the interests of ethnic groups. Gift-giving is a more prominent correlate of partisan preferences, despite the fact that gift-giving in Africa is generally candidate-driven and not organized by political parties.

Qualitative evidence from several of these countries further supports the claim that the basis for ethnic clustering of partisan preferences is unlikely to be the credible commitments of political competitors to improve the welfare of ethnic groups. The three countries in which clustering has the most consistent effect on partisan preferences are Kenya, Mozambique and South Africa. In Mozambique and South Africa, ethnic groups cluster on ruling parties that are well-organized and long-lived: FRELIMO in Mozambique was 43 years old in 2005, and the ANC in South Africa 93 years old (Beck, et al. 2001, Database of Political Institutions). These parties have undergone leadership changes and are associated with the interests of particular ethnic groups (e.g., the Xhosa in South Africa, only 22 percent of whom express partisan indifference, compared to 55 percent of Afrikaans respondents and 42 percent of Zulu respondents). They are exceptional in Sub-Saharan Africa: the average age of political parties in the rest of the continent was 17.2 years.

A closer look at Kenya, in contrast, indicates the improbability of parties making credible appeals to ethnic groups, suggesting that ethnic clustering must have other explanations. The Luo and Kalenjin are the only two groups of respondents who meet all clustering criteria in Kenya. However, the two parties on which they cluster (the LDP and the Kalenjin’s Kenya African National Union, or KANU) do not appear to make broadly credible appeals. Like the unclustered Kikuyu, of whom 35 percent express partisan
Figure D.2: Number of Firms in Model and Data <!-- Start of picture text --> Number of Firms Chile: Model vs Data Number of Firms China: Model vs Data<br>Model Model<br>Data Data<br>1984 1986 1988 1990 1992 1994 1996 1998 1999 2000 2001 2002 2003 2004 2005<br>time time<br>1.1 2.5<br>1.0<br>0.9 2.0<br>0.8<br>Relative to 1983 Relative to 1998<br>0.7 1.5<br>0.6<br>0.5 1.0<br><!-- End of picture text --> Note: The number of rms in Chile are aggregated from Chile's Encuenta Nacional Industrial Anual (ENIA) for the period 1983-1996. The number of rms in China is computed from the Annual Survey of Industries for the years 1998-2005.

the following fashion Based on this denition, our approach to addressing the decomposition is to construct two alternative counterfactual measures of _TFPR_ in which one distortion is shut down at a time where _log_ � _TFPRTFPRiK_ � is the log of _TFPR_ assuming the only distortion is the capital one, relative to the industry average _TFPR,_ and where _log_ � _TFPRTFPRiy_ � is the same object assuming the output distortion is the only active distortion.

Equipped with these alternative denition, we separately compute their regres- _TFP_ _<u>Qi</u>_ sion coecients with respect to _log ._ In the context of the model, where � _TFPQ_ � capital distortions create a wedge in the cost of renting capital, a decline in the capital-distortions' elasticity with respect to _TFPQ_ implies that, during acceleration episodes, more productive rms become more able to increase their capital labor ratios. A decline in the output distortion's elasticity, on the other hand, implies that the more productive rms become more able to increase size attracting labor and capital in proportion to their technological shares. With respect to _TFP_ , 54
Table 1: Summary Statistics

|||(1)||(2)||(3)|
|---|---|---|---|---|---|---|
||Mother (|6-year-old)|Mother|(3-to-4-year-old)|Women|without kids|
||mean|sd|mean|sd|mean|sd|
|Age|34.62|5.75|32.26|5.88|34.78|9.20|
|Migrant|0.00|0.02|0.00|0.04|0.00|0.03|
|Married|0.87|0.33|0.86|0.34|0.49|0.50|
|Divorced|0.05|0.22|0.04|0.20|0.08|0.27|
|Partner lives in household|0.92|0.27|0.92|0.27|0.54|0.50|
|Low-skilled|0.28|0.45|0.28|0.45|0.16|0.37|
|Middle-skilled|0.53|0.50|0.48|0.50|0.59|0.49|
|High-skilled|0.19|0.40|0.24|0.43|0.25|0.43|
|Youngest child six years old|1.00|0.00|0.00|0.00|0.00|0.00|
|Age of the youngest child in the household, aged less than 15 years|6.00|0.00|3.50|0.50|.|.|
|Number of children in the household aged less than 15 years|1.64|0.77|1.73|0.90|0.00|0.00|
|Number of persons in the household, whatever the age|4.32|1.40|4.43|1.58|2.77|1.30|
|Number of employed persons in the household aged 15 years and more|1.72|0.90|1.71|0.98|1.87|1.21|
|Number of inactive adults in the household aged 15 years and more|0.67|0.90|0.74|0.94|0.75|1.00|
|Employed|0.65|0.48|0.62|0.49|0.72|0.45|
|Part-time work|0.06|0.23|0.05|0.22|0.04|0.20|
|Seeking work|0.04|0.19|0.04|0.19|0.05|0.23|
|Monthly take-home pay from main job (deciles)|4.99|2.93|5.08|2.95|5.11|2.85|
|Number of hours per week usually worked in main job|34.53|14.11|35.71|12.72|36.68|11.90|
|Observations|8948||15837||189951||

Notes: Skill levels follow the official definition of the EU-SILC and is the highest education attainment level according to ISCED 2011. The low-skilled have at most lower secondary education, the middle-skilled at most upper secondary education, and the high-skilled tertiary education degress. Source: EU-LSF (20102019).

without children. In all three cases, we restrict the sample to women between 20 and 50 years old. The black, vertical line indicates the year of the reform. The graph reveals a clear gap between women with and without children in the age group analyzed. Based on these unadjusted trends there is no evidence of significant changes after the introduction of the reform. The right figure plots the magnitude of the employment gap between women without children and women with children aged six years old. The employment gap seems to increase after the introduction of the reform, mainly driven by an increase in the employment rate of mothers without children. This evidence might support our hypothesis that mothers of three- to four-year-old children might be the better control group as women without children might be subject to different unobservables.

10
**Figure 3. Predicted Poverty Rates in TA 1 Based on Imputation Using Different Price Adjustments, from NPS 2020/21 to TA 1, Tanzania**

<!-- Start of picture text --> Panel 1: IMF Panel 2: WB annually<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 3: WB quaterly Panel 4: WB monthly<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>30 30<br>25 25<br>20 20<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>15 15<br>30 30<br>25 25<br>20 20<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>15 15<br><!-- End of picture text -->

**Note:** 1000 simulations are implemented. The dashed lines represent the true poverty rates in TA 1 using the 2020/21 poverty lines. The light grey areas represent confidence intervals of the true poverty rates in TA 1 using the 2020/21 poverty lines. The dark grey areas represent standard errors of the true poverty rates in TA 1 using the 2020/21 poverty lines.

40
and to make the data available to outside users.

The CM is a complete enumeration of all nanufacturing establishments, as classified by the Census Bureau according to the Standard Industrial Classification System (SIC). In contrast to the CM, the ASM is a sample of establishments, selected after each census for data collection over the following five years. The annual data available in the LRD for all establishments from 1972 to 1989 include:

- @ the establishment name, address, four and five digit SIC codes;

- @® payroll statistics, including total salaries and wages;

- @® cost of materials and energy; @ capital expenditures; @® total value added.

In addition the LRD contains some variables that are only available for ASM establishments, and others that are only collected in census years. The additional ASM information relates to capital assets, rents, depreciation, retirements and repair. The data available only for census years include:

- @® the quantity and cost of material goods consumed; @ the quantity and value of product shipped; @ employment.

The product information collected by the CM (product quantity produced, product quantity shipped and product value shipped) is recorded at the 7-digit SIC level, which is so detailed that on average each facility reports under three or four product categories.

, 10
Figure **3 Fixed** Capital Formation **(CTC), 1960 - 91** <!-- Start of picture text --> Millions of US$<br>350<br>300 _.<br>250_.<br>200 _ .<br>Current prices -. I<br>100 D<br>50Cnstant 1977prices u- I<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990<br>Figure 4 Automatzabon and Digitalization of Lines (CTC), 1960 - 91<br>Percerntages<br>1 00 ._<br>Automaftizon--<br>60 -- - - - -- /-<br>40 - Diglizat.on<br>20 _<br>0 1 - -<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990<br><!-- End of picture text -->
# **Graph 3**

<!-- Start of picture text --> 3000<br>2000<br>PCA_2007-08<br><!-- End of picture text -->

- Correlations between PCA and EDI have improved in most of the states. The negative correlation indicates that worse off districts are getting better allocations.

- Chattisgarh’s EDI – PCA (2007-08) correlation is as high as -0.82. Gujarat and Jharkhand also seems to be allocating more funds per child in their educationally disadvantaged districts, and they seem to be driven more by equity oriented allocations (gender and social classification of districts)

- At least in half of the states, the fund flow per child seems to be mostly explained by the equity index.  It will be interesting to see the role of specific gender related interventions like National Program for Education of Girls at Elementary Level (NPEGEL) and Kasturba Gandhi Balika Vidyalaya (KGBV – residential schools for girls from marginalized and economically vulnerable groups) in such districts.

- In around six most laggard states and districts within them, funds follow an overall pattern of input related indices.  These are states and districts which are still struggling to complete the agenda of at least ensuring the enabling conditions for learning in place.

- In Bihar, teacher related index is more correlated with PCA rather than any other index. This could be attributed to the teacher appointment provisions in the state under SSA in the most poorly provided districts.

22
# **7. References**

Almeida, R. 2007. The Labor Market Effects of Foreign Owned Firms. _Journal of International Economics_ 72(1): 75-96.

Almeida, R. 2008. “Does the Workforce in East Asia Have the Right Skills? Evidence from Firm Level Surveys. Washington DC: World Bank, mimeo.

Almeida, R. and R. Aterido. 2008. _The Incentives to Invest in Job Training: Do Strict Labor Codes Influence this Decision?_ Human Development Social Protection Working Paper. Washington DC: World Bank.

Almeida, R. and Pedro Carneiro. 2009. Enforcement of Labor Regulation, Informality and Firm Performance. _Journal of Comparative Economics_ , Volume 47 Issue 1 (2009).

Almeida, R. and A. Fernandes. 2007. “Openness and Technological Innovations in Developing Countries: Evidence from Firm-Level Surveys.” IZA Discussion Paper No. 2907.

Aterido, R., M. Hallward-Driemeier, and C. Pagés, 2007. "Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations Across Firms." IZA Discussion Paper No. 3138.

Berman, Eli, Bound, John and Zvi Griliches. 1994. "Changes in the Demand for Skilled Labor within U.S. Manufacturing: Evidence from the Annual Survey of Manufacturers," _Quarterly Journal of Economics_ 109: 367- 397.

Di Gropello, E. and C. Sakellariou. 2008. “Industry and Skill Premia in South East Asia.” Washington DC: World Bank, mimeo.

21
# b. Remittances

High population growth rates, increasing pressure or the land and fragmentation of landholdings have contributed to the growing number of ruralurban migrants. These migrants (most of whom are male) leave -he rural areas in search of more remunerative employment opportunities in urban areas. The effective result of these migration patterns is a growing number of female-headed or managed households who, to varying degrees, benefit from remittance income. For some households, remittances can be an important contribution to household income and, depending on the particular circumstances, can help the household achieve a higher level of food security. Most transfers of income are within nuclear families or between close families, with 42.4 percent being from husband to wife (Rogers, 1985). In Siaya District, a recent survey shows that for one-third of the women respondents, remittances were the household's major source of income (SIDA, 1988).

While remittance income can help relieve labor constraints faced by female-headed households by enabling chem to hire labor, the potential unreliability of such income transfers and the absence of a resident male on the farm can create or exacerbate labor constraints for the household. This is directly related to the constraints faced by women-headed households.

# 4. Women as family caretakers

## a. Overview

Women's roles as family caretakers as they relate to household food security center on the provision of food and the care of children. A woman's traditional obligations as a wife and mother were to care for her infants and young children, rear her daughters (fathers or other male elders had responsibility for boys' upbringing starting at age eight) and provide the family's food and basic necessities' (GOK and UNICEF, 1984). While these responsibilities remain, their scope and complexity have increased as a result of economic, social and cultural changes. In many cases, women now face demands that exceed their available time and income.

Women are the "gatekeepers to child welfare' (GOK and UNICEF, 1984). They have primary responsibility for the health and wellbeing of children. These responsibilities include providing a clean and safe environment, fetching water, and procuring health services. The tasks associated with childcare have increased since more children survive, births are closely spaced, and recommended childcare practices often require additional time and cash.

Women play a critical role as providers and preparers of food. They choose from among foods available in the market or those produced on the farm, and they allocate food to individual family members. Their role in food preparation makes them aware of the quality and uses of different foods. Women play an important role in distinguishing between varieties for characteristics such as taste, storage life, digestibility, and cooking time (Clark, 1985). All of these factors have implications for agricultural and nutrition research and

* ~11