rafmacalaba/datause-encoder-data huggingface dataset:
who campaigned to convince farmers to join cooperatives, which members of the elite then led. Thus, it was not individual farmers’ characteristics that caused them to join cooperatives. Given that a cooperative exists, farmers do not self-select into cooperative membership because factories are not sufficiently close together for them to have a marketing alternative.<sup>11</sup> Descriptive statistics of the household survey administered in fall 2008 are reported in table 1. Consistent with the predictions of the theoretical model, on average, the per unit cultivation cost for survey respondents is highest in private factory non-reserve areas, followed by private factory reserve areas and is lowest in cooperative regions. The difference in the means of the per unit cultivation cost between farmers in private and cooperative factory areas is significant at the 5 percent level.<sup>12</sup> Most of the difference is due to higher costs for hired labor among farms in the private-factory areas.<sup>13</sup> This information is consistent with labor-intensive activities being the main means for farmers to improve cane quality.<sup>14</sup> 11 A referee suggested that cooperatives may have been more likely to form in areas with more productive agricultural lands, thereby lowering production costs in those areas. Placebo tests conducted and reported in table 7 using rice, the main alternative crop grown by survey farmers, show that production costs are not significantly lower within the reserve areas of sugar cooperatives, suggesting that significantly lower costs for cane production in these areas are not due to differences in soil quality.

12 A referee suggested the possibility that costs could be lower in cooperative factory regions because cooperatives provided some inputs to their members at volume discounts or subsidized rates, a common practice among cooperatives in developing countries. However, none of the AP sugar cooperatives provided inputs to their members.

13 Family labor was based upon the reported number of days family members worked on the farm. Family labor was valued using the state government’s daily minimum wage for farm labor in 2007–8 of Rs. 85. The minimum wage is strictly enforced by democratically elected village leaders, making it a very accurate measure of unit costs for agricultural labor.

17
**_Human Capital:_** Higher levels of human capital may have a positive or negative impact on attracting FDI inflows, depending on whether FDI is primarily directed to technology-based industries that depend on skilled labor (Lucas 1990; Zhang and Markusen 1999) or labor-intensive industries attracted to a lowwage labor force (Agarwal 1980).<sup>20</sup> In this study, we use the _educational attainment data set_ provided by Barro and Lee on their website.<sup>21</sup>

**_Quality of Institutions:_** The role institutions play in all globalization-related issues, including financial globalization, has gained increasing attention in recent decades.<sup>22</sup> Better institutions decrease all types of costs—such as financial, time, and effort costs—related to starting, continuing and even ending a business. They also help create a more business-friendly economic environment by increasing the transparency of rules and regulations and decreasing the information asymmetry in investment-related activities. In this study we use measures of _Control of Corruption_ and _Political Stability_ data available in the World Bank’s Worldwide Governance Indicators (WGI).

**_Investment Policy Openness:_** The more open the investment regime, the greater the expected FDI flows. Most countries have restrictions on investment, and, as noted above, these barriers are particularly in South Asia. Some nations impose different rules for foreign and domestic investment; some restrict access to foreign exchange; some impose limits on payments, transfers, and capital transactions; some close certain industries to foreign investment. The proxy for this variable comes from the Heritage Foundation’s _Investment Freedom Index_ and takes the value of 100 for no restrictions and subtracts points for each restriction found in a country’s investment regime.

**_Infrastructure:_** The availability and quality of infrastructure is a key determinant of FDI inflows.<sup>23</sup> Better infrastructure facilitates investment, decreases production costs, improves service provision, and increases investment returns. This study relies on the most frequently used data on infrastructure—the _ICT infrastructure_ data, which can be found in the World Bank’s WDI. Other data utilized for robustness, although not as complete, _quality of logistics and ports_ and _roads per capita_ are used for robustness. They are available at the same source.

**_Trade Openness:_** Free trade should be an important factor for attracting FDI, particularly in exportoriented businesses. In addition to allowing the FDI-related products to be easily exported, more open trade lowers the costs of imported inputs. On the other hand, high tariff barriers may also attract FDI by

> 20 For example, see (Root and Ahmed 1979), (Schneider and Frey 1985), (Narula 1996), and (Noorbakhsh, Paloni and Youssef 2001).

> 21 <u>http://www.barrolee.com/</u>

> 22 For example, see (Wheeler and Mody 1992), (Root and Ahmed 1979), (Agarwal 1980), (Wei 2000), (Asiedu 2002), (Dutta and Roy 2008), and (Solomon and Ruiz 2012).

> 23 For example, see (Addison and Heshmati 2003), (Asiedu 2002), and (Blonigen and Piger 2011).

22
**Table 1 Pension fund assets("),** 1988

||**Stoct? of**<br>assets(end-<br>**1988) $bn**|**%**of<br>personal<br>**sector**<br>**assets**|Iof<br>GNP|Total**net**<br>investment<br>**Obn**|Io<br>personal<br>**sector**<br>**saving**|Iof<br>**_ON"_**|
|---|---|---|---|---|---|---|
|UK|475.9|27.2%|57.0%|2 1 <sup>.9(c)</sup>|71.30)|**3.5%**|
|Us|1646.7|13.20|33.8%|72 <sup>**6(c)**</sup>|49.9%|1.5%|
|Canada|130.9|14.1%|26.7%|11.4Wd!|38.8%|2.4%|
|Japan(b)|134.1|2.1%|4.6%|17<br>o(d)|19.5%|0.6%|
|**oermany(b)**|41.1|:4|3-5%|**44**(d)|**3.9%**|0.3%|
|Netherlar1esf)|<br>177.4|39.6%|77.9%|:1 6(dl|37.9%|5.1%|
|Swedon(q)|51.2|-|28.4%|3 <sup>d'd'</sup>|())|2.1%|
|**Denmark(h)**|13.1||12.9%|1 <sup>2(d)</sup>|-|1.1%|
|Iwitserland|121. 1|-|68.0%|ll j(d)|95.0%|6.2%|
|**HemoJ**France|27.7|3.1%|3.0%|1 <sup>o(C)</sup>|1.5%|0.1%|

Sources National Flow-of-Funds Data

- Notes (a) The table covers only independent funded schemes, which are the main subject of the paper, and hence excludes pension funds managed by life insurers, which in 1988 had asoets of $100 billion in the United Kingdom, $628 billion in the United States, $80 billion in Japan and $6 billion in Germany.

- (b) The data exclude unfunded Japunese and German pensio4 reserves held directly on the balance sheet of the sponsoring firm (booking). In 1988 these amounted to $87 billion in Japan and $100 billion in Germany.

- (a) Flow.

- (d) Difference of stock (ie may include some revaluations)

- (e) The large t lancing item in the United Kingdom national accounts means this ratio may be inaccurately measured.

- (f) Includes both public (ASP) and private funds. Private funds alone were $104 billion.

- (g) Data for Sweden relate to the ATP scheme, which is a hybrid between social security and funded private schemes (it is nationally coordinated but relies on employers contributlons and employers **are** represented on the investment boards). There are also private schemes in Sweden (ITP/STP) but they are usually booked or unfunded.

- (h) 1987.

- (i) Not meaningful (saving negative).

5a
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Policy Research Working Paper 7310

# **Abstract**

In Turkey, female employment and education are still relatively low, while fertility levels are high compared with other European countries. However, Turkey stands just at the edge of an important social transition. Increasing female education and employment come along with important decreases in fertility. By mobilizing census and survey data, this paper finds that fertility decreases are mainly caused by fewer transitions to a third birth. Graduate women participating in the formal labor market are most at risk of deciding against child arrival in comparison with inactive or unemployed women. The third rank is particularly concerned, as women’s income contribution seems to be crucial for many families that already have two children, and the arrival of a third child risks reducing or stopping women’s working activities in the absence of institutional childcare support. Policies enabling women to combine work and family life, which have been proven effective in other European countries, emerge as useful to avoid a further fertility decline below replacement level in Turkey.

This paper is a product of the Poverty Global Practice Group and the Social Protection and Labor Global Practice Group, thanks to a grant by Sweden to the World Bank’s Umbrella Facility for Gender Equality. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Angela.Luci-Greulich@univ-paris1.fr.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
productivity and price shocks are not targeted to these industries in our scenarios. In addition, demand for these activities is almost exclusively domestic.

**Table 9. Export and productivity scenarios: GDP shares by sector (%)**

||**2012**|**base**|**agrpweff **|**indupweff **|**transeff**|
|---|---|---|---|---|---|
|Agriculture and other primary|10.3|9.4|11.6|6.7|8.0|
|Industry|24.3|23.2|24.3|32.5|20.7|
|Industry -manufacturing|16.4|14.9|15.8|24.0|11.7|
|Industry -other|7.9|8.3|8.6|8.5|8.9|
|Services|65.4|67.4|64.0|60.9|71.3|
|Services - private|53.3|53.6|50.4|46.7|58.2|
|Services - government|12.1|13.8|13.7|14.1|13.2|
|Total|100.0|100.0|100.0|100.0|100.0|

Note: Columns other than 2012 show results for 2020.

The share of government receipts and spending merits a comment. As direct and indirect taxes are adjusted through uniform scaling to balance the government accounts, whereas real government consumption is held constant between the scenarios, the GDP share of government is lower the faster economy grows. In other words, in the environment of fast growth, government uses the fiscal space to reduce the tax burden and its share of economy shrinks. In consequence, we see that the government’s share of GDP is at its lowest under _indupweff_ (Table 10).

**Table 10. Export and productivity scenarios: Government receipts and spending** **<u>(% of GDP)</u>**

|||**F**<br>|**inalyear**<br>||||
|---|---|---|---|---|---|---|
|**Indicator**||**2012**|**base**|**agrpweff **|**indupweff**|**transeff**|
|Receipts|Direct taxes|10.2|11.0|9.9|9.6|10.5|
||Import tariffs|1.7|1.7|1.4|1.5|1.5|
||Other indirect taxes|14.7|16.0|13.6|12.9|14.7|
||Private transfers|1.4|1.4|1.3|1.3|1.3|
||Foreign transfers|3.7|2.2|1.5|1.3|2.0|
||Factor income|1.6|1.8|1.9|1.9|1.8|
||Domestic borrowing|2.1|2.1|1.6|1.5|1.9|
||Foreign borrowing|1.2|1.2|0.8|0.7|1.1|
||Total|36.6|37.4|31.9|30.7|34.9|
|Spending|Consumption|18.7|18.3|17.1|17.0|17.3|
||Fixed investment|2.8|2.7|2.5|2.4|2.6|
||Private transfers|14.1|15.3|11.6|10.6|13.9|
||Foreign transfers|0.4|0.4|0.3|0.2|0.3|
||Domestic interests|0.1|0.2|0.2|0.1|0.2|
||Foreign interests|0.5|0.6|0.4|0.3|0.5|
||Total|36.6|37.4|31.9|30.7|34.9|

Note: Columns other than 2012 show results for 2020.

Given the labor market situation of Moldova, increases in productivity, exports, and GDP translate into higher wages, as we can see from Figure 15. The changes in wage growth are quite uniform across the different labor types: all types of labor benefit from improved efficiency and export demand in agriculture and food industry and even more strongly from higher efficiency and demand for industrial non-food exports. The only noteworthy difference is that the least educated labor type, primary labor, benefits most strongly from higher agricultural export prices ( _agrpweff)_ whereas high25
|||Not|Rate||Rate||Below||Mean||
|---|---|---|---|---|---|---|---|---|---|---|
|Province|District|Precise|($3.2)|S.E.|<br>($5.5)|S.E.|<br>MClass|S.E.|<br>Cons.|S.E.|
|Khorezm|Shovot||0.046|0.012|0.475|0.028|0.944|0.013|6.463|0.203|
|Khorezm|Urganch||0.075|0.009|0.361|0.017|0.885|0.011|9.767|0.366|
|Khorezm|Xiva||0.000|0.000|0.353|0.040|0.921|0.022|7.088|0.264|
|Khorezm|Xonqa|*|0.066|0.087|0.397|0.150|0.897|0.076|8.468|1.797|
|Khorezm|Yangiariq|*|0.065|0.087|0.395|0.150|0.894|0.076|8.492|1.796|
|Khorezm|Yangibozor||0.091|0.023|0.448|0.039|0.929|0.020|7.587|0.585|
|Namangan|Chortoq||0.076|0.011|0.213|0.017|0.777|0.017|12.201|0.598|
|Namangan|Chust||0.093|0.015|0.372|0.025|0.839|0.018|9.445|0.426|
|Namangan|Kosonsoy||0.006|0.005|0.184|0.027|0.811|0.027|9.361|0.313|
|Namangan|Mingbuloq||0.157|0.025|0.473|0.035|0.909|0.020|7.183|0.356|
|Namangan|Namangan||0.191|0.014|0.434|0.017|0.872|0.012|8.271|0.252|
|Namangan|Norin|*|0.273|0.091|0.647|0.150|0.949|0.080|6.457|1.887|
|Namangan|Pop||0.113|0.022|0.255|0.031|0.838|0.025|12.802|0.963|
|Namangan|To'Raqo'Rg'On|*|0.193|0.084|0.500|0.145|0.904|0.076|7.754|1.814|
|Namangan|Uchqo'Rg'On||0.000|0.000|0.251|0.031|0.890|0.022|9.144|0.353|
|Namangan|Uychi||0.124|0.023|0.322|0.033|0.873|0.023|9.521|0.797|
|Namangan|Yagiqo'Rg'On||0.114|0.016|0.376|0.025|0.868|0.017|7.673|0.221|
|Navoi|Karmana|*|0.149|0.082|0.309|0.142|0.907|0.076|8.486|1.791|
|Navoi|Konimex|*|0.115|0.082|0.269|0.142|0.939|0.077|8.726|1.792|
|Navoi|Navbahor|*|0.096|0.082|0.265|0.142|0.840|0.076|8.850|1.799|
|Navoi|Nurota||0.030|0.007|0.300|0.019|0.857|0.015|9.306|0.335|
|Navoi|Qiziltepa||0.029|0.011|0.276|0.029|0.754|0.028|10.507|1.606|
|Navoi|Tomdi||0.094|0.018|0.352|0.030|0.924|0.016|7.939|0.449|
|Navoi|Uchquduq|*|0.000|0.083|0.094|0.144|0.916|0.077|9.760|1.840|
|Navoi|Xatirchi||0.138|0.018|0.486|0.026|0.918|0.014|7.579|0.471|
|Samarkand|Bulung'Ur|*|0.181|0.082|0.424|0.142|0.826|0.076|8.012|1.804|
|Samarkand|Ishtixon||0.144|0.011|0.374|0.015|0.848|0.011|10.228|0.311|
|Samarkand|Jomboy|*|0.258|0.084|0.558|0.144|0.883|0.076|6.894|1.819|
|Samarkand|Kattaqo'Rg'On||0.230|0.023|0.483|0.028|0.915|0.015|8.686|0.696|

43
5 where the collection system is efficient and there are a large number of of collection windows<sup>4</sup> , large amounts of rice and wheat are bought up by the government, meaning the FCI, under this program.

A part of this grain collection is then stored as reserves for times of shortage in the future. But a part is meant to be sold to poor households, who have cards identifying them as Below Poverty Line (BPL) households. The sale to BPL households is done through what are called ration shops or public distribution shops. India has roughly half a million ration shops scattered through the nation. The FCI sells the food grain meant for such distribution at below market price to the ration shops with the instruction that the ration shops then sell these grains to poor households at a pre-fixed price, which is also below the free market price, and according to the maximum per-household quota specified by the government. The idea is that poor households should have the right to get some essential food grains at a low price.

A huge amount of effort goes into designing this system, which is now backed up by an act of government. The problem is that the law is widely violated. There are careful studies that show that over the last decade somewhere between 43% and 54% of the grain meant to be distributed under this system and released by the FCI for this purpose simply leaks out (see Jha and Ramaswami, 2010; Khera, 2011). Table 1 below presents some of this data. Wheat diversion is greater than rice. Overall food grain diversion peaked in 2004-5, when more than half the grain released for poor households did not make it to those households. There has been a slight improvement since then, but only slight.<sup>5</sup>

**Table 1. Diversion of PDS Food Grain in India** (% of grain released for the poor)

||**Rice**|**Wheat**|**Foodgrain**|
|---|---|---|---|
|2001-2|18.2|66.8|39.0|
|2004-5|41.3|70.3|54.0|
|2006-7|39.6|61.9|46.7|
|2007-8|37.2|57.7|43.9|

**Source:** Khera (2011)

> 4 The MSP is usually set fairly high. The way the glut of sales to the government that would result from this is kept manageable is by not having windows for receiving food grain in large parts of India. This indeed gives rise to a non-level playing field for farmers and deserves criticism but that lies beyond the focus of this paper. 5 I have described this food distribution in some detail in Basu (2015).
_Dbsussion_ 21 For the specific causes, the statistical adequacy of the equations varies by cause, as indicated earlier using coefficients of determination. Those causes of death that have distinctive patterns in Africa and the Middle East, and possibly in Asia, may be poorly estimated for these regions. This does not mean that diseases especially prevalent in these regions are underestimated: their prevalence might still be properly represented by extension of pattems across developed and Latin American countries. Rather it means that causes of death that show important discontinuities between reporting countries and nonreporting countries may be poorly estimated for the nonreporting countries. Unfortunately, without reports for all regions, one cannot tell defiritively which causes of death these are.

Even if the equations were entirely accurate for the present, projecting them into the future would still raise several issues. Future estimates also depend on assumptions about age structure, about current vital rates, and about trends in these variables. Furthermore, other unexpected changes could alter the outlook. Estimates for new causes of death cannot be generated by this procedure. For Human Immunodeficiency Virus (HIV) infection, in particular, this limitation may have significance for the broader picture. Besides changes in diseases, changes in medical technology, in life styles, in health and safety measures, and in the delivery of health services, as well as in the nature and quality of cause of death reports, could change the picture. All that the projections provide is a hypothetical picture based on somewhat contradictory assumptions: that mortality decline will be extended into the future, extending past trends, but that historical patterns associating particular disease categories with particular mortality levels will remain as they are.

Despite these caveats and uncertainties, the broad patterns in the estimates and projections are certainly plausible: the regional contrasts and the way some of them persist over time; the expected increase in circulatory system diseases as a cause of death; and the separate contributions of age structures, mortality levels, and epidemiological change to variation in distribution of deaths by cause. The implications worth drawing from these broad patterns depend on the questions one asks. As Table 8 showed with alternative percentage distributions, from different perspectives the figures reveal different patterns. One clear implication from any perspective, however, is that better data on cause of death are essential. In this regard, WHO is working with countries to strengthen their cause of death information systems as an essential support for health monitoring.

# Annex A. Methodology

This annex provides more detail about the data on causes of death, the projection of mortality levels, and the regressions to decompose overall mortality into major and specific causes. The reclassification of deaths from unspecified causes is also discussed.

## Table Al shows how countries are classified.
_Adult labor force participation_ In our analysis of the impact of the PAL program on labor supply, we focus on adult males and females between 18 and 60 years of age (in the baseline round). The dependent variable _Y_ ( _i_ , _t_ ) in equation (1) is specified by a binary variable indicating whether an individual _i_ works in the labor market in period _t_ . Specifically, a person is classified as working in the labor market ( _Y_ ( _i_ , _t_ ) = 1) if he/she reported having worked over the previous week (paid or unpaid) or had work but did not work. All others, such as those looking for work, students, doing household chores, and retired/pensioners, are classified as not working in the labor market (( _Y_ ( _i_ , _t_ ) = 0 ).29 Equation (1) is estimated using a linear probability model.<sup>30</sup> Table 3 presents the DiD _J_ estimates (summarized by the parameters γ in equation 1) of the impact of PAL on participation in labor market activities of male and female adults.31 Two specifications are used: in specification A we use same set of control variables in consumption (including binary variables for the round of interview) as well as binary variables for each village in the sample (and correcting standard errors for clustering of individuals at the village level). Specifically, the vector _X(i,t)_ in equation (1) consist of a set of binary variables identifying the date of interview of the household, and the age of the individual, his/her gender, years of education, binary variables for his/her marital status, the household demographic composition (i.e., the number of children separately by age group, adult men (and women separately) aged 19 to 54,

> 29  Individuals who reported permanent incapacity to work are dropped from the sample analyzed. In fact, the classification was based on questions 2.15, 2.16, and 2.18 in the baseline survey, (and questions 2.12, 2.14, and 2.15 in the follow-up survey). The set of three questions in each survey round is useful for verifying the nature of the work performed.

> 30 As Ai and Norton (2003) have demonstrated, the coefficient of the interaction term in nonlinear models, such as probit or logit, does not equal the marginal effect calculated by statistical software. We have also estimated the marginal effect of the interaction terms using the _inteff_ command in Stata proposed by Norton et al (2004) with similar qualitative results as with linear probability model presented here.

> 31 The complete set of parameter estimates is available directly from the authors upon request.

23