rafmacalaba/datause-encoder-data huggingface dataset:
-55-

following :..b-sections discuss the specific components of this general approach to poverty in more detail.

# VI.4 Policies for the Extremely-Poor.

Policies for the extremely-poor should be based on their special needs and behavior (cf. section VI.2). These policies must act as close as possible to the relevant margin: improving health and nutrition. To achieve this, the links among income, nutrition, health, female education and fertility must be considered. I deal with each in turn.

First, the econometric evidence on the link between income and nutrition is ambiguous<sup>94</sup> . It appears that when income increases the demand for food increases (i.e., a positive and high income elasticity of demand for food), but that the additional food may not enhance nutritional status: consumers prefer to diversify the types of food consumed, and improve along the lines of presentation and palatability. But while the income ela3ticity of demand for nutrients appears low, the same is not true of the price elasticities. These findings "..suggest that certain food subsidies may not only fail in improving the nutritional status of the poor, but that they may actually worsen it" (Behrman and Deolalikar, 1988, p. 677)95. These results, together with the possibly low expenditure elasticity for calories weaken the case for generalized food subsidies, even for the extremely-poor. Until more evidence is available for Mexico, one can sensibly make a case, on nutritional grounds, only for some form of provision of nutritionally adequate foods.

94For rural South India Behrman and Deolalikar (1987) find the income elasticity of demaiS for calories for low income groups to be very low; similar results for Nicaragua are found by Wolfe and Behrman (1982). On the other hand, some studies reviewed in Behrman and Wolfe (1984) find elasticities closer to unity. See Alderman (1989) for a recent review of the evidence; apparently the econometric estimates are sensitive to issues like aggregation and the specification of the food/nutrient conversion ratios. The nuu-itional content of even as finely a disaggregated commodity as 'rice' depends on its specific variety, as well as on the way it is cooked.

95The econometric studies of households' consumption demands for Mexico that I am aware of are too aggregated to pick up these effects; both Garcia Alba (1986) and Jarque (1987) estimate complete household demand systems, but. only one category of food is included. Lustig (1984) derives price elasticities for some food categories from a Linear Expenditure System, but does not compute the elasticities for calories. This is an area where using results from other countries may be misleading, given differences in the nutritional content of diets across countries.
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These results confirm that the threshold value was correctly identified, in that there exists a different relationship between the tariff reduction above of the threshold and the uptake of preferences.

**Table 5 Results of endogenous threshold regression**

||(1)|(2)|
|---|---|---|
|Ldist|0.212|-1.426|
||(0.054)**|(0.216)**|
|lgdp|0.103<br>**||
||(0.008)||
|lpop|<br>0.019<br>*||
||(0.010)||
|lgdpdecl|-0.626<br>||
||(0.047)**||
|lpopdecl|0.809||
||(0.048)**||
|freedomidicator|-0.016||
||(0.007)*||
|Frenchcolony|0.202<br>||
||(0.050)**||
|NONfrenchcolony|0.024||
||(0.026)||
|SouthAfrica|-0.969||
||(0.055)**||
|DUMagri|0.787|0.973|
||(0.029)**|(0.032)**|
|DUMtext|0.622<br>|0.609<br>|
||(0.045)**|(0.047)**|
|DUMfoot|0.353|0.374|
||(0.066)**|(0.070)**|
|DUMmach|-0.646|-0.609|
||(0.030)**|(0.031)**|
|DUMwood|1.191<br>|1.195<br>|
||(0.054)**|(0.060)**|
|DUMmineral|0.128<br>|0.336<br>|
||(0.151)|(0.158)*|
|DUMcloth|0.650|0.615|
||(0.035)**|(0.038)**|
|dutysmall|-1.760|-1.543|
||(0.942)|(1.017)|
|dutyhigh|1.599|1.621|
||(0.208)**|(0.233)**|
|Agriculture-hightariff|-1.006|-1.004|
||(0.152)**|(0.166)**|
|quota|-0.263|-0.378|
||(0.117)*|(0.127)**|
|Constant|<br>0.352|<br>12.000|
||(0.601)|(1.807)**|
|Country fixed effects||<br>included|
|Pseudo R2|0.209|0.324|
|Observations|23684|23641|

Absolute value of z statistics in parentheses, *significant at 5%; ** significant at 1%

# **Sample Selection Model Results**

In terms of estimating the selection model in equation (7) we used as the dependent variable a zero-one variable which takes the value of one if preferences were requested and the duty reduction was higher than 4%, as indicated by our threshold estimation. This includes 70% of 22
of analysis, notably in quantifying the benefits flowing to unlisted, privately held entities, while benefits may also be industry-wide rather than firm specific.

The network dimension of connections can be seen in some of the existing research. For example, Bertrand et al. (2004) argue that networks in France get formed around attendance at a common university, prior employment as a civil servant, as well as the political party of the government under which a CEO has served. Companies managed by connected CEOs have lower rates of return on assets than those managed by non-connected CEOs and have particularly weak financial performance and high labor to capital ratios in cases where the companies are located in politically contested areas. Connected firms also tend to make lower tax payments and receive more subsidies. Such firms also tend to reciprocate benefits by taking actions supportive to politicians at opportune moments.

The body of existing research also shows that political connections to firms exist across a wide range of economies, including at different levels of income and institutional development, as well as at differing degrees of de-centralization. They also cut across political systems, although autocracies seem to select and generate rents for connected parties in rather different ways than more competitive political systems.

Finally, severing, or at least limiting, political connections has motivated the adoption of policies for privatizing assets. Boycko et al. (1995), for example, argued that large-scale (and rapid) privatization in Russia was the principal way to ensure that politicians could no longer influence company decisions. Yet two decades later, evidence from Russia and other transition economies indicates that those objectives have, at best, been partially achieved. In fact, a striking feature has not just been the persistence of state owned enterprises (and even the reversal of policy, as in Russia) but also the persistence of networks tying former SOE managers – subsequently owners – to politicians, as well as to others of their own type. Part of this can be attributed to the way in which privatization has been engineered (Megginson and Netter, 2001), but also to the deep roots of the networks linking key players.

# **3 Data description**

We use two main data sources in the paper. The first is a database compiled using publicly available information that contains an exhaustive list of Politically Exposed Persons (PEP) in each country. These data will, henceforth, be referred to as _PEPData_ . In addition, we use Bureau van Dijk’s _Orbis_ dataset that provides ownership and shareholder information, along with balance sheet and financial information, for companies in each of the countries.

_PEPData_ classifies PEPs consistent with widely used definitions, such as that provided by the Financial Action Task Force (FATF), but also extends the definition to incorpo5
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P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. All models are estimated via three regression models: ordinary least squares (OLS), robust regression and instrumental variable model. In dropping outlier model, Top 5% samples on each dependent variable. The details of instrumental variable model are shown as below.

*1 IV1: 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Independence of Supervisory Authority - Political respectively

*2 IV2: 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies respectively

*3 IV3: 1st excluded IV is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies , and 2nd is Independence of Supervisory Authority - Political respectively

# <u>Panel C:  Client Protection: Transparency in Pricing</u>

||OLS|Robust Reg<br>Depe|OLS dropping<br>top 5% of dep<br>var<br>ndent Variable: N|OLS w/ the<br>same<br>observations<br>to IV<br>umber of borrow|Instrumental<br>Variable 1*<sup>1</sup><br>ers as % ofpopu|Instrumental<br>Variable 2*<sup>2</sup><br>lation|Instrumental<br>Variable 3*<sup>3</sup>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Client Protection: Transparency in pricing|1.9637***|2.0731***|1.6876***|1.8877***|3.9929***|1.8588|2.8433***|
||[0.000]|[0.000]|[0.001]|[0.003]|[0.002]|[0.200]|[0.002]|
|Inflation, GDP deflator (annual %)|-0.1038|-0.1601*|-0.1017|-0.0758|-0.0412|-0.1394|-0.0704|
||[0.303]|[0.083]|[0.287]|[0.588]|[0.787]|[0.141]|[0.599]|
|GDP growth (annual %)|0.1663|0.1632|0.1252|-0.0059|0.207|0.078|0.0886|
||[0.370]|[0.331]|[0.477]|[0.982]|[0.493]|[0.735]|[0.752]|
|Rule of law|-1.6228*|-1.7283**|-1.5783*|-1.4764|-0.9208|-1.5626*|-1.2612|
||[0.081]|[0.042]|[0.074]|[0.169]|[0.437]|[0.096]|[0.220]|
|Bank overhead costs / Total assets|-12.8061|-4.6985|-9.6165|-9.982|-9.8464|-7.359|-5.684|
||[0.298]|[0.671]|[0.411]|[0.501]|[0.539]|[0.626]|[0.736]|
|Ease of doing business index (1=most business-friendly regulations)|-0.0204*|-0.0182*|-0.0213*|-0.019|-0.0164|-0.0176|-0.0171|
||[0.092]|[0.097]|[0.065]|[0.189]|[0.290]|[0.136]|[0.221]|
|Constatnt|1.3644|0.5105|1.8526|1.7512|-3.3416|1.6103|-0.611|
||[0.528]|[0.794]|[0.369]|[0.503]|[0.396]|[0.675]|[0.850]|
|Observations (countries)|47|47|46|34|34|36|31|
|R-squared|0.375|0.463|0.345|0.398|0.091|0.421|0.345|
|Adjusted R-squared|0.282|0.383|0.244|0.265|-0.111|0.302|0.181|
|F statistics for excluded instruments on 1st stage|||||3.904|1.818|6.578|
|Shea's adj. partial R-sq|||||0.231|0.115|0.364|
|P-values of Hansen J statistics|||||0.534|0.535|0.112|

20
- 26 -

schools fail to produce desired results, principals are often moved instead of programs being abandoned. The evaluation or research emphasis is toward documenting the variables involved in "successful or effective schools" rather than toward documenting and improving successful programs. (Webster, 1987; p. 49).

This has resulted in a tremendous growth in the importance of testing. At the State level, these changes were increasingly reflected in the titles and responsibilities of evaluation offices. In the State of Washington, the Program Evaluation section became the section for Testing, Evaluation and Accountability; in Oregon, the evaluation unit is known as the Educational Program Audit Division. In at least two states (Oregon and Michigan), educational planning was subsumed within evaluation and testing units (Caully, 1981).

A major function of the evaltation units at the state level came to be the establishment and assessment of minimum standards for schools, with state fur,ding tied to compliance, and for students, often with secondary diplomas contingent on passing a minimum competencies test. While not universal, these functions have become increasingly widespread as education reforms proceed.

Most evaluation units at the school district level are devoting a considerable portion of their resources to testing. For example, Webster (ibid; p. 50) reports that large school district evaluation units increased the proportion of their evaluation budget allocated for testing from 11% to 23.5% between 1978 and 1983. (Comparable figures for medium and small units were from 23.4% to 33.3% and from 19.0% to 30.3%.) The resources directed to testing are necessarily being taken from other possible functions, and funds available for other aspects of evaluation have been reduced. For example, in Dallas, available federal evaluation resources dropped by 65% between 1978 and 1983 (Webster, 1987, p.37). The impact of these reductions has fallen mainly on local evaluations of process and curricula.

At the same time, data on student achievement at the school level are enabling researchers to choose successful schools for deeper inquiry into the factors of school effectiveness (Cohn and Rossmiller, 1987).

The current focus of on minimum standards, achievement testing, and monitoring is not without controversy. Many educators and evaluators are concerned that intense public and media interest in comparisons of test scores across schools, districts and student ethnic and racial groups distorts the meaning of test data. It is argued that achievement tests provide narrow measures of the broad goals and achievements of schools, and that short-term political pressures on schools are accompanied by little in the way of positive resources. Reduced resources for process evaluation make it more difficult for education managers to develop information-based strategies to overcome school deficiencies. One evaluator notes that his state provides no funding for remedial programs for the schools that do not meet standards.
for a number of focal points of father’s schooling distribution are reported in the lower panel of Table 2. The estimates show that the children born to fathers with low education (less than 9 years) have higher relative mobility in rural areas, but the pattern flips in favor of urban children for the households with higher educated fathers (more than 9 years of schooling). In contrast, expected years of schooling, a measure of absolute mobility, is consistently higher in urban areas across the whole distribution of father’s schooling, although the rural-urban differences are small at the tails of father’s schooling distribution. The estimates of relative and absolute mobility using the _IGMEk_ (equation (18)) and _ESk_ (equation (19)) as the relevant measures highlight important aspects of the ruralurban divide in intergenerational educational mobility missed by the standard linear model. Perhaps the key finding is that relative mobility rankings are opposite in the two tails of the father’s schooling distribution, in sharp contrast to the linear model that suggests higher relative mobility of rural children irrespective of father’s education level.

We provide estimates of weighted IGME (WIGME) and weighted ES (WES) as summary measures of intergenerational schooling mobility (see equations (20) above). The estimates of WIGME and WES are reported in the last row of Table 2. The estimated WIGME is 0.36 for the rural sample, and 0.42 for the urban sample. Having a father with one year more schooling thus translates into 0.36 year of additional schooling in the rural areas on average, and 0.42 year of additional schooling in the urban areas. The estimated WES are 9.07 (rural) and 10.18 (urban) implying that growing up in the rural areas results in the penalty of a one year less schooling for a child.

# **The Role of Cognitive Ability**

A central concern in the literature on intergenerational mobility is whether the observed persistence across generations in economic status is primarily a result of mechanical transmission of ability from parents to children, with little influence of the economic forces such as returns to education and school quality discussed in the theoretical model in section (2). The IFLS 2014 is especially suited to make some progress on this question in the context of a developing country because it collected high quality data on cognitive ability of children.

We construct a measure of cognitive ability of children as follows. First, we calculate the 18
**Table 2 Economic growth and economic freedom, civil and political rights and economic and entitlement rights – cross section estimates.**

||**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|
|---|---|---|---|---|---|---|---|
|**Column**|1|2|3|4|5|6|7|
|**EF**|1.099***|||0.946***|1.071***||0.969***|
|**dEF 1985-1994**|1.335***|||1.250***|1.045***||1.375***|
|**dEF 1995-2004**|0.994***|||0.827***|1.047***||0.928***|
|**CPR**||0.562***||0.428***||0.446***|0.332**|
|**dCPR 1985-1994**||0.426**||0.328*||0.335|0.230|
|**dCPR 1995-2004**||0.267*||0.210||0.264*|0.127|
|**ER**|||-0.255*||-0.111|-0.192|-0.524|
|**dER 1985-1994**|||0.013||0.108|0.029|0.121|
|**dER 1995-2004**|||0.016||0.308**|0.026|0.283*|
|**initial GDP per**<br>**capita**|-0.740***|-0.585***|-0.290**|-0.868***|-0.766***|-0.496***|-0.879***|
|**Tropics**|-1.694***|-2.179***|-2.413***|-1.660***|-1.803***|-2.343***|-1.748***|
|**Resources**|0.526|0.625|-0.099|1.041**|1.069**|0.616|1.360**|
|**Pop100km**|1.286***|1.660***|2.082***|0.813*|1.279***|1.599***|0.922*|
|**Remoteness**|-0.148**|-0.190**|-0.201**|-0.125*|-0.127*|-0.170**|-0.110|
|**Constant**|-5.202***|-1.281*|1.934**|-5.733***|-7.370***|0.181|-5.121***|
|**No of countries**|98|109|105|97|98|103|97|
|**Adjusted R-sq: **|0.562|0.429|0.405|0.588|0.599|0.429|0.608|

# **<mark>*** significant at 1%, ** significant at 5%, * significant at 10*</mark>**

**Dependent variable: average annual growth of GDP per capita in constant USD over 1975 to 2005.**

22
# **1. INTRODUCTION**

With the development community dedicated to meeting the Millennium Development Goals (MDGs), policy research has begun to focus on rigorous testing of models that explain MDG outcomes. Most basic, of course, is the goal of reducing poverty and hunger by half. Many “narrative” pathways out of poverty exist, but there are few quantitative models that have been tested over significant historical periods. One reason for this shortage of empirical results is a simple lack of data. Carefully tracing broadly representative pathways out of poverty requires long-term panel data on many individuals and households and are not available for many developing countries.

However, the number of panel data sets is growing and empirical pathways out of poverty are now being described. This paper reports on the pathways out of poverty in Indonesia during a period of economic and political turmoil. Since most poor people live in rural areas, special attention is devoted to rural pathways out of poverty. The generally positive results, even during this difficult period, hold broader promise for achievement of the MDGs.

Most of the world’s poor live in rural areas and are primarily engaged in low productivity agricultural activities. Thus the main pathway out of poverty will be connected to increases in the productivity of the rural poor, whether these increases are realized in farming, in rural non-farm enterprises or by rural-urban migration. Indonesia provides an interesting case study for several reasons. First, between the late 1960s and the mid-1990s it experienced high and sustained economic growth, pulling millions of people out of poverty; second, it is large enough to display spatial and sectoral diversity, which are reflected in the large-scale household expenditure survey (SUSENAS) conducted regularly by the Central Bureau of Statistics (BPS); and third, it has a large household panel dataset, the Indonesia Family Life Survey (IFLS), which allows a detailed investigation of the characteristics of households that move in and out of poverty.

This paper draws on this Indonesian experience by using both cross-sectional data from SUSENAS and panel household survey data from IFLS to assess the importance of 2
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income. Outlets for the products and services of secondary industries were often localized and hence were less subject to macroeconomic demand fluctuations than the wage income of the primary earner. A variety of household income sources spread market and other risks.

Furthermore, these sectors typically employed a high proportion of old age, child and female labor; groups which we bave now identified as the most vulnerable to extreme poverty. The fate of such industries in the course of industrialization can thus have great impact on poverty. The importance of these secondary activities to poverty is not very different in modern industrializing countries than what it was in the 19th century.

The role that secondary activities or occupations for the poor close to pauperism can be illustrated as late as the turn of the century. Secondary activities often appear among the occupations reported in the 1906 English pauper census. Table 20 lists some examples of high female pauper rates for various sectors. As the census officials noted, the female occupation list was dominated by jobs that older women entered in an attempt to avoid sliding into pauperism. The highest pauper rates outside of agriculture were found among charwomen, laundry women, hawkers and street sellers, all informal service or sweated occupations. These industries were those in wbich it was easiest for poor women, such as widows, to find work.

The high female pauper rate among agricultural workers reflects, in part, low wages of these. More importantly, by this time few women were working for wages in agriculture -- the absolute number of female paupers who had been employed in agriculture is not very higb. Those women who worked in the agricultural sector no doubt included many who were forced into the wage labor market by family crisis.

54
Figure 5: Dynamic Conditional Correlations of Soybean Oil Nominal Price Returns <!-- Start of picture text --> 0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>−0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013<br>(a) Crude oil (b) Coal (c) Natural gas<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>−0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013<br>(d) Barley (e) Maize (f) Sorghum<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>−0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013<br>(g) Rice (h) Wheat (i) Sugar<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>−0.21970 1978 1987 1996 2004 2013<br>(j) Coffee<br><!-- End of picture text --> Note. The solid thin lines plot the estimated dynamic conditional correlations (DCC), the solid thick lines represent their trends estimated by HP (Hodrick-Prescott) filter. The dashed lines are the estimated constant conditional correlations (CCC) based on Bollerslev (1990) and their corresponding 95% confidence intervals.

22