rafmacalaba/datause-encoder-data huggingface dataset:
32 horizontal links with the EU than Mexico with its NAFTA partners. In individual product groups, the HTS indices for CEEC trade with the EU are relatively high when compared with Turkey and Mexico-they are, however, mostly lower than for Malaysia's trade with East Asia.

The HTS indices for CEECs as well as for comparator countries significantly vary by a product group. The values of HTS indices are high for furniture parts for all countries including Turkey, Malaysia and Mexico. The exception is Slovenia with imports accounting for 28 percent of the value of exports to the EU. High levels of horizontal trade specialization can be also observed in switchgear parts for all countries except Latvia, Lithuania and Malaysia.

In other groups the situation is more diversified. Bulgaria and Slovakia seem to be highly involved in various stages of the furniture production process. Czech Republic and Slovakia specialize in automotive industry (piston engine parts and motor vehicle parts). Poland has the highest value of HTS index for engine parts. The Czech Republic also has together with Hungary the highest HST index for office equipment parts, which is, however, well below of that for Mexico. Most other CEEC have low HTS with the exception of Estonia, Slovakia and Slovenia.

The largest dispersion is in telecommunication parts. The values of HTS indices for only /three CEEC-Estonia, Hungary and Slovakia-fall in between the range set by Malaysia (0.86) and Mexico (0.47). The index for Estonia's trade in telecommunication parts is the highest among CEEC, but still considerably lower than that for Malaysia in trade with East Asia.

# 5.2. **Integration into global networks of production and marketing**

Since HTS only addresses the extent of mutual trade in a category of parts, it can only confirm or rebuff the suspicion that the increase in trade in parts has been due to production fragmentation. If its value is zero, one may suspect that there is no production sharing. There is one caveat, however. A part may be used in the assembly of a component or final product. Furthermore, if the value of the HTS index is close to unity indicating two-directional flows roughly equal in terms of value, these may merely indicate flows servicing different production processes not necessarily linked to production sharing.

Hence, one has to go a step further by examining developments not only in trade of parts but also that in components (i.e., those products that are not designated as parts in SITC. Rev. 2) as well as that in final products. A country may specialize, for instance, in the assembly of TV receivers. While the 'import' RCA should then indicate revealed comparative advantage in parts, this would not be captured by the HTS index. Neither would it provide evidence as to local firms' capture exports of piston engines assembled for Volkswagen-Audi in Hungary (see Section 6.2). Note also a dramatic decline in HTS index of parts for furniture whose value fell from 0.90 in 1993 to 0.54
are some nascent positive changes in the nature of domestic financial markets, with their reduced currency and maturity mismatches. Nonetheless, to a large extent, only a few firms seem to be able to use capital market financing. Latin America has not become a place with finance for all, at least based on the data analyzed in this paper.

What explains the lagging financial development in emerging economies and in Latin America in particular? What explains the persistent mismatch between expectations and outcomes? In this final part of the paper, we discuss and speculate on some of the possible reasons, based on evidence from various pieces of other work. We also discuss some of the possible avenues for the future.

While it is difficult to answer the question of whether the problems lie in the supply or in the demand side of funds, the findings in this paper suggest that the insufficient financial development does not seem to be determined just by the lack of available funds. In fact, financial underdevelopment seems to coexist with a large pool of domestic and foreign funds in the economy, not least because domestic residents are sometimes induced to save in marketbased instruments targeted to domestic markets only. Moreover, funds are also available from foreign investors eager to invest in emerging economies.<sup>24</sup> The availability of funds will naturally provide a continuing deepening of some markets. There also may be problems on the demand side, but there is not enough evidence to confirm this. Some surveys indicate that SMEs are not well served, but many owners do not want to lose the control of their firms and do not wish to subject their companies to market forces. Moreover, even when firms complain about poor access to financing, it is not clear that they have worthwhile investment projects.

The burden does not seem to rest on aggregate factors alone. The macroeconomic performance and institutional framework have likely hampered financial development in the past, but many developing countries have substantially improved their macroeconomic and institutional stances, and yet financial development has not progressed as expected. In the 2000s, there has been much less crowding out by the government in the financial sector, especially in bond markets and banking. Moreover, corporate governance and other institutional indicators have improved and are not likely to explain the cross-regional and cross-country variation in financial development.

> 24 One could argue that international financial markets are very volatile and that foreign investors are not reliable. But this is the case across countries, and it is difficult to explain the cross-country or cross-regional volatility. Furthermore, international investors seem to be favoring emerging economies in relative terms even in a period of global crisis, although they did pull back from all countries in the wake of the global financial crisis.

30
|Gold &<br>Livestock<br>Non-Housing<br>Silver<br>Wealth|(2)<br>(3)<br>(4)|)<br>(-0.42∗∗∗)<br>(-2.73∗∗∗)<br>(-0.38∗∗∗)<br>∗<br>-(0.19)∗∗∗<br>-(0.46)∗∗∗<br>-(0.15)∗∗∗<br>)<br>(-3.16∗∗∗)<br>(-0.66∗∗∗)<br>(-0.09∗∗∗)|∗<br>-(0.97)∗∗∗<br>-(0.99)∗∗∗<br>-(0.29)∗∗∗<br>)<br>(-0.80∗∗∗)<br>(-2.85∗∗∗)<br>(-0.72∗∗∗)|∗<br>-(0.73)∗∗∗<br>-(0.86)∗∗∗<br>-(0.23)∗∗∗<br>)<br>(-0.89∗∗∗)<br>(-0.16∗∗∗)<br>(-0.92∗∗∗)|∗<br>-(0.07)∗∗∗<br>-(0.08)∗∗∗<br>-(0.03)∗∗∗|∗<br>-13156∗∗∗<br>-13156∗∗∗<br>-8230∗∗∗<br>-0.18∗∗∗<br>-0.31∗∗∗<br>-0.34∗∗∗<br> high-confict if it experienced more terrorist attacks from 1973 to 2014<br> Household/Household-head controls include gender, age, marital status,<br>nifcance levels: ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|
|GSL|(1)|(-2.25∗∗∗<br>-(0.44)∗∗<br>(-0.22∗∗∗|-(0.96)∗∗<br>(-1.19∗∗∗|-(0.86)∗∗<br>(-0.47∗∗∗|-(0.08)∗∗|-13156∗∗<br>-0.22∗∗∗<br>ct is classifed <br> parentheses. <br>f dwelling. Sig|
|Variables||Variance of Income (log)<br>Confict×Variance of Income (log)|Confict|Income (log)||Observations<br>R-squared<br>The table shows the estimated coefcients of equation (2). A confi<br>compared to the mean of all provinces. Robust standard errors are in<br>education, household size, number of males per household, and type o|

27
# **Table 3f.  Trade Flows of SADC**

|||Imports|||Exports||
|---|---|---|---|---|---|---|
||USD in|||USD in|||
||<br>billion|% of total|% of supply|<br>billion|% of total|% of output|
|**Business Services**|||||||
|Air Transport|2.5|2.1|32.8|2.5|1.8|37.1|
|Communication|0.1|0.1|0.4|0.6|0.4|3.0|
|Insurance|0.8|0.6|2.3|0.8|0.6|3.1|
|Business Services nec|7.8|6.6|10.4|2.4|1.7|4.2|
|Financial Services nec|0.8|0.7|4.6|0.9|0.6|6.3|
|Transport nec|1.8|1.5|6.2|2.9|2.0|10.8|
|Water Transport|0.5|0.4|17.2|0.5|0.3|18.8|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|21.4|18.0|21.5|36.8|25.6|32.9|
|Energy and Minerals|8.8|7.4|39.5|60.2|41.9|83.2|
|Food Products|6.5|5.5|10.5|4.6|3.2|7.9|
|Petroleum and Coal Products|4.3|3.6|21.6|1.4|0.9|8.5|
|Other Manufacturing|43.8|36.9|37.9|13.2|9.2|16.6|
|Textile and Apparel|4.8|4.0|18.3|3.4|2.4|14.8|
|Wood and Paper Products|2.9|2.4|12.9|2.4|1.7|11.5|
|**CRTS**|||||||
|Agriculture and Forestry|1.6|1.3|4.3|4.8|3.3|11.9|
|Other Services|6.3|5.3|3.3|4.3|3.0|2.3|
|Trade|3.7|3.1|4.1|1.4|0.9|1.6|
|Utilities|0.7|0.6|3.5|0.5|0.4|2.5|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

52
At the end of December 2011, 19 non-life and composite insurers had foreign shareholders, most of which are regionally based, largely in Kenya. That said, South African interests are now having a greater impact. Recent market entrants have, however, largely been locally owned, but they are small, and there are no major local insurance groups operating in Tanzania.

With market shares from 25.4% to 10.7%, the six largest companies (listed according to market share) are Jubilee, Heritage, Alliance, AAR, Phoenix and Momentum.

Jafari (2014e) provides details of who owns the insurance companies of Tanzania, and calculates the market shares by region. The resulting market shares by regions of our model are as follows: **SADC, 7.6%; Kenya, 48. 2%; Rest of the World, 4.2%; European Union, 3.2%; others 0%.**

# **Kenya**

There were more than 35 insurance companies operating in Kenya in 2013. The largest companies and their market shares are the following: Jubilee (11.3%); CIC General (9. 2%); UAP (8.3%); APA (7.8%); ICEA Lion (5.6%); Heritage (4.8%); Kenindia (4.7%); AIG Kenya (4.5%); Britam (4.4%); First Assurance (4.1%).

Who owns these companies and their market shares by region of their model is explained in Jafari (2014e). The resulting market shares by regions of our model are as follows: **SADC, 4. 2%; USA, 4.7 %; Kenya, 84.6%; Rest of the World, 2.8%; European Union, 3.7%; others 0%.**

# **Uganda**

The largest insurance companies operating in Uganda, with their market shares and their primary country of ownership, are as follows: Jubilee (Kenya, 26.0%); AIG (USA, 14.5%); UAP (Kenya, 13.1%); Goldstar (Uganda, 6.3%); Lion (Kenya, 5.5%); Insurance Company of East Africa (Kenya, 5.4%); Phoenix of Uganda (Kenya, 4. 2%); East Africa Underwriters (South Africa and Kenya, 43.8%); APA (Kenya and South Africa, 3.5%); and Niko (Malawi, 2.3%). This identifies 86.7% of the market. Although we have not identified ownership from India, there are reportedly Indian ownership interests, so we allocate some ownership share of the unidentified portion to the rest of the world. Since it is likely that investors from Uganda, the 102
**Table 15. Multinoniial logit estimates of treatment choice for children under three reporting fever in the past two weeks (no modern sector treatment is reference choice). Model including cluster wealth and fever**

||_Wes_|_ternandC _|_entralAfrica_||_East_|_ernandSou_|_thern Africa_||
|---|---|---|---|---|---|---|---|---|
||_Public,_|_Public,_||_Private,_|_Public,_|_Public,_||_Private,_|
||_higher_<br>_level_|_lower_<br>_level_|_Private,_<br>_medical_|_commer_<br>_-cial_|_higher_<br>_level_|_lower_<br>_level_|_Private,_<br>_medical_|_commer_<br>_-cial_|
|Wealth index|0.195|0.211|0.044|0.057|0.067|-0.016|0.015|0.114|
||(1.40)|(1.71)*|(0.29)|(0.42)|(0.77)|(0.30)|(0.24)|(1.38)|
|Wealthindexsquared|0.058|-0.086|0.031|-0.018|0.004|0.012|0.019|-0.021|
||(0.87)|(1.42)|(0.45)|(0.24)|(0.17)|(0.64)|(1.09)|(0.84)|
|Clustermean|||||||||
|wealthindex|0.596|-0.135|0.518|0.407|0.421|0.074|0.532|0.273|
||(2.42)**|(0.59)|(1.99)**|(1.55)|(2.13)**|(0.81)|(4.92)**|(1.81)*|
|Clusterwealth|||||||||
|indexsquared|-0.013|-0.106|0.036|-0.698|-0.137|-0.074|-0.057|-0.110|
||(0.09)|(0.72)|(0.24)|(3.72)**|(1.79)*|(1.76)*|(1.20)|(1.76)*|
|Clusterprop.withfever|1.142|-0.684|-0.297|-0.096|-0.699|0.482|0.037|0.560|
||(1.48)|(0.98)|(0.39)|_(0.15)_|(2.22)**|(2.19)**|(0.15)|(1.75)*|
|1=Male|0.089|0.065|0.182|0.257|0.060|-0.008|0.029|-0.127|
||(0.33)|(0.31)|(0.65)|(1.01)|_(0.55)_|(0.11)|(0.32)|(1.12)|
|Age(months)|0.048|0.030|0.004|-0.051|0.046|0.053|0.063|0.009|
||(0.70)|(0.68)|(0.06)|(0.90)|(1.84)*|(3.34)**|(3.18)**|(0.38)|
|Agesquared(months)|-0.001|-0.000|-0.000|0.002|-0.002|-0.002|-0.002|-0.000|
||(0.33)|(0.25)|(0.02)|(1.23)|(2.43)**|(3.80)**|(3.22)**|(0.15)|
|Mother'sschooling(yrs)|-0.039|0.063|0.106|-0.028|0.046|0.052|0.055|-0.004|
||(0.83)|(1.71)*|(2.42)**|(0.58)|(2.30)**|(4.04)**|(3.16)**|(0.21)|
|Husband's|||||||||
|schooling(yrs)|0.014|0.031|0.084|0.047|0.035|0.032|0.022|0.015|
||(0.42)|(0.98)|(2.17)**|(1.25)|(1.86)*|(2.50)**|(1.30)|(0.76)|
|1=Drinking|||||||||
|watercovered|-0.564|0.202|-0.043|-0.132|0.041|0.260|0.136|0.170|
||(1.65)*|(0.67)|(0.12)|(0.40)|(0.30)|(3.12)**|(1.23)|(1.22)|
|1=Toilet|||||||||
|flushorpitlatrine|0.324|-0.144|-0.242|-0.105|0.313|0.138|0.237|0.209|
||(0.84)|(0.49)|(0.65)|(0.30)|(2.03)**|(1.44)|(1.90)*|(1.39)|
|1= Urban|1.102|0.315|-0.522|0.430|0.871|-0.482|-0.409|-0.177|
||(2.41)**|(0.89)|(1.16)|(1.10)|(3.91)**|(2.93)**|(2.37)**|(0.74)|
|Constant|-5.259|-1.420|-0.845|-0.365|-0.719|-0.353|-1.024|-1.898|
||(3.64)**|(1.86)*|(0.82)|(0.42)|(1.71)*|(1.32)|(3.06)**|(4.26)**|
|Observations|6394|6394|6394|6394|8170|8170|8170|8170|
|Joint tests(p-values)|||||||||
|Ownwealth|0.260|0.083*|0.820|0.893|0.523|0.814|0.289|0.386|
|Clusterwealth|0.037**|0.698|0.134|0.000**|0.097*|0.209|0.000**|0.131|
|Clustervariables|||||||||
|(wealthandfever)|0.040**|0.683|0.252|0.001**|0.044**|0.050*|0.000**|0.070*|

_Note:_ Model includes dummy variables for region (not shown) and a dummy variable for husband's data available. T- statistics, adjusted for clustering are reported in parentheses. * significant at 10%; ** significant at 5%. _Source:_ Author's calculations from pooled and weighted DHS.

_29_
**The Investment Decision:** A **Re-examination of Competing Theories Using Panel Data**

Even though business expenditures on plant and equipment gross investment (business fixed investment) in the United States constitute only about 10% of GNP, it is still perhaps the most important component of GNP because: (i) plant and equipment are durable goods and therefore variations in investment expenditures have long-term consequences for the economy's productive capacity; (ii) investment expenditures affect demands for the products of the construction and producer's durable goods industries. Therefore, changes in investment expenditures lead to shifts in the aggregate levels of employment and personal income through both direct and indirect effects; and (iii) since investment is the most volatile component of GNP, aggregate supply and demand are very sensitive to changes in investment.

Given these considerations, economists have been concerned with the analysis of the determinants of investment (capital) expenditures for a long time. This analysis has been conducted at all levels of disaggregation viz. economy, industry, and the firm. This paper compares the alternative theories of investment using a panel of U.S. manufacturing firms for the 1972-1990 period.

The study is organized into four sections. Section I begins with a discussion of the alternative models of investment, then examines the empirical evidence for these models, and considers the policy implications of the different models. Section II reports the results of the time-series regressions. Section III has results from cross-section regressions. Section IV deals with fixed effects models. The final section concludes the study and discusses the implications for developing countries.

1
across households.<sup>17</sup> From this more explicit formulation, household utility can now be seen as a function of n + 1 _aggregate consumption goods_ , x and the yi or equivalently the li, i = 1 to n. None of the goods in the household utility function in (1) can represent assigned consumptions. The demand functions in (5a,b) are _aggregate household demands_ for the market and domestic goods.

Models estimated on household survey data with missing information on individual consumptions are inevitably limited to the estimation of systems in which the demands of all family members, including children,<sup>18</sup> are aggregated. Since all family members face the same prices for all goods, it is valid to impose the restrictions required for aggregation. As Bergstrom (1997) points out, if the indirect utility function of each individual household member is of the Gorman polar form, and _all face the same prices_ , then aggregate demands are functions of these prices and aggregate household income. In other words, the pattern of household demands can be thought of as deriving from the behavior of a single representative individual endowed with total household income, Y, and facing prices, pi, i = 1 to n.<sup>19</sup> In this sense the model can be said to represent a single decision-taking agent.<sup>20</sup> Thus the model is consistent with an underlying individualistic decision taking process in which member i, i = 1 to n, maximizes ui = ui(xi, yi1, ..., yin) (8) where xi and yij are i’s consumption of the composite market good and of the domestic good produced by member j, yj = Σiyij, j = 1 to n, respectively. Individual preference parameters cannot be estimated due to missing data on the xi and yij. It is straightforward to show that the household parameters represent unidentified individual preference parameters weighted by unidentified shares of household full income. And so unless

> 17 Note that the Becker (1981) model of the household division of labor allows domestic productivities to vary, and therefore also treats the standard model as a special case.

> 18 For a model that includes children explicitly, see Apps and Rees (2002).  Systems that specify, for example health production and demand functions for children, and are estimated on anthropometric data, also represent models that include children explicitly.

> 19 The conditions are set out in Deaton and Muellbauer (1980), Ch 6.

12
# _Table 5_ . Stochastic Frontier Analysis on Select Subpopulations

_Dependent Variable = Log of Total Maize Grain Production (KG)_

||<br>|**PURE STA**<br>|**ND PLOTS**<br>||
|---|---|---|---|---|
||**Ove**|**rall**||**2**|
|HighestMembership Grade|MAPS|AFSIS|MAPS|AFSIS|
|Distance from S1|-0.088***|-0.071***|||
|Plot Area (Hectares, Logged)|1.040***|1.019***|1.046***|1.036***|
|Household Labor Days (Logged)|-0.077*|-0.082*|-0.031|-0.069|
|Hired Labor Days (Logged)|-0.005|-0.004|-0.011|-0.019|
|Inorganic Fertilizer (KG, Logged)|0.046***|0.039**|0.017|0.018|
|KG of Seed Planted (Logged)|0.032|0.03|0.042|0.052|
|Intercropping Rate (Logged)|-|-|-|-|
|Pure Stand Maize°|-|-|-|-|
|Flowering Season Rainfall (mm, logged)|-0.069|0.476|-0.07|0.189|
|Constant|9.015***|5.830***|8.193***|6.905***|
|_Technical Inefficiency_|||||
|Manager Completed Primary Education°|0.012|0.025|0.059|0.068|
|Manager Age|†|†|†|†|
|Manager Age (squared)|†|†|†|†|
|<br>Manager Received Agricultural Extension Services°|0.045|0.074|-0.059|0.021|
|Dependency Ratio|†|†|†|†|
|Count of Agricultural Assets|0.001|0.001|0.006|0.011|
|CV of Flowering Season Rainfall (1999-2014)|-2.442|-3.691*|-3.671|-2.858|
|Constant|7.068***|7.284***|7.181***|6.990***|
|_Random Error Term (v)_|||||
|Constant|-2.047***|-1.919***|-1.910***|-1.748***|
|N|367|367|274|323|

|_- continued next page -_|
|---|

21
who have no children at all. Since the calculation drops households with all female children, we would expect the results to understate preferences for girls. We also compute an “ideal” sex ratio using a subset of individuals who report both the total number of desired children by gender, which allows us to perform a similar computation for the gender mix in the “ideal” state.

For woman _i_ interviewed at time _t_ , our basic estimating equation is a linear specification for outcome _Yit_ , where where _P_<sup>_w_</sup> _i_<sup>is an indicator of program eligibility (1 if the household qualifies under the</sup> BPL or SC/OBC rule and the woman had two or fewer children as of October 1994 and 0 otherwise). _Tt_ are dummies for each survey wave. This is a standard difference indifference methodology, which controls for common (additive) time trends and preprogram differences between the two groups. In this interpretation,  2 and  3 are estimates of short and medium-term program effects respectively. For each of these outcomes, we estimate Equation (1), controlling for _X_ , a vector of household level background characteristics such as urban location, religious affiliation, BPL and SC/OBC status across waves. We also include relevant individual characteristics, notably education, number of children at the time of the program introduction and age in quadratic form. In this and all other specifications going forward, we use robust standard errors clustered at the primary sampling unit, and apply the NFHS state-sample weights. For discrete outcomes, we use probit regression and report marginal effects; for continuous outcomes, we report OLS estimates. The complete regression results are reported in Appendix Table A.

For eligible women, the program positively and significantly affects the ratio of living daughters to living sons--and the effect becomes larger in the medium-term (Table 3, Column 1). In terms of fertility preferences, we observe mixed results: first, a smaller, insignificant negative, then eventually positive impact on the ratio of ideal daughters to ideal sons. We see a similar effect on the likelihood of a woman expressing the desire for at least one daughter (Table 3, Columns 2 and 3). These findings are suggestive of a positive change in women’s preferences for girls, but not conclusively so.

Our results are consistent with the MODE evaluation report (MODE, 2000) which showed that 70% of beneficiaries reported positive behavioral changes among their family members, but only 2% felt that actual discrimination against girls would decrease. Note also that the total number of ideal children appears to have fallen both in the short and medium term but not significantly so. (Table 3, Column 4).

# **4.2. Child Mortality, Health Outcomes and Investments in Health**

For this analysis, we use as the primary dataset the child health history collected from mothers with young children, in which detailed vaccination and health outcomes are recorded. In NFHS-1, the detailed child health history is taken for 1790 children born up 11
42 Table 14: Brazil - Distribution of Minor Chlldren and Share in Female-Headed Households (FHH) by Income Class and Metropolitan Area, 1984 (percent) Share of Minors

||Recite||Sio Paulo||Porto Alegre||
|---|---|---|---|---|---|---|
|Income Class|All minors|% In FHU|All minors|% In FIlE|All minors|% In FEB|
|Lowest 5%|9|37|10|19|11|22|
|5-10%|10|23|10|15|10|15|
|10-25%|23|14|23|13|22|12|
|25-50%|28|12|27|6|26|9|
|Above 50%|30|9|30|_5_|32|7|
|All|100|15|100|9|100|I 1|

source: authors ubadons