rafmacalaba/datause-encoder-data huggingface dataset:
decrease in wealth.<sup>29</sup> The probability of being neither in work nor in school, on the other hand, increases with distance to school. The younger the child is, the more likely she is to be in school only and the less likely to be in work only. Boys are generally less likely to be in school only than girls. Children are less likely to be in school if the household head never attended school. Children are also less likely to attend school only and more likely to work only if the household is engaged in agricultural activities.

# _Baseline balance in the Mexican data_

For the analysis of child work and schooling in the Mexican data, we tested for balance along the following individual and household characteristics: age, gender, and an indicator variable taking the value 1 if neither of the child’s parents live in the household, a wealth index (with weights derived from the first principal component of the following dwelling characteristics: electricity, three indicators for roof material ((i) sheets made of metal, fiber glass, or plastic, (ii) sheets made of cardboard, or (iii), concrete), indicators for wall material ((i) wood, (ii) bricks, or (iii) adobe), dwelling owned by the household, the household has no access to toilet facilities used exclusively by the household, the household has access to piped water on the household's dwelling or plot, and ownership of the following assets: TV, video, stereo, blender, refrigerator, washing machine, fan, gas stove, gas heater, car, and truck.), whether the household belongs to an indigenous people group, whether the household head ever attended school, whether the household is engaged in (nonlivestock) agricultural activities, the total number of household members, and the number of household members aged 0 to 5 and 6 to 17. Finally, we also test for balance in whether there is a 29 We get similar results if we include annual baseline per capita income instead of the wealth index: the probability of being in school only increases significantly with 1.7 percentage points for every additional 1,000 Philippine Peso of per capita income, while the probability of being in work only, in school and in work, or idle each decreases by about half a percentage point (statistically significant at the 5% level).

45
Preliminary demographic analysis reveals that over a quarter of Armenian households are composed of a male and female couple with children, with other adults also living in the household (Fuchs and Gonzalez 2022). Most single-headed households – where the self-identified household head is not living with a spouse – are sustained by females; single female-headed households account for almost one-fifth of all households.

## a. Definition of Household Types

Three typologies were tested for the fiscal incidence analysis (see Table 3). Ultimately the T3 category was used most often while T2 was used in some analyses due to sample size limitations.

# Table 3. Household Types Based on Armenia ILCS 2020

Demographics + Income (proxy for intrahousehold roles and bargaining power)

- (T1) A. (Labor) earners are a female majority • Calculated from the gender of Gender of B. (Labor) earners are a male majority household members who report household earners C. Equal number of male and female positive labor earnings in labor markets labor earners • Labor earnings include salaries and D. No (labor) earners in the household self-employed earnings; exclude incomes from agricultural sales and own consumption

- (T2) A. Larger share of female earnings • Calculated over total labor earnings Gender sustaining (>60% of household labor earnings) reported by gender of household household earnings B. Larger share of male earnings members (>60% of household earnings) • Labor earnings include salaries and

- Labor earnings include salaries and self-employed earnings; exclude incomes from agricultural sales and own consumption

- C. No earners in the household

Demographics + Income (proxy for intrahousehold roles and bargaining power) + Presence of children and elderly (proxy for care needs and domestic responsibilities)

- (T3) • Typology (3) of gender sustaining Gender sustaining household labor earnings household earnings • Combined with the presence of + presence of retirees who may contribute to children and domestic tasks and care elderly • Combined with the presence of dependent household members:

- Calculated from gender and age of all household members (self-reported) in household interview roster; labor earnings reported by gender of household members

- Children <6 years old

- Elderly 80+ years old

_Note:_ ILCS = Integrated Living Conditions Survey.

18
29 that could have been devoted to the health sector given Vietnam’s system of revenue- and expenditure-sharing between provincial and central government; some of the revenues collected at provincial level are passed to the center and distributed across provinces as transfers.<sup>22</sup> In both columns (1) and (2), the coefficient on ODA is significantly larger than that on the provincial government’s total resources, and is not significantly different from one. This suggests that fungibility of domestic and external resources between the health sector and other sectors may not be a major issue in Vietnam, and that a value of one for the parameter  in eqn (17) may be a reasonable approximation pending more indepth econometric work on intersectoral fungibility.

> 22 On fiscal decentralization in Vietnam, see Government of Vietnam-Donor Working Group on Public Expenditure (2000).
# **_Table A4.8a Relationship between project design and IEG binary outcome rating, ordered probit_**

|||**Dep**|**endent vari**|**able: IEG 1-6 o**|**utcome rati**|**ng**||
|---|---|---|---|---|---|---|---|
|**Design index components**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Number of project|-0.163**|||||||
|components|(0.069)|||||||
|Number of project||-0.024||||||
|sub-components||(0.015)||||||
|Number of PDO indicators|||-0.038*|||||
|at entry|||(0.023)|||||
|Number of intermediate||||-0.030***||||
|indicators at entry||||(0.011)||||
|PDO rating|||||0.029|||
|(1 high - 4 low)|||||(0.145)|||
|Results framework rating||||||-0.228*||
|(1 high – 4 low)||||||(0.123)||
|Extent of changes to|||||||-0.294**|
|project components|||||||(0.129)|
|Observations|120|120|120|120|120|120|120|
|Pseudo R-squared|0.059|0.051|0.051|0.066|0.044|0.053|0.058|
|||**Dep**<br>|**endent vari**<br>|**able: IEG 1-6 o**<br>|**utcome rati**|**ng**||
||**(8)**|**(9)**|**(10)**|**(11)**||||
|**Design index**|0.230***<br>(0.072)|||||||
|Project Characteristics||0.131***||||||
|Index for comparison<sup>1</sup>||(0.056)||||||
|CPIA at Approval|||0.290<br>(0.184)|||||
|Change in CPIA over||||-0.294||||
|Project Lifetime||||(0.247)||||
|Observations|120|120|120|120||||
|Pseudo R-squared|0.072|0.059|0.043|0.040||||

Notes: This table provides estimates of IEG Outcome Ratings regressed on common design features and quality ratings. Using Principal Component Analysis , an Index is created for Design based on these features and ratings. (1) For comparison, an Index is created for Project Characteristics based on the log of original commitment, preparation cost, first and second half supervision cost, the change in project size from approval to closing, and the time it takes projects to move from one key milestone in the project lifecycle to another. The table also provides the estimates of regressing IEG Outcome on two country variables, CPIA score of project country at approval and the change in the score over the project lifetime.

60
# Table 5. Bond Pricing OLS Regression Results

|Dependent Variable: Fixed yield-to-|maturity of Bond T|ranche|||||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VIX|0.550*<br>(0.297)|1.141***<br>(0.300)|||||||
|RISK|||1.219***<br>(0.299)|1.266***<br>(0.376)|||||
|FED|||||-1.125***<br>(0.295)|-0.837***<br>(0.308)|||
|LIBOR|||||||0.335**<br>(0.146)|0.556***<br>(0.145)|
|GDPPC|0.777**||0.742**||0.740**||1.227***||
||(0.320)||(0.323)||(0.325)||(0.238)||
|GROWTH|-0.0187||-0.0206||-0.0200||-0.0284||
||(0.0229)||(0.0231)||(0.0219)||(0.0299)||
|EXT|(0.00754)||(0.00761)||(0.00761)||(0.00950)||
||0.0225||0.0217||0.0213||0.0236||
|CA|(0.0215)||(0.0215)||(0.0214)||(0.0218)||
||0.00867||0.00857||0.00835||0.0139||
|PCRED|-0.000675||-0.000986||-0.000631||0.000119||
||(0.00571)||(0.00576)||(0.00569)||(0.00666)||
|Log of Size of Bond Issued|-0.0441|-0.0666|-0.0495|-0.0684|-0.0506|-0.0683|-0.0318|-0.0479|
||(0.0479)|(0.0706)|(0.0472)|(0.0706)|(0.0473)|(0.0718)|(0.0471)|(0.0759)|
|Maturity of Bond issued|0.0537**|0.0344***|0.0542**|0.0347***|0.0536**|0.0341***|0.0548**|0.0326***|
||(0.0205)|(0.00766)|(0.0207)|(0.00761)|(0.0203)|(0.00774)|(0.0215)|(0.00764)|
|UST10Y|<br>0.370*|<br>0.850***|<br>0.378*|<br>0.848***|<br>0.432**|<br>0.822***|<br>0.416*|<br>0.932***|
||(0.201)|(0.175)|(0.202)|(0.174)|(0.187)|(0.163)|(0.218)|(0.190)|
|Bond Tranche fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|No|Yes|No|Yes|No|Yes|No|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-period fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|
|Includes China issuance|Yes|No|Yes|No|Yes|No|Yes|No|
|Observations|5,881|3,153|5,881|3,153|5,881|3,153|5,593|2,863|
|R-squared|0.805|0.703|0.805|0.702|0.805|0.704|0.795|0.687|
|<br>No. of Countries|63|70|63|70|63|70|63|70|
|No. of Industries|187|192|187|192|187|192|187|191|
|No. of Bonds|5437|2865|5437|2865|5437|2865|5176|2602|

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 33
- 16 -

## DESCRIPTION

## ADVANTAGES

## DISADVANTAGES

|ll. CLINIC-BASED<br>SERVICES<br>|||
|---|---|---|
|Clinic-based service is a<br>reasonable approach in<br>areas where health<br>|Patients are seen at each<br>visit by health care<br>professionals.<br>|Patients are primarily<br>limited to those living<br>close by.<br>|
|workers are available and<br>|Problems can be spotted<br>|Followup depends upon<br>|
|users do not live far from<br>the clinic. With some<br>|and treated at visit.<br>A switch in<br>|user's returning to<br>clinic.<br>|
|physician supervision,<br>trained nurses and<br>midwives examine<br>|contraceptive method<br>can be quickly done at<br>the clinic.<br>|The nurse or midwife<br>may not be familiar to<br>the patient.<br>|
|women, prescribe the<br>appropriate family<br>planning methods, and<br>manage problems.|Start-up costs low if<br>Maternal Child Health<br>services (MCH) already<br>available.<br>More complete services<br>are offered.|Patients are expected to<br>come on their own<br>initiative.<br>Patients may have to<br>wait in long lines.<br>The doctor or nurse may<br>be a male, which<br>would not be<br>acceptable to women<br>in some cultures.|

_Source:_ Centers for Disease Control (1983): p. 283.

complications, and how to deal with them. The need for a _full_ choice of methods is important, since there is no - and may never be - one ideal contraceptive to suit all couples throughout their reproductive lives. The 'cafeteria approach', offering the widest possible range of methods, appears to be the most acceptable arrangement, leaving the final choice to the contraceptive user, in consultation, as necessary, with a health adviser. Informed choice is as important as informed consent. To limit the number of methods available is to restrict access to family planning for couples whom the available options may not suit. The need of _voluntary_ choice is also of ethical importance - without it the entire concept of family planning would risk being seriously undermined and fa.lling into disrepute.

# **_Pr6vider attitudes_**

The attitude of the health worker is crucial for the provision of ethically acceptable family planning programmes. The experience of a health project in Zaire illustrates the importance of provider characteristics with reference to the patterns of acceptability of intrauterine devices (IUDs). IUDs were new to the community and early acceptors were reluctant to try them. After encouragement by members of the health team, several of whom accepted IUDs themselves, a number ov women accepted them. Subsequently two local women suffered excessive bleeding, after which there were almost no new acceptors. Service providers decided to be especial!y encouraging and supportive of women
**Table 8:  Productivity Effect of Services Liberalization, Structural Break Approach. Ackerberg et al. TFP measure**

|Banking Break 2001|2.626***|||||2.269***|
|---|---|---|---|---|---|---|
||(0.641)|||||(0.549)|
|Rajan-Zingales Break 2001||0.484***|||||
|||(0.081)|||||
|Telecom Break 2002|||8.126***|||6.226***|
||||(2.347)|||(2.223)|
|Insurance Break 2002||||5.218**||3.015|
|||||(2.227)||(1.937)|
|Transport Break 1997|||||8.103***|8.528***|
||||||(2.628)|(2.633)|
|Tariffs|0.000|0.003|0.000|0.000|-0.000|0.001|
|(t-1)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Input Tariffs|-0.004|-0.004|-0.003|-0.003|-0.010|-0.009|
|(t-1)|(0.009)|(0.009)|(0.009)|(0.009)|(0.007)|(0.006)|
|Forein Dumm|0.029*|0.030*|0.034**|0.035**|0.043***|0.034**|
|g y|(0.017)|(0.017)|(0.017)|(0.017)|(0.016)|(0.016)|
|Observations|22,558|22,558|22,558|22,558|22,558|22,558|
|R-squared|0.030|0.034|0.029|0.028|0.032|0.036|
|Number of firms|3771|3771|3771|3771|3771|3771|

Notes: The dependent variable is the log TFP estimated using the Ackerberg et al. method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry-year level, are reported in parentheses. *** denotes significant at the 1 percent level, ** at the 5 percent level, * at the 10 percent level 43
has electricity. Second, the household can gain directly from having electricity inside the home. We first outline the model specification and then discuss identification.

**_Model specification:_** To quantify these two factors we exploit the unusually long time period in the REDS panel. This allows us to observe household consumption at a base date (1982), denoted _Cij_ 82 for household _i_ in village _j_ , and at the follow-up survey date 17 years later (1999), denoted _Cij_ 99 .  We postulate that village electrification allows household consumption to grow at a rate _Gij_ for household _i_ in village _j_ that has been connected to the electricity grid for<sup>_T_</sup> _j_ years. Thus the consumption level at the end of the period attributable to the returns from village electrification is given by: _Cij_ *99 = 1( + _Gij_ ) _T j Cij_ 82 (9)

* We do not observe either _Cij_ 99 or _Gij_ . To address this problem we make two assumptions:

<u>Assumption 1: The proportionate deviation of observed consumption from</u> _Cij_ *99 is:

* ln( _Cij_ 99 / _Cij_ 99 ) = α +γ ( _Eij_ 99 − _Eij_ 82 ) + π _X ij_ +η _j_ + ε _ij_ (10) Here we allow household electrification (whether grid-supported or private) to yield a consumption gain—represented by the term γ ( _Eij_ 99 − _Eij_ 82 ) —relative to the consumption level attributable to village electrification. We also allow for a vector of household and community characteristics as controls, denoted _X ij_ (which can include changes in characteristics), and geographic effects, η _j_ . The innovation error term is ε _ij_ .

<u>Assumption 2: The return to having the village connected to the electricity grid depends</u> on the household’s own access to electricity (such as through a private generator) at the time the connection becomes available. Thus we write: _Gij_ = β 0 + β 1 _Eij_ 99 −τ _j_ (11) where _Eij_ 99 −τ _j_ = 1 if the household has electricity at the time of connection to the grid τj years earlier and _Eij_ 99 −τ _j_ = 0 otherwise.

> Notice that if β 0 + β 1 = 0 then _Gij_ = β 0 1( − _Eij_ 99 −τ _j_ ) , in which case the private return to village grid connection is zero if one already has electricity privately. This is the asymmetric <u>externality discussed in section 3. We do not, however, assume that this is the case.  As a</u>

20
15 to $102 million in 1990 to $177 million in 1991, is attributable to this improvement in data gathering. The bulk of the spending is done by the federal government, with FWS itself comprising about half of the federal total. For all three years, the federal total of conservation expenditures is $248 million, while the state total is $74 million. Expenditure data is collected on all listed plant and animal species. However, as already noted, we confine our attention to the vertebrates. Since approximately 95 percent of the identifiable conservation budget is spent on vertebrates, we are confidan **t** hat any patterns uncovered here would be robust in the complete sample of listed species.

B. Regression *72: Determinants of Spending Regression #2 uses the log of total spending from 1989 to 1991 (LNTOTAL) as the dependent variable. Since we only observe spending on a species when it is greater than $100, the dependent variable is censored at ln(100) and the appropriate estimating procedure is a Tobit regression.?5 The independent variables are the same as those in Regression #1, with the addition of a SUBSPECIES dummy for listed taxonomic units below the full species level.

Before discussing the regression results, it is helpful for the exposition to introduce a hypothetical variable which we call CHARISMA". We think of this variable as the unmeasurable part of existence value, and we mechanically define it to be orthogonal to all of the independent variables used in Regression #1.i Although it may seem to be an unorthodox construction, CHARISMA is just a statistically harmless fiction that enables us to discuss a possible bias in our estimates. In writing about this hypothetical variable as if it actually exists in the real world, we seek only to simplify the exposition. For this purpose, we treat CHARISMA as a 'real' variable omitted from the right-hand-side of Regressions #1 25Because there are only two sored observations, the results of the Tobit esfimation are practically identical to those of an OLS regression using the same variables.

26We can do a thought expeiment to envision what the CHARISMA variable reprsents. First, imagine that we could create a perfect measure for the existence value of each species. Next, regress this perfect measure on the set of independent variables used in Regression #1. Define the residuals from this regression to be the CHARISMA variable.
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE double the implementation period. Furthermore, inadequacies in the system for supervising construction contracts have cut quality and shortened the life of public works. The government has taken steps to improve capital budget execution ratios, and it is hoped that the establishment of the new National Construction Authority will improve the quality of public works.

Beyond the trunk network, accessibility falls off. About 30 percent of Kenya’s population lives within two kilometers of an all-weather road—well above the benchmark for low-income countries, but only half the level found in middle-income countries. The clustering of Kenya’s population along the MombasaNairobi-Kisumu corridor makes it comparatively easy to achieve significant increases in rural accessibility by improving the quality of the existing rural network, without adding hugely to the length of the classified network. When making the necessary improvements, it will be important to ensure that road investments are spatially synchronized with other interventions aimed at raising agricultural productivity. The need to provide a basic level of connectivity for the north of the country should also be taken into account.

# Rail

## **Achievements**

Kenya’s rail corridor is of strategic importance to the region. Linking the port of Mombasa to Nairobi and continuing onward into Uganda, it is a key conduit for bulk freight, easing pressure and providing additional capacity along the northern corridor (table 3). Owing to deterioration of the infrastructure, freight traffic on the rail corridor has declined to less than 1 million tons per year and handles less than 6 percent of the cargo passing through the northern corridor that links Kenya, Uganda, Rwanda, Burundi, the Democratic Republic of Congo, parts of Tanzania, southern Sudan, and Ethiopia.

**Table 3. Railway indicators for Kenya and selected other countries, 2000–05**

||Kenya<br>(KRC)|South Africa<br>(SPOORNET)|Malawi<br>(CEAR)|Tanzania<br>(TRC)|Tanzania-<br>Zambia<br>(TAZARA)|Uganda<br>(URC)|Zambia<br>(RSZ)|
|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state-run (0)|0|0|1|0|0|0|1|
|Traffic density, freight, 1,000 ton-km/km|690|5,319|112|510|460|815|379|
|**EFFICIENCY:**||||||||
|Staff: 1000 UT per staff|185|3,037|204|228|300|181|452|
|Coaches: 1000 passenger-km per coach|1,015|596|1,285|3,157|3,120|NA|2,772|
|Cars: 1000 ton-km per wagon|200|925|212|692|502|166|180|
|Locomotive availability in %|44.8|—|89.9|74.2|25.2|69.5|31.2|
|**TARIFFS:**||||||||
|Average unit tariff, freight, US cents/ton-km|3.8|—|5.8|4.0|3.0|15.2|3.9|
|Average unit tariff, passenger, US<br>cents/passenger-km|0.6|—|1.0|1.6|1.1|2.3|0.8|

Source: Bullock 2009, derived from AICD railways database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

Through a combination of track rehabilitation and improved operational performance it should be possible to boost traffic volumes to 5–10 million tons per year, which should be enough to accommodate 8
Under this assumption, equation (4) may be written as This equation forms the starting point of my empirical analysis. The results will be presented in the following section.

# 4 Data Analysis

My empirical analysis uses data from the 1980 and 1990 US Censuses.<sup>7</sup> The dependent variable of the Mincerian earnings equation is the natural logarithm of the annual wage or salary income in the year preceding the census. The set of control variables includes potential experience, dummies for each year of schooling, years spent in the US and its square, married with spouse present and dummy variables indicating whether the respondent speaks only English or speaks English very well, health limiting work, residence in SMSA, eight census divisions and year of immigration. To control for changes in labor market conditions a dummy indicating Census year 1980 is added to the regression and interacted with regional and educational dummies. Interaction terms with the Census year dummy and other explanatory variables are not statistically signi...cant and are therefore excluded from the speci...cations. Descriptive statistics are provided in Table 1.<sup>8</sup> The summary statistics of trade used in this analysis is the natural logarithm of the mean of Open, where the mean is calculated from 1970 to 1980. I will refer to this measure as log Open.<sup>9</sup> Using the logarithm of this summary measure of trade implies that the e¤ect of a one percentage point increase in Open on the dependent variable is larger the lower the level of openness.

Table 2 presents the results of the Mincerian earnings equation for the two censuses. As can be seen in the last column of Table 2; which combines the 1980 and 1990 Census, the estimated return to home country experience

> 7 The data are available online at http://www.ipums.org. For more information on the data, see Ruggles and Sobek (1997).

> 8 A detailed description of the data can be found in the data appendix.

> 9 Open is de...ned as the ratio of imports plus exports in exchange rate US$ relative to GDP in purchasing-power-parity US$. Other studies that use this measure are Alcala and Ciccone (2001) and Dollar and Kraay (2002).

8