rafmacalaba/datause-encoder-data huggingface dataset:
schooling effect in the United States (see, for example, Neal, 1997, and Figlio and Ludwig, 2000). The importance of availability of private schools in schooling choices has been demonstrated in the developing country context (see, for example, Alderman _et al_ ., 2001).

Data on the presence of public and private schools are available at both the district and the village level. These two measures are moderately correlated, with a correlation coefficient of 0.33. Using district-level information, we measure the percentage of schools that are public in the district of junior high school attendance. For the sample of students from the 321 original IFLS communities, we can measure the share of public schools within 25 miles of the village center. This latter specification can only be estimated on the sample of students that were interviewed in the same sub-district where they attended junior secondary school.<sup>12</sup> The consistency of the two stage least squares estimate is based on the critical assumption that local private school proximity is uncorrelated with unobserved determinants of student test scores. This assumption has been questioned in the US context, where evidence suggests that proximity to catholic high schools is correlated with unobserved determinants of 12<sup>th</sup> grade math and reading test scores (Altonji, Elders, and Taber 2002). However, that conclusion is largely based on the implausibly large differences between OLS and 2SLS estimates of the effect of Catholic schooling on scores, which we do not find in the Indonesia data. Also, the location of American

> included in these sub-samples.

> 12 In principle, considering only students that remained in the same sub-district that that they attended junior secondary school could cause selection bias. To probe this, we utilized the fact that student’s test scores are ranked based on their deviations from annual means, and estimated a Heckman two step model excluding the year the test was taken from the test score equation. The estimated public school premium remained essentially the same in the presence of the selection correction term, even though the years since the test was taken was strongly and positively correlated with whether the student had subsequently moved.

20
c

# FIGURE 5b

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We also find evidence that beliefs about enforcement and the likelihood of the community following rules has an impact on if the respondents consider firewood reduction as a legitimate cost. This result is implying that the strength of institutions and the enforcement of rules can change perceived opportunity costs of the contracts. It needs to be further investigated is these differences are caused by respondents acknowledging a potential for leakage or some other belief about the role of institutions. Further studies should be conducted that include different forms of enforcement as an attribute. This will allow a better understanding of how the form of enforcement will influence adoption decisions.

We also find that preferences for REDD+ contract attributes depend on the levels of other attributes. In particular we find that at higher payment levels respondents are willing to accept a higher grazing restriction (the marginal disutility of grazing decreases) and that at higher levels of required reduction of firewood collection respondents require more of the payment to be given to the household (as opposed to the community). These results show the importance of incorporating interaction terms when analyzing choice experiment data and in the context of REDD+ programs indicate that higher payments will not only increase the likelihood of contracts being adopted but also decrease the perceived opportunity cost of grazing restrictions.

We verify the robustness of the WTP estimates by analyzing the data using the “WTPSpace” approach and find that the results are robust to both estimation in “WTP-Space” and “Preference-Space”. Perhaps the most intriguing finding is that Ethiopian households appear to value cash payments much more than losses in access to forest resources. These results suggest that the opportunity costs of carbon sequestration may indeed be low. The results also find that personal beliefs about the community, enforcement of contracts and the impact of the REDD+ program can significantly influence respondents’ likelihood of adopting contracts.

17
private savings in the 1990s. The following conclusions, and ideas for future research, emerged: (i) The behavior of private savings between 1991 and 1993 is explained by a deterioration of unadjusted disposable income more than by growing consumption. The increment in taxes explains part of the deterioration of disposable income in the 1990s. This evidence cannot be interpreted as a causal relationship between taxes and savings, but existing empirical evidence suggests that tax increases is not the most efficient way of rising national savings due to its large negative effect on private savings (see, among others, Corbo and Schmidt-Hebbel (1991)). The magnitude of this effect is an empirical matter that deserves a careful investigation in Colombia.

(ii) Although the increase in private consumption explains part of the deterioration of the private saving rate in the 1990s, it only increased in 1992 and the rise was moderate (2.2% of GNP). It is doubtful that the deterioration of the private saving rate can be explained by arguments suggesting a process of stock adjustment of durable goods as a consequence of the trade reform. In fact, the consumption of durable goods only had a moderate increase in 1993 (0.7% of GDP) after a period of decline.

(iii) Given that disposable income of corporations is equivalent to corporate savings, the behavior of households and corporations was analyzed separately. Household savings show a secular decline since 1970 and two elements deserve special consideration to understand this behavior. On one hand, it is important to establish the main determinants of private consumption, step that has already been taken by L6pez, et. al (1996b). On the other hand, the secular decline of household savings seems to be closely related to the GOS generated by households. Therefore, understanding the reasons behind the declining trend of household's GOS should be a priority of future saving studies in Colombia.

(iv) As far as corporate savings is concerned, the period 1983-1994 was characterized by the rise and fall of this aggregate. According to the National Accounts, the decline in the GOS is the main cause of its recent deterioration and, eventually, this could be related to the trade reform as it increased external competition. However, micro data suggests that the decline in corporate profits was temporary, reducing the potential 18
Republic, the Maoists held the majority of seats in the new Constituent Assembly, and their leader was elected the first Prime Minister.

Nepal‟s geographical terrain served as an important determinant of the intensity and likelihood of civil war violence. Since government forces outnumbered the insurgents, insurgent forces depended on mountainous and forested terrain to help them hide and maneuver. Economic development also played a role since the Maoists could find greater support for their cause among the poor and disenfranchised. These arguments are supported in Do and Iyer (2010) who find conflict-related deaths were substantially higher in districts with higher poverty, and in districts characterized by higher elevation and forest coverage. Their results indicate that geographic conditions explain approximately 25 percent of the variation across districts in conflict intensity, with the pre-1996 rate of poverty at the district level also serving as a positive predictor of conflict intensity. The relationship between the intensity of violence and Nepal‟s geography and terrain is also supported in Bohara _et al_ . (2006).

The civil war entailed enormous social costs. The death toll reached over 13,300, with about two-thirds of those deaths being caused by government forces and the remaining one-third caused by Maoist insurgents (INSEC 2010). As shown in Panel A of Figure (1), the conflictrelated deaths increased sharply in 2002, an escalation that coincided with Prime Minister‟s announcement of a state of emergency and his mobilization of the Royal Nepal Army to combat the insurgents. In addition, the drawn-out conflict caused substantial destruction to the country‟s infrastructure as well as the postponement of new projects. This crippling of the country‟s infrastructure not only restricted access to education and health systems, it also stifled economic growth and development. Panel B of Figure (1) shows that the conflict tended to be more intense in the mid-western and far-western regions of the country.

5
sector specific fixed utility to have linear time trends. This specification potentially captures changes in employment opportunities in different sectors over time. Modifying the assumption on fixed utility only affects the second stage regression.

The second stage regression for the “Alternative Specification” is where _η_ �1<sup>_i_istheintercenpand</sup><sup>_η_�</sup> 2<sup>_i_isthetrend.Weset</sup><sup>_η_�</sup> 1<sup>1= 0and</sup><sup>_η_�</sup> 2<sup>1= 0forthefirstsector.</sup> _Data_ Artuc, Chaudhuri and McLaren (2010) use CPS data for males between 25 to 64 years old, from the year 1976 to 2001; we use the same data and sample section procedure. CPS is a repeated cross-section, and its March supplement provides retrospective industry questions regarding workers’ industry in the previous year, along with their current industry. These retrospective questions allow us to construct number of workers moving from industry _i_ to _j_ , denoted as _yt_<sup>_ij_.Inadditiontoflowdata,weuseaveragewagedataforeachindustry.Artuc,</sup> Chaudhuri and McLaren (2010) aggregate industries to 6 major sectors<sup>10</sup> . Different from them, we aggregate industries to 16 sectors. The sectors are: 1. Agriculture, 2. Mining, 3. Construction, 4. Non-durable manufacturing, 5. Durable manufacturing, 6. Transportation, 7. Communications, 8. Utilities, 9. Wholesale trade, 10. Retail trade, 11. Finance, 12. Business, 13. Personal services, 14. Entertainment, 15. Professional, and 16. Public.

In addition to increasing the number of choices, we consider sector specific iid utility shocks, _η_<sup>_i_</sup> , and let the deterministic part of moving cost, _ct_ , change over time. These two changes improve their theoretical model significantly because some sectors may be more preferable by workers for non-pecuniary reasons and the moving costs may change over the twenty six year sample. These possibilities are now addressed in the model. We use PPML regression in the first step and IV regression in the second step. Everything else is exactly

> 10They were not able to disaggregate sectors further because their method did not allowed zero cells in the transition matrix. The were able to estimate at most seven structural parameters.

14
16

# Table 4. Road Sector Financing Plan

(TSh million)

|**Revenues**|1985/86|<br>1986/87|Budget<br>1987|Proposed<br>Financing<br>Plan||
|---|---|---|---|---|---|
|H.V.Taxes and Licenses|72.11|<br>99.56|<br>94.74|<br>150.00|(a)|
|M.V. registration tax|8.04|<br>8.54|<br>12.66|<br>13.00|(b)|
|H.V.transfer tax<br>|36.20|<br>38.15|<br>55.28|<br>10.00|(c)|
|M.V.road license|55.74|<br>55.74|<br>105.58|<br>150.00|(d)|
|Road toll**tax**|122.88|<br>137.63|<br>156.08|<br>-|(e)|
|Transport licensing fees|15.83|<br>17.34|<br>30.13|<br>6.00|(f)|
|Import Duties**&**Sales Taxes<br>________________________________||||||
|Gasoline (premium)<br>|C|C<br>|C<br>|C||
|Gasoline (regular)<br>|(<br>684.30|(<br>|C|C 7,809.00|(g)|
|Diesel fuel|20.63|C<br>|C|(||
|Motor Vehicles|555.42|C 3,500.00|C 6,000.00|<br>2,100.00|(h)|
|M.V. spare parts|214.56|(<br>|C|700.00|(j)|
|Other items|211.28|5<br>|C|<br>750.00|<br>(b)|
|||...__________<br>-----------|-----------|-----------||
|Total Revenue|1,996.99|<br>3,856.96|<br>6,454.47|<br>11,688.00||
|Expenditure:||||||
|Expenditure on roads<br>|1,337.09|<br>2,191.81|<br>2,652.71|<br>7,953.00||
|Contribution to General||||||
|Taxation|659.90|<br>1,665.15|<br>3,801.76|<br>3,735.00||
|||...__________<br>-----------|-----------|-----------||
|Total Expenditure|1,996.99<br> <br> <br>|<br>3,856.96<br> <br> <br>|<br>6,454.47|11,688.00||
|Shortfall of Regular|**nmmama**<br>_UUWWWW<br>**.f**|=fi<br>**nmmnmm**<br>**_ m **||**mam--**||
|Maintenance|(950.00<br>**_inMMWWUUi_**|) (3,300.00<br>WWWW**ammm**|) (5,300.00<br>**mina_**|)<br>-<br>**_aM**||

- Notess (a) Collection efficiency improved through better monitoring and revised tax structure.

- (b) No change.

- (c) Present tax replaced by requirement to re-register the vehicle at half the standard registration fee.

- (d) Collection efficiency improved through better monitoring.

- (e) Proposed to be eliminated due to poor collection performance.

- (f) Present tax replaced by nominal licensing fee to cover collection costs only.

- (g) Tax increased by TSh 5,300 million.

- (h) Revenue increased by lowering **tax** rates to reduce discretionary exemptions and avoidance.

- (j) Revenue increased by reducing sales tax levels to discourage avoidance.
Figure 4: Annual consumption growth in Figure 5: Annual consumption growth in Georgia based on IHS (2010-2015) Georgia based on WMS (2009-2015) <!-- Start of picture text --> Growth incidence 95% confidence bounds Growth incidence 95% confidence bounds<br>Growth in mean Growth at median Growth in mean Growth at median<br>Mean growth rate Mean growth rate<br>1 10 20 30 40 50 60 70 80 90 100 1 10 20 30 40 50 60 70 80 90 100<br>Consumption percentiles Consumption percentiles<br>Source : Author’s calculations based on IHS Source : Authors’ calculations based on WMS<br>12<br>12<br>8<br>8<br>4<br>4<br>0 0<br>Annual growth rate % Annual growth rate %<br>-4 -4<br>-8 -8<br><!-- End of picture text -->

# Armenia

## Main Result

The analysis of patterns of economic mobility for Armenia focuses on two episodes of poverty reduction. In the first episode between 2004 and 2008 poverty declined substantially, with the share of the population living below the national poverty line decreasing by 25.9 percentage points. The second episode covers the years from 2010 to 2015 during which the decline in poverty was a more modest 6.0 percentage points. Even though both periods witnessed poverty reduction, the macroeconomic conditions in the country and the associated drivers of poverty reduction were largely different.

Poverty reduction between 2004 and 2008 benefited from stable economic growth. But as Figure 6 illustrates, while a sizeable share of the population exited the state of poverty (34.2 percent), there was a non-trivial proportion of population moving back into poverty (12.2 percent).<sup>11</sup> Over the same period, 14.0 percent of the population lived in poverty in both periods. Conversely, only about 39.6 percent of households in the sample lived free of poverty in both periods.<sup>12,13</sup>

> 11 The synthetic panel methodology heavily relies on the use of time-invariant characteristics to impute household welfare in the second time period. Following best practice in the literature, the sample of households is typically restricted to households with household heads between 25 and 55 years. Annex 2 provides descriptive statistics which support the validity of these findings on patterns of economic mobility not only for the sample used for the analysis but for all households in Armenia.

> 12 The synthetic panel methodology determines patterns of economic mobility based on the welfare status in two periods of time. Findings are not informative about transitions which occur between the beginning and the end of the time window.

> 13 The documentation of results focuses on point estimates and does not report standard errors which are available from the author upon request.

11
_Leche_ , _de Tortilla_ and the National Institute of Indigenous people ( _INI_ ).<sup>4</sup> In addition to these direct costs, there are some indirect costs associated with complying with the program’s requirements. Such costs include the time costs of taking children to school and to health center, waiting in line at the health center, attending educational seminars on nutrition and hygiene, and traveling to the localities where payments are being distributed.

The data used in this paper consist of the sample of communities and households surveyed between November 1997 and November 1999, for the purposes of the evaluation of the PROGRESA program. In order to obtain a credible evaluation of the potential impact of the program the PROGRESA administration decided to adopt an experimental design that allows one to compare households before and after the initiation of PROGRESA with similar households that were not yet covered by the program. Specifically, the full sample used in the evaluation of PROGRESA consists of repeated observations (panel data) collected for 24,000 households from 506 localities in the seven states of Guerrero, Hidalgo, Michoacan, Puebla, Queretaro, San Luis Potosi and Veracruz. Of the 506 localities, 320 localities assigned to the treatment group (where PROGRESA was in operation) and 186 localities were assigned as controls.<sup>5</sup> As originally planned the localities serving the role of a control group started receiving PROGRESA benefits by December 1999.

The selection of beneficiaries into PROGRESA was a two stage process (Skoufias et al. 2001). In the first stage, using national census data, poor communities with schooling and health infrastructure were identified. In the second stage, households within the selected poor

> 4 Before the establishment of PROGRESA, previous government interventions in the areas of education, health and nutrition in the rural sector of the country consisted of many programs each intervening separately in health, education or nutrition with little prior coordination or consideration of the potential synergies that could result from a better coordinated and simultaneous intervention.

> 5 Behrman and Todd, (1999) conducted a careful investigation of the extent to which the selection of localities into treatment and control groups can be considered as random. Their analysis did not reveal any significant differences between village means for more than 300 variables in treatment and controls.

6
“soft” innovation and collaboration. The distinction between “hard” and “soft” innovation corresponds roughly to the distinction between technological and non-technological innovation discussed above. Thus “soft” innovation has referred to organizational innovation and skill upgrading, as opposed to “hard” innovation which corresponds to clear-cut technological innovation through the introduction of new processes or products.<sup>6</sup> There is also little formal modeling and theorizing on innovation in services and most of the conceptualization comes from the marketing literature (See for example, Tether et al. (2007)). The few papers that we are aware of that theorize on innovation in services have little formalization and are mainly conceptual (see Barras (1986), Howells (2000), Drejer (2004)). This implies that empirical papers have not adopted structural approaches or followed from theoretical models. Rather, they have tended to analyze more broadly how the characteristics of firms in the services sector correlate with the probability of innovating, and compared these findings with firms belonging to manufacturing sectors.

The main findings of the empirical literature (mostly using traditional innovation surveys) is that overall, service firms appear to be as innovative as manufacturing ones (Pires et al. (2008), Sirilli and Evangelista (1998)), but there is a large sub-sectoral heterogeneity in the way firms produce, innovate and sell (Fitzsimmons (1999); Pires et al. (2008); Miles (2005); Tether et al. (2007)). Second, skill intensity seems to be specifically important in services innovation, as the number of highly skilled employees is correlated with both higher process and product innovation, something not always observed in manufacturing (Pires et al. (2008), Eickelpasch and Vogel (2009)). Third, the intangible nature of most services makes it difficult to distinguish between process and product innovation, and to identify clearcut innovation. Often, there are cumulative small improvements so that for the same actual innovation effort, the measured innovation in manufacturing, which tends to be based on discrete identifiable efforts, might be higher than in services (Tatikonda and Zeithaml (2001) in Nijssen et al. (2006)). Fourth, 6