rafmacalaba/datause-encoder-data huggingface dataset:
enrolled in school, compared to only 47.7 percent of boys and 45.8 percent of girls in 1991/92, which is an overall gain of 52 percentage points over the 20-year period (Table 4). This intergenerational mobility in terms of human capital directly results, in part, from microcredit program expansion since children’s educational investment is higher among program participants than non-participants.

Intergenerational mobility can also be viewed through the lens of occupational mobility among adults in society. This study analyzed major farm and non-farm sectors of employment for participants and non-participants for the three survey years (Table 5). Employment categories were identified by income generation, and households engaged in multiple income-generation activities were assigned to the sector that yielded the highest income. The farm sector consisted of crop production and livestock and poultry, while the non-farm sector consisted of various areas of employment, including manufacturing and processing, transport, cottage industries, and the service sector. Employment has increased in terms of wage employment and self-employment in both the farm and non-farm sectors; however, wage employment is a lower-return activity in the farm sector than in the non-farm sector.

As Table 5 suggests, self-employment dominates wage employment overall for all three survey years. For example, 40.6 percent of households were involved in self-employment in 1991/92, of which the farm sector accounted for 25 percent (10.2 percent among all activities). But in 2010/11, although self-employment still dominated, at 57 percent of employment, the farm-sector share of self-employment grew to nearly 33 percent (18.6 percent among all activities). Selfemployment in the non-farm sector has always been the most dominant activity and was more so in 2010/11 (38.4 percent) than in 1991/92 (30.4 percent). As for wage employment, the non-farm sector is more dominant, except for 1998/99, when the shares of the farm and non-farm sectors 12
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while the same is not true of any other indicator. The nested inverse incidence scheme assigns a still higher weight of 0.37 to the food consumption indicator, and hence its contribution to �(�= 1⁄3) is even higher at almost 87%. By comparison, the contribution of the food consumption indicator for both NU and NII weights is moderated to less than 70% when union-based measures, �(�����; �= 1) and �(�����; �= 2) , are used. This is because union-based identification now allows other deprivations to be counted in more often. A major contrast in dimensional contributions is however observed with respect to subjective welfare weights. With SW weights, the standard of living and livelihood dimension accounts for the bulk of aggregate multidimensional poverty, its contribution in 2013 ranging between 53 and 62 percent. Education accounts for 9-12 percent, while health (food consumption) accounts for 26-38 percent.

Alongside other considerations, these results on dimensional decomposition can help inform judgements about the choice of weighting schemes and aggregate measures. For instance, a combination of NU or NII weights with dual cut-off measures (with � set at one-third) renders multidimensional poverty in the Philippines almost entirely a matter of food consumption deprivation. If this appears excessive, the dimensional decompositions remind us that it is largely a consequence of the particular weighting schemes and measures used, and it may be worthwhile to consider other reasonable alternatives.

# 4. Conclusion

Official estimates of income poverty for the Philippines over the decade or so since 2003 have indicated little decline despite respectable rates of economic growth over this period. Though the most recent estimates show a decline in poverty incidence in 2015, the earlier trend led to conjectures on whether this had been a lost decade for poverty reduction for the country. This concern also prompted inquiry into trends in non-income indicators of welfare to assess whether progress in the reduction of multidimensional poverty has fared any better. Recent work by Balisacan (2015) has suggested better performance on the multidimensional front. This paper has sought to contribute further to this line of investigation by constructing estimates of multidimensional poverty over the period 2004-13 using data from six rounds of the Annual Poverty Indicators Survey. A key emphasis of the paper has been on 15
- 1.28 -

population of Ghana, this approxlmation Is sufficient for present purposes. Data from the Survey for Cocoa Producer Pricing Indicate that most of the labor empioyed in agriculture Is family labor. For such labor, there Is no market transaction from which wage Information can be obtalned. However, It Is also clear that family labor Is not willing to work in the fields for Infinitesinal returns. At some point, the disutillity of labor exceeds the utility valuation of the product from an additlonal hour of labor. Therefore, there exists a reservation wage (In the sense of a return In real output) below which the individual will not work. While the reservation wage Is a subjective magnitude and thus specific to Individuals, It will have a mean value across a farm population. Since the mean reservation wage Is not observable, it is a parameter that Is estimated In the process of model callbratlon. The estimate used In our modeling Is 50 percent of the daily wage for casual labor.

Since labor Is mobile, Interregional migration Is feasible; and, in fact, It Is observed In Ghana. In our modeling exercise, seasonal migrants between the two regions, north and south, are paid a premium for the cost and disutility of migration. Thus, the representative farm household faces a rising supply curve for labor through the Increasing cost over family labor of regional casual labor ard the premium over the casual wage paid to Interregional migrants.

Given that unskilled labor Is an economywide resource, Its wage Is determined in the general equlilbrlum of the economy. Thus, sectorwide modeling should treat the wage of labor parametrically; and this Is the way we handle It. This approach does present a difficulty In a multiperiod model: What will GDP and the %age of labor be In the future? In practice, we have estimated future GDP by assuming a steady but slow annual growth In output per worker (about one percent) combined with labor force estimates based on demographic projections, In this framework, two alternatives for projecting the time path. of the wage of unskilled labor are feasible. One assumes nio growth in real wages, with all of the Increment In real product golng to capital, including service of external debt,
S

# Table I

# Profits and Foreign Direct Investment ln the V.S. Auto Industry

|Profits a/|Foreign Direct I|nvestment bl|
|---|---|---|
||Assembly|narts|
|1979<br>4.7|200.0|N.A.|
|1980<br>-5.0|200.0|**N.A.**|
|1981<br>-1.1|500.0|**NA.**|
|1982<br>-0.8|**65.0**|4.0|
|1983<br>5.1|101.4|32.0|
|1984<br>9.9|487.5|48.0|
|1985<br>6.8|6:8.0|71."|
|1986<br>2.2|850.8|234.8|
|1987<br>0.5|435.5|'3.7|
|1988<br>2.4|419.5|80.8|
|1989<br>-1.4|NLA.|N.A.|

Notes.-

# N.A.: Not available.

- al Billion of current dollars.

- Source: U.S. Department of Commerce, Survey of Current Business, _July._ various issues.

- b/ Millions of current dollars. Includes plant and other investment. Prior to 1982, data cover only FDI in assembl7 plants above $50 million. Source: U.S. Department of Commerce, Foreign Direct Investment in the United States, various issues and unpublished data of the Office of Trade Investment, U.S. Department of Commerce.
generally on tackling procurement irregularities, contract management issues and addressing institutional problems. The matrix of actions has been regularly updated until 2011, but the magnitude of the tasks is huge and progress seems to have been limited in some areas.

The second major factor to take into account has been the surge in road allocation: from 2009 to 2012, the total road allocation (both domestic and donor funds) increased fourfold: from 280 million USD to 890 million USD. Inevitably this increase places a significantly higher burden on the road institutions. The next section presents some indications as to whether that burden has been adequately managed.

The third factor has been the increase in domestic funding for roads and the decrease of the share of traditional donors, such as the EU, the World Bank and others. The Government of the Republic of Zambia has taken advantage of the growing fiscal space and increased the budget for select road projects. In subsequent sections, we identify how, since 2007, domestic funding for roads was multiplied by a factor of five but without a corresponding increase in the RDA’s staff or improvements in its technological capacity.

The data presented in this paper have been gathered via staff interviews, a thorough review of the RDA project database, observation of recent events in the sector and review of media articles.

# **RDA’s staffing dynamics**

**Technical expertise:** Successful management of contracts by road agencies requires that staff have a sufficient level of expertise, experience and confidence to challenge poor performance by consultants and contractors. But evidence points to structural problems within the RDA, such as poor planning and design of procurement and a weak internal monitoring and evaluation culture. For example, there are only two staff (out of more than 300), who are currently dedicated full-time to the project monitoring and evaluation (and not at a senior level). The risks that this low staffing capacity poses for the institution are recognized internally; During the RDA’s Managerial Accountability Workshop, the vulnerability assessment of the institution showed that the risks were “high” on almost all points of vulnerability that relate to implementation and monitoring. For example, the possibility of inflated payments, defects being overlooked due to connivance and deliberate ignoring of defects are all rated as _high_ risks.

**Increased workload:** While the number of staff in RDA has remained relatively constant since 2008 (between 320 and 350), since 2010, allocations to the road sector have increased tremendously leading to an increase in the RDA’s workload (see Graph 1). On average, RDA staff are now responsible for handling more and larger projects.

6
intensive goods remains high, Indian market access to the U.S. is likely to remain an important issue in its negotiation.

Many economists believe that the potential gains from liberalization in the service sector may be larger than gains available from liberalization of the trade in goods (e.g., Gervais and Jensen 2013 for the United States; Konan and Maskus 2006 for Tunisia; Chadha, Brown, Deardorff and Stern 2000 for India). The scope for gains from services trade liberalization may be higher since barriers to trade in services tend to be higher than the remaining barriers to trade in goods. Moreover, it is increasingly recognized that service sector trade liberalization not only directly affects service production and trade, but also has positive impacts on productivity or exports of goods, especially in developing countries (Arnold, Javorcik, Lipscomb and Mattoo 2012; Cebula, Mazumdar and Nair-Reichert 2011; Fukui and McDaniel 2010; Konan and Maskus 2006; Robinson, Wang and Martin 2002).

Analysis of the impacts of services trade liberalization is severely constrained by the lack of reliable services trade and protection data. The estimates of services trade barriers vary widely in the literature, depending on methodologies, how the barriers are defined, which sectors are included and whether the barriers include non-discriminatory impediments or apply only against foreign suppliers. However, many economists generally view the services barriers in India to be high, and those in the U.S. to be low (e.g., Borchert, Gootiiz and Mattoo 2014; Gervais and Jensen 2013; Hufbauer, Schott and Wong 2010; Petri, Plummer, and Zhai 2012).<sup>5</sup> As the best available educated guesstimate at the time of writing, we used the tariff equivalents of services

> 5 For instance, according to the Services Trade Restrictions Database which was released recently by the World Bank, India is found to be among the countries which have the most restrictive policies in services while service trade in the United States is found to be generally open (figure 4, Borchert et al.2014). The Services Trade Restrictions Indices (STRI), which range from 0 to 100, are found to be 17.7 and 65.7 for the U.S. and India respectively. However, the STRI does not cover cross-border trade in business processing services associated with the “outsourcing” phenomenon.

15
tuguese signals inclusion, but it also reflects high human capital. Similarly, education raises returns to economic acitivity but it also enables people to access and process information more effectively. Men are typically the main bread winners in households but being male might also indicate a greater involvement in community decisions.?° It will be important to contrast the impact of these variables with and without controls for economic resources.

Second, access to information is important. Informed citizens are better able to access services, exercise their rights, and hold others responsible. The number of radios or television sets owned by the household is a crude proxy for this dimension.

Third, empowerment is linked to local organizational capacity. Well-mobilized groups are more likely to have their voices heard and to solve common problems.” The TLSS includes information about participation in user groups over the last 12 months.

Finally, accountability of community leaders is another component to empowerment. The surveys do not include direct measures on the responsiveness of village and hamlet chiefs to the concerns of the population. However, the village census provides information on the education status and number of years lived in the village of the village leaders.

To summarize, these variables will serve as indicators of high social resources: having mother tongue Tetun; speaking the most common mother tongue in the sub-district as mother tongue; ownership of radios and television sets; participation in user groups; and village leaders with large human capital and life span in the village.

To facilitate the interpretation of the results, variables are, whenever possible, represented through a single index.?? For example, education is represented as a single variable, ranging from 0 of no schooling to 4 for post-lower secondary education. Monetary economic variables, like consumption and assets, are given as per capita Rupiah values. ?° Housing is represented by the prin20 For example, only one percent of the village chiefs and none of the hamlet chiefs interviewed for the village census were women. 21 Inman and Oberholzer-Gee (2003) uncover evidence for the US that democratic institutions which permit citizens to actively shape policies foster a sense of control and self-esteem. 2 As the ordering of occupational groups is not self-evident, each category is included separately (unemployed, non-coffee farmer, coffee farmer, pensioner, student, and wage earner).

23 Nominal values are deflated over time and space. The value of 10,000 Rupiah is about US$1.
In general communities and community leaders have played a somewhat limited formal role in the selection process, with many applicants coming forward on their own initiative. This is particularly the case in urban areas, with a greater role for chiefs in deciding how many workers should go and who should go in rural areas. More direct community involvement occurred in employer recruitment of husbands and wives from the island of Epi, and community decision-making in Ambrym and some communities in Tanna. In most cases the employer doing direct recruiting has established a geographic area to work with, based on advice, connections, or affinity with particular islands, and then engaged the communities there, with the communities nominating candidates who then go through the employer’s selection process.

As of May 22, 2008, 1,698 Ni-Vanuatu workers had been approved to come to New Zealand through the RSE.<sup>6</sup> As of April 19, 2008, 21 different employers had recruited from Vanuatu, although this includes five employers where the recruitment was carried out by Big Toe. Agents had been involved in recruiting 1,055 workers by April 2008, with 10 of the 22 licensed agents having succeeded in sending workers. The first employers recruited no women: Between October 2007 and January 2008, when the first 321 workers arrived in New Zealand, all were male. However, between one-quarter and one-third of those arriving between February and May 2008 were female. Out of the 1,698 workers recruited by May 22, 22.3 percent (379 workers) were female. A final point of interest from the official statistics is that only a minority of workers were recruited for the full seven months: as of May 2008, approximately 28 percent of RSE workers had been recruited for seven months, 28 percent for six months, 16 percent for

> 6 Official statistics were supplied by the New Zealand Department of Labour and the Vanuatu Department of Labour.

- 10 -
PHBK program costs are high. Further, according to data provided by Bank of Indonesia, 70 percent of total costs are, BI's "in-kind" costs (salary and allowance expenses of regular BI staff working on the project and rent for project office space). 45 It is therefore useful to examine program cost ratios considering total costs and "cash outlay' expenses only (all costs incurred by GTZ and BI that are in excess of regular BI staff salaries and rent for office space in BI buildings).

Even when only cash outlay costs are considered., the ratio of annual program costs to annual lending is high in 1995/96, but has declined rapidly in the last two years. In fiscal 1992/93, the program's ratio of total costs to funds lent was approximately 304 percent (or 86 percent if BI's in-kind costs are excluded from the calculation). This figure declined to approximately 94 percent by 1995/96 (or 28 percent if Bl's in-kind costs are excluded). The cost per group receiving a loan was about US$11,654 in 1992/93. (US$3,304 excluding in-kind costs) and had declined to US$1,764 (US$522 excluding in-kind costs) in 1995/96. Finally, if it is assumed that most program expenses are incurred for groups receiving their first loan, it is useful to compare annual program costs to the number of first loans received per year. The program spent an estimated US$16,856 per first loan received (USS4,729 excluding in-kind costs) in 1992/93, and about US$2,079 per first loan (US$615 excluding in-kind costs) in 1995/96 (Table 22).

> 45 Program costs considered here include all costs that Bank of Indonesi **a** nd donors attnibute to the program. These costs do not include the costs that banks incur in making program related loans. Banks' costs are excluded because information regarding these costs was not available. Further. it is assumed that banks are fully compensated for the costs they incur through the revenues they earn on program loans. Because banks receive no special incentive to participate in the program, it is unli:kely that thev would continue to do so if they were not recouping their full costs. Similarly, NGOs' costs are not included in this section because NGOs are compensated by the program for their services. Thus, their costs should already be captured in expenditures by BI and donors.

58
<u>Table S3: Features of Tweets</u>

|N-gram|Using|Using|
|---|---|---|
||Original<br>Tweet|Augmented<br>Tweet|
|accident|1|1|
|past|1|1|
|garden|1|0|
|city|1|0|
|near|1|1|
|thika|1|0|
|rd|2|0|
|and|1|1|
|kamiti|1|0|
|junction|1|1|
|accident past|1|1|
|past garden|1|0|
|garden city|1|0|
|city near|1|0|
|near thika|1|1|
|thika rd|1|0|
|rd and|1|0|
|and kamiti|1|0|
|kimiti rd|1|0|
|rd junction|1|0|
|#landmark-name#|0|1|
|#road-name#|0|2|
|past #landmark-name#|0|1|
|of #road-name#|0|1|
|and #road-name#|0|1|

Features defined using the number of occurrences of n-gram in the tweet.

We test two methods for determining whether a tweet reports a crash: Naive Bayes and support vector machines. Both techniques are commonly used in text classification for their ability to handle high dimensionality, e.g. when the number of features is greater than the number of observations [7, 8]. The Naive Bayes model is estimated as: where _y_ is whether the tweet is classified as crash related or not and _xi_ are all the n-grams that occur in a tweet.

The linear SVM solves the minimization problem: S8