rafmacalaba/datause-encoder-data huggingface dataset:
# **3.  Data**

I rely on disbursement data for individual World Bank projects over the period 1985‐2009. Over this period I have information on actual quarterly disbursements by project over the life of each project, for the universe of all projects financed by the two main lending arms of the World Bank: non‐ concessional lending to middle‐income countries by the International Bank for Reconstruction and Development (IBRD), and concessional lending to low‐income countries through the International Development Association (IDA).<sup>5</sup> For each project, I sum the quarterly data within each calendar year to arrive at annual disbursement flows for each project. Then for each country and year, I sum across all active projects to obtain _total disbursements_ on World Bank loans. I then subtract disbursements on projects approved in the same year to arrive at my measure of _disbursements on previously‐approved projects_ on World Bank loans. Disbursements on previously‐approved projects by construction reflect project approval decisions made in previous years, and my basic identifying assumption is that they are unlikely to be correlated with contemporaneous macroeconomic events. As discussed above, I also construct an alternative measure of disbursements on previously‐approved projects that excludes disbursements on projects approved in the current _and_ previous year.

Figure 1 shows the fluctuations over time in total disbursements and disbursements on previously‐approved projects as a fraction of GDP for one country in my sample, Zambia. The height of the bars shows total disbursements on World Bank loans. These are large in Zambia, averaging 3.4 percent of GDP, and moreover are also very volatile, ranging from close to zero in the late 1980s and late 2000s, to well over five percent of GDP in many other years. The dark‐shaded lower portion of each bar isolates my measure of disbursements on previously‐approved projects, while the remainder of the bar shows disbursements on projects approved in the same year. In most years, the bulk of disbursements on World Bank loans are associated with projects approved in previous years, and my core identifying assumption is that these can be thought of as plausibly exogenous to contemporaneous macroeconomic shocks. However, in a few years such as 1991 and 1999, there are large disbursements on projects approved in the same year. In 1991, for example, a large (for Zambia) project worth $210 million was approved, and approximately three‐quarters of it was disbursed in the same year. This project was an “Economic Recovery Credit” and was intended to “support economic reforms aimed at

> 5 My dataset in principle covers all 10475 projects approved between the first World Bank project in 1948 and early 2010 when my data stop.  However, electronic records on quarterly project‐level disbursement flows are not available before 1985, and the cost of manually entering this data from archived paper records is prohibitive.  I therefore rely only on data from 6529 projects approved since 1985.

11
Section 2 will review the historical context of Mode 1 services in India, while Section 3 describes the theoretical foundations that the discussions of services trade have been based on in the literature. Section 4 describes the applied general equilibrium methodology to be used and Section 5 analyzes the results. Concluding remarks are made in Section 6.

# **2. The Indian Context**

India has demonstrated rapid economic growth over the first half of this decade, clocking in an annual real GDP growth rates of more than six percent between 2001 and 2006, increasing the GDP per capita (PPP) from US$ 2513 to US$ 3736 (IMF 2007a). Much of this growth has been attributed to the export‐oriented services industries. This reputation can be attributed to service industries like information technology (IT), which was alone responsible for 3.8 percent of GDP in 2003‐04, up from 1.22 percent of GDP in 1997‐1998, as reported by Thatchenkery et al (2005). Based on these contributions of services to aggregate growth, some studies (Stough et al, 2005; Bhatnagar, 2003) argue that the growth of services, such as information communication technology , have allowed for a “leapfrogging” effect in Indian development, where India has been able to bypass the traditional development paradigm of developing agricultural and manufacturing industries before an export‐oriented services sector. The rapid Indian growth over this period is certainly concurrent with the increases in Indian services exports.

Based on data from the IMF’s Balance of Payments Statistics (2007b), Indian services exports grew at an average annual rate of about 34.45 percent between 2000 and 2006, allowing for exports to grow by 477 percent over the period (Figure 1). Over this period, the services exports increase was mirrored by a 280 percent growth in the services imports, although India was able to maintain a growing services trade surplus (Figure 1). As of 2001, 93 percent of these Mode 1 imports were demanded by the services sector<sup>3</sup> , illustrating their importance to India’s own services‐export sector. The manufacturing and agricultural sectors demanded the remaining 6 percent and 1 percent of Mode 1 imports, respectively.

> 3 Based on balanced data extracted and balanced from Dimaranan (2006), provided in Table A‐1 (Appendix A), and discussed in Section 4.2. While the share of imported services used in the production of Indian services may seem very high, it should be noted that it does not reflect the shares of domestically produced service inputs or total services inputs (domestic and Mode 1) used by the industries. The services and manufacturing industries use 46 and 37 percent of domestically produced services inputs, respectively. Looking at total services (domestic and Mode 1) inputs used the services and manufacturing industries use 48 and 37 percent of the total. Mode 1 services inputs are thus a very small share of all service used as intermediate inputs in India.

4
- (b) Who are the parties responsible for implementing those collective actions?

- (5) Have you been apprised of the National Plan for responding to HIV/AIDS, 20082011?

- (a) If so, would you say that the National Plan has been useful for creating your own Territorial Public Health Plan?

- (b) If so, would you say that the National Plan has been useful for developing the HIV/AIDS response in your Department/District?

65
Rola, A. C. and P. L. Pingali (1993), Pesticides, Rice Productivity, and Farmer’s Health: an economic assessment. Washington DC: International Rice Research Institute, World Resources Institute.

Rosenstock, L., M. Keifer, W. E. Daniell, R. McConnell, K. Claypoole (1991), ‘Chronic Central Nervous System Effects of Acute Organophosphate Pesticide Intoxication’, Lancet 338: 223-227.

Sabur, S.A. and A. R. Molla (2000), ‘Marketing and Economic Use of Pesticides: Impact on Crop Production’, Bangladesh Agricultural University, Mymensingh, Bangladesh (mimeo).

SOS-Arsenic.net (2004), ‘Agrochemicals: Imported Pollutants in Bangladesh’, Available at: <u>http://www.sos-arsenic.net/index.html.</u> SUNS (1998), ‘Pesticide Overuse Takes Serious Turn in Bangladesh’, Monday, Jan. 24, (Dhaka, Jan. 23 IPS/Tabibul Islam).

World Health Organization (WHO) (1990), Public Health Impact of Pesticides Used in Agriculture, 1990, New York: World Health Organization.

Wilde, J. (2000), ‘Identification of Multiple Equation Probit Models with Endogenous Dummy Regressors’, Economics Letters 69: 309-312.

World Resources Institute, UNEP, UNDP, World Bank (1998), ‘Environmental Change and Human Health’, in World Resources 1998-99, New York: Oxford University Press.

Xuyen, K., N. C. Hoi and P. Q. Trung (1998), ‘Occupational Environment and Skin Diseases in Pesticide Exposed Subjects in some Tea Farms in Vietnam’, Presentation given at the Third National Scientific Conference on Occupational Health, December 4- 5, 1998. Hanoi.

21
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 7 The first objective of the study was to test whether any variant of the intervention improved the quantity or quality of caregiver-child book-sharing. Varying the intensity of the training (one group session vs. two group sessions vs. group sessions plus a home visit) allowed us to test the hypothesis that the more involved interventions had larger effects, particularly on the quality of book-sharing **.** The second objective of the study was to assess the extent to which variants of our treatment increased child receptive and expressive vocabulary; we had hypothesized that the more involved interventions would have a larger effect size. The final objective of the study was to test whether the treatments had differential effects on disadvantaged children, specifically with respect to caregiver literacy.

# **Methods and Materials**

# **Storybook Selection and Modification**

For the book component of our intervention, we began by conducting a survey of all bookstores, markets, and grocery stores in the greater Kisumu area to identify all readilyavailable children’s storybooks. We were unable to find any storybooks intended for preschoolaged children that were printed in the dominant mother tongue, Luo. We pretested both existing, locally sourced English and Swahili storybooks and stories adapted from the African Storybook Project (http://www.africanstorybook.org), which we translated into English, Luo, and Swahili. We distributed these pilot books to households in peri-urban and rural communities surrounding Kisumu, and conducted follow-up interviews and focus groups to understand which books recipients had liked and why. Based on the feedback received, we partnered with Kenyan-owned Moran Publishers to adapt six of their (English-language) primary school level readers intended for beginning readers. In interviews and focus-groups, parents enjoyed the extremely detailed,
# **3.Diagnosing the Transaction Ecosystem: The Cases of Pakistan and Cambodia**

This study draws on transactions data from Pakistan and Cambodia to investigate actual FMIS budget coverage and determine the transactions profile to help interpret how data from actual system use can guide expenditure management reform.

## **3.1. The Pakistan Case**

### Background

The process of improving fiscal and financial reporting in Pakistan was initiated in the early 1990s after observations by both the World Bank and the IMF, and a diagnostic study under- taken by the Auditor General of Pakistan (AGP), that the (then manual) accounting and reporting system did not meet adequate standards for either financial or fiscal reporting. Notably, both accounting and auditing were directed by the Auditor General of Pakistan (an anomaly from pre-1947), budget reports by the Ministry of Finance (MOF) were not fully reconciled with accounting reports by the AGPR and provincial AGs, the Chart of Accounts (COA) was not compliant with IMF Government Finance Statistics (GFS) standards for fiscal reporting, and neither financial nor fiscal reports were timely or reliable. To meet these challenges, the Pakistan Audit Department (PAD) launched the PIFRA Program in 1995 with World Bank support. Under this program two projects have been executed, PIFRA I and PIFRA II. PIFRA I was completed in 2005, and PIFRA II in 2015.

As part of the project, a major mission critical country-wide integrated system, the Financial Accountability and Budget System (FABS), has been implemented to spearhead the reforms and assist the government in the functional processes associated with financial accounting and budgeting at the federal, provincial and district levels.

### **FABS functionality includes:**

- Budget preparation: assists in compilation of budgets, prints the budget book and associated reports, maintains a record of initial budgets, revisions, and budget releases.

- Budget execution:

- Records commitments, receipts of goods and services;

- Ensures expenditures are in accordance with budget appropriations, commitments and budget releases;

11
# **<u>7C: External DSA, Liquidity indicators</u>**

||||<br><br>Deviat<br>**Situ**|<br> <br>ion from Thr<br>**ation post‐sh**|<br><br>eshold<br>**ock**|**situ**<br>Deviati|<br>**ation pre‐s**<br>on from Th|<br>**hock**<br>reshold|
|---|---|---|---|---|---|---|---|---|
|**GROUP**|**DEBT BURDEN**|**STRESS TESTS**|**Max**<br>**Breach**|**Length (yrs)**<br>**of deviation**|**Average**<br>**deviation**|**Max**<br>**Breach**|**Length (yrs**<br>**of deviation **|**Average**<br> **deviation**|
|Whole sample|Debt Service to Revenue|A2|102.7|0.4|1.0|45.8|0.4|0.4|
||Debt Service to Exports|A2|9.7|0.9|0.1|6.1|0.4|0.1|
||Debt Service to Revenue|B1|57.9|0.4|0.6||||
||Debt Service to Exports|B1|6.1|0.4|0.1||||
||Debt Service to Revenue|B2|45.8|0.4|0.4||||
||Debt Service to Exports|B2|14.5|1.4|0.3||||
||Debt Service to Revenue|B6|6.1|0.9|1.0||||
||Debt Service to Exports|B6|80.6|0.4|0.1||||
|Fragile|Debt Service to Revenue|A2|102.7|1.4|3.3|45.8|1.4|1.4|
||Debt Service to Exports|A2|9.7|2.9|0.5|6.1|1.2|0.2|
||Debt Service to Revenue|B1|57.9|1.4|1.9|1|0||
||Debt Service to Exports|B1|6.1|1.2|0.2|1|0||
||Debt Service to Revenue|B2|45.8|1.4|1.4|1|0||
||Debt Service to Exports|B2|14.5|2.7|0.7|1|0||
||Debt Service to Revenue|B6|80.6|1.5|2.9|1|0||
||Debt Service to Exports|B6|6.1|1.2|0.2|1|0||
|Oil Exporting|Debt Service to Revenue|A2|0.0|0.0|0.0|0.0|0.0|0.0|
||Debt Service to Exports|A2|0.0|0.0|0.0|0.0|0.0|0.0|
||Debt Service to Revenue|B1|0.0|0.0|0.0||4||
||Debt Service to Exports|B1|0.0|0.0|0.0||4||
||Debt Service to Revenue|B2|0.0|0.0|0.0||4||
||Debt Service to Exports|B2|10.4|3.8|0.9||4||
||Debt Service to Revenue|B6|3.4|0.3|0.0||4||
||Debt Service to Exports|B6|0.0|0.0|0.0||4||
|Frontier LIC|Debt Service to Revenue|A2|102.7|1.6|3.7|45.8|1.6|1.5|
||Debt Service to Exports|A2|4.5|0.6|0.1|0.0|0.0|0.0|
||Debt Service to Revenue|B1|57.9|1.6|2.1||9||
||Debt Service to Exports|B1|0.0|0.0|0.0||9||
||Debt Service to Revenue|B2|45.8|1.6|1.5||9||
||Debt Service to Exports|B2|0.0|0.0|0.0||9||
||Debt Service to Revenue|B6|80.6|3.0|3.6||9||
||Debt Service to Exports|B6|0.0|0.0|0.0||9||
|HIPC (post CP)|Debt Service to Revenue|A2|102.7|0.5|1.2|45.8|0.5|0.5|
||Debt Service to Exports|A2|9.7|1.1|0.2|6.1|0.5|0.1|
||Debt Service to Revenue|B1|57.9|0.5|0.7|2|7||
||Debt Service to Exports|B1|6.1|0.5|0.1|2|7||
||Debt Service to Revenue|B2|45.8|0.5|0.5|2|7||
||Debt Service to Exports|B2|14.5|1.1|0.3|2|7||
||Debt Service to Revenue|B6|80.6|1.0|1.2|2|7||
||Debt Service to Exports|B6|6.1|0.5|0.1|2|7||

35
<!-- Start of picture text --> SAR<br>MENA<br>ECA<br>SSA<br>EU<br>LAC<br>HIC<br>EAP<br>0 100 200 300<br>Rate of non fatal road injuries warranting<br>hospital admission (per 100,000)<br><!-- End of picture text --> <!-- Start of picture text --> SAR<br>MENA<br>SSA<br>EAP<br>ECA<br>LAC<br>EU<br>HIC<br>0 500 1000 1500 2000 2500<br>Rate of non fatal road injuries<br>(per 100,000)<br><!-- End of picture text --> <!-- Start of picture text --> Low gasoline/diesel price i High gasoline/diesel price<br>1200<br>1100<br>1000<br>900<br>800<br>700<br>600<br>Nonfatal injuries rate *** Nonfatal injuries rate ***<br><!-- End of picture text --> <!-- Start of picture text --> @ Low gasoline/diesel price .t High gasoline/diesel price<br>140 ~<br>130 ~<br>120 -<br>110 ~<br>100 ~<br>90 -<br>80 -<br>70]<br>60<br>Road injuries Road injuries<br>admission rate *** admission rate ***<br><!-- End of picture text -->
3 <!-- Start of picture text --> Tabl 1 PBHM PROJECTS ;PPUCD ID V190<br>(In US$ millions)<br>Y90 n 90 FYW9 MF9<br>COOURkY PROJECT AEO?U REoGIW RCIOu RWCIO Ru6:<br>TOTAL $ * TOTAL 9<br>AFRICA<br>Kenya Population IV 35.0 IDA<br>Lesotho Population, Health &<br>Nutrition XX 12.1 IDA<br>NiLeria National Easential Drugs 68.1 IBRD<br>Tanzania Health and Nutritlon 47.6 IDA<br>162.8 4 81.3 4<br>ASIA<br>India Population VII 96.7 IDAIIBRD<br>IndLa Tamil Nadu Nutrition 1I 95.8 IDA<br>192.3 2 290.2 4<br>DHENA<br>Morocco Health Sector Developent 104.0 IBRD<br>Ymen A. R. Health IT 15.0 IDA<br>119.0 ? 79.5 2<br>LAs<br>Rollvia Intageated Health Development 20.0 IDA<br>Brazil Northeast Ba,Lc Health IX 267.0 IBRD<br>Colombia Child Care and Nutrition 24.0 IBRD<br>Ha'tL Health nd Population 28.2 IDA<br>3SS2 4 99.0 L<br>GRAND TOTAL 813.5 12 550.0 11<br><!-- End of picture text -->
Table A.1: Robustness check using alternative outside option measures

|VARIABLES||Non-o|wners||
|---|---|---|---|---|
|||Fish<br>|in 07<br>||
||(1)|(2)|(3)|(4)|
|Fraction of bad aid boats|**-0.776*****<br>**(0.193)**|**-0.605*****<br>**(0.168)**|**-0.505*****<br>**(0.192)**|**-0.651*****<br>**(0.164)**|
|D_bad house in 07|-0.212**|-0.166*|-0.134|-0.162*|
||(0.0891)|(0.0924)|(0.0980)|(0.0967)|
|Boat owner and captain pre-tsunami|-0.0206|-0.0187|-0.0248|-0.0120|
||(0.110)|(0.111)|(0.111)|(0.108)|
|D_large boat pre-tsunami|0.0150|0.0870|0.0714|-0.0308|
||(0.111)|(0.117)|(0.109)|(0.0972)|
|Ln (number of aid projects in 07)|**-0.661*****<br>**(0.186)**||||
|Ln (total housing aid in 07)||**-0.166****<br>**(0.0657)**|||
|Ln (distance to BA)|||**0.311****<br>**(0.126)**||
|Imputed market shadow wage in 07||||**-0.158***<br>**(0.0945)**|
|D_debt to Toke Bangku|0.189|0.105|0.149|0.208|
||(0.143)|(0.175)|(0.170)|(0.155)|
|High education pre-tsunami|-0.136|-0.110|-0.125|-0.138|
||(0.0923)|(0.0945)|(0.0948)|(0.0975)|
|Number of captains in 05|0.00665**|0.00471*|0.00292|0.000176|
||(0.00270)|(0.00270)|(0.00268)|(0.00223)|
|Observations|147|149|149|149|
|Sample||Orig|inal||
|Seasonality is controlled|Y|Y|Y|Y|
|Regional fixed effects|Kec|Kec|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; Estimates in coumn (1)-(4) are marginal effects of probit model.  Robust standard errors clustered at the village level are in parentheses.

44
In addition to the potential for real-time monitoring, the granularity and uniqueness of data captured by online job portals offer unique advantages over traditional survey instruments. Online job advertisements can provide highly granular data on labor market characteristics and demand dynamics at a specific time or location, or for a specific type of occupation. For example, while official statistics collect data on the current level and distribution of wages, online job-portal data focus solely on wage offers, which can help identify emerging trends in the demand for specific skills, the seasonality of hiring practices and firms’ production expectations for specific jobs. The data allow for both a dynamic and static evaluation of the labor market, as well as an examination of specific market segments. Information on the historical growth and spatial distribution of job openings, wage trends and patterns, and competition among job seekers can help policy makers understand how the labor market is evolving in different places and over time.

However, there are important caveats regarding the representativeness of online job-portal data. In order to ensure that findings are interpreted accurately, it is crucial to specify exactly what the data are representing and to clearly define their applicability to the broader labor market. Limitations on representativeness can be addressed by statistically weighting the data according to the industry structure derived from labor force surveys, focusing on segments of the labor market in which coverage bias is less of a problem, and using diverse data sources to conduct parallel analyses and confirm the robustness of findings (Kureková, Beblavý, and Thum-Thysen, 2015).

# **_Application of the Analysis to Babajob Data_**

As noted above, online job-portal data can facilitate an in-depth analysis of specific social groups and segments of the labor market. In this study, Babajob data was used to analyze gender differences in wage offers for similar positions. Women constitute 26 percent of the Indian workforce, and gender disparities in workforce participation rates are an important development issue (United Nations, 2012). An analysis of wage offers for 858,000 jobs posted to Babajob by 240,000 unique employers between 2007 and 2015 reveals how these offers evolved over time and varied by location, occupation and contract type. An econometric analysis found that the average wage offers for positions that specified a male hire were 7.1 percent higher than those that were open to both genders, while wage offers for positions that specified a female hire were 16.2 percent lower. This analysis controlled for a number of other factors that might affect wage rates, including employment period, location, occupation and contract type. Among occupations that were frequently offered to both male and female workers, cooks, garment, and teaching jobs exhibited relatively high wage differentials, while stewards, nanny, machinist and office helpers exhibited relatively small differences (Figure 2). Among the 20 cities with the largest number of job listings, Ahmedabad, 9
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