rafmacalaba/datause-encoder-data huggingface dataset:
experimental analysis of the Honduras _PRAF II_ program finds that the probability of matriculating to subsequent grades increases by 2 to 4 percentage points among beneficiary children. Comparable effects have also been documented for CCT programs in Mexico, Nicaragua, and Paraguay (Behrman et al., 2001; Maluccio and Flores, 2005; Veras Soares et al., 2008).

The literature also gives evidence on the indirect effects of conditional transfers on the labor market outcomes and marriage and fertility decisions of adolescents, which are thought to operate mostly through the conditionalities that require children to attend school regularly. In fact, rigorous evaluations of programs in Brazil, Cambodia, Colombia, Mexico, and Nicaragua confirm that treated children not only spend more time in school but also have relatively lower participation in income generating activities and domestic work (Behrman et al., 2005; Maluccio and Flores, 2005; de Janvry et al., 2006; IFS – Econometria - SEI, 2006; Ferreira et al., 2009). This effect is also observed among adolescent females. For example, the _Oportunidades_ program in Mexico lowers the participation of young girls in labor markets and child care by around 10 percent and 40 percent, respectively (Behrman et al., 2005; Dubois and Rubio-Codina, 2009). Furthermore, increased time spent in school and improved knowledge could also result in changes in marriage, sexual and fertility decisions. It is shown that CCTs have caused delays in marriage and onset of sexual activity in Malawi and Mexico, reductions in the number of sexual partners in Malawi, and reductions in pregnancy rates in Malawi and Turkey (Baird et al., 2009; Behrman et. al., 2005; Gulemetova-Swan, 2009; Ahmed et. al., 2007).

Evidence regarding the impacts of CCTs designed to explicitly address gender disparities in investments in human capital, such as the program evaluated in this paper, shows that they have succeeded in increasing school enrollment and attendance of girls. At least four interventions that were set up to address these issues achieve positive impacts in the short term. The _Bangladesh Female Secondary Stipend_ Program is shown to raise the enrollment of girls in sixth through eighth grade, by 8 to 12 percent (Khandker et. al., 2003). Similarly, the _Zomba Cash Transfer_ experiment in Malawi more than doubles the re-enrollment of girls between 13 and 22 years old who had dropped out of school before (Baird et. al., 2009). Another example is the _Japan Fund for Poverty Reduction Scholarship Program_ in Cambodia, which has a large positive effect (around 30 percent) on the school enrollment and attendance of girls (Filmer and 4
World Bank (2005A), “Republic of Tajikistan Poverty Assessment Update”, Report No: 30853-TJ.

World Bank (2005B) “Republic of Tajikistan: Health Sector Note”, Report No 29858TJ.

World Bank (2005C), “Tajikistan Energy Utility Reform Review”.

World Bank (2005D), “Tajikistan: Trade Diagnostic Study” Report No 32603-TJ.

World Bank (2006), “Europe and Central Asia Infrastructure Study”.

World Bank (2007), “Tajikistan: Programmatic Public Expenditure Review”.

34
# EXECUTIVE SUMMARY

The purpose of this paper is (1) to review the salient features<sup>of</sup> the World Bank's institutional development (ID) work in different sectors of the economy in the 1980s, (2) to assess the performance of<sup>ID components in</sup> Bank projects, and (3) to present the implications of the findings for Bank policy. Because of the wide spectrum of sectors<sup>being surveyed, cross</sup> sectoral, rather than, sector-specific features of ID work are highlighted. Investment projects, sector loans and sector adjustment<sup>loans in all regions</sup> fall within the purview of the paper. The paper draws<sup>upon the findings of</sup> sample surveys of projects, review papers on individual sectors<sup>and lending</sup> operations, Operations Evaluation Department (OED) data and analysis,<sup>and Bank</sup> staff interviews.

# Recent Trends

- The attention given to ID in Bank projects has increased significantly during the 1980s. Nearly 90 percent of the projects today have ID components in contrast to 72 percent in 1978. The awareness of ID issues on the part of Bank staff and<sup>their</sup> exploration of these issues during project preparation have increased steadily.

- O The scope of ID issues and of ID components in Bank projects<sup>has</sup> widened in recent years. By and large, this trend can be attributed to the newer policy-based lending instruments. Widening of the scope, however, has occurred through an expansion of the coverage of the institutions in the concerned sectors.

- Much greater attention is being given in recent projects<sup>to the</sup>

- sector- or subsector-level institutions and their structures and functions, while in earlier years, the preoccupation was solely with the project implementation unit (PIU) and its functions. This is a feature not only of policy-based lending<sup>operations but</sup> also of a growing number of investment projects.

- O The extension of ID work and the widening of the scope<sup>of ID have</sup> occurred primarily with elements and tasks which,<sup>for the most</sup> part, facilitate project implemencation, which is only one of the ID objectives. Much less attention is given to<sup>the ID objectives</sup> of sustainability and the long-term strengthening of sector-wide institutions. Country-specific sector ID strategies are relatively rare.

- Despite its expanded coverage in Bank projects, ID<sup>work that might</sup> facilitate the matching of beneficiary needs and<sup>demand to the</sup> design of project services and the choice of institutional alternatives for service delivery remains limited.<sup>Attention to</sup> the incentive framework necessary for long-term organizational performance and interorganizational collaboration and to issues of
_Figure 2.1 Cross-Country Relation between Educational Attainment and GDP, 1960–90_ <!-- Start of picture text --> 12<br>10 M exico 1990<br>8<br>6<br>4 M exico 1980<br>2 M exico 1970<br>M exico 1960<br>0<br>5.00 6.00 7.00 8.00 9.00 10.00<br>Ln (G D P per capita)<br>Average<br>years of schooling<br><!-- End of picture text --> Note: The scatter diagram is based on 317 observations from five years. The trend line represents the least squares regression line given by _S_ = -13.17 + 2.28 Ln _(GDPcap)_ Adjusted _R_<sup>2</sup> = 0.68. (-18.7) (26.0) _t_ -values in parentheses The application of Ramsey’s RESET test to this regression equation failed to detect a specification error; unlike with the alternative specification of the following type: _S = a + bX + cX2_ .

10
Table 8: Summary results

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Sample|All sch. in quintiles 3, 4, 5||Matched Sampl|e|
|Methodology|Fixed-E¤ects|Closest 5|Closest|p-score>15|
||Language||||
|Number of Years FTS|0.08<br>(0.07)|0.08<br>(0.07)|0.14<br>(0.09)*|0.21<br>(0.08)**|
|Constant|12.43<br>(0.35)***|11.56<br>(0.40)***|12.52<br>(0.56)***|11.89<br>(0.46)***|
|No obs.|11061|5910|3431|4313|
|No schools|152|83|49|63|
|R square|0.08||||
||Mathematic|s|||
|Number of Years FTS|0.16<br>(0.07)**|0.2<br>(0.07)***|0.2<br>(0.08)**|0.29<br>(0.08)***|
|Constant|10.35<br>(0.34)***|9.74<br>(0.40)***|10.73<br>(0.55)***|10.26<br>(0.45)***|
|No obs.|11115|5910|3431|4313|
|No schools|152|83|49|63|
|R square|0.08||||

Notes: This table reports the estimated program e¤ects under di¤erent sample restrictions. Column 1 reports the estimated treatment e¤ect using the full sample of schools in the 3, 4 and 5th socio-economic context quintile. Columns 2 and 3 report the results when the the sample is restricted to program schools and their 5 nearest neighbors and their closest neighbor, respectively. Finally, in Column 4 the sample is restricted to those schools with a propensity score higher than 0.15. All coe¢ cients and standard errors in columns 2, 3 and 4 are bootstrapped using 300 replications, and observations that do not belong to the support of the p-score function are excluded. Full results are reported in the next table. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

18
# **V. Did mark-to-market accounting contribute to the crisis?**

In the US, accounting rules consist largely of generally accepted accounting principles (GAAP) and generally accepted auditing standards (GAAS). GAAP sets rules that constrain (but do not fully determine) the information systems that financial-institution managers may use to document their firm’s economic condition and performance. GAAS sets procedures to be used in examining and verifying a firm’s reports and records for compliance with GAAP.

In a world of diverse and evolving circumstances, reporting systems inevitably convey options about where to book and how to measure risk exposures and changes in value.

The existence of these options and the ways that particular institutions use them to conceal losses is only imperfectly understood by outsiders. From a statistical point of view, accounting income and net worth are merely estimates of a firm’s economic income and ownership capital. Because contemporary reporting systems only provide imprecise point estimates of the current and future earning power of a firm, careful users of accounting data must acknowledge the existence of a margin for error. In different firms and in the same firm at different times, measures of income or net worth may be biased up or down and may vary substantially in their exposure to estimation error. GAAP has never required a firm to report interval estimates or to disclose helpful supplementary information about the degree of imprecision or bias inherent in the methods its managers adopt.

Observers who fail to acknowledge the many reporting options that fair-value accounting still allows blame it for causing the crisis. They argue that thin markets can cause a downward spiral in asset prices by encouraging institutions to sell troubled assets quickly and “forcing” them to take writedowns that understate the “true” value of the underlying assets. However, in creating and deepening the securitization crisis, the most serious impact of accounting rules did not emanate from the values chosen to represent various on-balance-sheet positions. It came instead from using off-balance-sheet extensions of commercial and investment banks to warehouse risks that, for reputational 21
**Table** **<u>9a</u>** <u>:  Estimation of number of income sources and household welfare, urban areas</u>

|**Dependent Variables:**||**1990/91**|||**1995/96**||
|---|---|---|---|---|---|---|
|Income Diversification (S) and log of per<br>capita consumption(Cons)|1<sup>st</sup>stage<br>(1) S|IV<br>(2) Cons|OLS<br>(3) Cons|1<sup>st</sup>stage<br>(4) S|IV<br>(5) Cons|OLS<br>(6) Cons|
|S||-0.79|-0.37||0.53|0.03|
|||(2.3)**|(3.9)***||(2.0)**|-1.3|
|Head sex (Male)|0.05|<br>0.20|<br>0.14|0.07|<br>0.15|0.09|
||(6.2)***|(4.8)***|(4.9)***|(8.9)***|(4.3)***|(4.6)***|
|Head age|-0.02|0.25|0.26|0.02|0.18|0.16|
||(1.1)|(5.0)***|(5.6)***|(1.7)*|(5.6)***|(5.5)***|
|Age Squared|<br>0.00|<br>-0.03|<br>-0.03|<br>0.00|<br>-0.02|<br>-0.02|
||(0.0)|(5.2)***|(5.5)***|(3.1)***|(5.3)***|(5.1)***|
|Head education primary|<br>0.02|<br>0.28|<br>0.27|<br>0.04|<br>0.12|<br>0.08|
||(1.1)|(5.5)***|(5.5)***|(2.9)***|(2.8)***|(2.3)**|
|Head education secondary or higher|0.05|0.56|0.50|0.09|0.27|0.19|
||(3.3)***|(9.5)***|(10.1)***|(6.2)***|(4.7)***|(4.8)***|
|Household size|<br>0.00|<br>-0.18|<br>-0.17|<br>-0.01|<br>-0.21|<br>-0.20|
||(1.4)|(15.3)***|(16.1)***|(2.2)**|(22.8)***|(24.4)***|
|# Boys|0.00|0.07|0.07|-0.01|0.08|0.09|
||(0.2)|(4.0)***|(4.1)***|(1.4)|(6.2)***|(7.2)***|
|# Girls|<br>0.00|<br>0.08|<br>0.08|<br>0.00|<br>0.10|<br>0.10|
||(0.1)|(4.4)***|(4.5)***|-0.9|(7.5)***|(8.2)***|
|# Male adult with primary education|<br>0.00|<br>-0.02|<br>-0.02|0.01|<br>0.03|<br>0.03|
||(0.4)|(0.5)|(0.7)|(1.1)|(1.6)|(1.3)|
|# Male adult with sec./ higher education|-0.02|0.05|0.06|-0.03|0.10|0.12|
||(3.8)***|(2.5)**|(4.2)***|(6.2)***|(5.9)***|(10.3)***|
|# Female adult with primary education|<br>-0.02|<br>0.02|<br>0.04|<br>-0.03|<br>0.04|<br>0.07|
||(3.4)***|(0.7)|(1.9)*|(5.3)***|(1.5)|(3.8)***|
|# Female adult with sec./ higher education|<br>-0.02|<br>0.17|<br>0.20|<br>-0.02|<br>0.18|<br>0.20|
||(4.1)***|(8.0)***|(11.0)***|(4.1)***|(10.8)***|(14.5)***|
|Per capita asset holding|0.00|0.29|0.28|0.00|0.33|0.33|
||(1.3)|(32.6)***|(34.7)***|(0.2)|(41.6)***|(43.6)***|
|Access to credit (yes)|<br>0.07|<br>-0.01|<br>-0.03|<br>-0.04|<br>0.38|<br>0.42|
||(2.7)***|(0.1)|(0.4)|(2.4)**|(7.6)***|(9.5)***|
|Secondary city (yes)|-0.04|-0.32|-0.31|0.01|-0.34|-0.35|
||(3.7)***|(10.6)***|(11.0)***|(1.8)*|(19.7)***|(21.6)***|
|Bulawayo city (yes)|0.01|-0.24|-0.24|-0.02|-0.14|-0.14|
||(0.6)|(7.0)***|(7.3)***|(1.9)*|(6.9)***|(7.6)***|
|Rainfall standard Deviations (planting)|<br>-0.20<br>|||<br>0.29<br>|||
||(29)***|||(62)***|||
|Rainfall standard deviations (harvesting)|.<br>0.02|||.<br>0.06|||
||(1.4)|||(2.8)***|||
|Constant|<br>0.93|5.56|4.58|<br>0.69|4.97|4.29|
||(24.7)***|(11.2)***|(37.1)***|(19.8)***|(14.4)***|(53.7)***|
|R Squared (adjusted)|0.36|--|0.52|0.32|--|0.49|
|Observations (N)|4561|4561|4561|7177|7177|7177|

Absolute value of t statistics in parentheses

* significant at 10%; **significant at 5%; *** significant at 1%

23
_Figure 8: Slavonian firms find high taxes and qualification of the workforce to be a major hurdle_ <!-- Start of picture text --> Major 5 Problems for Growth of a Firm<br>Croatian Firms, 2017<br>Other Regions Slavonia Zagreb<br>Lack of Credit Qualification of Workforce<br>Cost of Labour Excess of Govt. Procedures<br>High Taxes<br>60<br>40<br>20<br>Proportion of Firms (%)<br>0<br><!-- End of picture text --> With firms facing multiple constraints, there are several government programs that are available to help firms. In fact, of the 585 surveyed firms, nearly 59 percent of these firms have applied for some government program in the last three years, with the largest share being in Eastern Croatia (62 percent), relative to Zagreb (58 percent) and other regions (59 percent).<sup>10</sup> Nonetheless, nearly 40 percent of the firms do not participate in government programs. Why? Among the programs that are available, a large share of firms in Eastern Croatia do not find it appropriate to their needs, while others find it administratively burdensome to fulfill the application requirements (Figure 9).

_Figure 9: Customizing programs to firm’s needs and reducing administrative burden may encourage participation in government programs_ <!-- Start of picture text --> Reasons for Not Requesting Govt. Programs<br>Croatian Firms, 2017<br>Other Regions Slavonia Zagreb<br>No programs for the firm Lot of administrative work<br>40<br>30<br>20<br>Proportion of Firms (%)<br>10<br>0<br><!-- End of picture text -->

**Section 3: Critical Firm Capabilities: Management in Focus**

# _3.1 Why do we need a management survey?_

Although managerial and organizational practices have been conceived to be critical in affecting firm outcomes since the time of Adam Smith’s pin factory and Walker’s 1887 paper on business performance, these practices are not well measured in administrative data on firms or the commercial data on companies’ accounts. Some studies have used

> 10 The lowest participation rate was observed in Dalmatia (47 percent).

11
Longitudinal research in the United States indicates that age of entry into kindergarten shows a modest relationship to cognitive-academic achievement but not social-emotional functioning (NICHD Early Child Care Research Network, 2007), and researchers note that age-of-entry effects are dwarfed by other aspects of children’s family and early care and education experiences.

## Current Study

In order to better understand the developmental status of young children in Pakistan, and the impact of various risks on young children’s development, we conducted a nationally representative telephone survey of households with children aged 0-6 years. Health protocols precluded in-person interviews during the pandemic. To date, a nationally representative study of child development among children under age 3 or school readiness for those ages 3-6 has not been conducted in Pakistan. Thus, these data fill critical knowledge gaps.

This study shares the findings from the survey, conducted from December 2021 to February 2022. The aims of this study are to describe the current status of ECD and school readiness for children aged 0-6 in Pakistan and explore how these outcomes are related to six key risk factors—food insecurity, living in a rural area, low maternal education, lack of psychosocial stimulation at home, experiences of parental distress, and no ECE enrollment. This list of risks a child could face is not intended to be exhaustive. These are risks informed by the literature and identifiable in the current survey format.

This study aims to fill key gaps in the literature for ECD particularly around parenting capacity as related to parents’ education levels, mental health or distress levels, and engagement with their young children, specifically provision of psychosocial stimulation. The study also provides insight into the state of child development and subsequent risk factors during the COVID-19 pandemic. Section 2 discusses the data and methods and describes both the research design as well as key variables. Section 3 reports results and section 4 concludes with a discussion of the implications for policy and programming.

# **2. Data and Methods**

## Research Design and Data Collection Strategy

Gallup Pakistan was contracted by the World Bank to conduct a nationally representative survey. As pandemic health protocols precluded in-person interviews, interviews were conducted by telephone. Calls were made from December 2021 to February 2022 using random digit dialing (RDD) of mobile phones using all four telecom providers with active numbers across the country.

8
|Is a college graduate|0.04|0.21|11546|0.03|0.17|7783|
|---|---|---|---|---|---|---|
|Years mostly self-employed|1.62|3.08|11546|5.74|6.18|7783|
|Years mostly in government|||||||
|employment|0.41|1.74|11546|1.87|4.59|7783|
|Years mostly in private sector<br>|||||||
|employment|4.16|3.79|11546|4.19|5.46|7783|
|Industry: Agriculture, forestry, fishing|0.14|0.34|11507|0.24|0.42|6523|
|Industry: Mining and quarrying|0.01|0.09|11507|0.01|0.08|6523|
|Industry: Manufacturing|0.23|0.42|11507|0.12|0.32|6523|
|Industry: Electricity, gas, water|0.00|0.07|11507|0.00|0.06|6523|
|Industry: Construction<br>|0.08|0.26|11507|0.07|0.26|6523|
|Industry: Wholesale, retail, restaurants,<br>hotels|0.24|0.43|11507|0.26|0.44|6523|
|Industry: Transport, storage,|||||||
|communications|0.06|0.23|11507|0.05|0.22|6523|
|Industry: Finance, insurance, real<br>estate, business services|0.02|0.12|11507|0.01|0.09|6523|
|Industry: Social services|0.23|0.42|11507|0.24|0.43|6523|
|industry: other|0.00|0.00|11507|0.00|0.00|6523|
|Self-employed: no other workers|0.15|0.36|11546|0.26|0.44|7783|
|Self-employed: with family worker|0.09|0.29|11546|0.21|0.41|7783|
|Self-employed: with permanent|0.02|0.13|11546|0.02|0.15|7783|
|Government worker/employee|0.07|0.25|11546|0.15|0.35|7783|
|Private worker/employee|0.61|0.49|11546|0.30|0.46|7783|
|Age|28.19|5.79|11546|49.94|8.42|7761|
|In urban area|0.61|0.49|11546|0.58|0.49|7783|
|Status as household head: 1=yes; 0=no|0.20|0.40|9576|0.68|0.47|7480|
|Married: 1=yes; 0=no|0.66|0.47|11546|0.89|0.31|7765|
|Muslim: 1=yes; 0=no|0.90|0.30|11546|0.87|0.33|7765|
|House ownership: 1=owned; 0=not<br>owned|0.76|0.43|11458|0.84|0.36|7754|
|Electricity in house: 1=yes; 0=no|0.96|0.19|11458|0.95|0.22|7754|
|Toilet in house: 1=yes; 0=no|0.62|0.49|11458|0.62|0.49|7754|
|<br>Farm business: 1=hh with farm|||||||
|business; 0=no|0.28|0.45|11458|0.33|0.47|7753|
|Non-farm business:1=hh with non-<br>farming business; 0=no|0.49|0.50|11458|0.52|0.50|7753|
|Work history data incomplete|0.96|0.18|11546|0.58|0.49|7783|

16
_Africa’s Macroeconomic Story_ The same goes for the three clusters (regression [8]).<sup>24</sup> By contrast, macro policy outcomes such as average inflation rates and external debt burdens were not found to be statistically significant. We also found little evidence that countries with higher investment rates tended to grow faster.

# **Table 4: Cross-Country Regression Results for IDA-Eligible African Countries**

_Dependent variable:_ average per capita growth rate over the period 1995-2012

|**Regression:**|**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|**[7]**|**[8]**|
|---|---|---|---|---|---|---|---|---|
|Constant term|-2.51|-1.96|-2.59|-1.70|-3.08|-3.76|-3.42|-2.29|
||(1.03)|(1.41)|(1.10)|(0.92)|(1.44)|(1.78)|(1.78)|(1.39)|
||**_-2.43_**|_-1.39_|**_-2.35_**|_-1.85_|**_-2.14_**|**_-2.12_**|_-1.92_|_-1.65_|
|Oil-exporters|1.31|1.42|1.02|1.45|1.97|1.71|1.52|1.15|
||(0.69)|(0.79)|(0.70)|(0.73)|(0.81)|(0.79)|(0.84)|(0.77)|
||_1.88_|_1.79_|_1.46_|**_1.99_**|**_2.45_**|**_2.18_**|_1.80_|_1.49_|
|Volatility - output gap|-0.69|-0.75|-0.65|-0.81|-0.77|-0.79|-0.68|-0.71|
||(0.30)|(0.34)|(0.31)|(0.30)|(0.31)|(0.32)|(0.31)|(0.32)|
||**_-2.31_**|**_-2.23_**|**_-2.10_**|**_-2.69_**|**_-2.45_**|**_-2.51_**|**_-2.22_**|**_-2.18_**|
|**_CPIA ratings in 2005_**|||||||||
|Economic management|1.14||||||0.91||
||(0.31)||||||(0.56)||
||**_3.68_**||||||_1.64_||
|Macro management||0.91||||||-0.19|
|||(0.41)||||||(0.58)|
|||**_2.24_**||||||_-0.33_|
|Fiscal policy|||1.17|||||0.84|
||||(0.34)|||||(0.61)|
||||**_3.50_**|||||_1.36_|
|Debt policy||||0.89||||0.45|
|||||(0.27)||||(0.51)|
|||||**_3.27_**||||_0.87_|
|Public sector management|||||1.40||0.08||
||||||(0.47)||(0.82)||
||||||**_2.99_**||_0.10_||
|Structural policies||||||1.53|0.43||
|||||||(0.55)|(0.77)||
|||||||**_2.79_**|_0.56_||
|R<sup>2</sup>|0.485|0.366|0.470|0.451|0.427|0.410|0.492|0.483|
|F-statistic|10.0|6.2|9.5|8.8|7.9|7.4|5.8|5.6|
|Degrees of freedom|32|32|32|32|32|32|30|30|

_Note:_ OLS estimates. Standard errors reported in parentheses; t-statistics in _italics_ : **_bold-italics_** denotes statistically significant at the 5 percent level.

24 Similarly, CPIA ratings for 2005 and 2012 were each significant when entered separately but not when combined. The 2005 ratings were found to have a slightly better fit.

33