rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: _Growth and contemporaneousfinancial indicators, 1960-89_

|Dependant Variable<br>DEPTH|BANK|PRIVATE|PRIVY|
|---|---|---|---|
|Real Per Capita GDP Growth<br>0.024***|0.032***|0.034***|0.032***|
|[0.007]|[0.005]|[0.002]|[0.002]|
|R<sup>2</sup><br>0.5|0.5|0.52|0.52|
|Real Per Capita Capital Stock Growth<br>0.022***|0.022**|0.020**|0.025***|
|[0.001]|[0.012]|[0.011]|[0.001]|
|R2<br>0.65|0.62|0.62|0.64|
|Productivity Growth<br>0.018**|0.026**|0.027***|0.025***|
|[0.026]|[0.010]|[0.003]|[0.006]|
|R<sup>2</sup><br>0.42|0.43|0.45|0.44|
|[P-values in brackets]<br>Observations = 77||||

> * significant at the 0.10 level, * significant at the 0.05 level, *** significant at the 0.01 level.

|DEPTH=<br>Liquid liabilities to GDP|
|---|
|BANK=<br>Deposit bank domestic credit divided by deposit money<br>bank + central bank domestic credit|
|PRIVATE=<br>Claims on the non-financial private sector to total claims|
|PRIVY =<br>Gross claims on private sector to GDP|
|Productivity Growth=<br>Real Per Capita GDP Growth - (0.3)*Real Per Capita<br>Capital Stock Growth|
|_Other explanatory variables included in each of the twelve regressions:_log of initial income,<br>log of initial secondary school enrollment rate, ratio of government consumption expenditures to GDP,<br>inflation rate, and ratio of exports plus imports to GDP.|

_Source: King and Levine (I 993b)_
In accordance with Section 21(5) of the 2011 PRMA, four priority expenditures were approved by parliament in the annual budget funding amount (ABFA): (i) expenditure and amortization of loans for oil and gas infrastructure, (ii) roads and other infrastructure, (iii) agricultural modernization, and (iv) capacity building (including oil and gas). Table 2 shows the allocations to these areas for the 2012 budget year.

Table 2: Annual budget allocations to four priority areas, 2012

|Priority areas|GH₵ millions|
|---|---|
|Expenditure and amortization of loans for oil and gas infrastructure|100.00|
|Roads and other infrastructure|232.41|
|Agricultural modernization|72.47|
|Capacity building|111.95|
|Total|516.83|

Source: Government of Ghana Budget 2013.

Even though on paper the oil revenue allocation is skewed towards infrastructure, the overall decline in capital spending means that the total allocation to infrastructure is likely to have declined.

# **7 Loose monetary policy**

The Bank of Ghana is responsible for maintaining price and exchange rate stability in Ghana. To achieve this, in 2002 the bank adopted an inflation-targeting monetary policy framework, in the context of a market-determined exchange rate regime. Disciplined adherence to such a framework is important for success. In particular, the framework can easily unravel if the central bank becomes a primary source of financing for government fiscal deficits over time. Excessive fiscal expansion creates problems in many developing countries: it tends to be largely monetized, and the resulting excess injection of liquidity results in inflation and exchange rate depreciation. This has certainly been Ghana’s experience since oil was discovered. Specifically, there has been a dramatic increase in the central bank’s financing of the government (through the printing of money), in addition to an increase in borrowing to finance the fiscal deficit. Central bank financing (net claims on government) increased from GH₵1.4 billion in 2008 to GH₵10.6 billion by August 2014—a 640 per cent increase (Figure 6). As a result, inflation rose sharply ahead of the 2008 elections, and then from 8.5 per cent in 2010 to 17 per cent by the end of 2014 (Figure 7).

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16

# Lending: Summary of Findings

Positive: Most of the Bank's PHN projects incorporate efforts to develop outreach activities both for service delivery and for information, education, and communication.

Most of these outreach efforts take place through government health bureaucracies, but there is also a substantial effort to enlist the help of communities and NGOs (although not the commercial sector) in these efforts.

Negative: Many projects have no quantitative, aggregate performance targets, and only a small minority of projects aims to specifically target the most disadvantaged people in the communities served.

Iluformation acquired about clients during project preparation is mainly quantitative; there is little understanding of the determinants of client behavior, especially in the health sector. The gap in qualitative information is generally not filled during project implementation, when the focus is on collecting routine service statistics, rather than inquiring into whether IEC and services appropriately meet client needs.

These two findings raise serious concerns about the effectiveness of the outreach efforts built into the projects. Are project services and IEC efforts likely to increase existing low levels of utilization and acceptance if they are designed in substantial ignorance of what clients feel and why they behave as they do? Can field workers be expected to be providing services to the most needy in their communities, when there is seldom a special effort to identify these clients and to monitor whether workers are reaching them?

Inconclusive but Drobably negative: A desk study of project plans cannot throw light on the degree to which project interventions are in practice changed during implementation to better meet client needs. However, very few project designs feature special institutional arrangements to encourage learning and adaptation during implementation. (It should also be noted that for the majority of projects where qualitative knowledge of clients is weak, there may be no valid basis on which to redesign interventions anyway.) Many project interventions--such as targeting, MIS development and improvement of supervision systems--potentially affect worker performance incentives, but it is not known how they have actually done so in practice. In general, however, projects appear not to have taken a systematic approach to designing comprehensive incentive packages for particular worker cadres (and again, in most projects there may be insufficient qualitative knowledge about both clients and service providers to know what such packages should incorporate).
# **_b. Actual and potential tax revenues_**

_Chart 2_ (a) shows that there is a positive relationship between actual revenue collected and the revenue potential, both economic and legal. These are shown to be true in separate sub-charts below for ECA countries and the entire sample. Countries that have high revenue potential typically collect more revenue. Chart 2(b) shows that the tax potentials – both economic and legal – decreased after 2007. Among the ECA countries, the ECA/EU(6) group show the most tax potentials, while the CIS and South Caucasus group show the least tax potentials.

_Chart 2(a)._ **Actual and Potential Tax Revenues** <!-- Start of picture text --> SWEDNK SWEDNK<br>Actual and Potential: Legal<br>Actual and Potential Tax Revenues: Economic BLRITAFRANORFIN AUTBEL BLR FRAITAAUTFIN NORBEL<br>TJK SLVKGZIDNARMVENPERCOLARGAZEALBMEXGEOURYKSVZAFBRATURKAZSRBMDAUKRTUNMYSROMTHACHLMKDRUSBIHHRVMNEBGRJPNLTULVAGRCPOLPRTGBRESPDEUESTHUNKORCZECANSVNSVKCHEIRLNLD TJKIDN KGZKSVMYSTHAPERAZEARMMEXGEOKAZCOLALBSRBZAFTURCHEBIHBRAKORMKDCHLMNEROMJPNTUNRUSUKRLTUARGLVAURYCANBGRESTMDAGBRESPGRCHRVPRTCZESVKPOLSVNDEUHUNNLDIRL<br>SLV VEN<br>0 10 20 30 40 10 20 30 40 50<br>taxpotential1 taxpotential2<br>Fitted values taxgdpinmean Fitted values taxgdpinmean<br>BLR BLR<br>Actual and Potential: Economic - ECA BIHMNE Actual and Potential: Legal - ECA MNE<br>SRB BIH<br>MDAUKR RUSHRVBGR POL CZESVK SRB UKR HRVCZEPOL<br>ROMMKD RUS BGRMDA SVK<br>MKDROM<br>TUR<br>KAZ<br>KGZ ALBGEOKSV KAZ TUR<br>ARM AZE ALB<br>TJK KGZKSV GEO<br>AZEARM<br>TJK<br>5 10 15 20 25 30 10 20 30 40<br>taxpotential1 taxpotential2<br>Fitted values taxgdpinmean Fitted values taxgdpinmean<br>50 50<br>40 40<br>30 30<br>20 20<br>10<br>10<br>40 40<br>35<br>30<br>30<br>25<br>20<br>20<br>10 15<br><!-- End of picture text --> _Chart 2(b)._ **Actual and Potential Tax Revenues in the ECA Region** <!-- Start of picture text --> Tax Potential - Economic Tax Potential - Legal<br>40 2002 2007 2010 40 35.3 2002 2007 2010 35.1<br>34.8 34.3<br>32.8 32.6<br>30.8<br>29.4 30.0 28.3<br>3020 21.7 24.8 24.8 20.5 21.2 26.9 28.9 20.6 24.2 24.0 20.3 23.8 24.0 21.1 24.5 24.6 28.3 3020 24.2 26.7 26.8 21.7 26.0 26.8 22.6 26.2 26.0 23.7 27.2 27.4 25.0 28.3<br>16.1<br>10 10<br>0 0<br>Percent of GDP Percent of GDP<br>Balkans CIS-South Caucasus ECA/EU(6 ) Oil Exporters Turkey AVERAGE ECA (23) ECA Graduate d Balkans CIS-South Caucasus ECA/EU(6) Oil Exporters Turkey AVERAGE ECA (23) ECA Graduate d<br><!-- End of picture text --> 15
# **3 Results**

We first provide descriptive evidence on the unconditional average association between firm innovation and external finance. Figure 1, Panel A shows the mean percentage of fixed assets financed through internal funds for non-innovative firms, innovative firms, firms adopting innovations, and firms undertaking invention. Countries are grouped according to their income level using the 2019 World Bank classification. Overall, a large percentage of enterprises in EMDEs (i.e., countries other than high-income countries) finance working capital through internal funds, though there is a noticeable difference across income levels. The difference between innovative and non-innovative firms is wider in upper-middleincome countries, and it flips for high-income countries. In general, firms pursuing invention finance working capital with sources other than internal funds, especially in low income and lower-middle-income countries.

Firm innovation is higher for firms that collect funds through external sources (Figure 1, Panel B). Firms at the 25<sup>th</sup> percentile of the distribution of the percentage of fixed assets financed through internal funds have an innovation score of around 0.41, while firms at the 75<sup>th</sup> percentile of the distribution have an innovation score of around 0.30. Likewise, there is a strong association between firms pursuing adoption and firm financing of fixed assets, as the interquartile difference is negative and statistical significant at the 1 percent level. The magnitude of the interquartile difference is greater for firms undertaking invention, with a 0.15 interquartile difference between firms that finance working capital through internal funds.

# **Figure 1: Relationship between firm innovation and funding**

# **_Panel A: Mean percent of fixed assets financed through internal funds, manufacturing_**

<!-- Start of picture text --> Non-innovative firms Innovative firms Adoption Invention<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>All countries Low income Lower middle Upper middle High income<br>income income<br><!-- End of picture text --> Source: Own elaboration using data from the World Bank – Enterprise Surveys.

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Table A.11: Determinants of attrition (contact farmers and other farmers)

||CFs||Other farming HH|
|---|---|---|---|
|Treatment 1|0.053|Treatment 1|0.014|
||[0.066]||[0.013]|
|Treatment 3|0.002<br>[0.055]|Treatment 3|-0.013<br>[0.012]|
|Age|-0.006**<br>[0.003]|Age|-0.001**<br>[0.000]|
|Completed at least|0.056|HH head completed at|-0.004|
|primary school|[0.060]|least primary school|[0.012]|
|Single|-0.091<br>[0.190]|HH head Single|0.023<br>[0.020]|
|||HH head divorced,|0.045***|
|||widow, or separated|[0.017]|
|Total number|-0.011|Total number|-0.007**|
|of children|[0.013]|of children|[0.003]|
|Total landholding|0.005|Total landholding|-0.005|
|[hectares]|[0.011]|[hectares]|[0.003]|
|Total number of rooms|-0.037<br>[0.032]|Total number of rooms|-0.003<br>[0.008]|
|Number of years|-0.034*|Number of years|0.004|
|since formal training|[0.020]|since formal training|[0.003]|
|Missing above|-0.145*|Missing above|0.001|
|variable|[0.076]|variable|[0.015]|
|Household head|-0.035|Household head|-0.002|
|was female|[0.088]|was female|[0.013]|
|% of household|-0.436|% of household|0.171***|
|members was away|[0.350]|members was away|[0.065]|
|HH has non-own|0.023|HH has non-own|-0.013|
|farming work|[0.078]|farming work|[0.012]|
|HH has outside|-0.015|HH has outside|0.003|
|employment|[0.073]|employment|[0.017]|
|2012 precipitation|-0.001|2012 precipitation|-0.001*|
|shock|[0.002]|shock|[0.000]|
|Constant|0.352|Constant|0.043|
||[0.308]||[0.058]|
|N|178|N|3662|
|R2|0.099|R2|0.014|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Regressions include district xed eect. CF=contact farmer; HH=household. Household attrition measured by whether a household surveyed in 2012 could not be interviewed in 2013. The CF attrition outcome reects whether the village had at least one CF interviewed in 2012 but not in 2013.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

54
**Table S28. Estimated total number of school-aged children in households without educational engagement**

||All<br>Countries|Ethiopia|Malawi|Nigeria|Uganda|
|---|---|---|---|---|---|
|Total|68,465,008|31,681,834|6,116,277|<br>24,741,134|5,925,768|
||(1,797,619)|(614,013)|(156,190)|(1,489,912)|(313,951)|
|Observations|6,190|1,911|1,193|1,417|1,669|

_Note_ : Each column reports the estimated total number of school-aged children living in households that report their children have not been engaged in any sort of educational activity since schools closed. Data are only from the first phone survey round in each country. Standard errors are in parenthesis.

55
# **_Table A4.7e Relationship between staff experience, staff predicted performance, and IEG outcome rating, results for IBRD/IDA_**

||**Dependen**<br>**B**<br>|**t variable: Sta**<br>**Performance**<br>**y lending sour**<br>|**ff Predicted**<br> <br>**ce**<br>|**Depend**<br>**o**<br>**By**<br>|**ent variable:**<br>**utcome ratin**<br>**lending sour**<br>|**IEG 1-6**<br>**g**<br>**ce**<br>|
|---|---|---|---|---|---|---|
||**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|
|Number of projects prepared before by the|0.043**|||-0.003|||
|preparation TTL|(0.017)|||(0.014)|||
|(Log) Number of ISRs signed before by the mid-||0.049*|||0.006||
|point supervision TTL||(0.030)|||(0.039)||
|(Log) Number of ISRs signed before by the final|||0.005|||0.081**|
|supervision TTL|||(0.028)|||(0.037)|
|Constant|0.294***|0.223**|0.267***|3.077***|3.063***|2.842***|
||(0.054)|(0.088)|(0.090)|(0.453)|(0.479)|(0.445)|
|Observations|942|1,144|1,129|942|1,144|1,129|
|Adjusted R-squared|0.005|0.002|-0.001|0.005|0.002|-0.001|
||**Dependen**|**t variable: Sta**<br>**Performance**|**ff Predicted**<br>|**Depend**<br>**o**|**ent variable:**<br>**utcome ratin**|**IEG 1-6**<br>**g**|
||**B**<br>|**y lending sour**<br>|**ce**<br>|**By**<br>|**lending sour**<br>|**ce**<br>|
||**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|
|Number of projects prepared before by the|0.039*|||-0.032|||
|preparation TTL|(0.023)|||(0.021)|||
|(Log) Number of ISRs signed before by the mid-||0.006|||-0.005||
|point supervision TTL||(0.024)|||(0.025)||
|(Log) Number of ISRs signed before by the final|||0.046*|||-0.020|
|supervision TTL|||(0.024)|||(0.031)|
|Constant|-0.356***|-0.341***|-0.472***|1.899***|1.909***|1.882***|
||(0.049)|(0.067)|(0.073)|(0.045)|(0.444)|(0.462)|
|Observations|1,096|1,416|1,384|1,531|1,527|1,527|
|Adjusted R-squared|0.002|-0.001|0.002|0.090|0.092|0.086|

Note: Robust standard errors clustered at the country level are reported in parentheses. (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, weighted by original commitment amount. Columns (1) (2) (3) do not include any controls. Columns (4) (5) (6) include controls for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

59
America, Du and Dong 2013 in China, Jain 2016 in India, and Clark et al. 2017 in Kenya) by exploring the relationship between preschool expansion and FLFP in a region with little evidence to date.

After the passing of The National Education System Act (NSEA) in 2003, the Government of Indonesia accelerated efforts to expand preschool education with a medium-term plan to increase enrollment between 2004 and 2009. Block-grants were set up to incentivize private provision, while district-level governments were responsible for the public provision of preschools. In 2004, less than a quarter of children aged 3 to 6 ever attended preschools. By 2016, the gross pre-primary enrollment rate had increased to 60.3 percent (World Development Indicators). We exploit the spatial and temporal variations in preschool access and the exogenous overlap with the time when mothers have a preschool-aged eligible child to infer the causal effect of preschool access on maternal employment in a difference-in-difference-in-differences (DDD) framework. Our constructed panel tracks the same mothers on average for 22 years and allows us to include an individual fixed effect that accounts for unobserved individual career and family preferences, biological dispositions, and cultural traits.

We find that mothers of preschool-aged eligible children increase their work participation by 7.4 percentage points, or 13.8 percent from the mean, if they are exposed to an additional public preschool per 1,000 children. We further find that private preschools do not have a statistically significant effect on work participation, but eligible mothers are more likely to hold a second job. The results further indicate that public preschools increase the likelihood of mothers being unpaid family workers and agricultural workers, while private preschools induce mothers to become artisanal production workers. We do not find any effects on earnings or hours worked. An increase in informal sector employment that does not require a full-time commitment is compatible with the fact that preschools typically only operate 3 hours per day during typical working days. We note that public preschools are likely substitutes to private preschools, although the extent of substitution is likely limited because they cater to different sub-populations (Chang et al. 2006). Contrary to other studies that use the eligibility of the youngest child,<sup>1</sup> we offer some evidence that preschool access may be more meaningful for the first two children. Our results are robust to

> 1 See Gelbach (2002), Cascio (2009), Contreras and Sepulveda (2017), Berlinski et al. (2011), and Baker et al. (2008) for some examples.

3
||INFRASTRUCTURE, EQTI<br>|PMENT &<sup>TECHNOLOGY BARRIERS</sup><br>|dd Ai t Ril Ll|
|---|---|---|---|
|Bariers|Possible Corrective Measures|Progress with Current Measures|Recommene ctons a egona eve<br>|
|Computerization of<br>Customns Procedures|- Massive investment in computerization so<sup>that a</sup><br>fuU range of electronic data interchange (edi)<br>services can be provided.<br>- Systematic accelerated training for personnel<br>working as inspectors and customs officers<sup>in</sup><br>transit companies.<br>- Retraining of personnel not already familiar<br>with computerized procedures.<br>- Extension of terminals linkied to customs<sup>offices</sup><br>in order to accommodate some importes,<sup>who</sup><br>would thus benefit from being able to clear<br>merchandise through customs on their own<br>premises.<br>- Extension of computerization measures in<br>Algeria, where customs procedures suffer<br>because of the huge number of entry operations<br>and the distances characteristic of<sup>the country.</sup>|___|IM - Harmonize the procedures employed<br>by the Maghreb countries.<br>IM - Avoid hardware and software<br>incompatibilities among the Maghreb<br>countries, and ensure that links are possible<br>with banking, port, and maritime service<br>providers.<br>IM - Identify the necessary conditions for a<br>master plan to link the Maghreb countries<br>with the "just-in-time' system in<br>accordance with international standards.<br>IM - Extend the system to border controls.|
|Lack of Ro/Ro<br>Facilites<br>.________________<br>-cabotage|- Provision of ro/ro installations in all<sup>ports</sup><br>where they are needed as a step toward<br>development of multimodal transport systems<sup>that</sup><br>will allow domestic deliveries by container<sup>and</sup><br>TIR trailer and breakbulk operations through<br>vessels (for some types of cargo).|- Studies for and installation of ro/ro<sup>ramps</sup><br>now well under way in primary and<br><sup>secondary ports.</sup><br>_||

IM = Intra-Maghreb EM = Europe/Maghreb
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