rafmacalaba/datause-encoder-data huggingface dataset:
# Table A4: Robustness checks

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||_Ou_|_tcome: Sur_|_vey comple_|_ted_||
|Phone survey|0.427***<br>(0.038)|0.423***<br>(0.038)|0.425***<br>(0.039)|0.427***<br>(0.038)<br>[0.000]|0.417***<br>(0.038)|0.347***<br>(0.053)|
|Mean of Dep. Var (WhatsApp)|0.299|0.299|0.299|0.299|0.299|0.386|
|R-squared|0.212|0.234|0.212|0.212|0.242|0.217|
|Observations|597|597|597|597|597|358|
|Controls (Age, Sex)|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls (Education)|No|No|No|No|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Controlling for baseline completion||✓|||||
|Controlling for mode of initial recruitment|||✓||||
|Computing randomized inference p-values<br>Imputing missing values||||✓|✓||

_Notes:_ This table shows the estimated impacts of the phone-based survey relative to WhatsApp-based survey on completion rate. “Phone survey” is a dummy variable equal to one if the survey was per call and zero if was assigned to the WhatsApp group. The dependent variable is a dummy equal to one if the survey was completed. Column (1) presents the estimated coefficient of the main model (same as in column (3) in Table 3, which includes age and sex as control variables, and municipality fixed effects). Columns (2) and (3) include indicators for completing baseline survey or type of recruitment (=1 if Tigo, = 0 if other channel), respectively. Column (4) present results after adjusting standard errors using randomization inference, which are presented in squared-brackets. Column (5) includes education level as control variable, imputing zero for the missing values for caregivers that did not complete the baseline survey. Finally, column (6) presents estimated coefficients of the same model as in column (5) but with no imputation of missing values on the education level variable. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

39
Agricultural value added increases in all scenarios. The increase is mainly due to the increase in production in irrigated activities for the climate change scenario, in rainfed activities in the urbanization scenario and in the world price increase scenario. Other agricultural activities deteriorate in all scenarios. The value added of other activities falls as much as 64 percent. Industrial activities also experience a decline of approximately 2 to 9 percent. A change in the non-agricultural value added is also higher in the climate change scenario. The changes in the real value added are milder. Furthermore, the industrial value added increases in real terms within the urbanization scenario.

The comparison of changes in nominal and real GDP and the sectoral value added show that an important account of GDP change is due to adjustment in prices. In real terms, effects are milder, and in the opposite direction.

**Table 4.2. Macroeconomic results of simulations**

||**Base**<br>|||**% Ch**|**ange**|||
|---|---|---|---|---|---|---|---|
||**Level**<sup>**a**</sup><br>|**Sc. 1**<br>|**Sc. 2a**<br>|**Sc. 2b**<br>|**Sc. 3a**<br>|**Sc. 3b**<br>|**Sc. 3c**<br>|
|Absorption|384,423,455|-0.5|0.3|0.4|-1.9|-2.5|-2.2|
|Household Consumption|245,160,549|-0.2|1.2|1.7|-1.8|-2.4|-2.1|
|Investment|75,017,087|-1.7|-2.3|-3.2|-3.8|-5.1|-4.5|
|Export|98,447,183|-1.4|-0.6|-0.9|-2.1|-2.6|-2.4|
|Import|103,183,886|-1.0|-0.6|-0.9|-2.0|-2.4|-2.2|
|PPP Real FX Rate|100|-0.5|0.1||-4.2|-5.0|-4.2|
|Nominal FX Rate|100|-1.8|-0.1|-0.4|-5.9|-7.1|-6.0|
|Export Price Index|100|0.9||||||
|Import Price Index|100|0.7||||||
|World Price Index|100|0.8||||||
|Domestic Price Index|100|-0.5|-0.2|-0.4|-1.8|-2.2|-1.9|
|Consumer Price Index|100|||||||
|Terms of Trade|100|0.2||||||

_Source: Results from TACOGEM-W._ _Notes:_<sup>_a_</sup> _1000 TRY for GDP accounts. Sc1: Agricultural prices are increased according to FAO and OECD 2008; Sc2a: 30 percent water transfer from urban to rural areas; Sc2b:50 percent water transfer from urban to rural areas; Sc3a: Yield of rainfed crops falls by 30 percent; Sc3b: Yield of rainfed crops falls by 30 percent and irrigated crops falls by 10 percent; Sc3c: Regional yields of all crops falls at various degrees._ For demand side macro variables we can conclude similar results. The change is higher in climate change scenario. Total consumption falls for the first and third scenarios, while increasing in urbanization. However, when water consumption is removed from the scenario, consumption falls for the urbanization scenario as well. For all scenarios, agricultural consumption decreases while the manufacturing goods consumption increases.

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**Table 3: Do Women invest differently?**

||Capital<br>Stock<br>FE|Capital<br>Stock<br>FE|FE<br>Adjusted<br>profits|Males<br>FE<br>Adjusted<br>profits|Females<br>FE<br>Adjusted<br>profits|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Treatment Amount 100|1.38**|0.63*|13.58***|||
||(0.649)|(0.317)|(3.70)|||
|Amount 100 * Female|-1.39**|-0.41|-14.15***|||
||(0.708)|(0.402)|(5.25)|||
|Treatment Amount 200|1.22***|1.43***|12.95***|||
||(0.430)|(0.386)|(4.06)|||
|Amount 200 * Female|0.475|-0.46|-14.83**|||
||(0.777)|(0.758)|(6.46)|||
|Amount * % inventories||||8.53**|1.39|
|||||(3.35)|(4.39)|
|Amount* % equipment||||5.3<br>(6.53)|-5.8<br>(5.88)|
|Observations|3519|1135|3697|1772|1775|
|Number of Firms|365|365|365|173|176|

Notes: Robust Standard errors in parentheses, clustered by enterprise

*, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. The sample in Column 2 is limited to controls and the first wave after treatment Adjusted profits are real profits less the value of the owner's time, estimated in six education / gender cells using a simple production function run on the baseline data.

36

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past, and therefore to reagecss also the risk attached to such loans. Banks, generally speaking, have become increasingly sensitive - some might even argue over-sensitive - to risk, having perhaps failed to pay sufficient attention to it and too much attention to nominal return during the earlier phases of lending. 14/ In essence provisioning reflects a reassessment of the risks associated with a given portfolio of loans. As perceivcd risks rise, so there will be a tendency for provisioning against these risks to rise as well.

Economic performance amongst the highly indebted countries is most unlikely to be perfectly positively correlated, even though, to a certain exte t, all debtor countries will be similarly affected by some world economic developments such as rising interest rates and increasing protectionism. This lack of correlation will encourage banks to examine countries individually, to form views on their separate creditworthiness and on the extent to which they should provision against them. This is the case even though the banks usually publicly provision against a group of countries rather than individual countries. Given this case by case approach, it may be somewhat misguided to look at data for an aggregated group of countries. Yet, if this is done, plenty of evidence may be found to support the view that the underlying economic performance of the major debtor countries had been deteriorating over recent yearso Although some debtor nations have achieved a notable strengthening in their current account balance of payments, this has frequently been achieved against a background of stagnating growth, and

> 14. For a critical review of banks' approaches to country risk analysis, see Graham Bird, "New Approaches to Country Risk", Lloyds Bank Review, October 1986.
correctly matched with districts in the SDIP dataset, I compared district names reported in the 1993 and 2000 waves, examined sub-district names and where necessary, referred to detailed district level maps.

District level descriptive statistics of the school construction program are provided in Table 2. IFLS data were representative of only 222 of the 293 districts that existed in Indonesia when the school construction program was launched<sup>2</sup> . Comparison of summary statistics in Panel A (full sample of districts) and Panel B (IFLS sample of districts), indicates that school construction program intensity was slightly lower on average in the IFLS sample of districts than in the country as a whole.

> 2 Duflo (2000, 2001) and Beirerova and Duflo (2004) used all 293 districts to construct the instrument as they used Census data for their analysis.

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> - 35 -

Table 9: (Cont'd.) Determinants of the T-Bill Rates in the Philippines Variable Parameter Estimate T-Statistic III. Philippines (1976-87) Quarterly Using Monetary Base (M2)

# **DEP** Vaxiable - rPHL

|CONST<br>T|-0.063<br>|0.5<br>|
|---|---|---|
|SGP|0.137|0.6|
|e|0.004|1.3|
|GDP/M2|0.032<br>|0.4<br>|
|-n|0.573|3.9|
|TPHL-1|0.766|9.0|
|||R-Square-<br>0.833<br>|
|||Durbin Watson-<br>2.2|

*Note<sup>T</sup> PHL here is the Treasury Bill Rate as opposed to rTHA and rIDN which are the Money Market Rates.

Also note that e is the exchange rate between the home country's currency and the Singapore Pound.

Source: IFS.
6 26th level corresponding to a female life expectancy of 82.5 was added. The appropriate level for each time period was chosen given life expectancy, with interpolation if necessary between adjacent levels. Choice among life table families was based on region and mortality level, as follows: Region and mortality level Life table family Sub-Saharan Africa Below 12 South 12-17 North 18 and above West North Africa, the Middle East, Latin America, and the Caribbean Below 18 South 18 and above West Northern America, Europe, Asia, and Oceania All levels West Over time, countries in particular regions may therefore switch from one life table family to another. To avoid discontinuities, the switch is made gradually, using life tables linearly interpolated between the two families involved for five intervening periods.

# Fertility level and trend

Base-period total fertility rates (TFRs) are derived from the same mix of sources as mortality rates, including the U.N. 1988 revision; official sources, as cited in Population and Vital Statistics Reports or in government publications; the U.N. 1984 assessment; the U.S. Bureau of the Census (1985); Eurostat (1987); and Celade (1987). We relied on two sources more frequently for information on fertility than mortality: various surveys, especially Demographic and Health Surveys and World Fertility Surveys; and the Institut National d'ttudes Demographiques (Monnier 1987). Often, all these sources give fertility for a different period or in terms of the crude birth rate, and corresponding TFRs had to be estimated using available data on age-sex composition and age-specific fertility, or obtained from a projection using an estimated TFR for an earlier period. Where no reliable source was available, an unattributed Bank estimate may be indicated in the Appendix.

Future fertility trends are specified by selecting a year when the net reproduction rate (NRR) reaches unity, i.e., when the average woman's fertility results in exactly replacing herself. (This is referred to here as the replacement year.) We explain below how the replacement year was selected; what was done for countries with NRR already below replacement; how the path of fertility between the present and the replacement year was specified; and what patterns of fertility by age were applied.

For all countries, the replacement year is assumed to be before
firms is to look for evidence on the mediating channels through which advocates claim formalization should benefit firms. Impacts through these intermediate channels may be quicker to materialize. The intermediate channels may also suffer from less variation, enabling more precision.

Table 5b presents the impact of formalizing on channels through which formalization might be expected to influence firm outcomes. The assumption that the grants are not independently affecting these channels is more plausible here than for profits or capital stock, and consistent with this, we find that the upper and lower bounds are quite close to one another. We therefore just present the upper bound estimates, which do not control for capital stock.

The results show little evidence that formalizing has impacted firms through most of the main channels through which formalization is often hypothesized to have benefits. We find no significant effect on relationships with the financial sector – applying for business or personal loans or having a business bank account – on relationships with the government – having an electricity connection in the business name, applying for a government contract, making sales to the government, or participating in any government SME program. Moreover, the point estimates on most of these variables are close to zero, with relatively small confidence intervals in most cases. The point estimates also suggest businesses are not any more likely to pay taxes, and the point estimate is actually negative on the amount of taxes paid. We do not find any evidence that businesses are changing location after formalizing.

The only intermediate channels which show significant impacts are the use of receipt books (p=0.007), and an increase in advertising (p=0.003). Both outcomes are still statistically significant at standard levels even if we conservatively control for multiple hypothesis testing of 14 intermediate channels by multiplying each by 14 to obtain Bonferroni p-values. This is consistent with the nonexperimental evidence in McKenzie and Sakho (2010) who suggest the main effect of formalizing in Bolivia is to expand sales through increasing the use of receipts. However, to date the evidence from Figure 3 and Table 5a suggests that for most firms this channel has not yet resulted in significantly higher profitability.

# **5.5 Impacts on Attitudes**

The tea shop owner who contacted the health department about an inspection raises the question of whether formalization may have changed attitudes about the government and market regulations more generally. Di Tella et al. (2007) study formalization in a different domain – formal titles among land squatters – and find very large effects of obtaining formal property title on beliefs that favor the workings of the market. Motivated partly by this, in the first follow-up survey we asked a number of questions on trust and attitudes which enable us to examine the impact of formalizing on the attitudes that firm owners have towards government, the courts and police, and taxation.

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**Table 11: Determination of Employment and Daily Wage at the Regional Level in the Philippines (2003-2007) –** **<u>Using Years of School as an Indicator of Human Capital</u>**

|||_NCR_|_L_|_uzon_|_V_|_isayas_|_Mi_|_ndanao_|
|---|---|---|---|---|---|---|---|---|
|**Estimati**|**on of inc**|**ome equation(d**|**ependent**|**variable = dail**|**y wage i**|**n log)**|||
|age|0.04|(19.06)***|0.04|(19.46)***|0.05|(22.63)***|0.06|(31.93)***|
|age * age|-0.0004|(14.28)***|-0.0003|(13.76)***|-0.001|(17.30)***|-0.001|(23.98)***|
|year of school|0.09|(76.92)***|0.08|(142.09)***|0.08|(110.22)***|0.08|(119.18)***|
|sex (female=1)|-0.24|(31.01)***|-0.17|(19.76)***|-0.24|(23.83)***|-0.25|(26.07)***|
|year dummies (2003 as reference)||yes||yes||yes||yes|
|constant|3.60|(72.51)***|3.67|(82.86)***|3.18|(62.65)***|2.97|(72.91)***|
|||**Estimation of**|**selection**|**equation**|||||
|age|0.16|(48.76)***|0.15|(88.56)***|0.15|(58.94)***|0.15|(68.89)***|
|age * age|-0.002|(48.29)***|-0.002|(88.95)***|-0.002|(58.99)***|-0.002|(67.58)***|
|year of school|0.04|(23.57)***|0.02|(17.33)***|-0.002|(2.15)**|0.03|(26.06)***|
|sex (male = 0, female=1)|0.25|(15.28)***|-0.10|(9.76)***|-0.04|(2.41)**|-0.29|(21.32)***|
|househoold head (household head =1)|0.32|(17.51)***|0.27|(21.99)***|0.32|(18.08)***|0.36|(20.02)***|
|married (married =1)|0.33|(16.30)***|0.20|(15.30)***|0.23|(12.41)***|0.25|(13.50)***|
|child of younger than 5 years old|-0.02|(1.48)|-0.03|(4.55)***|-0.04|(3.26)***|-0.06|(6.19)***|
|female * married|-0.91|(35.11)***|-0.69|(42.34)***|-0.67|(27.85)***|-0.78|(33.15)***|
|female * headcount of child(ren)|-0.09|(15.16)***|-0.07|(19.86)***|-0.07|(14.69)***|-0.04|(10.78)***|
|year dummies (2003 as reference)||yes||yes||yes||yes|
|constant|-3.34|(60.94)***|-2.66|(96.45)***|-2.58|(62.79)***|-2.93|(78.45)***|
|Log likelihood|-58|765.12|-1|70018|-7|4502.09|-9|1684.48|
|Number of observations||66,654||200,976||85,428||114,443|

_Note:_ *, ** and *** imply significance at 10 percent, 5 percent and 1 percent, respectively.

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# **II.** BACKGROUND

The Ghana road system consists of 31,700<sup>kms, of which 14,400 kms are trunk roads</sup> and 1,800 kms are town roads. Over the<sup>years the system suffered from inadequate</sup> maintenance, resulting in a deterioration to<sup>the point at which, at the time of writing, 36</sup> percent of the trunk roads are defined by<sup>the Ghana Highway Authority (GHA) as being in</sup> poor condition and 42 percent are regarded<sup>as fair; only 40 percent of the rural network are</sup> regarded as inotorable. (According to<sup>GHA,two thirds of the feeder roads are in poor</sup> condition; see Table 3.) Table 3. Distribution of Roads by Region<sup>and Condition, 1989</sup>

|||Trunk ro<br>|ads<br>|||Feede<br>|r roads<br>||
|---|---|---|---|---|---|---|---|---|
||Total<br>length<br>|C<br>|ondition (%<br>|)<br>|Total<br>lengthl<br>|C<br>|ondition (<br>|)<br>|
||(kin)|Good|Fair|Poor|(kln)|Good|Fair|Poor|
|Greater Accra|<br>468|37|45<br>|18<br>|817<br>|1<br>|43<br>|56<br>|
|Volta|1,511|38|20<br>|42<br>|1,855<br>|11<br>|26<br>|63<br>|
|Eastern|1,890|23|35<br>|42<br>|2,337<br>|19<br>|36<br>|45<br>|
|Central|1,409|7<br>|67<br>|26<br>|1,634<br>|12<br>|21<br>|67<br>|
|Western|1,502|12|48|40<br>|1,076<br>|21<br>|5<br>|74<br>|
|Ashanti|1,395|24|17<br>|59<br>|3,284<br>|14<br>|5<br>|81<br>|
|Brong Ahafo|1,839|31<br>|48<br>|21<br>|1,790<br>|16<br>|26<br>|58<br>|
|Northern|2,790|25|45|30|1,991<br>|15<br>|16<br>|69<br>|
|Upper East|535|--|94|6<br>|1,071<br>|8<br>|14<br>|78<br>|
|Upper West|1,091|7|27|66|1,404|9|11|80|
|Total|14,430|22|42|37|17,262|13|20|67|

Source: Bhandari (1990).

The exact number of motor vehicles using this<sup>road system is unknown. In the second</sup> half of 1987 the Vehicle Examiting and<sup>Licensing Department issued roadworthiness</sup> certificates to 76,000 vehicles (the number<sup>for the first half of 1987 was 68,500, and</sup> incomplete data for the first half of<sup>1988 show 67,100 applications for certification).</sup> Government vehicles are exempt from the roadworthiness<sup>test, and the rules of certification</sup> are rarely enforced. The prevailing feeling<sup>is, therefore, that the number of certificates</sup> issued is nowhere near the real size<sup>of the vehicle fleet. Crude estimates prepared by</sup> TecnEcon for 1986 (1987, Appendix A-3) claim<sup>that only 80 percent of the non-government</sup> fleet obtain certificates, and that the government<sup>fleet includes over 18,000 vehicles (of</sup> which 7,000 are private cars, 5,000 are trucks,<sup>and 3,000 are buses). By this estimate, the</sup>
In stages 2 and 3, multiyear estimates can be constructed using either finance ministry projections of current spending trends, spending agency costings of existing programs or a combination of both. More often than not these estimates take as their source historical spending data, rather than constructing total cost estimates based on the actual costing of existing activities or programs. Finance ministries will tend to apply general costing assumptions to existing programs as part of forecasting methodologies and require spending ministries within the budget process to apply more specific costing methodologies when preparing cost estimates for new – or expanded existing – programs. Therefore, the accuracy and consistency of new program costing take on a higher priority within the incremental decision making of the annual budget process. In summary, there are two types of ‘costing’ exercise that inform an MTEF budget process: the way general costing assumptions inform forecasting methodologies, and the way costing methodologies inform consideration of programs in the annual budget cycle, generally restricted to new or expanded programs.

Cost information has been a fundamental component of PFM reform over the last 25 years. In large part, this can be attributed to the general focus of these reforms on _programmatic_ budgeting, which requires that budget expenditures be _allocated and controlled_ on the basis of objectives-based programs and subprograms. Because program structures constitute instructions to organizational units on how to manage and control their budgeted resources, this has two important consequences (Robinson 2013b). The first is that program structures need to be defined in a feasible way to ensure allocations can be mapped between programs and organizational structures, as well as to permit a workable approach to assigning indirect costs – usually internal support services – to programs. In this context, programs costs include all direct costs<sup>11</sup> and indirect costs.<sup>12</sup> The second consequence is that program structures must be integrated with both budget expenditure classifications and the chart of accounts. Cost information, and the costing systems used to collect and report cost information, must be closely aligned with the program structure (Robinson 2007).

_In principle_ then, the budget and forward years’ estimates within an MTEF should be constructed on the basis of estimating the actual cost of existing programs, rather than taking historical levels of expenditures as a starting point and mechanistically escalating for changes

> 11 Costs that can be directly traced to a cost object in an economically feasible way; an example of a direct cost is the salary of an employee whose work relates entirely to a single project

> 12 Costs that cannot be directly traced to a cost object in an economically feasible way; an example of an indirect cost is the depreciation on a corporate information technology system or shared service centres such as human resources or fleet management

12