rafmacalaba/datause-encoder-data huggingface dataset:
substantially lower than that for male farmers (0.115). In fact, the data shows that Sri Lankan male and female farmers cultivated an average of 1.36 and 1.19 crops, respectively, implying an overall low level of diversification.

# _(c) Descriptive statistics_

Table 1 shows the summary statistics for the full, male, and female samples. The last column shows the _p-_ values resulting from the test of mean differences by the gender of farmers.

- <u>Table 1. Summary statistics of self employed farmers</u><sup>a</sup>

|||Mean||_t_-test|
|---|---|---|---|---|
||Total|Male|Female|(p-value)|
|Monthly real gross crop income (Rs.)|15,487|16,970|11,086|0.000***|
|Cultivated area (acres)|1.724|1.945|1.044|0.000***|
|Productivity (Rs./acre)<sup>c</sup>|16,905|15,976|19,809|0.000***|
|Household head|0.793|0.876|0.546|0.000***|
|Age|51.7|51.9|51.1|0.000***|
|Years of education|8.524|8.461|8.711|0.007***|
|Access to nonfarm labor income|0.385|0.449|0.193|0.000***|
|Access to nonlabor income|0.470|0.451|0.529|0.000***|
|Share of nonfarm labor income (%)|0.248|0.297|0.103|0.000***|
|Share of nonlabor income (%)|0.174|0.144|0.262|0.000***|
|Number of unpaid ag family workers|0.179|0.220|0.059|0.000***|
|Agricultural inputs (Rs./month)|4,812|5,449|2,923|0.000***|
|Tractor (=1 if tractor(s), =0 otherwise)|0.104|0.122|0.048|0.000***|
|Incidence of natural calamity|0.114|0.109|0.132|0.071*|
|Access to finance|0.090|0.098|0.066|0.000***|
|Access to ICT|0.880|0.882|0.874|0.267|
|Incidence of crop subsidy|0.337|0.389|0.180|0.000***|
|Value of crop subsidy (Rs./month)|898|935|658|0.150|
|Export Orientation Index|0.216|0.177|0.339|0.000***|
|Simpson Index of Diversification|0.102|0.115|0.060|0.000***|

Source: authors’ calculation using HIES 2016. Notes:<sup>a</sup> Self-employed farmers are defined as those who had income from at least one crop activity. Those farmers having only livestock activity are not included due to the lack of land information. c Productivity is measured as gross crop income divided by cultivated area in acres. *, **, and *** indicate that the male-female difference is significant at the 10, 5 and 1% level, respectively. ICT = information and communication technology.

11
# **Figure 27: Majority Foreign-Owned Firms by Sector**

<!-- Start of picture text --> 0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br><!-- End of picture text --> _Source_ : Ruslana. Share of all active firms listed; data for the most recent year available (usually 2012).

0
improve this situation. Statistics about statistics are vital if improvements to statistical systems are to be made. This concern applies across the range of administrative data sources. In the area of geospatial data, there is an opportunity for the Global Geospatial Information Management (GGIM) community to have a significant beneficial impact.

Third, data about statistical infrastructure is patchy. In particular it will be useful to collect further data on the financing of statistical systems. One of the Sustainable Development Goal indicators (17.18.3) relates to whether the national statistics plan is fully funded but there remains incomplete data on this. Financing is also a complex issue since the costs that are associated with producing high-quality and more frequent data vary across countries and are likely to reflect, at least to some extent, country statistical capacity. For instance, a study suggests that the average cost of implementing a recent household consumption survey (in 2014 or later) ranges from approximately US$800,000 to US$5 million, depending on the context and sample sizes (Kilic _et al._ , 2017). Countries with a better data infrastructure (and higher levels of data skills) may possibly offer lower costs regarding data collection.<sup>18</sup> Related to this, certain countries heavily rely on donor funds and technical assistance to implement data collection activities (e.g., household surveys). Everything else equal, these countries likely have lower statistical capacity than countries that implement surveys on their own. As such, it is useful to investigate further how each country produces its data and whether some aid-dependent countries, in a counterfactual scenario, can continue their statistical activities without interruptions in the absence of donors. Such scenarios are not far-fetched. Donors such as the Swedish Aid Agency (SIDA) or the United Kingdom Foreign, Commonwealth and

> 18 On a related note, further research into the different strengths of correlation between the five data pillar scores can be useful. For example, does a stronger correlation of the overall SPI score with data infrastructure justify prioritized investment in this area?

18
Gender, Generations, and Non-farm Participation M. Shahe Emran<sup>1</sup> George Washington University Misuzu Otsuka Johns Hopkins University Forhad Shilpi World Bank Key Words: Intergenerational Correlations, Occupational Mobility,<sup>Non-FarmParticipation.</sup> Gender effect, Two Stage Conditional Maximum Likelihood JEL **Classification :J62, 012**

> 1Emails for correspondence: semran@ggwu.edu, fshilpiSworldbank.org, motsuka©jhu.edu. We would<sup>liketo</sup> thank the participants at NEUDC, 2002 at Williams College, and<sup>BryanBoulier, JoseCanals-Cerda,Gershon</sup> Feder, Mary Hallman, Hanan Jacoby, Shabbir Jilany, Peter Lanjouw,<sup>MarkLong,Don Parsons,DavidRibarand</sup> Stephen Smith for useful discussions and/or comments on earlier drafts.
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

||**IX. Social impacts**|||
|---|---|---|---|
|**Focus area**|**Indicator**|**Data Source**<br>**(e.g. UK)**|**Source/reference**<br>**(example)**|
|? Consumer prices|**? End-user prices – Avg. residential,**<br>**industrial, commercial prices with and**<br>**without taxes (per KWh)**<br>? Price levels for different income groups (%)<br>? Consumption levels for different income<br>groups (MWh)<br>? Expenditures on electricity for different<br>income groups<br>? Ratio of daily disposable income for 20%<br>poorest income groups to price of electricity|IEA Statistics:<br>Energy Price and<br>Taxes<br>IEA Statistics:<br>World Energy<br>Statistics<br>Surveys|Delfino and Casarin<br>(2001)<br>Torero and Pascó-<br>Font (2003)<br>IAEA-IEA (2002)|
|? Economic welfare|? Welfare effect among income groups<br>? Welfare distribution effect among<br>government, consumers, producers||Ennis and Pinto<br>(2002)<br>Mota (2003)|
|? Access to service|**? Level of electrification (n o., % of total**<br>**households connected)**<br>? Rate of change in electrification (no. of new<br>connections)<br>? Level of urban electrification (no., % of total<br>urban households connected)<br>? Level of rural electrification (no., % of total<br>rural households connected)|||
|? Energy use|**? Electricity consumption per capita (KWh)**<br>? Commercial energy use per capita (KWh,<br>annual growth rate %)<br>? Share non-commercial energy as total energy<br>consumption (%)||Bacon and Besant-<br>Jones (2002)|
|? Continuity of service|? Dis-connections from service (no.)|||

# **X. Environmental impacts**

|**Focus area**|**Indicator**|**Data Source**<br>**(e.g. UK)**|**Source/reference**<br>**(example)**|
|---|---|---|---|
|? Climate change|**? CO2 emissions by the sector (0,000 tones)**<br>? Rate of change in CO2emissions by the<br>sector (%)<br>? CO2intensity of the sector (‘000<br>tones/MWh)|IEA Statistics: CO2<br>Emissions from Fuel<br>Combustion<br>National Statistics<br>Online||
|? Polluting emissions|**? SO 2, NOx, particulates, CO, VOC**<br>**emissions by the sector (quantity)**<br>? Rate of change in SO2, NOx, particulates,<br>CO, VOC emissions by the sector (%)<br>? Concentration levels of SO2, NOx,<br>particulates, CO, VOC in urban areas (values)|Department for<br>Environment, Food<br>and Rural Affairs:<br>Digest of<br>Environmental<br>Statistics|IAEA-IEA (2002)|

26
ternative estimates of caloric intake using NSS data, reported in column 5, provide a sense of the magnitudes of this source of measurement error. The details of the procedure are discussed in Appendix C, but the main departure from the standard NSS practice is imputation of calories for most expenditures in the beverage and processed food categories (which includes purchased “meals”). These expenditures rise from about 5% to 9% of average household food expenditure, and as they lack quantities or report units like “number of cooked meals” or “number of cold beverages: bottled/canned,” untested assumptions about how to impute their caloric value have become more important. The procedure used here yields very similar levels of estimated intake for 1983 through 1994, but a substantially smaller decline between 1994 and 2005, such that the total decline between 1983-2005 is only 66% as large as estimated by (NSSO 2013) or (Deaton and Dr`eze 2009). – The estimates begin to diverge even more after 2005 while there is a similar pattern of decline from 2005-2010 and recovery from 2010-2012, overall the decline between 2005 and 2012 is about 40 calories per day as opposed to an increase of 47 per day according to official estimates. The alternative assumptions are not necessarily better than the official procedure, as there is no “ground truth” against which to assess either measure. Rather, the point is that measurement assumptions can have a large effect even using the _same data_ , particularly in recent years when the importance of cereals and other unprocessed agricultural staples has fallen. These imputation assumptions have the potential to generate bias of a similar magnitude as the recall errors that potentially bias down both the level and decline in caloric intake.<sup>7</sup> Returning to the other side of the calorie equation, columns 6 and 7 again report estimates of population TEE and average TEE for adults. Relative to the official estimates using the 30-day recall period, the estimated population – TEE is almost perfectly negatively correlated over different time periods it is rising from 1983-1994, stable over 1994-2000 when intake appears to increase, peaks in 2005 when caloric intake is near its minimum, and then declines from 2005-2012 when caloric intake rises. Furthermore, by 1994 intake using 30-day recall is below TEE and by 2005 it is _much_ lower. The levels of TEE estimated in later periods are more consistent with the esti-

> 7Note that per capita caloric intake levels under any methodology are somewhat sensitive to outliers, and the analysis here follows the standard practice of censoring the distribution around the 1% and 99% percentile cutoffs (corresponding to 960 and 4400 calories a day per capita). However the trend in population averages over time are not very sensitive to outliers and generally track the population median.

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<!-- Start of picture text --> 15. "Firms in my line of 70 .<br>business usually know in advance<br>about how much this "additional 60<br>payment" is."<br>so<br>This is true<br>40<br>(1) always<br>(2) mostly 3<br>(3) frequently<br>(4) sometimes<br>(5) seldom El2<br>(6) never El<br>World [DC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or (3) for question 15**

Results show that more than 40 percent of corruption in developing countries is of the "greasing" type. In the CIS about 70 percent of entrepreneurs usually know beforehand what the additional payment will be. In developed countries only 10 percent of entrepreneurs possessed this knowledge.

# _Question 16: Corruption and Blackmailing_

<!-- Start of picture text --> 16. "Even if a firm has to<br>make an "additional payment" 5<br>it always has to fear that it will<br>be asked for more, e.g. by<br>another official." 40<br>This is true<br>30<br>(1) always I<br>(2) mostly E<br>(3) frequently I<br>(4) sometimes i I<br>(5) seldom I<br>(6) never I1 1<br>Wc_ d LDX DC SSFA NM CEE LAC SSA cis<br>Percentage of firms who ticked (1), (2), or (3) for question 16<br><!-- End of picture text --> This is true 32
unprecedented spike in rule suspensions and escape clause usage that occurred through the COVID-19 pandemic did not dampen the effect of fiscal rules on spreads for countries temporarily abandoning their rules. Instead, our empirical results imply that fiscal rules still provide a spread-compressing effect even if abandoned. We interpret these results to reflect the expectation that rule abandonment will be temporary, and that countries will return to fiscal rule compliance after a short duration of time.

**Figure 1. Trends in Fiscal Rule Adoption**

<!-- Start of picture text --> Number of countries<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1986 1991 1996 2001 2006 2011 2016 2021<br>High Income Low- and Middle-Income<br>a. Adoption of fiscal rules, by income group<br><!-- End of picture text --> <!-- Start of picture text --> Number of countries<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1986 1991 1996 2001 2006 2011 2016 2021<br>Debt rule Balanced budget rule Any rule<br>b. Fiscal rules in low- and middle-income countries, by<br>type<br><!-- End of picture text --> Source: Davoodi et al. (2022a)

**Table 1. Average Structural Deficit (% GDP) for Countries with and without Fiscal Rules (2007-2021)**

||**Average **|**Standard Deviation**|
|---|---|---|
|**Countries With Fiscal Rule**|2.78%|1.01%|
|**Countries Without Fiscal Rule**|3.41%|0.81%|

Source: Davoodi et al. (2022a) and World Economic Outlook Database (April 2022 Vintage) Note(s): Sample size of 41 middle and low-income countries.

Perhaps the most notable development in fiscal rule usage recently has been the unprecedented spike in escape clause usage and fiscal rule suspension witnessed during the COVID-19 pandemic. In 2020 and 2021, 39% and 36% of low- and middle-income countries with fiscal rules, respectively, either temporarily suspended a rule or used an escape clause.<sup>13</sup> The previous peak occurred in 2010, when suspensions and escape clause usage stood at a 7% rate, as only three countries abandoned their fiscal rules (see Figure 2). With the growing importance and relevance of fiscal rules in the years leading up to the pandemic, such a sudden breakdown in fiscal discipline raises the question of how long it will take for fiscal balances to return to their pre-pandemic trend, thus allowing

> 13 See Appendix Table A1 for a list of all low- and middle-income countries that either enacted an escape clause or temporarily suspended a fiscal rule during 2020-2021.

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# **Table 2.  Ethno-religious groups of Ghana and Instrumental variables**

|Ethno-religious<br>group|Number of<br>households|Percent<br>of households|International remittances (from<br>African or other countries)<br>received as percent of household<br>incomeingroup|International migrants(to<br>African or other countries)<br>as percent of population in group|
|---|---|---|---|---|
|1|205|5.28|7.2%|2.75%|
|2|239|6.15|6.1%|4.62%|
|3|550|14.16|7.0%|3.16%|
|4|44|1.13|10.2%|3.45%|
|5|110|2.83|6.5%|2.06%|
|6|193|4.97|4.9%|0.87%|
|7|245|6.31|8.2%|0.77%|
|8|572|14.73|5.0%|0.38%|
|9|42|1.08|2.9%|1.19%|
|10|159|4.09|2.9%|0.38%|
|11|234|6.02|1.4%|0.52%|
|12|104|2.68|7.9%|0.23%|
|13|269|6.93|1.6%|0.45%|
|14|547|14.08|8.3%|0.98%|
|15|371|9.55|2.9%|0.32%|
|Total|3884|100.00|||

Source: 2005/06 Ghana GLSS 5 Survey (sub-sample)
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of poverty in each location. As a benchmark case with which to compare our results, we make the extreme assumption of no knowledge whatsoever about the spatial distribution of poverty – in which case our given budget is distributed uniformly to the entire population. We set up a series of comparisons to this benchmark, where we assume knowledge about poverty levels for administrative units with progressively smaller populations. For a given level of disaggregation, we ask how knowledge about poverty outcomes across regions can be incorporated into the design of a transfer scheme so as to improve the overall targeting performance relative to the benchmark case. We consider a variety of transfer schemes that make use of this knowledge in different ways. The schemes range from simple, intuitive transfer schemes to more sophisticated ones, in the latter of which expected poverty at the national level is minimized given the information and budget constraints. We are interested in comparing performance across schemes, as well as the relative performance of each scheme at alternative levels of disaggregation. We consider performance in terms of the squared poverty gap – a measure of poverty that increases not only with the number of people below the poverty line but is also particularly sensitive to the distance between a poor person’s income level and the poverty line. To avoid making strong assumptions about the exact value of the poverty line or the size of the budget, we also show results for two poverty lines and two hypothetical budgets.<sup>6</sup> Finally, we report how close “optimal geographic targeting” in combination with our poverty maps, comes in terms of poverty reduction to the hypothetical scenario of “perfect targeting”. From this we can get a sense of the potential benefit from combining detailed geographic targeting with additional targeting mechanisms such as

> http://econ.worldbank.org/programs/2473/topic/14460/.

> 5 For a more detailed discussion, see Demombynes et al. (2002).

> 6 We have tried more poverty lines and budget sizes, but do not present them in this paper for brevity.  For a formal discussion of using “program dominance curves” to assess the poverty impact of different programs, see Duclos, Makdissi, and Wodon (2003).

6
from the series the growth accelerations that would be due to changes in the terms-of-trade.<sup>5</sup> This is done in the statistical analysis of section 5 where we control for the impact of changes in the terms-of-trade.

**<u>Fiscal Events.</u>** The core of this study is the definition of a fiscal event. Ideally, we would like to carry out the equivalent of what Wacziarg and Welch (WW, 2003) have done for trade liberalization episodes, that is use a combination of criteria that qualify a fiscal reform to center an event which could then be checked against detailed reports identifying significant changes in the fiscal regime. In a second step a “before and after” analysis would be carried out around the fiscal event for selected outcome indicators (e.g. growth and other indicators like investment in the case of WW).

Unfortunately, carrying out a similar exercise for changes in fiscal policy is much more difficult. First, as mentioned above, is the issue of trying to disentangle the stabilization objective from the growth objective which is not addressed here. Second, there is much more fungibility in fiscal policy than in trade policy, so it is more difficult to identify the fiscal space levers, and it is much more difficult to identify the expected effects of changes in these levers. Here, we restrict fiscal reform to a change in total primary fiscal expenditures and, in a second step, we study the underlying evolution of many components of potential interest (e.g. education, health or transport and communication).

Faced with these limitations and with limited data availability, we rely on changes in consolidated central government total fiscal expenditures, TFE (taken from the GFS, see details in annex A.1) as “event” changes in government expenditures. Since we are looking for autonomous fiscal expenditures, events are defined on expenditures purged of non-discretionary components such as wages and interest payments, _IP_ .<sup>6</sup> Lacking information on the wage component for each functional expenditure category, we consider as discretionary TFE purged of interest payments. So, we define discretionary fiscal expenditure, DFE, as _DFE_ = _TFE_ − _IP_ which is equivalent to focusing on primary spending.

We also compute the primary fiscal deficit _, def,_ as the difference between the total revenues and _grants_ and the discretionary fiscal expenditure, DFE (so a deficit is negative).

As discussed below, for the developing countries in the sample used here, average _DFE_ is 24% of GDP and average central government primary fiscal 5 While HPR also face this problem, because they use an 8-year (rather than a 5-year) window, they are less likely to be capturing terms-of-trade fluctuations in their growth events.

6 Heller (2006) considers wages and interest payments as the 2 non-discretionary expenditures in developing countries. In our fiscal data set which is decomposed by “function”‘ rather than “economic” use we do not have a wage component for each function so we cannot include wages as non-discretionary. See the annex A.1 for the definitions of these components.

7
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