rafmacalaba/datause-encoder-data huggingface dataset:
Table C3: ITT Effects of Incentives on Birth Outcomes

||(1)|(2)|
|---|---|---|
||Intervention Period|Post‐Intervention Period<br>(Jan – Dec 2011)|
|**A. Birth Weight**|||
|Treatment|‐34.88<br>(45.38)|24.48<br>(39.63)|
|Large Sample_p_‐value|0.44|0.54|
|Wild Bootstrapped_p_‐value|0.46|0.57|
|Control Group Mean|3304.82|3279.13|
|Sample Size|555|802|
|**B. Low Birth Weight**|||
|Treatment|0.01|‐0.01|
||(0.02)|(0.01)|
|Large Sample_p_‐value|0.63|0.60|
|Wild Bootstrapped_p_‐value|0.61|0.63|
|Control Group Mean|0.05|0.06|
|Sample Size|555|802|
|**B. Premature**|||
|Treatment|0.03|‐0.04*|
||(0.03)|(0.02)|
|Large Sample_p_‐value|0.31|0.08|
|Wild Bootstrapped_p_‐value|0.32|0.09|
|Control Group Mean|0.09|0.12|
|Sample Size|414|708|

Notes: This table reports ITT estimates of the treatment effect of the modified fee schedule for on indicators of birth outcomes. The LATE estimates are reported in Table 6. The observations include woman for whom we are able to obtain information on birth outcomes provided in public hospital birth records. The differences are estimated from OLS regressions of the dependent variable on an indicator for clinic treatment random assignment. The _p_ ‐values are for 2‐sided hypothesis tests of the null that the difference is equal to zero. We present both the _p_ ‐value computed for large samples and a Wild bootstrapped _p_ ‐value that is robust in samples with small numbers of clusters (Cameron et al. 2008). Our Wild bootstrap procedure assigns symmetric weights and equal probability after re‐sampling residuals (Davidson and Flachaire 2008) and uses 999 replications. Column (1) reports the results for the sample observed in an 8‐month intervention period (May 2010 – December 2010). Column (2) reports the results for the sample observed in the 12‐month period following the end of the intervention (January 2011 – December 2011). Standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

52
poorer rural households tend to have a higher rate of participation in agricultural wage employment. Similarly, the share of income from agricultural wage labor is more important for poorer households in these countries, and the relationship holds regardless of the level of development.

# **Diversification and specialization**

The results presented thus far suggest that rural households employ a wide range of incomegenerating activities—though perhaps rural households in the African countries in our sample are more dependent on agriculture then rural households in other countries. The question remains, however, whether households specialize in activities—with diversity in activities across households in the rural space—or, whether households themselves diversify income-generating activities. We examine the degree of specialization and diversification by defining a household as specialized if it receives more than 75 percent of its income from a single source and diversified if no single source is greater than that amount.<sup>10,11</sup>

# **Figure 13. Figure 14.**

<!-- Start of picture text --> Share specializing on farm Share with diversified income portfolio<br>6 7 8 9 6 7 8 9<br>GDP (log) GDP (log)<br>Africa Non-Africa Africa Non-Africa<br>Overall Trend African trend Overall Trend African trend<br> Africa without NGA Africa without NGA<br>80 80<br>60 60<br>(%) 40 (%) 40<br>20 20<br>0 0<br><!-- End of picture text --> 10 Other definitions of diversification and specialization are possible. Davis et al (2010) used 100% and 50% of income from a single source as alternative thresholds in order to examine robustness. They find that the extent of diversification is affected by the choice of the threshold, which drops to around 10% or less in all cases when using the 50% definition of specialization, climbing to around 90% with the 100% definition. The broad patterns by country and by level of welfare, however, did not change with choice of the threshold. Alternative groupings of income categories are also possible, such as joining together agricultural and non-agricultural wage labor, or non-agricultural wage labor and non-agricultural self-employment, which would increase the share of household specializing in these new categories.

11 Note that we are constrained from delving into the details of diversification due to how household survey data are often collected. The apparent diversification may derive from aggregation across seasons (with seasonal specialization by households) or across individuals (with specialization by individual household members).

14
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International Monetary Fund (1987), World Economic Outlook, Oct. 1987.

- Jorgensen, D. W. (1971): "Econometric Studies of Investment Behaviour; a Survey," Journal of Econometric Literature, 9 (December), 1111-1147.

- Jorgensen, D.W. and C.D. Siebert (1968): " A Companion of Alternative Theories of Corporate Investment Behaviour, I American Economic Review, 58 (September) 681-712.

- Kuh, E.(1971): Capital Stock Growth: A Micro Econometric Approach, Amsterdam: North Holland Publishing Company.

- Nickell, S.J. (1978): The Investment Decisions of Firms, Cambridge Economic Handbooks, Cambridge University Press.

- Sundarajan, V. and S. Thakur (1980),"Public Investment, Crowding Out and Growth: A Dynamic Model Applied to India and Korea", Staff Papers, International Monetary Fund, Vol 27, December 1980, pp 814-55.

- Tunwai, V and C. Wong (1982): n Determinants of Private Investment in Developing Countries," Journal of Development Studies (London), Vol 19 (October 1982), pp 19-36.

- van Wijnbergen, S.(1988), "External Debt, Inflation and the Public Sector: The Role __.,_ Fiscal Policy", mimeo, The World Bank.

- van Wijnbergen, S. (1982): "Stagflationary Effects of Monetary Stabilization Policies: A Quantitative Analysis of South Korea," Journal of Development Economics, Vol.10, pp 133-69.
**Table 3. Direct and indirect exporters**

||Direct<br>Only|Indirect<br>Only|Both Directly<br>and Indirectly|Total<br>Exporters|Total Firms|
|---|---|---|---|---|---|
|2020|18|6|2|27|198|
|2016|25|4|1|30|198|
|2013|18|7|5|30|198|

Notes: Sample is restricted to the 2013-2016-2020 panel and excludes firms missing information on number of employees. The above table reports the number of firms within each survey round that export (directly or indirectly). Firms exporting directly are those that sell to other countries without any intermediary. Firms exporting indirectly are those which sell to a wholesaler or other who then resells to customers in other countries. Source: Authors’ elaboration using Enterprise Survey for Egypt.

**Table 4. Simulated Effects of an International Price Increase**

|Groups|No. of<br>Firms|Average Firm-<br>level<br>Employment<br>Change|Total<br>Employment<br>Change|Average Percent<br>Employment<br>Change|
|---|---|---|---|---|
|Always exporters|46|13.960|642.169|0.067|
|New exporters|59|30.824|1,818.644|0.353|
|Never exporters|176|-0.052|-9.183|-0.003|

Notes: The above table reports the simulated changes to employment following a 5 percent increase in the international price. Never exporters are firms that only sold to the domestic market in periods 1 and 2. New exporters are firms that sold to the domestic market in period 1 and switched to exporting in period 2 following the international price increase. Always exporters are firms that exported in period 1 and 2. Source: Authors’ simulation.

**Table 5. Changes in log Employment relative to Changes in Sales and Export Share between 2016 and 2020**

|Export Share|Sales|Increase<br>Decrease or unchanged|
|---|---|---|
|Increase||(1)<br>(2)|
|||N= 50<br>N= 21|
|||Δln emp. mean =0.86<br>st. dev.=0.74<br>Δln emp. mean = 0.17<br>st. dev.=0.77|
|Decrease or||(3)<br>(4)|
|unchanged||N= 243<br>Δln emp. mean = 0.16<br>st. dev.=0.69<br>N= 179<br>Δln emp. mean = -0.26<br>st. dev.=0.66|

Note: This table reports the mean change in log employment, for four scenarios: (1) sales and share exported increased, (2) sales did not increase and export sales did, (3) sales increased while the share going to exports did not, (4) neither sales nor export shares increased. Excludes upper and lower one percentiles of change in log employment. The sample is limited to exporting firms. Calculated using 2016-2020 panel weights. Source: Author calculation using Enterprise Survey 29
the underlying population of shippers or carriers. Notable examples collecting surveybased data on trucking costs in developing countries are Teravaninthorn and Raballand (2009) for Africa, Osborne et al. (2014) for international trade corridors in Central America and Lam et al. (2019) for Vietnam.

Naturally, survey design and sampling presents researchers and practitioners with multiple choices to be made such as whether one is interested in trucking costs throughout the entire country at both urban or intra-city scales, or just in some key corridors. The studies cited above exemplify different choices on this front. To facilitate comparison and guide standardized data collection, survey data is typically collected for a fixed freight scenario that is representative for a typical shipment, such as a 20 ton trailer truck. Many countries have separate fleet inventory surveys that can guide this choice. Similarly, respondents are asked to consider a scenario involving commodities that satisfy certain criteria, such as not being irregularly shaped. The length and detail of the survey is another key design choice: facing a trade-off between detail and response rates, a common approach is to complement questionnaires with in-depth interviews from a small number of respondents.

Most studies in the literature aim to quantify several variables for the case under study: the ton-mile cost of truck shipments to the carrier, the price charged to shippers, the composition of costs to variable versus fixed costs, capacity utilization rates and total vehicle operation costs. As expected, relative and absolute costs differ across countries, but some consistent results also emerge from a range of studies: in developing countries, a larger share of total costs in trucking is borne by variable costs since older vehicles with low fuel efficiency have a larger share in the national fleet. In France and the U.S., two developed economies for which comparable data exists, the cost composition is the reverse, with a higher fraction accounted for by fixed costs. This finding resonates with firm-level models of technology choice that posits a trade-off in operating with a low marginal cost technology at a high fixed cost, or vice versa.

One concern in asking truck operators about their variable and marginal costs is that economic agents do not always explicitly or accurately calculate these economically relevant variables. When asked about them on the spot, they may give noisy guesses. They are, however, more likely to be well-informed about the levels of separate cost components. Therefore, information collected through surveys can be leveraged as an input to imputing transport costs. For example, a survey can simply ask operators about hourly wages of drivers, the cost of vehicle maintenance and the fuel efficiency of their fleet with the goal of precisely measuring each component. Putting all this information together, under assumptions on the importance of each component—which itself can be informed by surveys or interviews—the researcher then imputes freight costs per ton-mile. Since this approach goes one step beyond surveys, the next section 8
<!-- Start of picture text --> VAS Republic ANNUAL CORPORATE INCOME TAX Rwanda @\/@ || Glossary:<br>(643) of Revenue @ @<br>Xs Rwande TAXPAYER RECEIPT hathorty TKR<br>aT axpayer and Tax Identification Net Profit = Total Income — Tota! Expenses<br>Paymentbate<br>Start and End of Tax Period [[|__ Due Date | | | Tax Credits<br>Tax Liability<br>[5-Calculation | ETR = Tax Liability / Net Profit<br>5-Business Income/ Sales 1+ Useful stock details<br>10- Cost of Goods/Services Sold / 6-Opening Stock 95- Net Income (Substract Line 90 from Line 55) =<br>15-Gross Profit ( Line 5 - Line 10) F 7-Purchases 100- Reintegration of non-deductible expenses +<br>20- Operating expenses (Excluding Rental Expenses on Line 85) 8-Closing Stock 105- Depreciation adjustment (+/-)<br>25- Depreciation + 110- Loss carried forward from previous five tax periods =<br>30- Total expenses and depreciation (Line 20 + Line 25) F > In 115- Non-taxable Dividend received =<br>35- Net operating Income (deduct Line 30 from Line 15) = 120- Taxable Income [(Add Line 95 and Line 100 and +/- Line 105) minus (Line 110+Line 115)] =<br>40- Investment Income (Line 15 of Annex A) | 125-Corporate Income Tax ( Line 120 * 30%) [=<br>45- Non operating & Extraordinary Income h 30- Tax Discounts from employment (Broughtforward from Line 265)<br>50-Rental Income I 135- Tax Discounts from Exports (Brought forward from Line 225) RRA-CIT-DF1-E11<br>55-Total Income( Add Line 35 through Line 50) FE ~> 140- Tax Discounts from Free Trade Zone (Broughtforward from Line230)<br>60-Investment expenses (Line 35 of Annex A) 145- Tax Discounts for Micro Finances(Brought forward from Line 235)<br>65- Non operating & Extraordinary expenses a 150-Tax Discount for newly listed companies on Capital market(Line 245)<br>70-Training & Research expenses if 155-Venture capital companies registered with Capital market( Line 250)<br>75- Investment allowance +<br>160-Collective inveistiment chemes and employees’ shares scheme(Line 255)<br>80- Bad debts +<br>165-Agricultural and livestock activities (Line 260)<br>85- Rental expenses( Only 10% of Gross Rental Revenue) + \ 70- Foreign Tax Credit (Line 65 ofAnnex B) .<br>90- Total deductions (Sum of Line 60 through Line 85) ir > F 175- Corporate Income Tax Payable [( Line 125-(Line 130+135+140+145+150+155+160+165+170)] | =<br><!-- End of picture text -->
# **6. Benchmarking EAP’s cross-border investments**

As discussed in the Introduction, the literature has emphasized that developing economies have been gaining space in global finance and, since the 2000s, have become a significant source and destination of FDI, bank lending, and portfolio investments.<sup>17</sup> For example, whereas South economies received about 9% of global capital inflows in 1990, by 2012 they received 33% of total world flows. As sources of capital flows, South economies sent about 34% of global capital outflows by 2012, up from 12% in 1990. Importantly, South economies have broadened and deepened their connections not only with the North, but also with other South economies. The patterns of EAP’s financial integration shown in the previous sections suggest that the region has followed these overall trends for the South, but little else is known about the role that EAP economies have played in this new phase of financial globalization. Thus, in this section, we expand the existing evidence by benchmarking EAP against both North and South regions and test how EAP has been integrating financially with itself and with the rest of the world.

To benchmark EAP’s cross-border financial investments, we estimate a gravity model similar to that in Section 4. In particular, we estimate three specifications assessing EAP’s intra- and interregional financial connectivity, both from the sender and the recipient point of view, using the PPML estimator described earlier. In all specifications, the dependent variable is the value of a certain crossborder financial investment between a pair of economies. The explanatory variables are intra-regional dummies (when benchmarking intra-regional investments) or inter-regional dummies (when benchmarking inter-regional investments). Intra-regional (inter-regional) dummies take the value of one whenever both sender and recipient economies belong to the same (different) region, and zero otherwise. These dummies are individually defined for each of the seven geographical regions

> 17 See, for example, Aykut and Ratha (2003), World Bank (2011 and 2013), de la Torre et al. (2015), and Broner et al. (2016).

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The study uses individual data from the Demographic and Health Surveys (DHS) which are large scale, repeated cross-sectional household surveys that use standardised questionnaires which are comparable across countries.

# Intimate partner violence

Since 1998 the DHS has included a standardised domestic violence module which asks women if they have ever experienced specific acts of physical, emotional and sexual abuse. Since 2005, the DHS also asks women if they experienced any of these three forms of violence in the twelve months preceding the survey date. The study analyses reports of abuse _in the last twelve months_ so as to exploit the timing of unemployment shocks. A potential concern with self-reported measures of IPV is that women may systematically underreport its incidence (Kishor and Johnson, 2004). The DHS surveys attempt to minimize this through methods of women selection, interviewer training, and interview technique. Specifically, only one eligible woman per household is selected for the domestic violence module so as to reduce the likelihood that others in the household are aware of the nature of the questions. Women are asked the questions only towards the end of the interview so that a rapport has been built up between interviewer and respondent before the questions are posed, questions on IPV are only asked if absolute privacy is ensured, and translators are avoided to ensure privacy. Finally, interviewers are provided with extensive training regarding the appropriate way to ask questions of such a sensitive nature.<sup>16</sup> Another possible concern is that reporting behavior changes endogenously with male vs female empowerment. It seems plausible that secular (permanent) changes in relative unemployment rates will empower or disempower women to report violence. However, it is less plausible that

> 16 Some recent, limited, evidence from Peru suggests that underreporting may occur for highly educated women (Aguero and Frisancho, 2017), but it is unclear how generalizable this is for other country contexts. In any case, in all cases, this study controls for education in our analysis. The estimates should remain unbiased and consistent with possible measurement error in the dependent variable provided this measurement error is uncorrelated with the explanatory variables

7
# **2. FINANCE IN AFRICA: GROWING FROM A LOW LEVEL**

Given the central importance of finance for economic development and poverty alleviation, the superficiality of African finance is alarming. African financial systems are small, both in absolute terms as in relative terms. Figures 1 and 2 show the ratio of liquid liabilities to GDP and the log of liquid liabilities in US dollars, respectively, with data for 2007. With a few exceptions—such as Mauritius, South Africa, and a handful of offshore financial centers— African financial systems are among the smallest across the globe, both in absolute terms and relative to economic activity. Many African financial systems are smaller than a mid-sized bank in continental Europe, with total assets often less than US$1 billion. Small size is connected to low productivity and skill shortages, and prevents banks from exploiting scale economies; in addition, it might deter them from undertaking large investments in technology. In addition, Africa’s financial systems are characterized by very limited outreach, with less than one in five households having access to any formal banking service—savings, payments, or credit (Figure 3). Again, this in stark contrast not only to continental Europe, where access to a checking account is taken for granted, but also to other regions of the developing world, where penetration rates are typically between 30 and 50 percent. Behind these low numbers, however, is some intra-regional variation, with banking penetration surpassing 40 percent in South Africa but remaining below 20 percent in most of East Africa.

Banking is also very expensive in Africa, as reflected by high interest spreads and margins (Figure 4).<sup>7</sup> This spread between deposit and lending interest rates provides disincentives for both savings and lending, as it depresses the returns for savers and pushes

> 7 We denote _spreads_ as the difference between ex-ante contracted loan and deposit interest rates and _margins_ as the actually received interest (and non-interest) revenue on loans minus the interest costs on deposits (minus noninterest charges on deposits).

4
52. As noted in Section II, the MICs are of increasing importance in relation to a range of global public goods. The HCAC category, for example, while only 15 in number, accounts for more than 60 percent of all private capital flows to developing countries and is, therefore, important from the standpoint of regional and international financial stability. The LCAC and MCAC categories are particularly important for the global objectives of poverty reduction and protection of the environment and health. More than half of the world's poor live in these countries. Because it includes China, even the HCAC category accounts for about a quarter of the world's poor (Table 6).

**Table 6. Selected Indicators for MICs, 1997**

|Country Groupings|GDP<br>(US$ billions|Population<br>) (millions)|<br>Tr<br> <br>(US$ b|ade<br>illions)|Estimated<br>Number of Poor|
|---|---|---|---|---|---|
||||Exports|Imports|(millions)|
|LCAC|1,020|804|291|287|448|
|MCAC|2,337|1,539|358|445|1,006|
|HCAC|2,547|1,581|826|798|742|
|Total MIC|5,904|3,924|1,475|1,530|2,196|
|World|29,126|5,817|6,826|6,704|2,802|
|MIC (as % of World)|<br>20.3|67.5|21.6|22.8|78.4|
|_Percent Share of MICs_||||||
|LCAC|17.3|20.5|19.7|18.7|20.4|
|MCAC|39.6|39.2|24.3|29.1|45.8|
|HCAC|43.1|40.3|56.0|52.2|33.8|
|Total|100.0|100.0|100.0|100.0|100.0|

Notes: 1/ Based on latest poverty data ($2 a day) for countries where available. Source: World Bank staff estimates.

53. Inevitably, the proposed classification is not watertight and exceptions may be found in each group that do not confirm to the norm. China, for example, is an HCAC, but its poverty incidence of 54 percent and CPIA rating are well out of line with others in this group. South Africa is another HCAC where a high poverty incidence of 36 percent-a legacy of the apartheid regime-is well above average for this group. Lower on the scale, Brazil provides the opposite example as an MCAC with a low poverty incidence of 17 percent (though with high regional concentrations of poverty). Algeria is something of an exception among LCACs as it has low poverty (estimated at 15 percent) although its policy performance is well in line with the average for the group.

54. It should be noted that while average per capita income rises as we move up the capital market access categories, there is substantial variation around the averages. The two extreme cases are those of China and India of course. The average per capita income for the HCAC category is close to $4,000 but that of China is only $780. Similarly, the average per capita income of the MCAC category is about $2,700, but that of India, which belongs to this category, is only $450.

21