rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Maccini, Sharon, and Dean Yang (2009). “Under the weather: Health, schooling, and economic consequences of early-life rainfall.”</mark> _<mark>The American Economic Review</mark>_ <mark>99(3): 1006-1026.</mark> Marques, H. (2015). “Does the gender of top managers and owners matter for firm exports?” _Feminist Economics_ 21(4): 89–117.

<mark>Mascia, Danilo V. and Stefania P.S. Rossi (2017). “Is there a gender effect on the cost of bank financing?”</mark> _<mark>Journal of Financial Stability</mark>_ <mark>31: 136-153.</mark> <mark>Moro, Andrea, Tomasz Piotr Wisniewski, and Guido Massimiliano Mantovani, (2017). “Does a manager's gender matter when accessing credit?” Evidence from European data.”</mark> _<mark>Journal of Banking & Finance</mark>_ <mark>80: 119-134.</mark> <mark>Muravyev, Alexander, Oleksandr Talavera, and Dorothea Schäfer (2009). Entrepreneurs' gender and financial constraints: Evidence from international data.”</mark> _<mark>Journal of Comparative Economics</mark>_ <mark>37(2): 270-286.</mark> Niederle, M. and L. Vesterlund (2007). “Do women shy away from competition? Do men compete too much?” _Quarterly Journal of Economics_ 122(3): 1067–101.

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Phelps, Edmund S. (1972). “The statistical theory of racism and sexism.” _The American Economic Review_ 62(4): 659–61.

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<mark>Peng, Congmin and Po-Wen She (2020). “Are women less likely to be managers in the UK labour market?”</mark> _<mark>Economic Modelling</mark>_ <mark>85: 317-324.</mark> Rajan, R. and L. Zingales (1998). “Financial dependence and growth.” _<mark>The A</mark> merican Economic Review_ 88(3): 559–586.

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included in the SMPD. Other UN agencies providing mostly technical assistance; it does not count as budget support and rarely uses PFM systems, consistent with the large negative coefficient for the UN dummy in equation 4 of Table 8.

The DAC bilaterals are already split into two groups in equations 1-3, the _Nordic Plus_ group and all others. Equations 4-6 add dummies for two large bilateral donors - the U.S. and Japan - that are well-known for favoring project-based aid over budget support. Corruption concerns are voiced often in U.S. debates on foreign aid, e.g. in Congress and in Wall Street Journal editorials. “Congress insists that aid is spent on identifiable sets of measureable activities” and its earmarks and reporting requirements severely restrict the use of budget support or reliance on recipients’ procurement and other PFM systems in managing project aid (OECD, 2006). Negotiators for the U.S. insisted on weakening or eliminating Paris Declaration targets for budget support and use of country procurement systems (Mokoro Ltd., 2008b: 23).<sup>30</sup> Results in equations 4-6 confirm the expectation that Japan and (particularly) the U.S. use country systems less than do other bilaterals.

For bilateral donors, Table 9 tests three different survey measures of domestic constituents’ trust in aid’s effectiveness, from _Gallup International_ , the _WVS_ , and _Eurobarometer_ . As expected, public support for aid is associated with greater use of country systems, as shown in Table 9. Coefficients on the public opinion variables are all positive, and significant in most cases, with t-values ranging from 1.43 to 3.75. For each 5-point increase in the percentage of _WVS_ respondents who are strongly in favor of their country increasing economic aid to poorer countries, _PFM_ increases by nearly 4 percentage points and _DBS_ increases by 1 percentage point. The U.S., unsurprisingly, is somewhat of an outlier in terms of weak support for aid, but results are not dependent on inclusion of its observations in the sample. The strongest effects of public opinion on _DBS_ are obtained using _Eurobarometer_ (equation 6), which excludes U.S. observations.

It might be objected that public opinion is partly endogenous to how aid is delivered. If use of country systems improves aid’s effectiveness in reducing poverty, support for aid may increase. We find this argument implausible for several reasons. At this point, the assertion that use of country systems increases aid effectiveness is an untested hypothesis, and any positive effects would likely be too small to account for much of the large cross-donor variation in public support for aid. Moreover, public opinion in each of the three surveys is measured at least

> 30 In contrast, the U.S. led the effort to include a Paris Declaration provision on “managing for results” because “reporting on results is critical to demonstrating aid effectiveness and to sustaining public and Congressional support for U.S. assistance” (USAID, 2006).  However, USAID’s own reporting system “focuses mainly on physical deliverables” such as number of schools or clinics, rather than on outcomes (OECD, 2006).

26
medicine, recommends involving the extensive network of domestic NGOs to enhance the quality and scope of basic services provided while simultaneously curtailing implementation costs. When discussing the financing of preventive health care services that require subsidies to be produced or demanded at optimal levels, Bishai et al. [2008] suggest that governments may subsidize network providers by financing non-state actors to act as intermediate agents. In addition to expanding the reach of basic services, leveraging adequate local capacity can also improve the outcomes of social interventions by targeting them to areas with well-organized non-state actors [Maluccio, 2010, de Renzio, 2005]. The Nicaraguan CCT program, _Red de Protecc ́ıon_ , targets areas with significant local institutional capacity. As Rawlings and Rubio [2005] point out, CCTs must often take into account local supply capacity constraints in deciding which areas or populations to target; hence, incorporating local private capacity might increase the reach of such programs.

There are also possible pitfalls from such state-non-state collaboration. Batley [2006] finds that non-state provision of basic services can often be hindered by unsupportive, mistrustful relationships between governments and their non-state counterparts. However, if successful, such partnerships can lead to improved service standards, particularly when large NGOs support smaller local actors. Awortwi and Helmsing [2007] study the decentralization of the provision of basic services in Sub-Saharan Africa from central governments to non-state actors. They find that the coverage of primary education, primary health care, sanitation, and drinking water improved from this decentralization, although the quality of services continued to vary and geographic inequalities persisted.

What might distinguish a successful government-NGO collaboration from a troubled one? The International Finance Corporation [2011] recently completed a detailed comparative study on the systematic collaborations between African governments and the private health sector. Evidence points to particularly successful collaborations on disease control and immunization programs. The report finds significantly greater collaboration with NGOs that are better organized and have been established for a long period of time and also highlights the importance of a policy of engagement and information exchange between the government and the private sector. Finally, the report notes that the government’s ability to regulate the private health sector depends on adequate enforcement capacity and consistent oversight.

2
**Table 7 Performance indicators of the parastatal sector**

|_Efficiency indicators_|_1986-91_|
|---|---|
|Total value added (annual percent)|0.5|
|Value added in manufacturing (annual percent)|-0.1|
|Change in total factor productivity/ (annual percent)|-1.7|
|Growth rate of labor use/growth rate of labor in pnvate sector (ratio)|0.6|
|Growth rate of capital inpuits/grovvth rate of canital In pnvate sector (ratio)|2.1|
|Relationship<br>to budget and_external_accounts|_1986-91_|
|Net lending and equity to parastatals/overall net lending by government (percent)|35.4|
|Profits, interest and dividend payments/tax revenue|**3.7**|
|Parastatal extemal debt/public and publicly guaranteed debt (I 990) (percent)|17.0|
|Parastatal extemal debt servicing/total (I 990) (percent)|25.5|
|Net exports of parastatals/total net exports (percent)|-27.9|
|Relationship<br>to_bonking_system and near_bank_financiol intermediaries|1986-91|
|Parastatal depositsAotal commercial bank deposits|6.7|
|Parastatal deposits/total near bank financial intermediaries deposits|10.0|
|Parastatal credit/commercial bank credit|**6.0**|
|Parastatal credit/near bank financial intermediaries credit|1.3|

Source: Worid Bank data

**Tabl. 8 The effects on intermediate variables**

||Crisis period|The adjustm|ent period|
|---|---|---|---|
|Intermediate vanable|_(1976-81)_|Phase I _1(981-84)_|Phase_I/_(1985-91)|
|Change in fiscal deficit/GDP (percentage points)|5.7|-5.8|4.5|
|Inflation rate, (change in rate)|3.3|-1 1.1|4.7|
|Real average interest rate (loans)|2.1|-0.3|4.6|
|Real effective exchange rate (percentage change)|16.9|5.7|-42.3|
|Change in current account/GDP (percentage points)|9.9|-8.2|**4 **<sup>**.0b**</sup>|
|Debt service/exports average (percent)|8.0|26.0-28.0|30.0|

Note: A minus indicates a decline in deficit or rate a December to December b. 1984-90.

Source: Statistical Abstracts, Economic Surveys (various issues), IMF, _and_ World Bank staff estimates.

67
technology; this wait-and-see process accounts for some of the observed lags in the adoption of high-yielding seed varieties in India at the time (Foster and Rosenzweig 1995; Munshi 2004). In rice villages, the proportion of farmers who adopted the new seed varieties rose from 26 percent in the first year following the introduction of the technology to 31 percent in the third year; in wheat villages, the proportion of adopters increased from 29 percent to 49 percent. Farmers who did not have neighbors with comparable attributes (such as farm size or characteristics unobserved in available data such as soil quality) may have had to carry out more of their own experimentation, which was probably a more costly form of learning because the farmers bore all the risk of the choices they made (Munshi 2004).

The learning process at work during the Green Revolution is similar to that described by Miguel and Kremer (2003, 2004) about the importance of social networks in the adoption of new health technology, in this case deworming drugs. Survey data on individual social networks of the treatment group in rural Kenya reveal that social links provide non-treatment groups better information about the deworming drugs and thus lead to higher program take-up. Two years after the start of the deworming program, school absenteeism among the treatment group had fallen by about one-quarter on average, and there were significant gains in several measures of health status, including reductions in worm infection, child growth stunting and anemia, and gains in self-reported health. But children whose parents had more social links to early treatment schools were significantly less likely to take deworming drugs. The authors speculate that this disappointing finding could be due to overly optimistic expectations about the impact of the drugs or to the fact that the health gains from deworming take time to be realized while the side effects of the drugs are immediately felt.

Providing information about a program, hoever, is no guarantee of higher program 19
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|Parameter|Data source|
|---|---|
|No impediments to natural flow upstream|FAO Africa dams; Meridian Global Dam Database<br>(2006);andpowerplants - CARMA(www.carma.org)|
|Gauging station data: location, discharge, and<br>year(minimum of 4 in between 1988-2009)|Global Runoff Data Centre|
|Greater than 100 Km from major water bodies|Global Lakes and Wetlands Database(2004)|
|Sufficient amount of rain for detection|SSM/I|
|International River Basin|Transboundary Freshwater Dispute Database (TFDD,<br>2008)|
|Catchment area upstream of gauging station is<br>as large as possible to provide many<br>observations and degrees of freedom for the<br>model<sup>12</sup>|15 second accumulation and flow direction grids<br>(HydroSHEDS, 2006)|

**Table 1 : Selection of river basins criteria and catchment upstream of gauging station data source.**

<!-- Start of picture text --> Mekong Zambezi<br>Length (km) 4,350 2,574<br>Area (km 2 ) 787,836 1,390,000<br>WB Region EAP SSA<br>Population<br>71 21<br>density / km 2<br>Population 13 55,800,000 28,800,000<br>Treaty with<br>water Yes Yes<br>quantity<br>River basin Zambezi<br>Mekong River<br>organization River<br>Comission<br>Authority<br>Riparians Zambia,<br>Angola,<br>China, Burma,<br>Namibia,<br>Thailand,<br>Botswana,<br>Laos,<br>Zambia,<br>Cambodia,<br>Zimbabwe,<br>and Vietnam<br>and<br>Mozambique,<br>Snowmelt No No<br><!-- End of picture text -->

**Table 2: Selected river basins have a range of physical and socio-economic characteristics**

|Mekong|Zambezi|
|---|---|
|-1.0|-12.6|

**Table 3: Palmer et al. predict the relative change (%) of discharge in selected river basins from 1960-1990 (km**<sup>**3**</sup> **yr**<sup>**-1**</sup> **) compared to 2050s (km**<sup>**3**</sup> **yr**<sup>**-1**</sup> **)**

> 12 The selection is based on greater or equal to 50,000 Km<sup>2</sup>

> 13 Population statistics based on Landscan 2000 estimates summarized by basin (TFDD, 2008).

32
# **1. The Indian Conundrum**

Since the 1990s, India has witnessed high economic growth, accompanied by rising real wages, a steady decline in fertility and higher educational attainment for both males and females. But a strange conundrum has simultaneously unfolded. It has to do with women’s employment. In most other countries, overall prosperity, the freeing of women from the onus of childbearing and their increased educational qualifications have gone hand in hand with their increased entry into the labor market. However, in India female labor participation rates have stayed below 35 percent for more than two decades, whereas the developing country average has been stable at around 50 percent (World Bank 2015a). In fact, women’s employment in India is among the lowest in the world, and lower than in most countries of the same income level (Verick 2014). During the past few decades, other countries in South Asia have experienced rising or flattened rates of female employment, but in India the overall women’s labor force participation has declined since 2004.

For urban women in the 25-55 year age range, employment rates rose slightly from 22.7 percent in 1993 to 24.6 percent in 2004 but afterwards declined gradually to 22.2 percent in 2011. In contrast, nearly all men in this age group (97 percent) are working in India and this proportion has fluctuated only marginally (see Table 1). The aggregate decline in female labor force participation has been driven primarily by the withdrawal of rural women from the labor market, without commensurate increase in urban areas. Essentially, India has one of the lowest labor force participation rates (LFPR) for urban women – one that has stayed consistently below 25 percent, and in some states below 15 percent. This contrasts with our expectations that women in cities and towns would be more likely to engage in market work, since urban areas are hubs for jobs..

**Table 1. Labor Force Participation Rates (percent) for Women and Men Aged 25–55 Urban Areas, 1983–2011**

||**1983**|**1993**|**2004**|**2007**|**2009**|**2011**|
|---|---|---|---|---|---|---|
|<sup>Women</sup>|22.7|22.7|24.6|20.6|21.2|22.2|
|<sup>Men</sup>|97.3|97.1|96.9|97.0|97.0|97.2|

Note: Data is presented for women aged 25-55 to exclude those who may be in education. This includes all women in the age range – not just wives of household heads. Source: India National Sample Survey data based usual principal status.

If we look at Indian women’s employment patterns in the context of broader demographic change and drawing upon the experience of other countries, we find that the classic facilitators of employment are in place. India’s total fertility rate (TFR) saw a 20 percent fall from 3.0 in 20012
obtained from the multilevel logistic model. Thus, I am confident that I did not lose substantial statistical information by dichotomizing this variable.

# **6.2 Independent Variables**

## **6.2.1 Socio-Demographic Variables**

I control for standard socio-demographic variables that can affect citizens’ acceptance of government’s right to make people pay taxes. A question probing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their income can be problematic in the context of developing economies, where individuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also affect household resources.

## **6.2.2 Experience with Paying Taxes or Fees**

It is difficult to assess just how ubiquitous taxes are in ordinary Africans’ lives. There has not been any systematic effort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states’ revenue raising capacity is generally low. On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3).

The types and amount of taxes citizens pay varies both within and between countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the majority of local revenue in South Africa (Hoffman, 2007). In Tanzania, Fjeldstad and Semboja (2001) count ten major categories of taxes, eighteen major categories of licenses, forty groups of charges and fees, and seventeen items listed as other revenue sources. In some countries including Kenya, Malawi, 11
acyclical if either (i) commodity revenues are not important in the budget (low ) or (ii) the volatility of the commodity price is low relative to the business cycle’s volatility (high ).<sup>41</sup> QQMM<sup>�</sup> /GG̅ Motivated by these concerns, we now introduce a new empirical specification zz pp σσ /σσ where estimates of procyclicality do not depend on the relative size and volatility of commodity revenues.

# _B. Empirical Specification of the MPS_

Our empirical specification is: x F + M P + = �M yy FFLLNNAAFF GG yy iitt iitt iitt iitt (4) iitt iitt ΔGG SS FFAATT SS x MMPPGG � x ΔCCRRC yy iitt ii tt iitt + ηηΔNNCCRRC + ζζ + δδ + εε where , , and are the annual change in expenditure, yy yy yy commodity resource revenue, and non-resource revenue in the country iitt iitt iitt in year , ΔGG ΔCCRRC ΔNNCCRRC respectively, each as a share of three-year moving average GDP (MAGDP).<sup>42</sup> ii tt Variables and represent country and year fixed effects, respectively. The country fixed effect captures country-specific time trends in expenditure growth. ii tt ζζ δδ By including year fixed effects, we control for the global level of commodity prices, as well as aggregate shocks that affect all countries at a particular year, such as the financial crisis of 2008–2009. The dummy variable is equal to one if country adopted a fixed ER regime in period (otherwise iitt= 0 if the ER is floating). MMPPGG F indicates a floating regime: F iitt . The parameter M ii tt MMPPGG jj is the MPS commodity revenues for countries with floating (iitt iitt iitt ) or fixed iitt FFAATT FFAATT ≡1 −MMPPGG SS jj= F FFAATT 41 A similar argument applies to measures of cyclicality over the business cycle based on correlations between expenditure and GDP. 42 The variables in levels used to calculate , , and are denominated in current yy yy yy local currency. Nominal series are deflated using the national CPI, from the IMF World Economic iitt iitt iitt Outlook Database. ΔGG ΔCCRRC ΔNNCCRRC 30
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