rafmacalaba/datause-encoder-data huggingface dataset:
12 other policies which affect the inflation rate, when considering financial disintermediation. Is any observed cross-section relationship between real interest rates, efficiency and growth simply due to the negative correlation between real interest rates and inflation, and the impact of the latter? Or, given the importance of the means of payment Ml relative to quasi money in the financial systems of most developing countries, is it realistic to expect a large role for interest rate policies in the face of high and volatile inflation rates? These issues are further investigated by Neal. 1989's study of the determinants of financial depth in 117 countries for 1985.

Thatkhate. 1288, tests cross-country relationships between economic variables such as growth and investment and the level of interest rates for a -sample of 64 LDCs during the period 1971-80 using a nonparametric Mann-Whitney test. No statistically significant difference was found between countries above and those below the mean real interest rate. The study concluded that the interest rate level itself had little or no impact on macroeconomic variables, at least when considered in isolation from other policy variables **.16** Fry, 1988 and Gonzalez Arrieta. 1988, summarize existing studies in this area. While the former is inclined to support a positive association, the latter argues that the evidence is far from clear. In our view, despite considerable doubt on the extent to which liberalized interest rate or financial sector policies can, on their own, contribute to efficiency and growth, the weight of evidence of the above and similar studies supports a positive association. No known studies present strong evidence in favor of repressing financial systems.

# 4. Some Cross-Section Relationships for 34 Countries

# 4.1 Data.

The data base for the present study covers 34 countries over a period of 21 years, 1965-85. The countries chosen are in the Hanson-Neal, 1987, data set and the interest rate data are based on their study which tried to inelude only countries with meaningful interest rates. The interest rates are generally rates on deposits of around 3-6 months. These are complemented by data on output and population growth, investment and savings rates, narrow and broad money aggregates, current account and 1975 income per head, from Interational Financial Statistics World Bank sources and, in some cases, 16 The difference between the high and the low-interest rate country groups 'n this study is only 3.5%, so that much of any statistical power in the "tails" of the distribution is eliminated. Also, the rank ordering between real interest rates, growth and incremental capital-output ratios is as predicted by the McKinnon-Shaw view. The results of the study are therefore not so much against this view as providing little support.
effect on child survival, holding constant the proportion of deliveries by medically trained personnel.

The Wald tests at the bottom of Table 3 indicate that while the x's are jointly significant, and the interactions between the x's and the 'poor' dummy are jointly significant, the interactions between the x's and the year dummy are _not_ jointly significant. Dropping the year interactions from the model, however, produces predicted infant and under-five mortality rates that are, on average, inferior to the predictions produced by the model in Table 3-see Table 4. In what follows we kept with the estimates reported in Table 3 and used all reported coefficients in the decompositions.

**Table 3. Weibull parameter estimates**

||_Ba_<br>|_se_<br>|_Searinte_<br>|_ractions_<br>|_Poorint_<br>|_eractions_<br>|
|---|---|---|---|---|---|---|
|_Variable_|_Coef:_|_p-value_|_Coef:_|_p-value_|_Coef_|_p-value_|
|NorthemUplands|2.054|0.13|-2.862|0.17|-0.418|0.87|
|RedRiverDelta|-0.087|0.95|-1.170|0.62|-3.129|0.21|
|NorthCentralCoast|-0.067|0.96|0.695|0.78|0.857|0.73|
|Central Coast|0.609|0.66|-1.134|0.58|-0.998|0.67|
|CentralHighlands|2.573|0.06|-2.550|0.23|-4.412|0.06|
|<br>SouthEast|2.869|0.04|-2.704|0.20|-5.827|0.06|
|Child=male|-0.657|0.31|-0.877|0.37|-1.405|0.17|
|Child'sage|-0.094|0.46|-0.061|0.74|0.084|0.62|
|<br>Log equivalentHHconsumption|-1.099|0.18|1.034|0.40|0.072|0.97|
|<br>Years schoolingofmother|0.294|0.01|-0.118|0.51|-0.017|0.94|
|<br>Safewater|1.920|0.06|0.212|0.89|-2.619|0.15|
|OKsanitation|0.490|0.64|-1.995|0.20|2.714|0.36|
|Vaccinationcoverage|0.032|0.06|-0.012|0.52|-0.009|0.67|
|<br>Antenatalvisitcoverage|0.000|1.00|0.005|0.82|-0.058|0.03|
|<br>Medicallytraineddelivery|||||||
|<br>coverage|-0.017|0.27|0.008|0.68|0.071|0.00|
|<br>Facilitydelivery coverage|0.016|0.31|0.003|0.88|-0.010|0.72|
|<br>Constant|19.600|0.01|-4.968|0.62|-0.512|0.97|
|Waldtestofjointsignificance|35.53*|0.00|17.70|0.41|31.97|0.02|
|p|0.236||||||

* Excludes constant term in Wald test ofjoint significance.

19
Further effort is expanded on collection of high-quality data, often the first set of sector data an agency will have access to. IE-generated data provide a client with at least two rounds of survey data (84 percent) and support to help improve administrative data systems (48 percent). Survey data are generally not available for projects without IE.

Evidence-based decisions are taken at multiple stages of IE implementation. Policy changes are recorded following baseline results discussions (36 percent), and when different mechanisms are tested (23 percent). Policy scale-up and -down decisions are recorded following discussions on IE results in 30 percent of the cases.

The relative importance of these different mechanisms will be the subject of future research as we expand the IE coverage and frequency of data collection of these monitoring indicators.

# 6. CONCLUSIONS

This is the first study that investigates the effect of IE research on contemporaneous measures of aidfinanced project implementation. It builds on the notion that the effectiveness of aid greatly depends on the quality of projects through which aid is delivered, and builds a case for a fruitful complementarity between aid financing and IE research. The study presents evidence that impact evaluation may help improve project implementation. Since project implementation is a necessary condition (although not sufficient) for projects to reach their development outcomes, then impact evaluation might be an enabling mechanism to secure developmental effectiveness.

We find that impact evaluation has large and significant effects on the disbursements of World Bank projects, increasing the average level of quarterly disbursements by 40 percent by midterm, helping projects reduce delays relative to plan by half, and increasing by 40 percent project disbursements relative to the average in the typical project in the same sector.

We conclude that projects with IE are more likely to implement their activities as planned and, in so doing, are more likely to achieve their objectives. This is of significance for both the Bank and development policy. For the Bank, the introduction of IE might help avert the need to push disbursements out toward the latter half of project duration, or to restructure and cancel nonperforming projects. It can also help manage the potential reputational risk of committing funding without being able to secure their disbursements. More important, for developing countries, avoiding delays may mean that schools, health facilities, or roads are built now instead of next year or not at all, securing the potential for educating one more generation, saving more lives, or connecting to market more people and goods.

Furthermore, our results support the idea that project financing and IE research are complementary outputs of the World Bank. This has implications for the use of IE as an operational tool to improve 18
During the period of the effectiveness of this measure, only one bank was constantly within the prescribed monthly rates. On average, seven banks were above the acceptable growth rates, per month. The average excess during the period of one and a half years was 3.88 percentage points above the acceptable growth rates, with the highest excess in the whole period was 70.8 percentage points. However, it should be pointed out that these excesses were more frequent in the first several months after the introduction of the measure. Even though there were still banks that were above the monthly rates,<sup>27</sup> the achieved annual growth rate on loans to households for 2008 was 37.4 percent, which was 2.4 percentage points below the acceptable maximum. This trend continued in 2009, which was clear proof that the measure had served its purpose.

After the introduction of the measure there was a significant decline in the growth of the loans to households, from almost 60 percent at the beginning of 2008 to 5.7 percent at the end of 2010, or a decline of more than 90 percent (Chart 15).

**Chart 15. Annual growth of loans to households before and after the NBRM measure**

<!-- Start of picture text --> 58.77%<br>5.70%<br>0.51%<br><!-- End of picture text --> <!-- Start of picture text --> Loans to households (lhs)<br><!-- End of picture text --> Source: NBRM

# <u>Liquidity ratios</u>

The fourth measure that was introduced by the NBRM was intended to enhance the liquidity position of banks. In general, banks had a prudential and conservative approach to liquidity risk management in the pre-crisis period, which enabled them to accumulate adequate levels of liquid assets to address the challenges that emerged during the crisis period. However, the global financial turbulence and the contraction of the international capital flows had certain impacts on the banks’ liquidity, which resulted in a decline of the growth of the liquid assets in 2008 (Chart 16).

The expectations were that the impact of the international financial and economic crisis over the economy could accelerate in the following period. Therefore, there was a necessity to

> 27 At the end of 2008, there were six banks that were above the prescribed monthly rates, which was one of the reasons why the NBRM decided to extend the use of this measure for 2009 (in addition to the fact that the risk from the high credit growth of loans to households was still relatively present).

20
Policy Research Working Paper 8818

# **Abstract**

Using unique direct observations of patient-provider interactions linked to patient exit interviews and detailed household surveys, this paper assesses the relationship between patient wealth and the quality and price of antenatal care in the Democratic Republic of Congo. Overall, the analysis finds a significant wealth-quality gradient, with a standard deviation increase in wealth being associated with an increase of 4 percentage points in protocol compliance. This increase in compliance represents 8 percent of the average quality of care received by women in the lowest wealth quintile. Over half of the wealth-quality gradient is driven by lower facility quality in poorer areas. However, the analysis also finds statistically significant within-village and even within-facility wealth-quality relationships. Within villages, wealth-quality gradients are primarily driven by wealthier women seeking care at higher-quality even if more distant facilities. Within the same facilities, poorer women tend to receive worse care, but on average they also pay less for the same quality of care compared with wealthier women. The price gap increases in the local ratio of wealthy to poor households, suggesting that providers do not charge different prices only for redistributive reasons.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gshapira@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
countries. Subsidy design factors (red dots) are in general around or above 1. These indicators suggest that policy makers should mainly focus on increasing access to services, although enhancing subsidy design could also be beneficial.

**Figure 8. Factors driving the targeting performance of subsidies (Ω) across countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Of access factors, _SA = SAP/SAH_ is the share of households located in a service area for the poor relative to the population as a whole; _C = CP|SA/CH|SA_ is the relative share of households that are connected to the service conditional on potential access (being located in a service area). Of subsidy design factors, _T = TP|C/TH|C_ is the relative proportion of households with a subsidy conditional on usage; _R = RP|T/RH|T_ is the relative rate of subsidization. Factor values below 1 indicate that the factor lowers targeting performance, whereas factor values above 1 indicate that the factor boosts targeting performance. BGD = Bangladesh; ETH = Ethiopia; JAM = Jamaica; MLI = Mali; NER = Niger; NGA = Nigeria; UGA = Uganda; PAN = Panama; SLV = El Salvador; VNM = Vietnam.

Various factors influence the degree to which targeting performance is inadequate across countries. In table 3 we explore relevant access and subsidy design factors in more detail.

Four African countries—Ethiopia, Mali, Niger, and Uganda—show an estimated value of Ω lower than 0.1. In all four the main factors that decrease the efficiency with which subsidies target poor households relative to all households include (i) poor households’ lower probability of being located in a service area and (ii) their lower probability of being connected to a water network, conditional on being located in a service area. Additionally, in Ethiopia and Niger, the relatively low water quantities consumed by the poor significantly decrease targeting performance.

Nigeria shows an Ω above 0.5, revealing a significant problem, although it is less acute than in Ethiopia, Mali, Niger, and Uganda. _SA, C,_ and _Q_ are closer to 1, and _T_ = 1 and _R_ = 1.03. This is consistent with the evidence in the previous subsection, which shows that Nigeria’s distributional incidence of subsidies by decile is regressive, although to a less pronounced degree than in the other four African countries.

17
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 17_ <!-- Start of picture text --> Papua New Guinea − Food Price Index (LCU, 2018 = 1) 2017−01−01 / 2022−07−01<br>1.50<br>1.45<br>1.40<br>1.35<br>1.30<br>1.25<br>1.20<br>1.15<br>1.10<br>1.05<br>1.00<br>Price Inflation (Year on year, %) 16.01<br>30 30<br>20 20<br>10 10<br> 0 0<br>−10 −10<br>−20 −20<br>Trust Level 8.5<br>10 10<br> 8 8<br> 6 6<br> 4 4<br> 2 2<br>2017 2018 2019 2020 2021 2022<br>Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul<br>2017 2017 2018 2018 2019 2019 2020 2020 2021 2021 2022 2022<br><!-- End of picture text --> Figure 1. Estimated food price dynamics in PNG, 2017-2022.

_Note:_ Food basket price in local currency, average across markets, January 2018 = 1. The candle-chart on top shows the estimated Open, High, Low and Close prices for the month. Red candles indicate months where end-of-month prices are lower than start-of-month prices. The size of the candle is an indicator of intra-month price variation. Along with estimates of prices, the shows the 12-month moving averages as a dotted line, as well as Bollinger Bands in the grey shaded area, which can help assess whether prices are high or low, and whether prices increases are sharp or smooth, on a relative basis. The middle chart shows the 12-month percentage change rate in prices, as a measure of inflation. The trust score at the bottom is a metric that factors in both data availability and accuracy of imputations to express confidence in the estimates on a scale of 1-10. _Source:_ Figure prepared by the authors for this paper.
but in sectors such as roads and water it plays a very small role in total investment—something unlikely to change in the near term.

# **Exception of specific public projects**

Another option is to exclude from fiscal targets certain investments undertaken by the public sector. A recent proposal would exclude projects financed by multilateral institutions, on the grounds that such projects are more likely than others to be carefully screened and designed. However, this idea has not garnered much support, among other reasons because the fungibility of money means that the marginal financing from multilateral institutions would not necessarily support the intended projects. Furthermore, total multilateral flows to Latin America are at present quite small (less than 1 percent of GDP), so they are unlikely to make a big difference.

A second proposal, developed and refined by the IMF (2004b), is to exclude from fiscal targets investments made by public enterprises deemed to be commercially run.<sup>23</sup> In principle, the proposal is potentially important for Latin American countries, since public enterprises are typically included in the public sector aggregates monitored under fiscal programs. In most other industrial and developing regions, this is not the case.

In practice, this approach poses several problems. First, appropriate criteria to identify commercially‐oriented public enterprises are difficult to establish. Second, enterprises that meet likely criteria are the exception rather than the rule in Latin America,<sup>24</sup> and this naturally detracts from the practical relevance of this approach for public investment.

Another difficulty is that excluding commercially‐run public enterprises from targets may further restrict investment elsewhere in the public sector if those 23 In fact, this proposal is not new: back in 2002, the IMF had already agreed to exclude the investments of Brazil’s public oil company PETROBRAS from the country’s fiscal performance targets. However, the authorities apparently never took advantage of the agreement, see Afonso (2005). This exceptional treatment was predicated on the basis of the company’s profitability, its corporate governance, and the fact that some of its shares were publicly traded.

24 In fact, very few were found by the recent pilot country studies summarized in IMF (2005) 17
# **1 Introduction**

During 2020 and 2021, an unexpected shock dramatically affected the lives and jobs of everyone in the world: the COVID-19 pandemic. Latin America was not an exception: the pandemic produced a staggering death toll and transformed most aspects of the daily lives of Latin Americans, including their jobs and incomes.

The COVID-19 pandemic reached the region in March 2020. In an attempt to contain the spread of the disease, all the governments imposed national lockdowns, school closures, travel restrictions, and social-distancing measures. These measures contributed to saving lives but at the same time they inevitably brought negative consequences in the labor market. The economies of the region experienced an unprecedented increase in job losses, unemployment and income reduction.

Not surprisingly, the impact of the crisis was highly asymmetric. In particular, the magnitude of the employment and income loss was mediated by some job characteristics; mainly the ability to work from home (WFH), and access to labor benefits that define a job as formal or informal. Workers who could continue working from home and/or had contracts that protected them from shocks were more able to cope with the crisis (e.g. De la Vega and Gasparini 2021).

One of the asymmetries that were early noticed is that of gender: women were hit harder by the crisis than men. In particular, job losses were significantly larger among women. At least two factors could account for this gap. The first one is occupational segregation: if women are employed in sectors more affected by the pandemic and work in occupations that are more difficult to perform from home, the impact would be understandably harsher.

A second factor that could contribute to explain the gender asymmetry is the presence of children at home and the associated childcare activities. Given the long school closures, someone should stay at home to look after the children and help them with the school chores. For different reasons, including deeply rooted cultural factors, women are more likely than men to play this role, and then suffer the consequences in the labor market. In that scenario, only mothers with the ability to work from home might be able to reconcile their labor market activities with their family responsibilities.

We study these issues with the help of one of the most ambitious data sets collected immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). These surveys were carried out in three waves between May and August 2020 in over 100 countries around the globe, including 13 in Latin America. Variables in the HFPS were harmonized by the World Bank, which helped foster a growing literature (Ballon et al. 2021; Cucagna and Romero 2021; Khamis et al. 2021; Kugler et al. 2021; Mejia-Mantilla et al. 2021).

We contribute to this initiative by codifying the occupation variables in the HFPS. These variables were not previously codified because the surveys include only open questions for occupations and hence the codification becomes very arduous. In this paper 2
# **_Brazil 1872_**

|Occupational income<br>(in milreisper annum)|Number of people in<br>occupation|Average annual income<br>relative to mean|
|---|---|---|
|72|223|0.23|
|100|1065836|0.32|
|108|1586|0.35|
|109<br>|15<br>|0.35<br>|
|118|64263|0.38|
|120|62662|0.38|
|126|140|0.40|
|132|15|0.42|
|144|14261|0.46|
|155|45229|0.50|
|157|6736|0.50|
|161|239|0.52|
|163|426|0.52|
|175|677987|0.56|
|177|411664|0.57|
|178|86|0.57|
|179|874|0.57|
|180|292066|0.58|
|191|150|0.61|
|199|261|0.64|
|206|1466|0.66|
|207|16160|0.66|
|208|22|0.67|
|213|109|0.68|
|214|7|0.69|
|215|57619|0.69|
|218|60|0.70|
|229|142|0.73|
|232|272965|0.74|
|233|82|0.75|
|236|67294|0.76|
|237|182|0.76|
|240|6717|0.77|
|245|2872|0.79|
|247|962|0.79|
|250|18778|0.80|
|251|81|0.81|
|255|31|0.82|
|262|120545|0.84|
|266|623196|0.85|
|269|6088|0.86|
|270|64280|0.87|
|271|1925|0.87|
|272|2|0.87|
|282|24835|0.90|

36