rafmacalaba/datause-encoder-data huggingface dataset:
number of these studies find conflicting results regarding the growth impact of different types of sectoral spending. For example, Easterly and Rebelo (1993) show that public investments in transport and communication in developing countries is positively correlated with growth, with a very high coefficient. On the other hand, using data from 43 developing countries over 20 years Devarajan, Swaroop, and Zou (1996) find that capital spending-in particular, public investments in transport and communication-has a negative correlation with per-capita real GDP growth.

In addition to the work on the relationship between public spending and economic growth, many researchers have examined the link between sectoral public spending (mostly in the health and education sectors) and outcomes in those sectors. For example, Harbison and Hanushek (1992) examined 12 studies on developing countries that look at the association between public education spending and educational outcomes. Six of these studies report a statistically significant positive relationship between the two; others found no evidence of any measurable impact of spending on outcomes. Elley (1992), Hanushek (1995), Mingat and Tan (1992, 1998) also find that that there is little if any relationship between public education spending and educational outcomes. Using cross-section data-1994 data for 50 developing and transition countries-Gupta, Verhoeven, and Tiongson (1999) find that primary health care spending is positively associated with child and infant mortality rates. In an earlier study, Bidani and Ravallion (1997) looked at 35 developing countries in 1990 and found a positive effect of public health spending on outcomes such as life expectancy and infant mortality rates. Filmer and Pritchett (1999) provide a good survey of studies linking public spending with health outcomes. In their own work, they find that the two are very tenuously related. According to their results, doubling public spending from 3 to 6 percent of GDP would improve child mortality by only 9 to 13 percent.

What do these weak links between public spending and development outcomes indicate? Does it mean that these governments (mostly in developing countries) are spending on unproductive activities? Should they not be spending on education and health? Generally, it is difficult to draw such policy conclusions from cross-country data, as much depends upon the country specific situation. However, it is possible that these studies do not shed light on the "true" relationship between public spending and development outcomes. The link between public _3_
simply is not a measure of the extent of reform - although it has been used in this way. For example, the third report oun adjustment lending presents a table of regression results for the macroeconomic impact of adjustment policies "corrected for implementation" (World Bank, 1992: 28). This correction has been applied by 'making the adjustment variable equal to intensity by the percentage of conditions fully or substandally implemented". The study of Mosley _et al._ (1991) includes a compliance indicator as an explanatory variable, as well as the value of Bank adjustment loans and drawings from the IMF.

The second problem with the use of dummy variables is that there may be a structural break in the slope of behavioral relationships, whereas the use of the dummy allows only for changes in intercept. For example, financial market liberalization may make private investment more responsive to changes in the interest rate as administrative controls on credit allocation are relaxed - this change is a rotation, not a shift, of the investment function. This problem can be handled by using a slope dummy for the adjusting countries/period of adjustment - a solution that can be less clearly applied if a compliance indicator is being used. Slope dummies allow for changes in the nare of economic relationships rather than just imparting exogenous shifts to these functions. Such allowance is particularly important if the approach adopted is, as recommended below, to model policies rather than the presence of a program.

The third measure of adjustment, that is the value of adjustment-related borrowing, bears no relation to either the extent of intended reforms or compliance with these intentions. As Mosley _et al._ themselves indicate, lending is little related to compliance. Nor can the approach be interpreted as separating out the effects of the aid funds from those of the policies. As explained, the compliance indicator is not a good measure of policies. To measure the effects of aid monies then all aid, not just adjustment lending must be included (though, at least initially, as separate variables).

None of the existing measures of adjustment therefore seem very satisfactory: but in fact it does not matter. There is an analogy here with the literature on the macroeconomic effects of aid. Attempts to estimate the aid-growth link direcdy were misplaced - analysis should concentrate on the channels through which the link is expected to operate.<sup>5</sup> Similarly, we cannot regress macro performance direcdy on adjustment programs - we should instead trace and quantify the links from reform to changes in exogenous (policy) variables to changes in outcomes.

75
Policy Research Working Paper 7417

# **Abstract**

This paper presents trends in monetary and nonmonetary dimensions of wellbeing in Ethiopia using data from the Household Consumption and Expenditure and Welfare Monitoring surveys implemented in 2000, 2005, and 2011. The paper provides evidence on changes in overlapping deprivations using a non-index approach to multidimensional poverty. It assesses the performance of various dimensions in education, health, and living standards, taking one indicator at a time. It then examines the overlap between different dimensions of poverty and examines how this has changed over time in Ethiopia and across rural and urban areas. It highlights that although Ethiopia’s multidimensional poverty index is very high, there have been improvements in overlapping deprivations and, as a result, the number of individuals deprived in multiple dimensions has fallen.

This paper is a product of the Poverty Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at aambel@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
Columns (4) to (7) add the different technology indexes. The results indicate that firms with FTM have lower labor productivity, which is consistent with our initial descriptive statistics and other findings in the literature (Allison et al., 2023).

Column (1) shows that firms with FTM are, on average, 38.4% less productive than their male counterparts. As we control for management human capital in column (2), the gap reduces slightly, in line with the small differences in FTM education and experience. Furthermore, the gap reduces significantly as we control for capital stock and technology in our specifications, highlighting the significant capital gap between men- and women-managed firms. For instance, in column (4), the difference in labor productivity between male- and female-managed establishments is 25.8%, a reduction of over 10 percentage points compared to column (1). The results also suggest that adopting and intensively using advanced technologies linked to GBF positively correlate with firm productivity. In the case of SBF, the association between labor productivity and technology seems weaker and only significant at the extensive margin.

Table 6: OLS estimates of productivity and technology gaps

||(1)<br>Ln(VAPW)|(2)<br>Ln(VAPW)|(3)<br>Ln(VAPW)|(4)<br>Ln(VAPW)|(5)<br>Ln(VAPW)|(6)<br>Ln(VAPW)|(7)<br>Ln(VAPW)|
|---|---|---|---|---|---|---|---|
|Female Top Manager|-0.485***<br>(0.0880)|-0.458***<br>(0.0885)|-0.309***<br>(0.0968)|-0.302***<br>(0.0972)|-0.296***<br>(0.0969)|-0.281**<br>(0.129)|-0.284**<br>(0.130)|
|Log(Capital per worker)|||0.276***|0.256***|0.265***|0.258***|0.259***|
||||(0.0282)|(0.0272)|(0.0279)|(0.0413)|(0.0420)|
|Log(Employment)|||-0.0470<br>(0.0391)|-0.117***<br>(0.0402)|-0.0941**<br>(0.0410)|-0.0889<br>(0.0544)|-0.0613<br>(0.0526)|
|GBFs - Extensive margin||||0.285***<br>(0.0606)||||
|GBFs - Intensive margin|||||0.382***<br>(0.0834)|||
|SBFs - Extensive margin||||||0.146**<br>(0.0652)||
|SBFs - Intensive margin|||||||0.115<br>(0.0895)|
|Formality|2.215***|1.987***|1.726***|1.662***|1.622***|1.771***|1.791***|
||(0.156)|(0.167)|(0.182)|(0.183)|(0.183)|(0.229)|(0.228)|
|Management human capital index||0.683***|0.554**|0.292|0.365*|0.210|0.245|
|||(0.193)|(0.218)|(0.222)|(0.217)|(0.321)|(0.325)|
|Sector|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|8579|8579|6717|6717|6717|4370|4362|
|R-squared|0.342|0.346|0.488|0.495|0.494|0.500|0.495|

Note: This table presents the OLS regression results. FTM = Female Top Manager, GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. Estimates were performed using sample weights. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 7 shows the 2SLS model results using the regional share of FTM and female ownership as instrumental variables. First-stage F statistics are much higher than 10, which 17
while slumps cause firm exits. But before getting to the dynamics of firm entry and exit, the next task is to understand the characteristics of slumps and surges in the Russian economy.

Slumps and surges have two characteristics: depth and endurance. In the case of slumps, the depth is characterized by how much the economy shrinks. Similarly, to determine the endurance of a slump, the task is to determine from beginning to end, how long a slump lasted without interruption of at least one period of positive growth. With respect to the data, to ascertain the depth of slumps, one looks at period when a slump takes place and one asks how often these slumps are characterized by rates of 0, -1, -2, or -3 percent average annual growth, for example. To get a picture of how long slumps last, one records how long (how many years) each slump remained in negative territory once the slump began.

<!-- Start of picture text --> Figure 5: The average slump in Russia is deeper than in other<br>economies (1993-2009)<br><!-- End of picture text -->

**Figure 5: The average slump in Russia is deeper than in other economies (1993-2009)**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Depth<br>OECD Resource Rich Russia China<br>India Brazil Korea<br>5<br>4<br>3<br>2<br>Density Function<br>1<br>0<br><!-- End of picture text --> Source: Author’s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE) To illustrate the depth of Russian slumps and compare these to that of other economies, a kernel density estimator 15 is used. Figure 5 is a kernel density plot where the horizontal axis, from left to right, indicates progressively deeper slumps (higher negative growth rates). The vertical axis, from bottom to top, records how often a particular negative growth rate is recorded. The data lines record how often a negative growth rate is recorded for all of the slumps that took place in these economies between 1993 and 2009. The respective top of each hill marks the most common negative rate of growth registered in slumps for each economy.

This graph confirms that for Russia—because the top of the hill is to the right of all other comparator economies—the common slump is characterized by higher negative growth than that found in any of the economies to which it is compared.

> 15 Smoothing the duration of slumps data with a kernel density estimator can be more effective than using a histogram to identify features that might be obscured by the choice of histogram bins or sampling variation.

10
the 8 manufacturing sectors, slightly over 29 percent of its tariff lines. In contrast, Bolivia has a low NTB coverage rate of about 3 percent, but only 43 percent of firms reported a product innovation. Hence it is possible that trade policy has little to do with product innovation, but econometric estimations might help to clarify this potential link between innovation and trade policy by controlling for other factors that might be correlated with both sets of variables.

# **4. Is R&D Related to Product Innovation?**

To answer this question we estimated reduced-form models of product innovation, but also considered the possibility that R&D expenditures and perhaps the sales variables that were recorded in the firm surveys are measured with error. If they are, the standard direct regression model with product innovation as the dependent variable and the R&D/Sales variable (our proxy for research-cost intensity derived from the theoretical model) might be biased, possibly suffering from attenuation bias if the measurement error is random.

# 4.a. _Econometric strategy_

Due to the dichotomous nature of our variable of interest, the direct empirical model of product innovation can be written as: where P is the probability of observing a value of one for product innovation, y. Subscript i represents firms, the s’s are manufacturing sectors, and c’s are countries. The Xs are matrices of the relevant explanatory variables, measure at the three levels of aggregation (firms, sectors, and countries). The betas, alphas, and deltas are the parameters to be estimated with a Probit estimator, which assumes a standard normal distribution of the relevant parameters with respect to the latent threshold variable. ε _isc_ is, therefore, the standard white noise error. Below we report results that are robust to heteroskedasticity of regression errors clustered around the observations of each country, ε _c_ . This correction becomes particularly important for the estimation of the delta parameters associated with industry and country variables when the dependent variable is a micro unit (see Moulton 1990). Allowing for error clusters around industries or industry11
functional-data (henceforth KS test) rejects the hypothesis that the observed maturity structures of pension funds and mutual funds are generated by the same underlying distribution (Column ix).<sup>12</sup> In fact, in unreported results, when we compare the maturity structures at monthly frequency with a coarser distribution (as the one used in Figure 4), the hypothesis that the maturity structures of these two types of investors are generated by the same distribution cannot be rejected at conventional levels.

Although the maturity structures of Chilean mutual and pension funds look short term, it is necessary to compare them to that of other institutional investors that can serve as benchmarks. To do so, we analyze the maturity structure of US fixed-income mutual funds. US funds provide a good benchmark of how funds operating in a developed capital market (with a different set of investment opportunities) behave. To compare Chilean mutual and pension funds to US mutual funds, we present the maturity structures grouped within eight large bins, determined by the availability of US mutual fund data from Morningstar.

Figure 4 shows that Chilean mutual and pension fund holdings are much more tilted toward the short term than US mutual funds. Notably, this is even the case when 12 This test was proposed by Cuesta-Albertos et al. (2006), and consists on applying a standard two-sample Kolmogorov-Smirnov (KS) test to the random projections of each set of functional data; in our case the samples of maturity structures of all pension funds and mutual funds, respectively. We start by forming two groups of vectors of length _M_ corresponding to the time-average maturity structures of all individual pension and mutual funds _WD_ , _k_  (1 / _T_ )  _WD_ , _k_ , _t_ , discretized by month, with _M_ corresponding to the _t_ longest maturity observed (in months). Each of these vectors is projected on a random direction ���<sup>�</sup> , obtaining two samples of random projections (one for each type of investor) of sizes<sup>_n_</sup> 1<sup>and</sup><sup>_n_</sup> 2<sup>, the number</sup> of pension funds and mutual funds respectively. The standard two-sample Kolmogorov-Smirnov test is then applied to these samples. The process is repeated _M_ times using different random directions, and the resulting set of p-values is adjusted for false discovery rate under dependency as in Benjamini and Yekuteli (2001). The p-value reported in the table corresponds to the minimum of the adjusted p-values, which indicates the level of confidence with which at least one of the _M_ hypotheses can be rejected. An alternative statistic proposed by Cuesta-Albertos et al. (2007), based on the fraction of rejections among the _M_ hypotheses, yields similar conclusions (not reported).

14
eliminate this risk. We will nonetheless discuss the robustness of our results to the addition of Kampala.<sup>5</sup>

# **3.2. LSMS-ISA household data**

We use the Living-Standards Measurement Study - Integrated Studies on Agriculture (LSMSISA) data set for Uganda provided by the World Bank. These LSMS-ISA data sets are derived from the Uganda National Panel Survey (UNPS) which comprise 5 waves of interviews (20092010, 2010-2011, 2011-2012, 2013-2014 and 2015-2016). The UNPS collects information for a sample of households which is representative at the national, urban/rural and main regional levels (North, East, West and Central regions). The LSMS-ISA data set provides household and individual level information including household welfare measured by consumption aggregate per adult equivalent, indicators of participation and performance in the workforce within the agricultural and non-agricultural sectors.

We retain only the first 3 waves as they minimize attrition and have a similar structure. In the consecutive waves, parts of the sample were replaced by new households obtained from an updated sampling frame developed from the 2012 Uganda Population and Housing Census by Uganda Bureau of Statistics (UBOS). Additionally, a new household identification system was implemented after the third wave, making it difficult to construct a balanced panel data set across all 5 waves.<sup>6</sup> Our household data is therefore a strongly balanced panel data set comprising 2,458 households distributed across 320 enumeration areas (EA) and surveyed in the first 3 rounds of the UNPS. The data cover 106 districts out of the 111 listed in 2010 (127 districts in 2017).

# **3.3. Descriptive statistics by district**

We first compare households and individual characteristics between refugees-hosting and nonhosting districts between the first wave (2009/2010) and the last wave (2011/2012) of UNPS data collection. We do so using our main variables of interest (household welfare measured by consumption per adult equivalent, individual labor market outcomes, household nonagricultural income and output) and some control variables (socio-demographic characteristics). Tables 1 presents the comparison over socio-demographic characteristics at

> 5 The updated UNHCR annual aggregates (Figure A2; Appendix) only include Kampala totals from 2009 to 2016. 6Indeed, the household ID format has been modified for the last 2 waves, a change that complicates the matching across waves. Balancing the panel across all five waves of the longitudinal survey results in a significant drop in the number of enumeration areas/clusters from 320 to 211. The number of households also drops from 2,462 interviewed across the first 3 waves to 1,431 households interviewed across the 5 waves. Attrition is too much of a concern for robust inference.

11
on the Correspondence Between Developing Countries' RCA and Heckscher-Ohlin Indices Bureau of Economic Research Labor Intensive Product Group and Other Manufactures Table I

# Summary Statistics for the National

|||%of individual<br>pro-||Individual<br>pro|mucts with|
|---|---|---|---|---|---|
|||duct manufactures with|Total valueofexports to|RCAsexceedin|g unity|
|Product/Country<br>Group|Year|RCAs exceedingunity1/|developed countries<br>(Smill)|<br>Value (Sill)|<br>**S**oftotal|
|NBERlabor Intensive Manufactures||||||
|Alldeveloping countries|1965|30.2|2,945.7|2,499.0|64.8|
||1975|45.3|20,864.7|17,592.3|84.3|
||1985|51.3|98,785.0|83,186.0|84.2|
|Asian exporters<br>of manufactures|1965|29.4|956.9|869.6|90.9|
||1975|39.5|11,043.1|9,250.6|83.S|
||1985|49.5|55,968.4|49,777.4|88.9|
|Other South-Asian counlries|1965|19.7|202.4|191.8|94.8|
||1975|33.6|1,33t.0|1,179.7|a8.7|
||1985|29.4|9,267.6|7,734.8|83.5|
|All Other ManufacturedGoods||||||
|Alldeveloping countries<br>2/|1965|6.8|906.5|482.0|53.2|
||1975|9.1|4,641.8|1,930.1|41.6|
||1985|14.8|26,311.5|d,212.2|31.2|
|Asian Exporters<br>of mnuf,<br>*ures 3/|1965|1.1|41.2|1.0|2.4|
||1975|2.3|860.7|241.9|28.1|
||1985|6.8|7,033.2|1,919.4|27.2|
|Other South-Asian countries<br>4/|1965|3.4|19.1|3.4|17.8|
||1975|2.3|97.1|8.6|8.9|
||1985|0.0|848.6|0.0|0.0|

- I/ The NBER labor intensive group is composed of 119 distinct three, four and five-digit SITC products. See Appendix Table I for a lisTing of these Items with their corresponding labor intensity and revealed comparative advantage indices. The "all other" (capital intensive) group Is composed of 88 three, four and five-digit SITC products.

- 2/ Outside the NBER labor intensive product group these developing countries had a 1985 RCA above one in the fol lowing SITC groups (SITC no. in parenthesis): inorganic chemicals (513); coal and petroleum based chemicals (521); dyes anC tanning products (532); explosives (571); unhardened rubber products (69292); cement (6612); nonindustrial diamonds (6672); pig iron (671); iron and steel tubes (6783); iron and steei anchors W6NW4; television receivers (7241); ships and boats (735); and base metal office supplies (8951).

- 3/ Outside the NBER labor intensive product group these developing countries had a 1985 RCA above one in the fol lowing SiTC groups (SITC no. in parentheses): rubber tyres and tubes (6291); iron and steel anchors (6984*; nonelectric domestic appliances (7194ki television receivers (7241); ships and boats (735); and base metal office supplies (8951).

- 4/ Coal and petroleum products (SITC 521); medicinal products (541); unhardened rubber products (6293); cameras and projectors (8615); and developed cinema f its (863) were the non-NBER products in which these countries developed RCAs greater than one.
A New Algorithm to Estimate Inequality Measures in Cross-Survey Imputation: An Attempt to Correct the Underestimation of Extreme Values Gianni Betti<sup>*</sup> Vasco Molini<sup>†</sup> Lorenzo Mori<sup>*‡</sup>

**_Keywords:_** Inequality indicators, Bias reduction, Survey-to-Survey Imputation, Moroccan HBS, Moroccan LFS **_JEL Classification:_** I32, C13 and E24

> *University of Siena. Department of economics and statistics. Email: gianni.betti@unisi.it

> †50x2030 Initiative. Email: vmolini@worldbank.org

> ‡University of Bologna. Department of Statistics P. Fortunati. Email: lorenzo.mori7@unibo.it