rafmacalaba/datause-encoder-data huggingface dataset:
63

# Mauritania 1 PFP - Projections and Actuals

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<!-- Start of picture text --> Ne GO DPG<br>/e<br>_-/5 Yew<br><!-- End of picture text -->
# **Table 5.1. Policy Response to the Pre-Crisis Credit Boom**

|||**Panel A. Polic**|**y measures to**|**curb cred**|**it growth**||||
|---|---|---|---|---|---|---|---|---|
|ALB<br>ARM<br>BIH|BUL|HRV<br>CZE<br>EST<br>GEO|HUN<br>LTV|LIT<br>M|DE<br>MDO|POL|ROM RUS<br>SRB|SVK<br>SVN<br>TUR<br>UKR|
|Monetary<br>measures|||||||||
|tightening of<br>reserve<br>X|X|X<br>X|X|X|X|X|X<br>X|X|
|requirements|||||||||
|tightening of<br>interest rates||X|X|||X|X||
|Fiscal measures<br>(fiscal|||||||||
|tightening or<br>reductions in<br>fiscal<br>incentives)|X|X<br>X|||||X||
|Tightening of<br>regulations and<br>supervision<br>X|X|X<br>X|X|X|X|X|X<br>X|X<br>X|
|Market|||||||||
|development<br>measures (e.g.<br>introduction of<br>credit bureau)|X|X||X||X|X||
|Moral suasion|||||||||
|and<br>communication<br>tools|X|X<br>X|X|X||X||X|
|Administrative<br>measures (e.g.<br>credit controls)|X|X||||X||X|
3 complementary, but combining approaches is only valuable if each supplies useful information.

The objective of this paper is therefore to provide an initial analysis of firms’ perceptions of constraints on the business climate, as reported across a number of the World Bank’s Enterprise Surveys, with special emphasis on Africa.<sup>1</sup> How does the reported severity of these constraints compare across countries and across different types of constraints, such as macroeconomic management and governance, regulation, factor markets and infrastructure? Does the probability that a firm rates a constraint as serious appear to vary systematically by type of firm as well as across countries? Do firms’ ratings conform to patterns expected from other survey data, where available? Do the crosscountry patterns confirm to other cross-country evidence?

In this paper, we look at perceptions of firms regarding the investment climate across twenty-six African countries where similar questions have been included—Burundi, Democratic Republic of Congo, Ethiopia, Malawi, Guinea-Bissau, Eritrea, Uganda, Gambia, Madagascar, Mozambique, Tanzania, Mali, Burkina Faso, Zambia, Benin, Kenya, Mauritania, Senegal, Lesotho, Angola, Cape Verde, Swaziland, Namibia, South Africa, Mauritius, and Botswana—in surveys encompassing almost five thousand firms. The Enterprise Surveys were carried out between 2002 and 2006 by the Africa Private Sector Group of the World Bank.

In Section II we review some existing literature on firm perceptions, including studies bearing on the choice of whether to ask firms to rank or to rate constraints. Section III provides a picture of how views on constraints differ across countries, and also considers whether cross-country perception patterns are reasonably consistent with other data. Section IV shifts towards firm-level analysis--we offer a simple conceptual model of firms’ responses and test this using Probit regressions, to see whether factors such as size,

> 1 More information on the World Bank’s Enterprise Surveys can be found at <u>www.enterprisesurveys.org.</u> These surveys are also sometimes referred to as the World Bank’s Investment Climate Surveys or ICA data.
IPCC. 2014. _Fifth Assessment Report_ (AR5). Geneva, Switzerland.

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Kunreuther, Howard, Meyer, Robert, and Michel‐Kerjan, Erwann. 2014. “Strategies For Better Protection Against Catastrophic Risks,” In _Behavioral Foundations of Policy_ , edited by Eldar Shafir. Princeton, NJ: Princeton University Press.

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Lawrence, Anne T. 2001. “The Emergence of Conservation Banking in Southern California.” In _Ahead of the Curve: Cases of Innovation in Environmental Management_ , edited by Ken Green, Peter Groenewegen, and Peter S. Hofman, 93–105. Dordrect: Kluwer.

Linnerooth‐Bayer, Joanne and Hochrainer‐Stigler, Stefan. 2015. “Financial Instruments For Disaster Risk Management And Climate Change Adaptation.” _Climatic Change_ , forthcoming.

Mechler, Reinhard. 2005. _Cost‐Benefit Analysis of Natural Disaster Risk Management in Developing Countries._ Eschborn: Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ).

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29
evidence. The paper ends with a summary of the main ndings and their implications for the broader literature on intergenerational mobility.

# (2) Related Literature

The contributions of this paper are at the intersection of two major areas of economic research: intergenerational mobility and the eects of school expansion. There is a vast literature on intergenerational mobility in the context of developed countries, focusing primarily on intergenerational persistence in permanent income. See, for example, Solon (1992), Mazumder (2005), Chetty et al. (2014), Black et al. (2020), Carneiro et al. (2021), Acciari et al. (2022), Adermon et al. (2021), Abramitzky et al. (2021), Card et al. (2022), and Berman (2022). For excellent surveys, see Solon (1999), Black and Devereux (2011), Heckman and Mosso (2014), Mogstad and Torsvik (2021), and Cholli and Durlauf (2022). The literature on developing countries is limited, with most of the studies analyzing intergenerational persistence in educational attainment because of the paucity of long-run panel data required for a credible analysis of permanent income. Recent contributions in the context of developing countries include Azam and Bhatt (2015), Agüero and Ramachandran (2020), Alesina et al. (2021), Emran and Shilpi (2015), Neidhofer et al. (2018), and Asher et al. (2023).<sup>21</sup> Excellent surveys of the recent literature on developing countries are provided by Torche (2019), and Iversen et al. (2019).

A second strand of literature that our analysis contributes to is the eects of access to schools, especially public schools, on children's outcomes, including the intergenerational effects (for surveys, see Orazem and King (2008), Hanushek (2002), and Filmer (2007)).<sup>22</sup> Neilson and Zimmerman (2014) report that elementary and middle school construction projects in a poor urban school district in the United States raised test scores, enrollment, and home prices. Currie and Moretti (2003) nd that availability of college in a county in the United States improved children's birth outcomes by increasing maternal education. Khanna (2023) analyzes the general equilibrium eects of a large scale school expansion program in India,

> 21 Among unpublished papers, see Yu et al. (2020), Emran et al. (2021).

> 22Intergenerational eects refer to the eects of policies on the second generation: does improving education of parents by a policy improve the outcomes of their children? For an excellent discussion on the distinction between intergenerational eects and intergenerational mobility, and the related literature, see Bjorklund and Jantti (2020).

7
category. Withincommercial debt,acuriousphenomenon is observed: CSFR and Romania both have more ST than long term obligations. Poland has a very low share of commercial debt altogether, of which a relatively tiny share has been in the form of ST obligations, even as far <!-- Start of picture text --> Figure 13: 1992 (Prolected)<br>60%.~ ~ ~ ~ ~<br>50%.~~0%UII<br>40%F 80%.~~~~~~~~~<br>1 0% #20%<br>10% wr<br>*Su~~rt1,rmCoEmwc,eI Co,r,,u.cIaI ~ I<br><!-- End of picture text -->

# Proportion of Merebandise Imports Financed by ST Commercial Debt

All countries except Poland and Romania appeared to rely on short term credits in 1989. We assume that an important proportion of these represented trade lines. It may be argued that these figures reflect inhenited condi-

**Flaure 15: 1900**

<!-- Start of picture text --> 100%.<br>~~0%I IIIli<br>a0%I It .. .... I<br>60%- 1 9UM 14J Fm::<br>.- IE.W.iTm.wcwoij<br>0*<br><!-- End of picture text --> <!-- Start of picture text --> Figure le: 1991 (Estlmete)<br><!-- End of picture text --> back as 1986.4 Also, Poland's entire ST obligations are owed to commercial creditors. Commercial banks have thelargestexposure of allcreditorcategoriesin Bulgaria. A majorpartof commercial exposure is in STclaims, but overall these form arelatively small shareof the total debt. Romania's external debt is very low for well-lown reasons. The share of ST commercial obligations is expectedtodeclinefrom99%in 1989toabout9O%in 1992, as shown in Figure 13.

<!-- Start of picture text --> 1 00%<br>o|<br>- X-<br>c<br><!-- End of picture text -->

Finally, the following chart provides a rough indication of the region's reliance on trade finance, by comparing hard currency merchandise imports to short term commercial borrowing in the corresponding period.

<!-- Start of picture text --> Figure 14: 1989 |<br>40Xi l -<br>1 0 .2<br>,~ L S<br>Ig 40<br>8<br><!-- End of picture text -->

Flour, IT: 1992 **(Prolected)** <!-- Start of picture text --> 1 00%0<br>20% OX<br>I%,.JjJ<br>0%l I 3 =~~~~~~~0%<br>0 X rf<br><!-- End of picture text --> tions, and are based on borrowing and lending decisions made according to outdated assumptions, such as the Soviet umbrella or the presumption that all loans carried a 4. Perhaps Poland was not perceived by banks to be ftilly under the Soviet umbrella since the early 1980s.

7
> This framework characterizes long-run portfolio equilibrium. However, costs and frictions

> to instantamous portfolio reallocation **-** arising from sources such as investors' imperfect

> information, congestion effects or investment adjustment costs **-** may drive a wedge between short-run and long-run portfolio equilibrium.<sup>3</sup> Further, these frictions, and hence portfolio

> dynamics, may differ across countries. The paper's empirical analysis focuses on the estimation of the long-run portfolio equilibrium condition, while allowing for unrestricted cross-country heterogeneity in the short-run dynamics.

The paper extends previous literature along three dimensions. First, it builds on a recent strand of the literature that adopts an international portfolio equilibrium approach to the analysis of the current account (Ventura 2002).4 Our paper shares with this literature the emphasis on risk and adjustment costs as essential ingredients for explaining the observed patterns of international asset portfolios. However, that literature has focused primarily on the impact of wealth changes on capital flows (what has been termed the _portfolio growth_ effect). In contrast, the present paper also brings into focus the determinants of international investors' portfolio shares, for given levels of their wealth (the _portfolio rebalancing effect)._

> Second, the paper implements empirically the portfolio diversification model using a

> comprehensive data set on foreign assets and liabilities that covers a large number of developing and industrial countries and spans the years from **_1965_** to **1997.** Importantly, the data encompass not only industrial economies, which have been the focus of previous empirical literature, but also a large number of emerging markets and developing economies. Using this information, we can assess the empirical robustness of the portfolio model across different country groups and

> alternative measures of risk and return.

Third, the paper follows a novel econometric approach to the estimation of the long-run portfolio equilibrium condition in a heterogeneous dynamic panel setting, using the Pooled-Mean Group estimator recently developed **by** Pesaran, Shin, and Smith **(1999).** This approach combines the efficiency gains from restricting long-run parameters to be the same across

> See Bacchetta and van Wincoop **(1998)** for a theoretical discussion of portfolio dynamics arising from these and other sources.

> _4_ Along similar lines, the paper's portfolio equilibrium approach to capital flows also brings it close to a strand of the literature on 'current account sustainability' that underscores the role of international investors' portfolio choices in shaping the sustainable current account (see Mann 2002 for references). **By** shedding light on the factors that shape

> international portfolio diversification and its time path, the analysis in this paper could be readily adapted to identify

> current-account trajectories consistent with portfolio equilibrium.

**3**
# _5.3 Propensity Score Matching and Treatments_

We now turn our attention to the findings from our nearest neighbor propensity score matching models. Table 8 presents the average effect of each treatment on frequency of Mirt stove use per week. These effects are compared with the counterfactual of one of the other treatments. Also included in each cell are the number of actual matched treatment and control households, the two-tailed p-value based on the estimated t-statistic and whether the average treatment effect is significant at the 1%, 5% and 10% levels. Additional information on the number of blocks, region of common support, absolute propensity score difference between treated and controls and average frequency of stove use for matched treated and control households are available from the authors.

Table 8 Average Treatment Effects of Row Treatment vis-à-vis Column Control

||Treatment1|Treatment 2|Treatment 3|Treatment 4|Treatment 5|
|---|---|---|---|---|---|
|Treatment 2|**0.01 **(0.99)<br>[236/135]|||||
|Treatment 3|**0.11**(0.77)<br>[240/165]|**0.23**(0.30)<br>[240/179]||||
|Treatment 4|**-0.86**(0.03)<sup>**</sup><br>[200/162]|**-0.53**(0.04)<sup>**</sup><br>[200/181]|**-0.12**(0.62)<br>[200/201]|||
|Treatment 5|**0.57**(0.03)<sup>**</sup><br>[240/130]|**0.59**(0.01)<sup>***</sup><br>[240/370]|**0.28**(0.28)<br>[240/165]|**0.89**(0.00)<sup>***</sup><br>[240/146]||
|Treatment 6|**0.43**(0.33)<br>[236/140]|**0.53**(0.02)<sup>**</sup><br>[236/196]|**0.01 **(0.99)<br>[236/202]|**0.17**(0.51)<br>[236/126]|**0.50**(0.07)<sup>*</sup><br>[236/185]|

ATT in bold. Positive numbers indicate the row treatment has a larger effect on frequency of use than the column treatment. P values in parentheses. Number of matched treated/number of control in square brackets. ***, **, * indicate significant at the 1%, 5% and 10% levels Results in Table 8 are consistent with results already discussed, but utilize explicitly matched samples. We find that those who receive stoves under treatment 4 use their Mirt stoves less than other treatments, except for treatments 3 and 6, which are statistically insignificant. The treatment 4 effect is statistically significant at least at the 5% level for treatments 1, 2 and 5. Marginal effects of treatments 1 and 5 versus treatment 4 are particularly large at 0.86 and 0.89. These effects are about 1/3 of the pooled sample mean.

The average treatment effect of treatment 5 is also large vis-à-vis treatments 1 and 2 at 0.57 and 0.59 more times per week (about 25% of the sample mean). In the matched sample we find that households receiving stoves under treatment 6, which includes the network treatment along with free distribution, on average use the Mirt stove 0.50 times (22%) more per week than

**22 |** P a g e
<!-- Start of picture text --> 25 180 + 1.8<br>@Goods.and semices<br>20 mGoods 160<br>15 BServices 140 an 14<br>| ~ /<br>10 ~ 1.0<br>| 100 pa<br>it] | | Se| | | 0 aa Volume Share, index 06<br>- =———— Value Share, percent (rhs)<br>— AQ 02<br>1990 1993 1996 1999 2002 2005 2008 1980 1985 1990 1995 2000 2005<br>Source: WEO Source: Comtrade, WEO<br><!-- End of picture text -->

Share of Imports of Services from Ireland

||2004|2005|2006|2007|2008|
|---|---|---|---|---|---|
|UK|9.2|9.3|92|10.7|11.1|
|US|2.2|1.6|2.0|22|1.8|
|France|40|3.8|3.4|49|5.0|
|Germany|3.0|3.6|3.3|3.5|3.5|
|Italy|3.2|3.5|46|5.0|5.1|
|Spain|2.3|2.5|2.6|29|3.0|

Source: Eurostat
# **3. Improvements in Living Standards between 1995 and 2010**

The poverty reduction was accompanied by an improvement of living standards across all segments of the population. Figure 2 shows changes in real per capita consumption and consumption shares across 10 components between 1995 and 2010 for the bottom 40 percent, middle 40 percent (40<sup>th</sup> to 80<sup>th</sup> percentile) and top 20 percent of Nepal’s consumption distribution in 1995 and 2010. The top panel shows that in real terms, per capita consumption of the bottom 40 percent registered a cumulative growth of 62 percent, which translates to an average growth of 4.1 percent per year between the two survey rounds. The middle 40 percent saw a slightly higher cumulative growth of 66 percent than the top 20 percent group (50 percent).

With such growth in living standards over a 15-year period, one might expect notable changes in the way households allocate their resources among competing needs and wants. However, the share of food consumption remained almost unchanged between 1995 and 2010; households in the bottom 40 percent on average spent 70 percent of the total budget on food items in both years, and those in the middle 40 percent spent 61 percent in 1995 and 57 percent in 2010. The only exception was educational expenses. All three groups more than doubled their shares on educational expenses between these two years: 4 percent to 10 percent for the top 20 percent, 3 to 7 percent for the middle 40 percent and 1 to 5 percent for the bottom 40 percent. This increase is offset by a modest decline in the shares of general nonfood items and services.<sup>14</sup> F **igure 2: Real per capita consumption and consumption shares in 1995 and 2010**

||**NLSS-I**<br>**Real per capita consu**|**mption**|**(2010/11**|**NL**<br>**National Average Pric**|**SS-III**<br>**e)**|
|---|---|---|---|---|---|
|**Bottom 40%**|11,111|||12,250<br>17,972||
|**Middle 40%**<br>|13,589<br>22,537<br>|||21,049<br>37,379||
|**Top 20%**|14,015<br>10,757<br>22,070<br>58,8|80||15,563<br>32,223|15,583<br>8,561<br>88,104|
||0K<br>20K<br>40K<br>60K|80K|100K|0K<br>20K<br>40K|60K<br>80K<br>100K|
||**NLSS-I**<br><br>**Consu**|**mption**|**Shares (**|**NL**<br><br>**% of total)**|**SS-III**<br><br>|
|**Bottom 40%**|69%|17%|8%|69%|13%<br>7%|
|**Middle 40%**|61%|23%|8%|57%|11%<br>15%<br>7%|
|**Top 20%**|24%<br>43%|16%|9%|1<br>39%|10%<br>17%<br>7%<br>9%|
||0%<br>20%<br>40%<br>60%|80%|100%|0%<br>20%<br>40%|60%<br>80%<br>100%|
|**Food**|**Non-food items**<br>**Housing**|**El**|**ectricity**|**Garbage**||
|Source: World Ban<br>**Tobacco**|k Staff Calculation based on Nepal Livi<br>**Telephone**<br>**Fuel**|ng Standar<br>**Du**|d Surveys<br>**rables**|1995/96 and 2010/11<br>**Education**||

> 14 This includes items such as clothing, footwear and personal care items. See World Bank (2013) for more details.

8
# 1. **Introduction**

Recent work on efficiency wage models has produced an internally consistent macro-theory of involuntary unemployment (underemployment)' whose basic assumptions and implications have largely been corroborated by empirical micro-studies.<sup>2</sup> Although the microeconometric work was motivated by macroeconomic theory, it has never been fully integrated into a macroeconomic framework.<sup>3</sup> In this paper we offer one version of such an integrated approach. More precisely, we first develop an efficiency wage model with labor turnover (Phelps 1968, Stiglitz 1974, Salop 1979, Hoon and Phelps 1992) and show that the worker's decision problem gives rise to a quit-rate function. We then use microeconomic data to estimate this quit-rate function and to test the specification suggested by economic theory. Finally, microeconomic evidence and macroeconomic model are combined to evaluate the quantitative effects of changes in economic policy and other macroeconomic shocks on the wage rate, the turnover rate, and employment in the long-run (steady state analysis).

The efficiency wage model with labor turnover we employ is in principle applicable to any type of movement of labor across sectors in any country (Bulow and Summers 1986). However, the original literature on this type of efficiency wage model has mainly focused attention on unemployment in developed countries (Phelps 1968, Salop 1979, Phelps and Hoon 1992). We, on the other hand, test and calibrate our model using panel data on the movement of Mexican workers between the formal salaried and the informal self-employed sector. Our choice of the data set was motivated by the following two considerations.

'See Katz (1986) and Woodford (1994) for surveys.

2See Katz (1986) and Layard, Nickell, and Jackman (1991) for surveys.

3The quantitative papers by Danthine and Donaldson (1990,1995) and Kimball (1994) on efficiency wage models with shirking (Shapiro and Stiglitz 1984) consider microeconomic evidence when calibrating the macroeconomic model, but do not incorporate a microeconomic estimation equation into the macroeconomic model as we do. In this sense, we feel that we have come closer to a full integration of the two fields of labor economics and macroeconomics. See also Blanchard and Katz (1997) for a statement in favor of such an integrated approach.