rafmacalaba/datause-encoder-data huggingface dataset:
19

**Figure 7. France: Industrial Employment**<sup>**and Inflation.**</sup> **Source: IMFIFS.**

<!-- Start of picture text --> 0 04<br>E ~~~~~~~~~~~~~~~~~~~~~~~~o 0 g<br>0~~~~~~~~~~~~~~~~~~~~~~~~00<br>8, 00 00 4 0 U 0W. 000<br>eV 92 0. as 0 al 7 go<br><!-- End of picture text --> Again, figure 7 shows that the behavior of industrial<sup>employment and inflation fit the classical</sup> recessionary scenario.

# **IV. Theoretical Aspects of Exchange Rate Based Stabilizations**

Two questions arise after comparing the behavior of real variables<sup>during stabilization both</sup> among European countries and with Latin America. The<sup>first, is whether the expansion observed in</sup> Denmark after 1982 was of the same nature as those observed<sup>in Latin America and Israel. In other</sup> words, was it mostly speculative in nature, based on lack<sup>of credibility about the final success of</sup> disinflation?. If not, what explains it?. The second question<sup>is how to understand what happened in</sup> France and Ireland as opposed to what happened in Denmark.<sup>Why were the former contractionary while</sup> the latter was expansionary?. As we will see, the comparison of Denmark<sup>with France and Ireland</sup> provides important insights regarding the role that credibility<sup>plays in ERBS in low inflation countries.</sup>
For the probit model, the MNLM, and the Heckman selection model we use a crosssectional sample, where we include data from the six countries (depending on the regression), and take the last rounds available as of fall 2013.

For the panel data analysis we have data for Nigeria for four survey rounds: post-planting and post-harvest data for the years 2010/11 and 2012/13. Due to a limited questionnaire for the post-harvest interviews, we use the data of the post-planting round only.<sup>5</sup> In the case of Uganda, we have data for three survey rounds: 2009/10, 2010/11, and 2011/12.

# **3.2.1 Probit Model**

To answer the first question on the determinants of enterprise operation, we use a discrete choice model for our analysis, and estimate a probit model.

Formally we estimate, where the dependent variable _Yi_ is a binary variable equal to one if a household operates a non-farm enterprise, and zero if not. **v**<sup>_′_</sup> _i_ is a vector of individual characteristics including a constant, and comprises the variables gender, age, the marital status, and education (proxied with the ability to read & write) of the household head. **w**<sup>_′_</sup> _i_ is a vector of household characteristics including the number of adult household members, annual net household income, the number of rooms in the dwelling, and a binary variable if a household member has taken out credit over the past 12 months, indicating the possibility of accessing financial support. _mathbfx_<sup>_′_</sup> _i_ records whether or not the household has experienced food shortage or a shock over the last 12 months. Finally **z**<sup>_′_</sup> _i_ is a set of location variables (geo-variables), including the household’s distance to the next population center and annual precipitation.<sup>6</sup>

# **3.2.2 Multinominal Logit Model**

We use a multinominal logit model (MNLM) to estimate determinants of the type of business activity that households operate.

Formally we estimate, where _b_ is the base category or comparison group. The log odds of an outcome compared

> 5 The shock variables are taken from the post-harvest questionnaires due to data availability.

> 6 See Appendix B.2 for summary statistics and a detailed definition of the variables used in the probit model.

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<!-- Start of picture text --> Policy, Research, and External Affairs<br>Women In Development<br>WPS 403<br><!-- End of picture text --> This paper -a product of the Women in Development Division, Population and Human Resou'rces - Department is part of a larger effort in PRE to document cost-cffective ways of raising the productivity of women farners in Africa. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Mila Villar, room S9-133, extension 33752 (53 pages with tables).

Women are responsible for 70 percent of staple food production in Africa, but agricultural extension is still geared to men. The North-West Development Project in Cameroon demonstrates that this scenario of neglect is avoidable.

And groups allow socially acceptable contacts with inaic agents, a supporlive environment, a chance to develop leadership and management skills, and the use of more effective communication methods.

The project design team that set out to improve agriculture in Cameroon's poor Northwest Province did not extend extension and credit to women for ideological reasons. Food crops were a primary concern, and women were growing them.

Despite stif'f academic requirements, the ratio of female extension workers incrcased to I X percent for workers and 14 percent for supervisors. Before the project, the Ifew femrale workers had been restricted to home economics and kitchen gardens. Af'ter three months of intensive training, female agents did as well as men.

The project worked more through contact groups than contact farners. The advantages: cost-effectiveness, the benefits of group dynamics, and the shared use of expensive equipment.

Maize credit was given to producer groups whose membership is 90 percent female. The members have collective liability for the loans and achieved virtually 100 percent repayment each year.

The short-term strategy of "gender targeting" was olten used. Groups were initially contacted by same-gender extension agenLs. Once trust and credibility were established and farmers knew the system, the same-gender agent could tum an area over to an agent of the other gender.

These and other techriques used in Cameroon are widely replicable, providing three principals are observed: focusing on small farmers, redressing male biases, and recognizing women's reics.

The PRE Working Paper Serics disseminates the findings of work under way in the Bank's Policy, Research, and External Affairs Complcx. An objectivc of the serics is to get thcsc findings out quickly, even if presentations are Icss than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily reprcsent official Bank policy.

Produced at the l'RE Dissemination Center
- 16 -

|**Ave**|Table<br>**raie Value Index**of Manuf|5<br>actured E|xports from LDCs|
|---|---|---|---|
||L .AMRICA<br>AFRICA|ASIA|URUOPC|
|1965|100<br>100|100|100|
|1970|169<br>264|203|227|
|1975|536<br>408|685|595|
|1980|1361<br>710|1793|918|
|1965|1993<br>1532|3517|911|

Soun **es Riveros** & **Kateus** (1988), Table 2.

# VI. **ftforc _nt** of **MHC I*ulatioms.**

Analysis of **the** actual enforcemnt of NMC **regulations requires** underssnading the institutions affecting the operation of labor markets in LDCs. In fact, in analyzing the impact of NWC on employment and exports, examination of mandatory **payments** is not enough, given the likely existence of a varying degree of *nforcem_nt across countries and through tim. A pure quantitative analysis of persistent wage differentials within the economy to **assess** the actual enforcement of NMC is not sufficient because there would still be the problem of sorting out labor cost **increases** that are *ndogenous to a growth process from **those** that **are** imposed through government regulations. Thus, quantitative examination of this issue is severely limited by lack of adequate empirical data. Consequently, in order to test the idea that **there are** enforcement mechanisms in most LDCs which keep labor cost levels above **the** supply price of labor, **we** must resort to more Indirect evidence One major characteristic of formal labor markets in LDCs **refers** to **the** existence of very detailed job security regulations. Recently, Lucas & Fallon (1988a,b) have investigated this issue in IndLa and Zimbambe, and **they**
# Regional Development Overview: Challenges, Adopted Strategies, and New Initiatives

Randall W. Jackson<sup>1</sup> Geoffrey J.D. Hewings<sup>2</sup> Serge Rey<sup>3</sup> Nancy Lozano Gracia<sup>4</sup> JEL: R10, R15, R58

> 1 West Virginia University 2 University of Illinois 3 University of California-Riverside 4 World Bank
A shadow price does not a market make. (Anonymous, quoted by Joskow [4])

# 1 Introduction

Over the last decade, network industries have undergone a dramatic transformation: competition has been introduced in industries that had long been viewed as text-book examples of natural monopolies, such as gas, electricity, and telecommunications. Production and transport have been unbundled to foster the introduction of competition: the capacity provider, i.e., the owner of the infrastructure, now often differs from the service provider. This situation raises new challenges for economists and policy makers. Chief among them is the design of institutions that will lead to "optimal" network expansion. Different arrangements have been suggested, ranging from indicative planning to decentralization of investment decisions through congestion pricing. Two questions lie at the core of the debate: is the infrastructure network still a natural monopoly? and what role could congestion pricing play? This article shows how simple economic theory sheds light on the argument, and informs the policy discussion.

In the early 80's, network industries in most countries were composed of vertically integrated utilities, subject to some form of governmental control. In the United Kingdom and in South America utilities were publicly owned. In the United States they were (for the most part) privately owned, and subject to regulatory oversight.

In many countries, today's industrial organization is dramatically different (see the survey by Klein [5]). First, utilities (or more exactly, their successor companies) are now privately owned. Second, and more importantly for this analysis, network industries are now competitive: production and transport have been unbundled, and vertical separation has been introduced. Usually, firms compete to provide service to customers, using the network infrastructure that often remains a monopoly. In the electricity industry, the transmission and distribution networks are sofar considered natural monopolies, while generation and retail sale to customers are competitive. In the United Kingdom, the monopolistic owner of the power transmission grid is not involved in generation nor retail sale of electricity, and the owner of the railway track does not offer passenger or freight service. In the telecommunications industry, the local network is (at least today) a natural monopoly', while long-distance telecommunications and value-added 'MCI **acknowledged** in **July 1997 a loss of $800** million in its **efforts** to **penetrate local telecommunications**

**1**
# **Upping the Ante: The Equilibrium E↵ects of Unconditional Grants to Private Schools**

_By_ Tahir Andrabi, Jishnu Das, Asim I Khwaja, Selcuk Ozyurt, and Niharika Singh<sup>_⇤_</sup>

**_JEL Codes:_** _I25; I28; L22; L26; O16_

**_Keywords:_** _Private schools, Financial innovation, Educational Achievement, Education Markets, Return to Capital, SMEs_

> _⇤_ Pomona College; Development Research Group, World Bank; Harvard University; Sabanci University; and Harvard University. Email: tandrabi@pomona.edu; jdas1@worldbank.org; akhwaja@hks.harvard.edu; ozyurt@sabanciuniv.edu; and niharikasingh@g.harvard.edu. We thank Narmeen Adeel, Christina Brown, Asad Liaqat, Benjamin Safran, Nivedhitha Subramanian, and Fahad Suleri for excellent research assistance. We also thank seminar participants at Georgetown, UC Berkeley, NYU, Columbia, University of Zurich, BREAD, NBER Education Program Meeting, HarvardMIT Development Workshop, and the World Bank. This study is registered in the AEA RCT Registry with the unique identifying number AEARCTR-0003019. This paper was funded through grants from the Aman Foundation, Templeton Foundation, National Science Foundation, Strategic Impact Evaluation Fund (SIEF) and Research on Improving Systems of Education (RISE) with support from UK Aid and Australian Aid. We would also like to thank Tameer Microfinance Bank (TMFB) for assistance in disbursement of cash grants to schools. All errors are our own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
increases in child labor in households with fewer assets (which are used both as buffer stocks and collateral for borrowing). Guarcello, Mealli and Rosati (2003) use data from Guatemala and show that child labor increases in response to broadly defined income shocks and self-reported credit rationing. If the household has assets, including pension assets (which they can either use as a buffer or as collateral to borrow), child labor does not increase with the negative income shock (Edmunds, 2004). Hence, access to credit is likely to be important. Using cross-country analyses, Dehejia and Gatti (2005) find that financial development in the period 1960-95 is associated with a reduction in child labor.<sup>10</sup> Finance can also affect intergenerational persistence of relative income differences through child health. Foster (1995) studies the fluctuations in child growth in rural areas of Bangladesh during and after severe floods in 1988. He shows that growth patterns in children in landless households were influenced by credit market imperfections and concludes that because the high cost of transferring resources between periods appears to be an important contributor to fluctuations in child growth in the poorest households, small-scale credit programs targeting these households may provide significant benefits in terms of reducing these fluctuations.

In another study, Gertler, Levine, and Moretti (2003) examine the roles of both large commercial and microfinance institutions in helping families insure consumption against major illness in Indonesia. The authors use measures of individuals’ physical abilities to perform activities of daily living to identify serious health problems and find that families cannot insure against the costs of a major illness and see their consumption decline. They also show that wealthier households reduce consumption considerably less as a result of illness. Finally, they estimate the effect of geographical proximity to financial institutions on consumption smoothing. They find households that are closer to financial institutions are better able to insure consumption, and that the estimated effects of being close to a commercial bank are similar to that of being close to a BRI branch – the largest microfinance lender in the country. While it is difficult to interpret the effect of bank branches – since commercial banks are likely to be located in prosperous areas – being close to a BRI branch is less likely to suffer from endogenous

> 10 There is also a theoretical literature that models the role of household access to credit in determining the extent of child labor: see Parsons and Goldin, 1989; Baland and Robinson 2000; and Jafarey and Lahiri 2002.

29
these shocks on non-agricultural output are somewhat larger than on the aggregate output, especially for the terms of trade shocks.

When it comes to domestic variables, several results are worth noting. First, shocks to government consumption show an interesting contrast in their effect on agricultural and non-agricultural GDP. Namely, these shocks lead to a reduction in agricultural GDP, but they tend to increase nonagricultural GDP. One potential explanation for this result is the negative wealth effects that would arise if the majority of government consumption is allocated towards non-agricultural goods. Increases in government consumption, in such case, will raise the relative price of non-agricultural goods, leading to a negative wealth effect for agricultural workers and thus having contractionary effects on that sector.

Shocks to domestic interest rate have almost no effect on agricultural GDP, but significantly reduce non-agricultural GDP. This may be not surprising if higher interest rates reduce investment demand and non-agricultural sector is more capital intensive. Shocks to investment have large positive effects on output in both sectors.

**Figure 1. 6: Impulse Responses of Agricultural Output to Various Shocks**

<!-- Start of picture text --> tot shock r_us shock Log(yf) shock<br>.015 .02<br>.005<br>.01 .015<br>0<br>.005 -.005 .01<br>0 -.01 .005<br>-.005 -.015 0<br>Log(gc) shock Log(inv) shock tby shock<br>.015<br>0 .005<br>.01<br>-.005 0<br>.005<br>-.01 0 -.005<br>-.015 -.005 -.01<br>r shock Log(yA) shock Log(yNA) shock<br>.01 .04 .005<br>.03<br>.005<br>.02 0<br>0 .01<br>-.005 0 -.005<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>quarters quarters quarters<br>95% CI IRF<br><!-- End of picture text --> _Source:_ authors.
observed and notional wage changes are allowed if measurement error is present: ∆yit<sup>o=</sup> xit−1βt + εit + ∆eit.

It is worth stressing once more that measurement error is not forced onto the data. Assuming that ∆eit is normally distributed with a positive variance in the population would imply that the probability of a firm correctly reporting individual wages would be zero, a feature most likely inconsistent with the nature of our data. Instead, our assumption that a fraction, qt, of the wages, is measured without error allows us to examine the relative importance of this feature in the two data sets we study.

Under these assumptions, the likelihood of each observation is calculated and all parameters are estimated by maximum likelihood. Intuitively, the estimator uses the local asymmetries in the wage change distributions that we have discussed in the descriptive section in order to identify DNWR and DNWR. However, instead of identifying them unconditionally, by, for example, visually inspecting the previous histograms, the estimator exploits the asymmetries in wage changes conditional on individual and job characteristics. As for the identification of potential measurement error, the estimator has two main features that help assess its relevance. First, since measurement error is expected to follow a continuous distribution, its presence is less likely when the size of the spike at zero is large. Second, since measurement error is expected to displace actual wage freezes, we should observe excess probability mass around zero and relatively smooth asymmetries in other parts of the wage change distribution when measurement error is important.

It is important to note that not all forms of measurement error will necessarily result in an underestimation of DNWR. Most notably, in the presence of rounding errors, wage freezes would actually be over-represented and not the opposite. For example, Smith (2000) uses data from the British Household Panel Survey (BHPS) and finds that self-reported reported wage freezes between two consecutive years are less likely to happen when individuals double check their pay slip before reporting wages. As a consequence, measurement of DNWR in the BHPS which does not take into account rounding errors would tend to over-estimate rigidities. We believe our data are unlikely to suffer from rounding errors since wage information is directly extracted from firms’ 13
<!-- Start of picture text --> Initatons and CompletIons of '301' Cases, by Year<br>Number of CasZ<br>14 -<br>12 -.............................. . . . ..................... ....<br>10 . ............................ .. ........ . .. . . .. . .. . .<br>12- . .. . . . .. . . . . . . .. ......<br>10<br>Year<br>| Inrdiations U Completons<br><!-- End of picture text -->
developing countries is consumption expenditure per person ( _C_ ) given by the total household expenditure, including spending on food (purchased and home-produced and food received as gifts), non-food items, estimated flow of services from consumer durable goods and actual or self-estimated rental cost of housing.<sup>27</sup> Although this is a comprehensive consumption aggregate, there are well-known concerns about how well it reflects welfare, even when thought of as the narrower concept of ‘economic welfare.’<sup>28</sup> _n_ A strict interpretation of our theoretical model would imply that _Y_ = _y_ +τ ( _y_ , _y_ ) = _C_ . However, our theoretical model simplifies reality in a number of respects, leaving concerns about how well consumption reflects perceptions of own economic status. The literature has pointed to concerns about ( _inter alia_ ) the use of a market-price weighted aggregate of consumption and the calibration of deflators for demographic differences and cost-of-living differences. In that light, we cannot rule out the possibility that our results using consumption as the measure of _Y_ will be biased by correlations between the perceived economic welfare of friends and neighbors and the errors in measuring own welfare using consumption.

For these reasons, we also consider a measure of self-assessed economic welfare based on the own-economic welfare ( _OEW_ ) question in which respondents are asked: “Imagine six steps, where on the bottom, the first step, stand the poorest people, and on the highest step, the sixth, stand the rich (show a picture of the steps). On which step are you today?” We also need measures of the economic welfare of relevant groups for social comparison or risk-sharing. People in geographic proximity to the respondent are an obvious source of comparators, co-insurers and help in other ways, such as finding employment. Using the

> 27 For details on how the consumption aggregate was formed see World Bank (2005b).

> 28 On the identification problem in consumer demand-based measures see Pollak (1991). Slesnick (1998) surveys alternative empirical approaches. Subjective welfare measures have been seen as a promising route to avoiding the identification problems in deriving a metric of welfare from observed demand behavior; for overviews of this approach see van Praag (1991) and Kapteyn (1994).

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