rafmacalaba/datause-encoder-data huggingface dataset:
**Rwanda** 7717-RW: Public Expenditure Program: An Instrument of Economic Strategy (October 19, 1989)

- __1w_ 6450-SE: A Review of the Three-Year Public Investment Program, 1987/88-1989/90 (February 26, 1987)

- Sierra ILone 5513-SL: Review of the Public Expenditure Program (September 13, 1985)

- **TIzania** 7559-TA: Public Expenditure Review (May 22, 1989)

- Zambia 6438-ZA: Public Expenditure Review (October 6, 1987)

- Zimbabwec Public Expenditure Review (February 1986)

- Bang adeh 7545-BD: Public Expenditure Review: A Framework for Public Resource Management in the Fourth Five Year Plan Period, FY91-95 (March 13, 1989)

- **Ehilizwines** 7473-PH: Education Sector Study (December 1988)

# Europe. Middle East and North Africa

- J.QFtdanl 5697-JO: Efficiency and Equity of Govermment Revenues and Social Expenditures (June 1986)

- 4156-MOR: Priorities for Public Sector Investment 1981-1985 (June 15, 1983)

- 5962-PAK: Economic and Social Development Prospects (February 18, 1986)

- **Tunisia** 5622-TUN: Financing of Education: Past Trends and Future Developments (April 22, 1985)

- lbrkey 6249-TU: Education and Training Sector Survey (September 1986)

# Latin America. Caribbean

- Biovia 7746-BO: Public Sector Expenditure Review with a Special Emphasis on the Social Sectors (September 15, 1989)

65
items were available at the voucher fairs, some items were forbidden, thereby affecting _voucher_ recipients’ choices. Second, while _cash_ households could spend the transfer where, how, and when they wished, _voucher_ households had to use the entire value of the voucher on the day of the fair and for specific food items, thereby affecting their choices.<sup>37</sup> And finally, as the transfer program primarily targeted women within the household, the transfer modality could have affected women’s control over purchasing decisions.<sup>38</sup> Table 5 shows how the in-kind transfer design affected households’ purchasing decisions. Unsurprisingly, all of the _voucher_ households used their transfer at the voucher fairs, whereas none of the _cash_ households did so (not shown). _Cash_ households were 98 percentage points more likely to spend the transfer at one of the markets outside of the camp, either at the primary market where they received the transfer or at a market that was closer to the camp but in a less secure zone.<sup>39</sup> In terms of the timing of the transfer, none of the _voucher_ households used their transfer over multiple periods (by design), whereas _cash_ households were 80 percentage points more likely to use the transfer over multiple periods. All of these effects are statistically significant at the 1 percent level.

# Table 5 about here

While the transfer modality affected where, when, and how program recipients spent the transfer, it did not appear to strongly affect intra-household decision-making with respect to the transfer (panel B). A majority of _voucher_ program recipients reported that they were responsible for spending all or part of the transfer, along with their husbands. Yet female program recipients in _cash_ households were 12 percentage

> 37 Concern Worldwide collected price data on the key regional market prior to the voucher fair and used these prices as the maximum prices for goods at voucher fairs. Appendix S1 shows that voucher households did not face substantially different prices at the voucher fairs (as compared with market prices). Nevertheless, the program design might have affected program recipients’ ability to bargain for a lower price. In fact, _voucher_ program recipients noted that traders often first cited the maximum price on the market.

> 38 An additional reason for the differences in purchasing patterns might have been “decision fatigue.” While most economic models are based on the assumption that agents are unconstrained in their ability to process information, people often use simple cognitive shortcuts when processing information, leading to systematic biases in decisionmaking (Simon 1955; Lacetera et al. 2012). While _voucher_ households were informed in advance of the availability of items at voucher fairs and in theory should have been able to plan their choices well in advance, this information might not have been easily processed.

> 39 There were only three markets within a 20 km radius of the camp (Masisi, Lumumbashi, and Nyabiondo), although only Masisi and Nyabiondo were frequented by camp residents.

21
13. Since there are no transactions costs in factor markets, this amounts to assuming that cii = O, v j = 1,.,J.

14. That is, the price of mature CIP is simply the rental value of the associated capital.

15. We consider the possibility of multiple equilibrium capital production technologies in section III.B.

16. If all households were behaving identically, 8s = Ps" Si"/w would hold.

17. Notice that C 'h w gives the value, in real terms, of the demand for type (j*,h) CIP. Division by piSh converts this demand into units of CIP.

18. The notion that transactions costs are larger for long maturity assets is certainly consistent with casual observation. For instance, the _Wall Street Journal_ of July 23, 1993, reported a bid/ask spread on three month treasury bills of the previous day equal to 0.005 percent of price. For a thirty year treasury bond, this spread was 0.062 percent of price, while for a thirty year treasury strip (a pure discount instrument, equivalent to a long-term bill) the spread was 0.7 percent of price. Thus transactions costs vary by a factor of 100 with maturity alone, despite the fact that these observations ignore the obvious likelihood that a long-term instrument will be rolled-over many more times during its lifetime than a shortterm instrument.

19. H(z) is a function iffj(z) is unique. We discuss below what happens when two or more technologies maximize the internal rate of return on investments.

20. See Bencivenga, Smith, and Starr (1 994a) for a formal proof of this assertion.

21. When j(z) is not unique, G(z) consists of a vertical segment for the same reasons as before. In particular, if technologies I and n > I both maximize the internal rate of return on capital invesments, then agents can invest exclusively in technology _£,_ exclusively in technology n, or in convex combinations of the two technologies. As a result, the fraction of wealth held in the from of equity can lie anywhere in the interval [G ([a R (Z)]R.), G{([AR(z)]})].

22. The assumption that resources collected in the form of fees or taxes are rebated to old agents prevents a transfer of the proceeds from those who bear these costs (by assumption sellers, or old agents) to those who do not (by assumption buyers, or young agents). A transfer of resources from old to young agents would, under our assumptions, raise the aggregate savings rate and, in and of itself, constitute a stimulus to capital formation. We, on the other hand, wish to isolate the effects of transactions costs alone. Therefore we rebate the proceeds of fee or tax collections to those who pay the fees or taxes. Given our preference assumptions, the result is that savings pattems are unaltered by the existence of fees/taxes.

39
Is your firm organized in a way that allows you to work efficiently and nimbly?

- 24 Organization _Sua empresa possui um layout (arranjo físico) definido para facilitar a entrega do produto/ serviço, comercialização e atendimento aos clientes?_ Are your premises designed in a way that facilitates delivery and sales of your product or service, as well as customer service?

- 25 Human _Você sabe quais são as obrigações trabalhistas de sua empresa, da contratação_ resources _até o desligamento?_ Are you aware of your firms’ obligations towards its employees, from hiring to firing?

- 26 Human _Você já definiu que conhecimentos e características as pessoas devem ter para_ resources _realizar as atividades em sua empresa, incluindo você e sócios?_ Have you assessed which knowledge and characteristics staff should have to successfully carry out tasks in your firm, including yourself and partners?

- 27 Human _Se você, seu sócio ou algum funcionário faltar ou tirar férias, está definido um_ resources _responsável para ficar no lugar desta pessoa?_ If you, your partner, or an employee is not able to come to work or takes vacation, is it clearly established who will act for this person?

- 28 Human _Você faz uma avaliação dos problemas recorrentes ou potenciais em seu negócio,_ resources _visando evitar que os mesmos ocorram?_

- Do you try to anticipate recurring or potential problems in your business with the goal of preventing them from happening in the first place?

- 29 Human _Se for necessário delegar alguma atividade (por motivo de doença, expansão,_ resources _etc.), você já sabe quais são as atividades que pode delegar?_ If you had to delegate some tasks (due to illness, expansion etc.) do you know which tasks you can delegate?

* Corresponds to a business practices question asked in our follow-up survey, based on McKenzie and Woodruff (2016).

29
The Inflation-Stabilization Cycles in Argentina and Brazil by Miguel A. Kiguel and Nissan Liviatan

# Table of Contents

|I.|Intr|oduction|1|
|---|---|---|---|
|II.|Infl|ation Before and After the Heterodox Shocks|4|
||i.<br>ii.<br>iii.|Inflation Before<br>The Change in Regime After the HSs<br>Process Inertia|4<br>6<br>8|
|III.|Alter<br>Cycle|native<br>vxplanatic-<br>for the Inflation-Stabilization<br>s|10|
||i.|The Fiscal View|11|
||ii.|The Inertia-Indexation<br>Appruach|14|
||iii.|An Alternative View:<br>Anticipations<br>of Recurrent<br>Controls|15|
|IV.|Analy|sis of the Cycles|16|
||i.|Phases of the Cycles|16|
||ii.<br>iii.|The Erosion of Credibility and Inflation<br>Instability Over Time<br>Macro Performance in the Cycle Regime|23<br>27|
|V.|Impl|ications of this Regime and Puzzles|28|
|Refer|ences||34|

Prepared for presentation at the conference on "Lessons of Economic Stabilization and its Aftermath," January 31-February 1, 1990. We are grateful to Mario Blejer, Michael Bruno, Stanley Fischer, Elhanan Helpman, Jose Luis Machinea, and Assaf Razin for comments on an earlier version, and to Jariya Charoenwattana for research assistance.
are ambiguous about the relationship between the degree of political stability in a country and stops in FDI inflows (Salomon and Ruiz, 2012).

The source for data on GDP, trade, and natural resource rents is the World Bank’s World Development Indicators. Financial openness is represented by the Chinn and Ito (2008) capital account openness index. The International Country Risk Guide is the source for the economic, financial, and political stability measures. Definitions and sources for the independent variables are presented in Appendix A2, while descriptive statistics can be found in Appendix A3.

# Econometric Results

The results from the complementary logistic regressions presented in Table 2 suggest that in general FDI surges are difficult to predict. The baseline regressions have low pseudo R<sup>2</sup> varying between 3% and 10% of the observed variance.<sup>16</sup> Stops appear easier to predict in that the pseudo R<sup>2</sup> varies between 10% and 34%. This difference can be attributed mainly to the fact that a surge in the preceding year is a good predictor of a FDI stop, but not vice versa. Hence, it can be inferred that surges are followed by stops. At the same time, a FDI surge in the preceding year is not a good predictor of a surge and a FDI stop in the preceding year is not a good predictor of a stop.

# _<u>FDI Surges</u>_

Global liquidity is associated positively with a FDI surge, regardless of its kind. Although global liquidity is only significantly correlated with the probability of a GF-led surge (Table 2), the effect of global liquidity on the likelihood of a GF-led surge does not significantly differ from the effect of global liquidity on the likelihood of an M&A-led surge (χ<sup>2</sup> =0.08, p=0.77). Furthermore, the effect of global liquidity on the likelihood of

> 16 This figure is based on McFadden’s R2.

15
# SCALING UP AID OR SCALING DOWN:

THE GLOBAL ECONOMIC CRISIS AND RWANDA’S MDGS Hans Lofgren, Hannah Nielsen and Kene Ezemenari<sup>∗</sup>

> ∗ The authors would like to thank Edith Kikoni, Annelie Gunborg Strath, Paul Cichello, Gaelle Le Borgne Pierre and Wayne Mitchell for valuable input. The authors would also like to thank Christian Arnault Emini who constructed the Rwanda national social accounting matrix (SAM), upon which the SAM used for this paper is based.  The SAM was constructed under a joint project with the Ministry of Economy and Finance (MINECOFIN), MINAGRI and the World Bank.  Parts of that study were undertaken with the financial support of the Belgian Trust Funds; and the financial support received was instrumental to completing the work.  The views, findings and conclusions expressed in this paper are entirely those of the authors and do not necessarily reflect those of the World Bank, its Executive Board, or member country governments.
**Table 16. Average predicted probabilities from Multinomnial Logit Estimation of Advice/Treatment sought as a result of a child under 3 having a fever in the past two weeks**

||_No/ Self_<br>_treat-_<br>_ment_|_Public,_<br>_higher_<br>_level_|_Public,_<br>_lower_<br>_level_|_Private_<br>_medi-_<br>_cal_|_Private_<br>_com-_<br>_mercial_|_Total_|
|---|---|---|---|---|---|---|
|_WesternandCentralAfrica_<br>Probabilities evaluatedat:|||||||
|Meancluster wealthin thepoorest<br>quintileofclusters|56.2|7.2|23.6|7.5|5.4|100|
|Meancluster wealthinsecond<br>quintileofclusters|46.9|8.5|20.9|8.0|15.8|100|
|Meancluster wealthinthirdquintile<br>ofclusters|39.0|9.8|17.0|8.6|25.6|lO1|
|Meancluster wealthinfourth<br>quintileofclusters|35.1|13.4|13.3|11.3|26.9|100|
|Mean clusterwealthin therichest<br>quintileofclusters|36.2|19.8|10.9|17.1|16.0|100|
|_EasternandSouthern Africa_<br>Probabilitiesevaluatedat:<br>Mean cluster wealthinthepoorest<br>quintileofclusters|38.7|7.9|31.4|11.8|10.2|100|
|Meanclusterwealthinsecond<br>quintileofclusters|36.3|8.8|31.0|13.2|10.8|100|
|Meanclusterwealthinthirdquintile<br>ofclusters|33.7|9.9|30.2|15.0|11.3|100|
|Meanclusterwealthinfourth<br>quintileofclusters|30.4|11.2|28.2|18.5|11.7|100|
|Meanclusterwealthinthe richest<br>quintileofclusters|27.0|11.3|21.0|30.8|10.0|100|

_Source:_ Author's calculations from pooled and weighted DHS data. See Table 10 for definition of categories. Predictions are the average probability averaging over all individuals with their observed characteristics but substituting all observations to have the average cluster wealth of the specified quintile of clusters.

Last, the results support the notion that experience with fever affects treatment choice, but only in Eastern and Southern Africa. The coefficient on the average incidence of fever in other households of the cluster is significantly negatively related to the use of public higher-level facilities; positively related to the use of lower-level public facilities; and (weakly) positively related to seeking care from private commercial sources. In Western and Central Africa, however, the coefficient is always insignificantly different from zero.

_30_
owned firms is a direct prediction from the non-separability theory, but could also arise as a prediction from firm competition theory since female-owned firms are less productive on average and so may find it harder to compete with new entrants; and could also occur in the occupational choice theory since females may be on average less attached to the labor market, and so may need less of a change in outside options to lead them to shut down their firm.

The U-shaped relationship between firm death and age of the owner, and the finding that firm death rates are higher in richer developing countries are most consistent with the occupational choice model. Young workers are likely to experience a higher arrival rate of alternative job offers than middle-aged workers, while older workers may be choosing between continuing to run the business and retirement or at least working less. Richer countries may offer a wider range of employment opportunities beyond subsistence self-employment, pulling people out of jobs.

The theories differ most in their predictions for the effect of firm exit on individual labor earnings, and in their predictions of the link between productivity and firm death. We therefore estimate the following regression for the outcome ( _Y_ ) of log total labor earnings (the sum of wage earnings plus profits+1, in last month): ��,� = �+ �∗��������h�,� + ∑���1(���, �= �) + ��,� (3) Where _FirmDeath_ takes value one if firm _i_ is dead at time _t_ , and 1(���, �= �) are a set of dummy variables for survey _j_ measuring follow-up in period _s_ . The standard errors are clustered at the firm level. The coefficient _b_ then measures the earnings loss or gain associated with the firm dying.

To test whether it is the more or less productive, or profitable, firms who die, we instead use baseline labor productivity (log of sales per worker) and log profitability, and estimate the regression: �� = �+ �∗�������� + ∑��1(���) + �� (4) Where _EverDie_ takes value one if firm _i_ is dead by the last survey round (within 5 years) of a survey. The coefficient _b_ then measures whether those firms who go on to die were lower or higher productivity and profitability to begin with than those that survive.

Panel A of Table 4 shows the results of estimating equations (3) and (4). In column 1, we see that firm death is associated with a sizeable fall in total labor earnings. The coefficient suggests 20
# **Introduction**

## _<mark>What is digital technology?</mark>_

<mark>“The term Digital Technologies is used to describe the use of digital resources to effectively find, [analyze], create, communicate, and use information in a digital context. This encompasses the use of web 2.0 tools, digital media tools, programming tools and software applications.”</mark><sup>1</sup> In recent years, digital technologies have penetrated virtually all facets of modern life, and even creative work has not been spared. Historically, technology has often been associated with mechanization or industrialization. While this advancement improved productivity, some have long feared that technological expansion would cost people their jobs and even diminish artistic output. “When small hand tools first grew into big machines over 150 years ago in England, the workers of that day showed their hatred of the new monsters by fighting them,” writes William F. Ogburn. “They threw stones at the factory windows and broke the machines inside.”<sup>2</sup> “That was 150 years ago,” he adds, “but the same sort of thing happens nowadays.” With respect to creative work, for example, just “a few years ago the talking pictures took away the jobs of 10,000 American musicians. They didn’t throw stones at the movie palaces; perhaps that is part of the Progress of the Century. But they did print advertisements in newspapers, protesting against ‘canned music’ and urging people to demand that theaters keep their orchestras.”<sup>3</sup> In today’s setting such tension still exists.<sup>4</sup> Nonetheless, while digital technology can disrupt the field of creative work it can also expand it.

The functional changes of technology have generally enhanced people’s lives. For this the eighteenth-century moral philosopher Adam Smith predicted. “In reading The Wealth of <u>Nations,” as Wassily Leontief observes, “one is struck by concrete descriptions of the</u> ongoing technological change and theoretical emphasis on the role it was bound to play in economic growth and the concomitant rise of private and public welfare.”<sup>5</sup> In the creative

> 1 “Digital Technologies,” New Zealand Commerce and Economics Teacher's Association Inc., accessed March 17, 2015, <u>http://www.nzceta.co.nz/pages/digital_technologies.htm.</u>

> 2 William F. Ogburn, _You and Machines_ (Chicago, Ill: University of Chicago Press, 1934), 5, accessed March 17, 2015, <u>http://babel.hathitrust.org/cgi/pt?id=mdp.39015063056744;view=2up;seq=14;size=150.</u> 3 Ibid.

> 4 Ryan Avent, “IT Was Fun While IT Lasted,” _Economist_ , July 7, 2014, accessed March 17, 2015, <u>http://www.economist.com/blogs/freeexchange/2014/07/productivity.</u>

> 5 Wassily Leontief, “The Present State of Economic Science” in _Adam Smith's Legacy: His Place in the Development of Modern Economics,_ ed. Michael Fry (London and New York: Routledge, 1992), 141.

2
**Figure 3.2: Costs of Identification (expressed as % of income per capita)**

<!-- Start of picture text --> Costs of identification<br>0.99<br>0.86<br>Fees charged for passport 1.32<br>0.44<br>0.04<br>0<br>Fees charged for NID 0<br>0<br>0 0.2 0.4 0.6 0.8 1 1.2 1.4<br>Percentage of income per capita<br>Chile Ghana India Vietnam<br><!-- End of picture text --> _Source: Living Life data. Notes: Cost expressed as percentage of GNI per capita using the Atlas Method in current (2016) U.S. dollars._ The efficiency of ID services can also be assessed by the time taken to obtain a NID and passport from the day of request until the day of delivery of the document. Legally prescribed timeframes vary significantly among the four economies from 1 day to obtain an ID in Ghana to 60-90 days in India. In India, obtaining a passport should take 3 days, and in Ghana the process lasts 15 days according to the law. However, time required in practice may deviate from the time prescribed by law.

Contributors reported much longer times required in practice for obtaining passports. For example, it takes Ghanaian citizens twice as long to get a passport at 30 days in practice compared to the 15 legally prescribed. Similarly, Indian citizens seem to experience significant delays while obtaining their passports at 30 days, as opposed to the 3 days prescribed legally. In contrast, the provision of national ID cards in India is more streamlined in practice at 30 days within the legal timeframe of 75 days. However, due to the limited number of respondents, the data on days required in practice should be interpreted with caution.

Figures 3.3. and 3.4 below provide a graphic of the time required to legally obtain an NID and passport in each of the four economies.

**Figure 3.3: Legally Prescribed Timeframes to Issue a NID**

**Figure 3.4: Legally Prescribed Timeframes to Issue a Passport**

<!-- Start of picture text --> Time to get an ID Time to get a passport<br>80 75 16 15<br>70 14<br>60 12<br>50 10<br>8<br>40 8<br>6<br>30 6<br>20 4 3<br>8 7<br>10 1 2<br>0 0<br>Chile Ghana India Vietnam Chile Ghana India Vietnam<br>Source: Living Life data<br>Note: In September 2017, Ghana introduced a new Ghana Card that is supposed to be issued in less than one day.<br>Time in business days Time in business days<br><!-- End of picture text --> 13 | P a g e