rafmacalaba/datause-encoder-data huggingface dataset:
the data for transition economies that tried to do just this, Commander and Svejnar (2009), found largely null results.

The three problems with attempting to uncover the relevance of elements of the business environment by estimating directly a production function augmented by business environment indicators can be avoided by taking a different approach. Following Carlin et al. (2006, 2010), we take as our starting point that the business environment is external to the firm and that to an important extent, firms in a country share the same environment. This suggests that firm level information be used in a different way from the augmented production function method. Specifically, we formulate predictions as to how the firm’s response to its business environment in terms of its expenditure on abatement and its evaluation of the costs imposed on it by deficiencies in the business environment varies with its characteristics, including its performance. In short, a firm-level assessment of the business environment is an endogenous variable in the modeling framework and as the dependent variable in estimations. The framework can be also used to address the relative importance of different aspects of the business environment in different countries.

# _Modeling framework: Summary_

The model is set out in Appendix 1 and we summarize it here. We use a simple single-period firm production function with 4 inputs, _L_ , _E_ , _B_ and _G_ , which are combined to product output _Y_ . _L_ is employment; _E_ is an intermediate input that is a flow of services which results from combining a public input _B_ with _G_ , an input the firm purchases in order to mitigate the effects of the unreliability of the public input. Firms also differ in productivity, which we capture with a productivity parameter _A_ . We index countries by _j_ and firms by _i_ . In our basic model, the public input _B_ that is supplied on identical terms to all firms in a country, so we write it as _B j_ . This captures the notion of a shared “business environment” – _B j_ varies across countries but not across firms within a given country. We then extend the model to cover the case where the public input varies with the firm’s productivity or profitability.

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macroeconomic stability and robust growth for the Argentine economy. The declining real effective exchange rate since 2005 suggests an undervalued exchange rate of approximately 30%, as of October 2015. Depressed price statistics decrease the REER and lessen the peso’s implied overvaluation. On the other hand, our inflation-adjusted REER,<sup>50</sup> exhibits an increasing trend and signals an overvalued US$/Peso exchange rate of 46% in 2015 relative to the base year 2005<sup>51</sup> (see Figure 10). Yet, the caveat of a assuming a constant real exchange rate remains.

Figure 10: Overvaluation Implied by the Big Mac Index and PPP <!-- Start of picture text --> % Big Mac Index (US$/Peso) S ource: The Economist Implied Overvaluation Relative to 2005 (%)<br>100 Source: BIS, INDEC, PriceStats, and authors' calculations<br>80<br>50 60<br>‐5.04 46.5<br>0 40<br>‐50 20<br>‐56.3<br>‐100 0<br>‐20 ‐30.5<br>‐150<br>Misalignment ‐40<br>‐200<br>Adjusted misalignment ‐60<br>‐250 Inflation‐adjusted REER REER<br>‐80<br>Figure 11: Overvaluation Implied by the iPad index in US$/LCU<br>iPad Index Implied Overvaluation (%)<br>Source: Commsec and authors' calculations, September 2015<br>80<br>60<br>40<br>20<br>0<br>‐20<br>‐40<br>‐60<br>Apr‐00 Apr‐01 Apr‐02 Apr‐03 May‐04 Jun‐05 Jan‐06 May‐06 Jun‐07 Jun‐08 Jul‐09 Jan‐10 Jul‐10 Jul‐11 Jan‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>ArgentinaBrazil Guatamala Israel Serbia Croatia UK Romania Greece HungaryPortugalBulgariaIreland Denmark Switzerland NorwayChina France ItalyNetherlands Sweden Poland GermanySpainAustria PhilippinesSouth Africa US (California)India UAE Hong KongSouth Korea US (ex‐tax)Mexico Russia Japan (with tax)Indonesia Taiwan SingaporeTurkeyVietnam New Zealand Thailand Saudi Arabia Japan (ex‐tax)Australia Canada (ex‐tax)Malaysia<br><!-- End of picture text -->

> 50 The adjusted REER is estimated by inflating domestic prices by the cumulative difference in monthly inflation estimates produced by PriceStats compared to INDEC.

> 51 2005 is considered to be a year of macroeconomic stability and therefore assumed to reflect equilibrium. Argentina’s economy had recovered from the 2001 crisis but not yet suffered from the global financial crisis nor the external (balance of payments) instability that has since prevailed.

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# **1. Introduction**

Volatility in market demand characterized by erratic, diurnal, seasonal, and cyclical fluctuations in the number of visitors and occupancy rates is a key feature of the hotel industry (see Alemayehu and Tveteraas 2020, Saito and Romão 2018, Park et al. 2016, Chen and Yeh 2012, Jang 2004, and Highman and Hitch 2002). While there are some benefits from higher volatility, most studies contend that the high cost of adjusting inputs in the short run (henceforth, adjustment cost) outweighs the benefits. Studies of the manufacturing sector suggest that the adjustment cost varies, being less for small compared to large firms. As a result, smaller firms suffer less or benefit more from higher volatility. The present paper makes a first attempt at exploring this idea in the case of hotels. We estimate the impact of volatility in occupancy rates on a slack-based measure of the efficiency of hotels in Malaysia obtained using the Data Envelopment Analysis (DEA) methodology. Our results confirm that smaller hotels are less negatively (or more positively) impacted by higher volatility in occupancy rates. We also find that hotels with more women’s ownership suffer less from higher volatility in occupancy rates, and those that use temporary workers suffer more.

The relationship between volatility in demand and the profitability or efficiency of hotels has been explored in several studies (Section 2.1 reviews the literature). While the evidence is somewhat mixed, most studies find that higher volatility has a negative impact on hotels’ performance. Studies for other service sectors and manufacturing sectors reach a similar conclusion. The key issue here is the shape of the short-run average and marginal cost curves. The steeper the slope of the cost curves, the larger the decline in firm performance when adjusting output to match fluctuating demand. Efficiency differences have also been linked to hotel size (see Pulina et al. 2010, Pérez-Rodríguez et al. 2023, Aissa and Goaied 2016, Salman Saleh et al. 2012, 2
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# Diego (October).

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Unobservable characteristics that are omitted from the analysis such as ability or wellconnectedness may be correlated with both the explanatory variables—migration and remittances—and the outcomes of interest. The direction of the bias is unclear a priori.

Ideally, an unbiased estimate of the causal impact would be the difference between outcomes of households with migrants (and/or remittances) and their outcomes in the counterfactual scenario when these same households do not have migrants. However, households with migrants tend to be ―selected‖ based on unobservable characteristics. Therefore, households without migrants will not be a good counterfactual for them.

One method to potentially account for such possible biases is the use of panel data to perform fixed-effects or first-difference estimation. The panel data structure allows us to control for unobserved fixed heterogeneity using household fixed effects. However, the identification assumption would be that there are no time-varying unobservable determinants of the outcome variables. We are concerned that there might be unobserved shocks, such as changes in the structure of the economy or weather shocks, which correlate with the migration decision as well as child outcomes and labor supply of the origin household.

Another method, the instrumental variable approach, will be used to separate the impact of migration from selection effects. Historical migration networks, defined as the percentage of households in the village with migrants in the past, will be used as instruments for migration and remittances in estimating their impact on the sending households. We expect that larger initial migration networks would lower the cost of subsequent migration, through information or through financing, and thus induce more migration. The first-stage regression reported in Table 3 confirms this relationship. The bottom row shows that 1993 and 1997 migration rates are very strong determinants of whether a household has a migrant in 2007 (as well as whether a household receives remittances). The F-statistic of joint significance of the instruments is reported at 38 or higher. By living in a community with high levels of migration in the 1990s, a household has a higher probability of having an international migrant than similar households in community with lower initial migration rates. For the 2000 regressions, we use only the 1993 network as the instrument since it can be difficult to argue for using the 1997 migration rate as historical networks in the year 2000.

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2011. With this trend, East Asia has become the region with the fastest rise in food prices, surpassing South Asia, the rest of the developing regions, and average prices worldwide (FAO 2010; UNICEF 2011).

Much has been written about the causes and consequences of the recent food crises.<sup>8</sup> Cuesta (2010) has summed up the causes in what he calls a “perfect storm,” where a series of factors and circumstances have converged to trigger a sudden surge in prices.<sup>9</sup> In the perfect storm hypothesis, the structural causes cited most often have to do with increased demand—as well as changes in its composition—in emerging countries, led by China and India. In an example of poor policy making, during 2007 and 2008, the governments of China, India, and Vietnam imposed bans or restrictions on the export of rice to neighboring importers within the region, such as Indonesia, Bangladesh, and the Philippines. Another recent example of this type of policy is the credit program for growers in Thailand, known as the Rice Mortgage Scheme. In this program, the Thai government guarantees domestic farmers prices well above market levels, which has resulted in substantial loss of competitiveness in Thai rice exports among other exporters in the region (to the point of threatening Thailand’s status of the world’s leading exporter of rice).<sup>10</sup> An important aspect of the volatility that surrounds international food prices is their high sensitivity to a variety of factors, including uncertainty about available food stocks. Again, the role that Asia plays in this uncertainty is a major determining factor, even more so in the current context of low stock levels, which in the case of corn have never been lower. The new G-20 Agriculture Market Information System (AMIS), which is designed to improve information about agricultural markets, shows substantial differences in the estimates of grain stocks in Asian 9
15 incomes is explained by the intergroup<sup>variance, where the groups are defined</sup> by decomposing the population into<sup>sectors of economic activity and occupational</sup> status. He estimates that including<sup>wealth-related income should increase the</sup> intergroup variance to 50% of the<sup>total variance. However, Glewwe and de Tray</sup> (1988,1989) estimate that only 15%<sup>and 25% of consumption inequality for Peru</sup> and Cote d'Ivoire respectively, is<sup>due to variation in consumption between</sup> socioeconomic groups. Information about<sup>intragroup variance can come from survey</sup> data or by assuming a frequency density<sup>function. Dervis, de Melo and Robinson</sup> (1982) and Thorbecke and Berrian (1987)<sup>discuss ways to generate the change in</sup> the intragroup variance from the model.

There are numerous examples of CGE models<sup>and a bibliography is available</sup> in Decaluwe and Martens (1988)<sup>and Devarajan, Lewis and Robinson (1986).'</sup> Recent examples which emphasize distributional<sup>issues are listed below. Some</sup> general conclusions are that the size<sup>distribution of income has been found to</sup> be insensitive to policy, but the<sup>extended functional distribution that</sup> distinguishes classes of income recipients<sup>by sector as well as by asset</sup> ownership, is sensitive to policy.1<sup>8</sup> Another general result is that structural differences in economies can lead<sup>to diff' rent winners and losers when the same</sup> adjustment policy is enacted.

De Janvry, Fargeix, and Sadoulet (1989)<sup>have developed a CGE model to study</sup> "<sup>1</sup> There are at least two such CGE<sup>models that are available in a "user-</sup> friendly" form on diskette: the Bourguignon, Branson, de Melo<sup>(1989) model</sup> described in the text and a fix-price,<sup>flex-price model by Maasland and Taylor</sup> (1987). '<sup>8</sup> Thorbecke (1985) suggest two major<sup>reasons for the stable size</sup> distribution of income. First, most<sup>of the policy simulations are conventional</sup> marginal changes in policy rather<sup>than structural reforms. Second, the household</sup> income distribution is mechanically mapped from the factorial income distribution.
- 15 -

taken as a base year, the levels indicate which countries did not allow their currency to appreciate significantly in real terms while the standard deviation provides a measure of stability. One notes, also in Table 6, that average values for the IAC Region indicate an anti-export bias through 1985 as average REER levels were high relative to 1978-80. The broad aggregate features are summarized in Table 2. It is noted that Asia and EHENA regions had the most stable REER over the period 1980-87 while the highly indebted group, HIC, had the most unstable. The HIC group had the steepest depreciation, an average value of -6 percent per year but with the associated high degree of instability it is difficult to assess the overall role of real effective exchange rates on economic performance for the group. The individual country values show wide divergencies in trend and stability. The problem is further compounded by not including in the analysis any measure of how 'appropriate' the level was at the beginning of the period. It is notable (Table 6) that the four NIEs maintained rclatively constant REERs. At the other extreme one finds countries such as Egypt and Sierra Leone that have allowed the REER to appreciate.and the level to fluctuate in an unstable manner.

22. There is increasing recognition that many of the HICs are suffering from debt fatigue (see World Debt Tables). The cycles of rising expectations and failure of most stabilization plans followed by further erosion of living standards have now begun to impact on their political situations. Opposition to current policies and to the governments pursuing them has been gaining. It is becoming increasingly difficult to impose sacrifices. Short-term expedients tend to prevail while sustainable medium-term objtectives continue to be
**Figure 2:** Consort Diagram

<!-- Start of picture text --> 254 clinics in 4 districts<br>(Bo, Bombali, Tonkolili, and Kenema districts)<br>Baseline Survey: September 2011<br>508 communities, 2,540 households (HH)<br>Control Community Non-Financial<br>Monitoring (CM) Awards (NFA)<br>Clinics = 84 Clinics = 85 Clinics = 85<br>Endline HH = 1,680 Endline HH = 1,700 Endline HH = 1,700<br>Endline Survey: May – June 2013<br>508 communities, 5080 HH<br>Ebola Outbreak: May 2014 – March 2016<br><!-- End of picture text --> Figure 2: the samples and timing associated baseline and endline surveys, the randomization, and the Ebola crisis. An initial end to the outbreak was declared in November 2015; however, a few additional cases emerged and the country was finally deemed “Ebola free” in March 2016.

# **3.4 Integrity of the Experiment**

Table C.1 reports balance across pre-specified covariates. Most notably, we find that the number of injuries or illnesses reported is lower in both treatment arms relative to control, household size is slightly smaller, and there are fewer households reporting a recent childbirth in CM. If anything, we expect such imbalances make it harder to find effects on general and maternal utilization. We also find that NFA communities have better cell phone coverage and a higher level of educational attainment, are less likely to belong to the Temne ethic group and are less likely to believe what a doctor told them. As a robustness check, we include unbalanced variables at baseline as additional controls.

We report manipulation checks in Tables C.2 and C.3 based on survey responses. Over 85 percent of CM communities report a meeting held by one of our implementing partners to discuss working with the clinic to improve health service delivery; the average CM community reports 2.5 such meetings. Around 44 percent of control communities report meetings as well. This does not reflect non-compliance by our implementing partners; rather, community meetings unrelated to our interventions are not uncommon across 17
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