rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Absenteeism and shirking among medical personnel are widely observed in public health facilities in many developing countries, where employment is characterized by fixed compensation – usually in the form of salaries – and ineffective monitoring. For instance, in Venezuela specialists and senior doctors miss about one third of their contracted service hours, while residents and nurses are absent about 13% and 7% respectively of these hours (Di Tella, and Savedoff, 2001).Similarly, several studies based on surprise visits to public health care facilities of Bangladesh, Honduras, India, Peru and Uganda found that 42%, 27%, 43 %, 26% and 35% of the medical personnel working at these facilities were absent (World Bank, 2003).

At the same time, many public physicians remain highly motivated, putting in lots of effort and working long hours to provide quality patient care for little financial reward. This mix of provider-types poses fundamental challenges in the design of compensation mechanisms and monitoring regimes in public facilities. On the one hand, as argued by the shirking version of economic models of efficiency wages (Shapiro and Stiglitz, 1984) higher wages or intensive monitoring or a combination of both is likely to induce workers to put forth more effort. On the other hand, as argued by industrial psychologists and sociologists (Kaufman, 1984; Deci et al, 1971, 1975, 1999; Lepper et al, 1973), stringent monitoring can be construed by some as indicative of employers’ distrust and may result in reduction of work effort. These contrary behavioral responses pose a design problem, since the ultimate objective of any reward-control paradigm must be the improvement of inoptimal physician performance in such a way that it does not compromise the effort of those already motivated.

Striking a balance between wages and monitoring in the design of compensation mechanisms is particularly important in the health sector, where production and delivery are both highly labor intensive. Indeed, worker motivation – loosely defined as an individual’s degree of willingness to exert and maintain effort necessary to meet organizational goals (Franco et al, 2002) – is the key determinant of health sector performance. While resource availability and worker competencies are necessary for the 2
# **Table A15: Impacts of mental health on labor outcomes – Intensity of trauma experience**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health<br>scores|-0.064***|-0.361**|
||(0.009)|(0.157)|
|_First stage of 2SLS (dependent v_|_ariable is standardize_|_d mental health scores)_|
|Trauma experience<br>(intensity)*Year after arrival|-0.009***<br>(0.003)|-0.009**<br>(0.004)|
|Kleibergen-Paap F stat.|23.158|28.882|
|AR 95-CIs|[-0.273, -0.041]|[-1.396, -0.205]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,687|
|Number of individuals|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005) is 16.38. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

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<!-- Start of picture text --> Russia's net debt-to-GDP ratio (percent)<br>30.0<br>600<br>40.0<br>20.0<br>00 ; ‘ ; r<br>40.020.0 <ni ol‘ : —SST Ea<br>60.0<br>eeSSSSSSSSSSSSSSAKA AAA AAAeeSSS Ssgsesggsgsegsssgaee ed<br>=—=Econ 2020<br>After crisis baseline (passive scenario)<br>—@ After crisis baseline (with a temporary fiscal adjustment 2010-2012)<br>“+ After crisis baseline (with a permanent fiscal adjustment 2010-2015)<br><!-- End of picture text --> <!-- Start of picture text --> 10g<br>=<br>=<br>70<br>60<br>ei<br>4030 (esi éusB bush ieaseessseachessbeac dae dept Abd eesieuseLesieas eons Qualbedi'es<br>a0<br>to<br>A See<br>ARARATAARAARARARAARRARARARRARARASSSSSeee SRB SERARRRAARRRASSSSSSESSSess<br>— Real oll price $/BBL (US$ 2008) — Averageoilprice $/BBL (US$2008}---- Medianoil price $/BBL (US$2008)<br><!-- End of picture text -->
_50_ 'union officialse and 9 percent answered 'the workers." When asked to identify the main criticism of their own union, _59.5_ percent of these workers answered 'gap between leaders and workers," 21 percent said "corruption," and 6.3 percent noted "excessive political involvement." TABLE 53 **SLengk Advanta= of Unio Manbaehip** <!-- Start of picture text --> Perceint of Total Who<br>Volunteered This Answer.<br>Protection of:<br>Wages 41.7 %<br>Working Conditions 21.0 %<br>Other 7.0 %<br>No advant_ges 17.7 %<br>No opinion 10.0 %<br>No answer 3.6 %<br><!-- End of picture text --> Source: Authors' survey of workers, 1986.

Combining the information from the 1963 and 1986 surveys of the workers' opinions, we obtain the impression that trade unions in Senegal are, and have been, generally aligned with powerful political groups and somewhat divorced from the workers' mterests. This view of the "cooperating" unions is also held by the the noncooperating (opposition) trade union organizations. The question that naturally arises in this context is why the majority of workers join the unions. The answer seems to lie in low union dues and a sense of protection against potentially extreme actions by employers.

# _5.4_ Trade Union Perspective

On the several occasions that we met with the leaders of the CNTS, we were struck by their low-budget operation, tbhir adversarial rather than cooperative approach to industrial relations, and the fact that although some of their positions were
employment in core activities (operations) at more than 90% in agriculture, but at only about 33% in coke and petroleum (figure 6). This variability carries over to differences across countries as well. On average, the core activities of manufacturing firms account for only 50% of employment, meaning that half the employees of manufacturing firms perform services activities. This suggests that the services value added embodied in manufacturing, and thus the contribution of services to GVCs, could potentially be much higher than that captured by traditional value-added measures.

**Figure 6. Decomposition of jobs embodied in gross manufacturing exports, by industry, percent, 2011**

<!-- Start of picture text --> Utilities<br>Other manufacturing<br>Transport equipment<br>Electronics<br>Machinery<br>Metals<br>Nonmetallic minerals<br>Rubber and plastics<br>Chemicals<br>Coke, petroleum<br>Paper, print, publishing<br>Wood<br>Textiles and apparel<br>Food products<br>Mining<br>Agriculture<br>0% 20% 40% 60% 80% 100%<br>Research and development, engineering, and related technical services<br>Operations<br>Transport, logistics, and distribution<br>Marketing, sales, and after-sales services<br>Horizontal support activities<br><!-- End of picture text --> _Source_ : Miroudot 2016.

# **The implications of services in global value chains**

As seen, services constitute the vital connecting links of value chains as well as a range of inputs sourced either at arm’s length or in-house. These value chains are “global” not just when transactions cross international boundaries but also when consumers or providers do so, especially by establishing a commercial presence abroad.

Growth theories have emphasized that trade in intermediate goods and services generally improves the allocation of capital and labor across sectors and countries (Jones 2011). The literature helps draw out the implications of services in GVCs for two key aspects of economic performance: the growth of productivity and the evolution of comparative advantage.

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# _3.2. The net financial return to the use of authentic inputs_

The ratio of crop output and input prices ultimately determines whether yield gains translate into increased profits for farmers. We now turn to examine the variation in relative prices across time to assess the profitability of input use for the average farmer.<sup>9</sup> We examine the agricultural inputs for which we were able to identify yields from controlled experimental studies undertaken in Uganda (mainly fertilizer and improved seeds) for crops that capture a high share of agricultural income in Uganda: maize, beans, matooke and coffee (see Table 2.2).

Although input prices are stable over time, crop prices are volatile. There has been some, but relatively modest, variation in the cost of inputs in Uganda over the period 2005/06‐2013/14, as shown in Figure 3.4.4.<sup>10</sup> However, there has been considerable variation in crop prices. This is particularly relevant for the adoption of inputs, as fluctuations in wholesale prices are often passed on to producers who then receive a smaller market share, particularly at the farm‐gate when they sell their crops (Fafchamps and Hill 2008). Figure 3.4 details the volatility in the real wholesale price of maize and matooke from 2000 to 2012 using data on wholesale prices collected by the Uganda Bureau of Statistics (UBOS) in 8 markets as part of price data collection for the consumer price index (CPI). The graphs show the distribution of prices using data only from months in which farmers typically sell (namely November to February and June to August, as reported in the UNPS survey). The price distribution is analyzed separately for each of the four regions.

**Figure 3.4. Distribution of real crop** **<u>prices</u>**

<!-- Start of picture text -->
1. Maize crop prices (2000 ‐ 2012) 2. Bean crop prices (2000 ‐ 2012)<br>1200<br>700<br>1000<br>600<br>800<br>500<br>400 600<br>300<br>400<br>200<br>200<br>100<br>0 0<br>0 50 100 0 50 100<br>Percentile Percentile<br>Central East North West Central East North West<br>3. Matooke crop prices (2000 ‐ 2012)  4. Input prices (fertilizer and pesticide, UG shs per<br>kg.)<br>Real price per kg (2005 UGX prices) Real price per kg (2005 UGX prices)<br><!-- End of picture text -->

> 9 As mentioned, prices are likely to be related to how easily farmers can access input and output price markets. In Section 4 we examine the role of distance to markets in influencing input adoption for farmers.

> 10 We only include fertilizer and pesticide, because there was no information on the quantity of hybrid seeds used.

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leveraged buy-outs (LBOs) that resulted in complete transfer of control to the PE investor. However, in recent years PE firms have also started to take minority equity stakes in already publicly-listed firms via PIPE transactions.

PE firms are usually credited with three potential value creating strategies. Kaplan and Stromberg (2009) define them as financial, governance and operational improvements. Financial engineering entails the use of higher levels of debt to create tax shields as well as to sharpen the incentives of the management team. Governance improvement is largely driven by providing a significantly larger share of equity to the operating management and through mitigation of agency problems via concentrated ownership. Operational improvement entails deploying significant industry-specific knowledge to create better-run organizations. Thus, unlike other institutional investors, PE firms are more likely to be involved in the day to day operations of the firms that they invest in. Successful PE investors are credited with the ability to source and screen investment ideas as well as the ability to monitor firms, especially those with poor corporate governance, using mechanisms such as board representation and active involvement in the strategic decisions of the firm.

# **3. Data and Summary Statistics**

## **3.1 PIPE and SEO Sample**

Our data on PE financed PIPE issues come from the Asian Venture Capital Journal (AVCJ) Database, a new electronic database that maintains information on private equity (PE), venture capital (VC), and mergers and acquisitions (M&A) transactions in Asia. We consider PIPEs from a set of nine countries, including Australia, China, Hong Kong, Special Administrative Region of China, India, Japan, Singapore, South Korea, Taiwan, China, and Thailand, all of which report at 8
# **_Summing up: Preferences—the good, the bad and the ugly_**

The bottom line is that the research suggests that preferences are widely used by many countries, but that the benefits are reduced by a variety of factors, whether by design, incidentally, or by domestic supply constraints. Much depends on how binding rules of origin are and how high are MFN tariffs. A recent analysis suggests that if one assumes away preferences – i.e., only MFN barriers are assumed to apply – developing countries would confront _higher_ protection in the OECD than other countries, i.e., the current structure of protection implies a _negative_ preference margin. This is illustrated by the darker bars in Figure 1, which reports an overall trade restrictiveness index (OTRI).<sup>29</sup> Alternatively, if it is assumed preferences are fully utilized – that is, we assume away rules of origin and other constraints, which are known in practice to reduce the utilization of preferences, and it is assumed that all rents created by preferences accrue to exporters – there is still much to be done to remove the negative effect of trade barriers.

**Figure 1. OECD Protection and Preferences**

<!-- Start of picture text --> Full utilization No utilization<br>40<br>30<br>20<br>10<br>0<br>-10 LIC LDC<br>-20<br>-30<br>-40<br>-50<br>Preference margin<br><!-- End of picture text --> _Source:_ IMF and World Bank, 2004.

The average reduction in OECD trade barriers due to the preference programs is not more than 20-30 percent of MFN rates (IMF and World Bank, 2004). In part this is due to (i) the effect of NTBs that apply to all imports, (ii) the fact that OECD countries

> 29 What follows draws on Hoekman (2004a). The OTRI is the uniform tariff equivalent of all the trade policies (tariffs and core NTBs) maintained by a country that would replicate the observed volume of imports. It includes estimates of the ad valorem equivalents of specific tariffs, core NTBs, and agricultural producer subsidies. See Kee, Nicita and Olarreaga (2004) for details.

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triggered. It appears that this type of situation occurs frequently. The South Centre (2009, p2) concludes that, in over 85 percent of import surges,<sup>1</sup> the import price did not fall more than 15 percent below the preceding 3-year moving average.

As the focus of the volume-based safeguard is on situations where the price of imports does not decline, we need to ask carefully why imports might rise when import prices are not declining. For most agricultural commodities, this is likely to be the case because of weatherinduced shocks to output which—in the case of crops—are largely reflected in yield changes (Roberts and Schlenker 2010). We considered following Roberts and Schlenker in using the deviations of yields from trends, but finally decided to use the three year average of previous yields as our benchmark against which to compare current yields. To the extent that changes in imports reflect changes in the volume of output, use of this average-yield measure as the base should better reflect the deviations in imports from their three-year moving average that trigger the volume-based SSM. We compared the deviations from the 3-year moving average with those from a linear trend and found those from the 3-year moving average to be smaller (Table 1).

Our analysis considers the impacts of yield shocks and subsequent application of the Q- SSM in a sample of low and middle-income countries (for the list of countries see Table 2) many of which are, or may become, importers of key commodities, particularly maize, rice and wheat. For all countries included in our sample, we have collected detailed data from household surveys on the sources of income and patterns of expenditure—with special emphasis on the consumption of staple foods whose price is typically very important for the poor, and on income from production of these goods—for a large sample of households. These data are used to capture the impacts of production and price shocks on the real incomes of households.

The first step in the analysis is to estimate the distribution of unfavorable shocks to agricultural output—such as might arise from a drought—and evaluate their implications for the national economy and for household welfare. We use historical data on yield shocks, available from the FAO, to estimate the distribution of shocks in productivity relative to the 3-year moving average. We then implement shocks in a CGE model (GTAP) as negative productivity shocks and, through a Monte Carlo exercise, calculate the resulting distribution of prices, household incomes and poverty rates.

> 1 Where an import surge is defined as imports exceeding 110 per cent of the preceding three-year moving average.

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# **VI.** Conclusions

In this paper we described the development of a simulation model which allows the user to examine in detail the interaction between a financial system and its economic environment. The model allows the user to compute and project indicators that are necessary to monitor the performance of a financial institution, and to examine how these indicators change in response to changes in the macroenvironment. The model should be useful to financial analysts that need to plan and forecast for growth and profits of a financial institution, or a group of them, and that are interested in capturing the linkages with the macro-economy in a fully consistent framework. It should also be vaiuable to economists interested in assessing the viability of the financial system, and in particular in assessing the impact of financial restructuring. When major financial restructuring is involved, model simulations can help policy makers and supervisors to quantify the impact. The approach in this paper may be applied to most countries and different financial institutions with minor modifications. The model which is implemented in Javelin has been designed to be used as a "template" that can be adapted to a particular country. The model may be used as an independent financial analysis model in which the user examines the performance of a bank. but without necessarily being "connected" to the macro-economy. Other option is to use the model as a macro-economic framework without a very detailed monetary system.

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identifying assumption.

# _Estimation Equation_

We measure pollution using data from monitoring stations at two different locations within the city, _ITO_ and _Siri Fort_ .<sup>14</sup> Table 2 presents pollution statistics at each location, along with weather conditions at _Safdarjung_ , Delhi. _ITO_ has much higher pollution compared to _Siri Fort_ : Average hourly _NO_ 2 and _CO_ at _ITO_ are 3.2 and 1.5 times their respective levels at _Siri Fort_ . This is not surprising given that _ITO_ is a major traffic intersection, while _Siri Fort_ is a mainly residential area. Ideally, we would have liked to know weather conditions specific to each location. However, we only have hourly weather data for _Safdarjung_ , which is fortunately located between _ITO_ and _Siri Fort_ . We use this as the best available proxy for weather conditions at each location. As the dynamics of pollution are likely to be different across the two locations, and also because they are at different distances from the various line expansions, we estimate impacts at each location separately.

At each location we estimate the impact of a particular metro extension using a time series of hourly pollutant data lying within a symmetric window around that extension’s opening date. We also ensure that there are no other extensions within this window. Thus, a window is characterized by a location _l_ ( _ITO_ or _Siri Fort_ ) and an extension _m_ . The RD approach is implemented by estimating the following OLS regression within each window:

> <sup>_y_</sup> _tl_ , _m_ is pollutant level (in logs) in hour _t_ , at location _l_ , when studying the effect of extension _m_ . _DM tm_ is the discontinuity dummy for extension _m_ : Within

> 14. In talking to experts at the CPCB, we were told that a monitoring station measures the quality of ambient air passing by it, and it is not possible to demarcate a precise catchment area for which the quality measure would apply. Given that the monitoring stations at _ITO_ and _Siri Fort_ are approximately 9 kilometers apart, we believe that they each measure air quality in two distinct geographies within the city. Some evidence for this is provided in table 2, which shows that average pollutant levels are very different across the two locations.

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- 2 -

# **I. INTRODUCTION**

It is natural to expect that aggregate agricultural growth is positively related to infrastructure development. However, how to strengthen such a relationship at the operational level remains debatable. Specifically, it is questionable what type of infrastructures need developing to promote agricultural production and competitiveness. Which farm product is the most important to stimulate overall growth and reduce poverty in developing countries? The current paper, casting light on the significance of agriculture in Africa, aims at examining the potential effects of infrastructure development on agricultural growth.

Following the general overview, the paper will pay particular attention to two commodities that are differently characterized at various levels: coffee and cocoa, and dairy—essentially cow milk. Sub-Saharan Africa is the geographic focus of this paper because the region is and will continue to be relatively heavily dependent on the agricultural sector into the foreseeable future. However, the empirical results are relevant to other regions as well. Methodologically, the paper takes the middle course between the micro and macro perspectives in the sense that it estimates the supply and demand system for a given commodity, while relying on existing aggregate data. There are considerable data limitations to directly answer the above-mentioned questions, and the possible answers may vary across commodities and across countries.<sup>1</sup> However, it can be shown that infrastructures would influence coffee and dairy production differently, and different infrastructure services have to be improved to accelerate agricultural growth.

The potential of agriculture to contribute to the overall growth of economies in Africa is regarded as high. However, in general, under current contributions such growth seems very moderate. Over the past five years about 75 percent of the countries whose data are available achieved relatively low growth rates in agriculture, compared with nonagricultural sectors

> 1 If there existed sector-specific input variables, the empirical growth model could be applied for agriculture sector growth. However, it is difficult to obtain sufficient data representing agriculture-specific physical and human capital and other macro variables, though some data on agricultural employment may be available.