rafmacalaba/datause-encoder-data huggingface dataset:
and the coefficients are increasing with size.<sup>4</sup> The workers employed by firms, employing 1500 or more workers receive the highest wages. In Zambia, Ghana, and Cameroon the highest wages were obtained in firms with more than 200 employees and in Kenya it was in the group with 400 or more workers that earned the most. Velenchik (1996a) also found a large premium associated with working for larger firms in Zimbabwe and the premium is found to be larger for white than blue collar workers. Findings suggest that larger firms use higher wages to increase the quality of the applicant pool, to reduce employment turn over, or to strengthen worker loyalty.

# **3. Data and Methodology**

Wages and productivity determination are examined by matched employee-employer manufacturing sector data; the Regional Program on Enterprise Development (RPED) survey data from 1993. The number of firms and employees interviewed are 201 and 1609, respectively. About 10 randomly selected workers from different occupational categories are interviewed in each enterprise. Matched employee-employer datasets have advantages compared to pure employee or employer data sets. An employee dataset may contain information about the sector in which a worker is employed but limited information about the firm. On the other hand, an employer dataset contains information about firms but limited information about individuals employed in the firms, apart from aggregate wage, education, and training costs. An employer-employee dataset allows detailed analyses of hypotheses related to both firms and individuals.

Empirical studies of determinants of wages and earnings inequality have focused primarily on factors affecting labor supply. Examples of long-run labor supply factors

> 4 Earnings per worker could increase with the size of the enterprise because of institutional factors that are correlated with firm size or economic factors. The non-institutional economic factors relating to wage increases are: (i) the inelasticity of labor supply of individual firms, (ii) factors that can be classified under 'efficiency wages'; (iii) profits, economic rent and labor productivity may increase with firm size and wages are set with profit-sharing considerations (see Mazumdar (1995) for details).

_5_
# 5. <u>CONCLUSION</u>

T&T has successfully achieved progress in the roadmap to diversification in which it has been engaged for several decades, albeit in the energy sector. Convergence towards the world average level of export concentration over the period 1980-2011 can be observed, and the country has transformed into an oil and gas economy from a solely oil-based economy. Nevertheless, further diversification away from the oil and gas sectors is possible and likely desirable as it would reduce the vulnerability of the economy to commodity price shocks and contribute to securing output growth for many years after the full depletion of the countries’ limited oil and gas resources.

We have attempted to determine the significant drivers of diversification episodes across the world and T&T using both descriptive statistics and econometric estimations. We have exploited a large panel dataset covering 183 countries over the period 1980-2010, and our econometric findings indicate that openness to FDI and access to finance are fundamental determinants of economic diversification over which the government has some control.

To diversify further its economy, these findings suggest that T&T could aim to attract more FDI in the non-energy sector and take measures aimed at increasing FDI induced spill-overs in the energy sector. Based on the findings of the FDI determinants’ literature, this could be achieved by participating more actively in regional trade agreement initiatives as it would increase the access to international markets its local and foreign producers enjoy. The country could push the Caribbean Community and Common Market (CARICOM) customs union members to implement more actively the necessary steps to establish a well-functioning common tariff zone in addition to potentiating the investment perception of the sub region. In addition, the government could envisage further free trade areas with large neighboring countries such as Brazil, Colombia or Venezuela. Improving the functioning of the domestic financial markets could also facilitate the creation of new business activities and generate employment opportunities.

While already quite good at the international level, there is scope to improve the quality and access to education in T&T. Secondary and tertiary education as well as research and development are important for maximizing FDI’s full benefits, and the authorities may consider investing more in these domains. Expanding the knowledge base of the economy would facilitate the development of new activities and also make the country more attractive to foreign investors.

Finally, improving the business environment for both local and foreign investors is crucial to diversify the economy of T&T. Easing the access to finance, improving property rights, reducing the burden of bureaucracy and rationalizing further the tax systems are reforms that have been proven to attract more investments in new activities.

20
for innovators also apply to firms that report themselves as non‐innovators. For example cognitive problems, framing or survey fatigue may bias some responses by non‐innovators.

Figure 3 compares combined product and process innovation rates as measured in the short questionnaire, the long questionnaire and the cleaned long questionnaire. In all countries the cleaning exercise reduces innovation rates compared to the long questionnaire, although by different magnitudes. The smallest differences are in Namibia (4 percentage points), Nepal (4 percentage points), and Malawi (6 percentage points) while the highest are in Bangladesh and Sudan, where innovation rates dropped by 20 percentage points and 33 percentage points respectively. Innovation rates measured by the long clean questionnaire are also smaller than in the short questionnaire for all countries with the only exception of Bangladesh. When measured using the clean long questionnaire, combined product and process innovation rates range from 6 percent in Sudan, 15 percent in Pakistan, and 21 percent in Nepal to 78 percent in Bangladesh and 84 percent in India.

# **4. 2. Which survey provides a more accurate measurement of innovation?**

Which questionnaire better captures innovation? Is a long questionnaire a good ‐or better‐ instrument to measure innovation in developing countries? The assumption is that a long questionnaire is more likely to capture an accurate picture of innovation as it gives greater information on the context and asks control questions that can potentially increase the accuracy of reported innovation. However, despite these reasonable assumptions, the subjective and self‐reported nature of innovation information makes it possible for firms to overestimate or even to underestimate innovation efforts also during in‐depth, single topic questionnaires. Moreover, long stands‐alone surveys can contribute to increase survey fatigue opening up space to underreport innovation in order to reduce the burden of the interview.

Insights on these possible scenarios can be obtained by looking at respondents’ answers in the experiment. Figure A decomposes the different combinations of responses to innovation in the surveys, including the short, long and long‐clean questionnaire. Six categories of firms are identified: firms that replied no to both short and long questionnaire (no>no); firms that replied yes to the short questionnaire and no to the long questionnaire (yes>no); firms that replied yes to both questionnaires and for which the answer was confirmed in the long‐clean questionnaire (yes>yes>yes); firms that replied yes to both questionnaires but for which the answer was not confirmed (yes>yes>no); firms that replied no to the short questionnaire and yes to the long questionnaire, with the yes confirmed after verification (no>yes>yes); and firms that replied no to the short questionnaire and yes to the long questionnaire, with the yes not confirmed in the long‐clean questionnaire (no>yes>no).

14
reduced intellectual function after adjustment for socio-demographic covariates and water Mn. Children with arsenicosis (exposure to water arsenic at level above that prescribed by WHO) achieved significantly lower performance than did children with water arsenic levels below WHO limit. Interestingly, the association was generally stronger for well-water arsenic than for urinary arsenic<sup>4</sup> .

Lastly, there is evidence that, when exposed to arsenic at early ages, children can develop visible symptoms such as pigmentation and arsenical skin lesions by the time they reach secondary school age<sup>5</sup> . Children with keratosis may become socially ostracised at school owing to the common belief that arsenicosis is contagious. If there are positive peer effects in classroom learning, affected children will be worse-off as arsenicosis would limit social interactions at school. Anecdotal evidence suggests that children with symptoms are often not sent to school in an effort to hide the problem and hence avoid such ostracism at school (Hassan et al., 2005).

School performance of children from affected households could be undermined for an additional reason. With nearly-complete awareness regarding the health implications of arsenicosis, affected households are increasingly seeking access to arsenic-free tube wells. Traditionally, children are sent to fetch water which cuts into study hours at home. If true, this would adversely affect school performance of children. However, the potential for this channel is a priori weak as arsenic contamination is highly irregular in a spatial sense. In many villages, unsafe tube wells are often near a safe well (Geen et al, 2005). Research on the choices of affected households suggests that a majority have managed to switch to arsenic free water wells after learning about the contaminated state of their own tube-wells. Using a large scale dataset on arsenic affected households in Araihazar district of Bangladesh, Madajewicz et al. (2005) find that 60% of the people whose wells were unsafe changed to another well within 12 months of receiving information by installing their own new well,

> 4 However, evidence from extant epidemiological and public health literature is not conclusive enough owing to very small size of the sample. For example, Calderon et al. (2001) utilizes a sample of 80 children where only 41 were exposed to high levels of arsenic. Similarly, Wasserman, Liu et al. 2004 used data on a pool of 400 children aged between 9.5 and 10.5 years of which only 201-176 could be assessed for cognitive skills. The sample came from only one region of Bangladesh-- Araihazar.

> 5 Upon examination of a large sample (N=5000) of children below 11 years of age from the affected Bangladeshi villages, Chakraborti et al. (2004) found Arsenical skin lesions in 6.1% of these children.

7
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or expenditure but they are typically administered only in selected areas or sub-samples of the refugee population.

In this paper, we combine proGres administrative data with survey data collected by the UNHCR in Jordan in 2014 and use a recently developed model for cross-survey imputations to estimate poverty among Syrian refugees. We also provide a sensitivity analysis to different poverty lines, estimate minimum sample sizes required for accurate estimation, compare the model proposed with alternative welfare measurement approaches, and test the performance of the model using targeting indicators. To our knowledge, this is the first experiment of its kind. Poverty studies that make use of cross-survey imputation methods have now become more frequent (see, e.g., Dang, Jolliffe, and Carletto (2019) for a recent review), but none of these works has focused on refugee populations.

We find that the imputation-based poverty estimates provided by the paper are not statistically different from the non-predicted consumption-based poverty rates (henceforth, the ‘true’ poverty rate), and that this result is robust to various validation tests, including alternative poverty lines and disaggregated population groups. These estimates are found to perform better or have smaller standard errors than other poverty measures based on asset indexes or proxy means testing. Moreover, our imputation models are rather parsimonious and use variables that are already available in the UNHCR’s proGres database, which is consistent with the findings in recent studies for imputation-based poverty estimates for regular populations. We also provide both theoretical and empirical evidence that relatively small survey samples can be combined with those from the census-type registration system to provide cost-effective and updated estimates of poverty.

While our estimation results are encouraging, they may not apply to other country contexts, sources of data or welfare measures. Further application of the proposed methodology to other 4
not be interpreted as policy experiments but rather as accounting exercises.<sup>9</sup> Two non-structural studies share similarities with our approach. Bourguignon et al. (2008) decompose country-level income distribution differences. Although their exercise is static, they also use simulations to aggregate semi-parametric models of each determinant of income dispersion, and generate counterfactual distributions in which the differences in the parameters governing each individual factor are shut down one at a time. Gayle et al. (2012) implement a decomposition that explicitly captures individual career dynamics but the object of their decomposition is the gap in the cumulative earnings of U.S. male and female executives, rather than a cross-sectional firm-level pay gap and hiring pipelines. Also, their approach relies on stationary analytical formulae rather than micro-simulations.

A second contribution of the study is to produce new evidence on where pay gaps are generated within a large, multicultural organization, using an original panel data set of sizable length and width, extracted from recent personnel records.<sup>10</sup> The novelty of our approach is to “locate” and quantify, rather than “explain”, pay gaps. It allows to focus follow-up investigations on those organizational processes where the gaps are highest and more malleable. If, for instance, most of the gender gap arose in entry salaries, a classic Oaxaca–Blinder decomposition could then be applied to entry salaries.<sup>11</sup> If instead, salary growth was the main pay gap source, follow-

> 9In fact, we lack the data to examine such behavioral responses where the gender gap is most salient – historical hiring patterns. For instance, when there is a change in the president of the WBG, churn increases sharply. It may be that the applicant pool changes in anticipation of this churn or immediately after. Unfortunately, the institution does not retain data on the historical applicant pool, and therefore incorporating behavioral responses is currently outside the scope of our work.

10Existing gender pay gap studies using single-firm data separately describe the gender differences in job assignment (Malkiel and Malkiel, 1973; Ransom and Oaxaca, 2005), entry salaries (Gerhart, 1990), salary growth and promotion (Jones and Makepeace, 1996; Hersch and Viscusi, 1996; BarnetVerzat and Wolff, 2008) and/or retention (Petersen and Saporta, 2004; Giuliano et al., 2005; Gobillon et al., 2014), but they do not provide, as we do, a quantitative comparison of these gender pay gap sources.

11The Oaxaca–Blinder decomposition evaluates the extent to which differences in an outcome vari6
> - 46 -

Table 17: **CoepowAd** Awel **r.wh 2bate** I **lcsefrs Output**

**ad** Nontradabl- Agricultural

**utpwut** UIdar Alto tive Agrlcultural Growth Rates

|Annual Growth<br>in Agricult rol<br>Ou put'|Manufact<br>FFrojction|Resul<br>urin<br>l1ige|ting Crowth Rate<br>Tertir<br>Projection|in Output ofOth<br>y<br>Range|_r Sectors<br>No<br>Projectio|nfarm<br>n<br>**Ra**|Nontradable<br>A<br>Pti|griculture<br>|
|---|---|---|---|---|---|---|---|---|
|i)GrowthOrigima|tesInIrrigated<br>A|riculture||||<br>**,g;**|rojecon|Range|
|<br>1.0|<br><br>872|038-072|||||||
|2.4<br>|.<br>1.9<br>|..<br>1.16-1.93<br>|1.06<br>2.64|1.67-1.06<br>1.80-2.66|0.86<br>219|0.46-0.86<br>136-221|1.89<br>|1.13-1.90<br>|
|3.26<br>|2.73<br>|1.78-2.74<br>|3.43|2.32-3.45|.<br>304|..<br>201-306|3.80<br>|2. 6-3.81<br>|
|4.0<br>|3.46<br>|2.36-3.47<br>|4.22|3.01-4.24|.<br>380|..<br>26E-382|4.81<br>|3.66-4.83<br>|
|6.1<br>|4.46<br>|3.22-4.47<br>|6.27|<br>3.94-6.29|.<br>482|..<br>364484|5.6o<br>|4.32-6.68<br>|
|6.6|6.46|4.13-6.48|6.36|<br>4.90-6.32|.<br>686|.-.<br>448687|6.77<br>|6.35-8.78<br>|
||<br>||||.|.-.|7.84|6.36-7.86|
|ti)Growth Ortigoatee<br>1.0|<br>**is Painfed**Agric<br>6.66|ulture<br>634-068|103||||||
|2.4<br>326|<br>1.80<br>|..<br>1.07-1.81<br>|.<br>2.6U|0.67-1.04<br>1.60-2.61|0.81<br>211|0.44-0.82<br>130-212|2.02<br>|1.21-2.01<br>|
|.<br>|2.67<br>|1.66-2.69<br>|3.39|2.32-3.41|.<br>296|..<br>194296|3.96<br>|2.77-3.96<br>|
|4.8<br>**66**|3.29<br>|2.23-3.31<br>|4.18|<br>3.01-4.20|.<br>370|.-.<br>280-372|4.99<br>|3.68-6.00<br>|
|**.**<br>**66**|4.27<br>|3.07-4.29<br>|6.22|3.94-6.24|.<br>471|..<br>346-473|6.85<br>|4.46-6.86<br>|
|**.**|6.27|3.97-6.29|6.26|4.91-6.28|.<br>6.73|..<br>4.38-6.76|8.96<br>8.63|6.60-6.97<br>6.52-8.06|

**a/** Tradable (crop) agricultural output only. b/ The **sam as** lower rang value our econometric **was** estimate. The upper range value is based on the assumption deriv ed by scaling all the national _SIO_ multipliers down **so** that the total nonfarm income multiplior **is** a.64, the to reflect technological chango and shifting expenditure patterns. that the full SIO multiplier increases by 2.6X per annum
30 Table 2 cont.

|Countrv|Year|Populatio<br>Size<br>(Milions)<br>(mid-1986|n<br>P<br> <br>Social<br>) Security|ooulation<br>Covere<br>Other Public<br> <br>Insurrnc _|d<br>by<br> <br>Private<br> <br>Insurance|Total**|Percent of<br>Population<br>Insured<br>(including<br>Family<br>Neffbers)|
|---|---|---|---|---|---|---|---|
|Tanzania|1987|23.0|--|-|-|||
|Ugnda|1987|15.2||--|--|||
|Zaire|1966|32.2||-||||
|Zanbia|1961|<br>5.6<br>(1960)||.342|--|.342|6.1X|
|Ziababie|1967|<br>8.7|.-||400,200|400.200|<br>_4.6X°_|

'The sources for the Information containfd In Tables 2, 3, **and** 4 are given for each country as the last entry for **each** country heading, in Table 4.

- **Am explained In the txt, in **saw** countries (2 L Zeire) private copanies my be required to furnish health care or pay for health care for thair employes. At som level of abstraction, this **my be** considered to be Ohealth insurance. Here, for purpoms of cornistency of treatment, "health Insurance" eans forml health insurance, where contributions (ether vowuntary or by law) are placed into an

- insuranmc pool, nd then withdraew to pay medical bills. What **the law** in Zaire does, in effect, ie force employers to self insure for its eloyees' health care. **Because** of space limitations in the Tables, **nu** bers of people **are always** given in the milions. Thus, for exaeple, the total population Burundi is 4.9 million, and the total ni 'r of people insured is .070 million (or 70,000).

- a mututlle de **La** Fonctfon Pthliquw. 3X of **salary** paid by employee and 4.5X paid by goverrmnt. Covers goverrmnt ployees and their faillies.

- b Report (see aources) ha an entry for Uationsl Social Inswrance Furd, data "not available".

- c Socalt Security and Pen ion Furd not inrolved In hse1th care-- long range plan to adopt ILO guidelines for health Insurnoe In Social Security plan.

- d Coverr_not *ptoyee receive 502 discowit at Goverr-ant facilities.

- Coverrurnt has been studying the possibility of National Health insurence (NH!) (mandated contributory) since 1985.

- f Equnvalent to more than half the urban population (NHIF-Nationtl Hospital Insurance Fund).