rafmacalaba/datause-encoder-data huggingface dataset:
The results from this equation (Table 12, third stage, column 4) are not very illuminating. The equation has a fairly poor fit (R2= 0.16) and the main finding is that children in Savannah work on average 9 hours longer than elsewhere. This is in addition to their already higher probability to work. The selection variable lambda has a large positive coefficient, indicating that any unobserved variables which make selection into child work more likely also contribute to increasing work hours above average. This result underlines the double disadvantage faced by children in Savannah and the need to make intervention in this region a top policy priority.

_Summary._ The results from the rural sequential probit model identify several key characteristics of the household which affect the child labor decision, but the overriding finding (and major difference with the results for urban areas) is the strong location effect. All other factors being the same, children in the Savannah have a far lower probability to go to school or to combine work and school than children elsewhere. This reflects the thin educational infrastructure in Savannah-a disadvantage that has been present for several generations as reflected e.g. in literacy rates in that region which are less than one-third the national average (Grootaert, 1993). The prerequisite for any successful child labor policy in rural C8te d'Ivoire is therefore to reduce the gap in education investment between Savannah and the rest of the country.

57
|Legume grown on plot (1/0)|0.177|0.382|0.198|0.398|
|---|---|---|---|---|
|Maize yield per hectare (kilograms)|1,154|1,649|1,282|1,718|
|Age of plot manager (years)|48.29|14.63|50.36|14.94|
|Phosphorus per hectare (kilograms)|10.28|22.11|11.93|23.38|
|Owned Household Assets (Thousand Naira)|140.9|548.2|93.55|147.5|
|Maize price (Naira per kilograms)|79.2|61.83|104.37|70.49|
|Fertilizer price (Naira per kilograms)|115.68|153.5|102.20|41.76|

_All prices are adjusted to 2012 prices using the cpi from the Nigerian National Bureau of Statistics_

# **4.1 Production function estimates and marginal physical product of nitrogen**

The production function estimates are presented for each farming system in Table 3. The results from a chow test (Chow, 1960) indicate that a pooled production function across farming systems is inadequate to explore the yield effects of nitrogen application in Nigeria<sup>16</sup> . Consequently we run separate production functions for each of the 3 farming systems under consideration. We present the pooled OLS and Fixed effects results for comparison. Table 3 indicates that applied nitrogen significantly affects maize yields in the Cereal- Root Crop Farming system (C-RCFS) and Tree Crop farming system (TCFS). The negative squared terms imply decreasing returns to applied nitrogen and indicate that the quadratic functional form used is likely appropriate. Only in the TCFS do we find evidence of increasing returns from the use of applied nitrogen alongside commercially purchased seed (likely to be improved varieties). This is in line with our expectation that improved varieties of seed and applied nitrogen are complementary inputs<sup>17</sup> . The general insignificance across other systems might reflect the poor quality of commercially purchased seed; often a problem in Nigeria (Ajeigbe et al., 2008). The seeding rate appears to be a major determinant of maize yields in Nigeria and this holds across all farming systems. The coefficients range between 14 and 40 and are significant at 1%. Higher labor supply, the use of a tractor or other mechanical equipment and animal traction tend to increase maize yields in the C-RCFS<sup>18</sup> . Where significant, maize plots on which two other crops (apart from maize) were grown had better yields than those on which 3 or more other crops apart from maize. This likely indicates that while mixed cropping (with a leguminous crop for

> 16 With F(51, 1927) =2.5, we reject the null that the reduction in the residual sum of squares from running separate regressions is not significant at 1 %.

> 17 We also tried to interact the applied nitrogen variable with irrigation but were unable to due to collinearity problems. 18 Whether a household member received advice on agricultural information (likely endogenous) was also considered and found not to be significant.

15
13 blindly follow their selection of important events rather than us cherry-picking the events ourselves. Selecting too many events by including events that might not appear so “important” should also lead to more conservative estimates because the included cohort fixed effects are then more refined, i.e. lump together fewer years than if we were not using these events. However, in the rest of the analysis, we will privilege the results Both approaches reduce returns for both developing countries and developed countries, to 0.8-1.1*** and 2.6-2.8*** respectively (N = 122; row 1). They accentuate the gap between developed and developing countries (to 1.7***-1.8***). However, while these two approaches help capture cohort effects, they do not fully solve the experiencetime-cohort problem since there may still be yearly cohort effects.

Web Appx. Section A.3 and Web Appx. Table A.8 show results hold if: (i) periods are constructed based on ages 18-40 or 18-30, in case important events are more consequential in the first decades or years after high school; (ii) we add as a control the country’s average per capita GDP growth rate for those years during which the individual was aged between 18 and 67, since the identified important years may not capture well uneventful periods of growth or recessions; (iii) add country-specific period dummies equal to one if the individual was aged between 0 and 17 during the period(s), and the individual’s corresponding growth rate, in case important events are consequential during childhood; (iv) combine the decade fixed effects, events fixed effects and growth rate approaches; and (v) use Lagakos et al. (2018b)’s HLT approach. Like them, we consider four versions of the HLT approach, based on the last 5 or 10 years of experience and using a depreciation rate of human capital of 0 or 1% (see rows 12-15).

# **4.3. Other Main Results**

**Hourly Wages vs. Hours Worked.** Results hold if we use returns based on hourly wages (row 2 of Table 1). Indeed, monthly wages can be decomposed into hourly wages and hours worked (N = 131; 105; 105). Hours worked slightly increases with development when ignoring cohort effects (Col. (1) in row 3). Yet, since this effect is very small, hourly wages, and thus hourly productivity, explains most of the returns based on earnings. However, using monthly wages allows us to use the full sample of 145 countries. **Selected Bins.** Results hold if we use returns (N = 145; 122; 122): (i) Based on the 0-25
- TSXV companies voluntarily graduate to the main board. Although it is more expensive, the main board is viewed as having more cachet.

Graduation has pros and cons. On the plus side for the SME exchange, graduation frees up often limited institutional investor appetite for SMEs and allows successful companies to benefit from the main board’s greater liquidity, investor pool, and credibility. Allowing graduation, however, implies by definition losing the more liquid and larger companies. Not all SME exchanges allow graduation. Companies that list on Chinext or the SME board of the Shenzhen Stock Exchange are not eligible to apply for a subsequent listing on the country’s larger exchanges.

Table 5 shows graduation requirements that must be met, in addition to first meeting the main board’s standard requirements.

||Table<br>Allow/Encourae|5. Graduation to the Main Board|No of Comanies|
|---|---|---|---|
|Exchange|g<br>Graduation?|Requirement|. p<br>Graduated|
|NSE India<br>Emerge|Yes|May graduate after a minimum of two<br>years on the SME platform; migration<br>policy is in place, specifying paid-up<br>equity, profitability|None|
|JSE AltX|Yes|Meet the requirements of the main board|24|
|GreTai|Yes|Nothing additional|312 to Taiwan<br>Stock Exchange.|
|Bovespa<br>Mais|Yes|Must comply with main board rules (if<br>Novo Mercado: 25% free float and 20%<br>independent board members, among other<br>rules)|None|
|WSE<br>NewConnect|Yes|Capital requirement to be listed on main<br>market is reduced for companies previously<br>listed on NewConnect (€12million vs. €15<br>million)|26|
|BIST ECM|Yes|Must have more than 250 shareholders|1|

_Source:_ WBG compilation of exchange information.

# **_A. Market Participants_**

# 1. Issuers and Issuance

What size company to allow on the SME exchange and whether to implement minimum size requirements are key considerations. The experience across emerging market exchanges varies considerably. Again, Table 1 notes how an SME is defined in different countries.

As shown in Table 6, a wide variety of company sizes are served by SME exchanges, from NewConnect’s 68 percent of companies with a market capitalization below $5 million to GreTai’s 30 percent above $100 million.

18
10 The above direct effect is the same concept as the nominal protection coefficient _(NPC)_ which is the ratio of the farmgate price to the border price after adjusting for all the relevant costs underlined above. Clearly, the _NVPC_ compares the farmgate price to the maximum that could be offered to producers (border price less than the costs advocated above). A ratio of less than one indicates that agriculture is being taxed. The _NPC_ has been criticized on the ground that as a measure of incentive distortion it ignores the exchange rate impact on policy distortion, and it also ignores the effect of exchange rate misalignment or implicit taxation. That is, the _NPC_ will understate the degree of agricultural taxation when exchange rates are overvalued at the same time it will be unable to provide an unambiguous answer as to the relative importance of one source to the net effects change over time in the sources of variation -- farmgate price, international price and exchange rate (Jaeger, 1992). The real protection coefficient _(RPC)_ is used to meet the above criticisms. It is a _NPC_ calculated at the equilibrium exchange rate.

"The indirect effect has two components. The first is the impact of the unsustainable portion of the current deficit and industrial protection policies on the real exchange rate and thus on the price of agricultural commodities relative to nonagricultural nontradeables. The second is the impact of industrial protection policies on the relative price of agricultural commodities relative to that of nonagricultural tradeable goods" (Krueger et al., 1988, p.255).

Another approach showing the linkage betwveen macroeconomic, policies (represented by real exchange rate) and real output price is that of the World Bank. Following the World Bank (1994, p. 271), the real producer price is decomposed as follows:

# _RPP =PF /CPI = PF/PB e e WYPIICPI Pu/WPI =NPCERPB_

where _RPP is_ the real producer price for export crops, _PF_ is the farmgate producer price, _PB_ is the border price in dollars, e is the nominal exchange rate defined as above, _NPC_ is the nominal protection coefficient, _RER_ is the real exchange rate, and _PB_ is the real price of country's exports (at the border).
Figure 1: Trade Reforms <!-- Start of picture text --> Final Goods Tariff Rates Change in Tariffs versus Pre−Reform Tariffs<br>1985−1997<br>year<br>5th Percentile 25th Percentile<br>50th Percentile 75th Percentile<br>0 50 100 150 200<br>95th Percentile 1985 Final Goods Tariff<br>(a) Final Goods Tariffs (b) Change in Tariffs vs. Pre-Reform Tariffs, 1985-<br>1997<br>Change in Tariffs versus Pre−Reform Tariffs Change in Final Goods versus Input Tariffs<br>1985−2004 1985−2004<br>0 50 100 150 200 −60 −40 −20 0<br>1985 Final Goods Tariff Change in Input Tariff, 1985−2004<br>(c) Change in Tariffs vs. Pre-Reform Tariffs, 1985- (d) Changes in Final Goods versus Input Tariffs, 1985-<br>2004 2004<br>19851986198719881989199019911992199319941995199619971998199920002001200220032004<br>200<br>0<br>150<br>100 −50<br>50 −100<br>Final Goods Tariffs (Ad Valorem) 0<br>−150<br>Change in Final Goods Tariff, 1985−1997<br>−200<br>0 0<br>−50<br>−100<br>−150<br>Change in Final Goods Tariff, 1985−2004 Change in Final Goods Tariff, 1985−2004<br>−200 −200<br><!-- End of picture text --> Panel (a) shows the 5th, 25th, 50th, 75th, and 95th percentiles of final goods tariffs by 3-digit National Industrial Classification (NIC) code in each year. Panels (b) and (c) show the relationship between 1985 final goods tariffs and the changes in final goods tariffs through 1997 and 2004, respectively. Panel (d) shows the relationship between the changes in final goods and input tariffs between 1985 and 2004. Source: Authors’ calculations based on TRAINS and various publications of the Government of India.

32
# **Figure 4**

# **Work-Intensity of Older Workers and Elderly in China and Four Countries**

(China, Indonesia, Korea, US and UK) Panel A Average Hours of Work Per Week (Unconditional on Work Status) <!-- Start of picture text --> China (CHARLS) Indonesia USA<br>80 80 80<br>60 60 60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 0<br>age age 45 50 55 60 65 70 75 80<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>China (CHNS) Korea UK<br>80 80 80<br>60 60 60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 0<br>age age 45 50 55 60 65 70 75 80<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>Lowess Plot bw=0.3<br>Working Hours orking Hours<br>W<br>Working Hours rking Hours o<br> W<br><!-- End of picture text --> Panel B Average Hours of Work Per Week (Conditional on Working) <!-- Start of picture text --> China (CHARLS) Indonesia USA<br>80 80 80<br>60 60 60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 0<br>age age 45 50 55 60 65 70 75 80<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>China (CHNS) Korea UK<br>80 80 80<br>60 60 60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 0<br>age age 45 50 55 60 65 70 75 80<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>Lowess Plot bw=0.3<br>Working Hours<br> Working Hours<br>Working Hours<br> Working Hours<br><!-- End of picture text --> Notes: Average hours worked per week by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. The following data sources are used: China: 2009 CHNS and the 2008 CHARLS pilot; Indonesia: 2007 Indonesia Family Life Survey (IFLS); Korea: 2006 Korean Longitudinal Study of Aging (KLoSA); United States: 2008 Health and Retirement Survey (HRS); United Kingdom: 2008/9 English Longitudinal Study of Ageing (ELSA).

32
alleviated by adjusting all time varying variables in equation (6) by appropriate measure of district size.

If panel data are available, as in our case, suitable _within transformation_ s of (6) can be used to remove the unobserved ® _i_ . Both deviations from district specific means (fixed effects) and first differences remove the ® _i_ , but the mean deviations transformation is not suitable if the model contains regressors that are only weakly exogenous. This is a relevant issue in matching functions because the explanatory variables (unemployment and vacancies) are predetermined by previous matching processes through the flow identities. In particular, omitting district subscripts for simplicity, the stock-flow identities imply that<sup>14</sup> Lagged outflows in (74) are in turn given by a lagged version of matching function (6) as Since any district mean is computed from all district observations over time, the means contain all values of the error term _{ετ: τ = T, T-1,...,1}_ . This creates correlation between transformed explanatory variables and transformed error terms and hence leads to biased estimates.

The first difference transformation contaminates the transformed variables only with recent error terms _{ετ: τ= t-1, t-2}_ , as may be seen by rewriting (6) in a first difference form as

> district and regional 1992-94 data from the Czech Republic, and Profit (1996), who uses Czech district data during 1992-94. For a brief survey of the principal studies se Münich et al. (1997).

> 14 These identities assume that all matches are brought about by the reported unemployed and vacancies (there being no out-of-register matching). Other forms of matching may create more complicated identities but will not eliminate the

− 15 −
# Table of Contents

|Introduction .................................................................................................................................................. 4|
|---|
|I.<br>An Overview of Haiti’s Labor Market Trends from 2001 to 2012 ......................................................... 5|
|A.<br>Labor Force Participation .................................................................................................................. 6|
|B.<br>Employment Rates ............................................................................................................................ 9|
|C.<br>Unemployment Rates ..................................................................................................................... 11|
|D.<br>Quality of Jobs ................................................................................................................................. 12|
|II.<br>Shining Light on the Determinants of Labor Income in Urban Areas (2007-2012) ............................ 15|
|A.<br>Labor Income Increased between 2007 and 2012 with Important Sectoral Disparities. ............... 15|
|B.<br>Returns to Endowment Drove the Wage Increase between 2007-2012 ........................................ 16|
|C.<br>Drawing Lessons from Lower Productivity Sector Analysis ............................................................ 19|
|Conclusion ................................................................................................................................................... 23|
|Bibliography ................................................................................................................................................ 24|
|Annexes ....................................................................................................................................................... 26|

3
two elements are likely to be found together more often in rich countries, inter-industry wage differences will be reduced more there.<sup>16</sup>

# **_Introducing different skill-intensities_**

So far we have assumed that the skill intensity of the two industries is the same: each had the same share of skilled and unskilled workers. This is clearly an unrealistic although helpful assumption. By removing it, we may affect the conclusions regarding the effects on inter-industry inequality.<sup>17</sup> We shall consider first the case of skill-level bargaining. The ratio between the average wages will be where σ1=low-skill labor intensity in the protected sector, and σ2=low-skill labor intensity in the unprotected sector.

Note that if the shares are the same, the second term in (12) becomes unity. In accordance with empirical studies reviewed above (Richardson 2000, Harrison and Hanson 1999; Currie and Harrison 1997; and also Winters, McCulloch and McKay 2004, p. 100) we assume that prior to trade reforms protection is biased toward the low-skilled sector in both poor and rich countries. Consider now the polar case where σ1=1 and

> 16 Since labor compositions are the same across sectors, the sources of inter-industry wage differences are either (or both) tariffs or unionization premium. If bargaining is skill-based, then liberalization will erode the unionization premium, and ∆ must go down. If bargaining is industry-based, then liberalization will erode protection rents and unionization premium, and ∆ must again go down. The only other possibility— which we explicitly exclude here—is a combination of protection for one sector and collective bargaining for the other. In that case, change in ∆ would be ambiguous.

> 17 Inter-occupational inequality will not be affected by different sectorial skill intensity. It is simply the ratio between ws and wu and that does not depend on what is the percentage of skilled and unskilled workers in different sectors.

19
These distributional predictions are not, however, borne out in the CMMHH data, which shows the highest rate of job losses in the middle wage quintile (35.7 percent), and lower rates of loss at the bottom (9.7 percent) and the top (8.3 percent). Data on the occupational and pay structures within firms is likely needed to better identify the distributional dynamics of employment outcomes both during a crisis and during normal times of workforce adjustment. In developing countries such data are lacking.

## **5.7.2 Georgia**

|_Table 5-25 Jordan: Percentage_<br>**National nominal hourly**|_of PFPS Jobs L_<br>**Wave 1**|_ost, by Wage Q_<br>**Wave 2**|_uintile_<br>CFUWBES||
|---|---|---|---|---|
|<br>**wage quintile**|CFUWBES1<br>Modeled|CFUWBES2<br>Modeled|2<br>Survey<br>Measured|**CMMHH**|
|**Q1 (bottom)**|19.0|30.4|30.1|9.7|
|**Q2**|16.6|26.5|25.5|26.7|
|**Q3**|14.0|22.6|22.6|35.7|
|**Q4**|14.1|21.0|23.5|17.1|
|**Q5 (top) **|13.5|21.4|25.0|8.3|
|**Total**<br> Sources: CFUWBES1, LMPS 20|15.8<br>16, and CMMHH|25.2<br>Jordan 2021.|25.5|23.3|

In contrast to _Table 5-25 Jordan: Percentage of PFPS Jobs Lost, by Wage Quintile_ Jordan, for **Wave 1 Wave 2** Georgia our **National nominal hourly** method **wage quintile** CFUWBES1 Modeled Modeled projects declining job **Q1 (bottom)** 19.0 <u>30.4</u> losses as wage **Q2** 16.6 26.5 levels rise. In **Q3** 14.0 22.6 the early **Q4** 14.1 21.0 stage of the **Q5 (top)** 13.5 21.4 crisis an **Total** 15.8 25.2 estimated 21.1 Sources: CFUWBES1, LMPS 2016, and CMMHH Jordan 2021.

## percent of

workers in the bottom wage range are projected to have lost their jobs and 16.4 percent of those in the top range (Table 5-26). By the winter, the top paying Georgian PFPS jobs are predicted to have completely recovered, whereas those jobs paying in the bottom range still are estimated to have experienced a cumulative job loss of approximately 11.2 percent. Because the GHFPS does not collect data on pre-COVID individual wages, we cannot provide any comparison to the larger population of private sector workers..

_Table 5-26 Georgia: Percentage of PFPS Jobs Lost, by Earnings Interval_

||**Wave 1**|**Wave 2**||
|---|---|---|---|
|**By monthly nominal net**<br>**earnings interval LCU**|CFUWBES1<br>Modeled|CFUWBES2<br>Modeled|CFUWBES2<br>SurveyMeasured|
|**400 or less**|21.1|11.2|10.2|
|**401 – 600**|19.1|7.5|8.6|
|**601 – 800**|18.4|4.0|6.1|
|**801 or more**|16.4|-3.0|4.3|
|**Total**|19.0|5.9|7.8|

Sources: CFUWBES1 and LFS 2019.

# **6. Conclusions**

This paper analyzes the channels of impact from COVID-19 on the loss of permanent formal private sector (PFPS) jobs in two upper middle-income countries — Jordan and Georgia — and provides a methodology to estimate such job losses in other countries where there is no timely or nationally representative labor market survey to measure actual losses. We take into account labor supply conditions (essentialness of industries, 42