rafmacalaba/datause-encoder-data huggingface dataset:
Evidence confirms that low-skilled, low-wage, individuals are trapped in poverty. About 85 percent of low-skilled workers in the formal economy are either very poor or working poor (Figure 3). On the other hand, almost 85 percent of people with high-skill levels are non-poor. Skill level is an important determinant of poverty within the working population.

**<u>Table 5: Average Earnings of Labor Market by Education Level</u>**

|Education Level|Annual|AverageEarnings(ran|d-2000 prices)|
|---|---|---|---|
||Formal Labor|Informal Labor|Self-employed|
|No Schooling|15,028|5,942|8,367|
|Grade 0-6|16,710|6,773|9,400|
|Grade 7-9|22,983|10,614|12,776|
|Grade 10-12<br>|44,327|16,139|35,696|
|National Technical Certificate<sup>12</sup>|60,628|43,417|72,725|
|Degree and Post Graduate|119,939|124,957|147,296|
|Overall Average|41,582|11,728|25,829|
|Source: Authors’ calculations from LFS2000.||||

To explore further the link between skill level and poverty, Table 5 reports three types of earnings by different level of education. For all three types of workers, earnings increase sharply with education, an attribute closely linked with skill level. For example, a worker with a college degree in the formal sector earns, on average, about 8 times as much as a worker with no schooling. The disparity between a degree-holder and a worker with no schooling is even larger in the informal sector or for the self-employed sector.

As the education level rises, average annual earnings also rise. On average, individuals with higher education earn more than twice that of individuals who have graduated from grades 10-12. When compared with different types of labor, self-employed with higher education earn the most compared to the formal and informal labor with the same level of education. On the other hand, at lower levels of education, formal workers earn more until grade 12. There are other dimensions of earning disparity among formal sector workers. Two most commonly discussed aspects are: rural/urban disparity and disparity based on gender. For instance, average earnings are significantly higher for those who are working in urban areas. The differential is largest for the self-employed.

**<u>Table 6: Average Earnings of Labor Market by Regions (2000 prices)</u>**

|Region|An|nual AverageEarnin|gs(rand)|
|---|---|---|---|
||Formal Labor|Informal Labor|Self-employed|
|Rural|21,872|9,360|13,299|
|Urban|48,028|13,310|41,692|
|Overall Average|41,582|11,728|25,829|
|Source: Authors’ calculations from LFS 2000.||||

12 National Technical Certificate includes three levels, NTCi- NTCiii, which are equivalent to high school grades 1012.

- 12 -
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# **2. Experimental design and descriptive statistics**

# **2.1. Experimental design**

The data come from the Tanzania Methodological Survey Experiment on Household Consumption Measurement, which was conducted from April to July 2022 by the Tanzania National Bureau of Statistics, with technical support from the World Bank Living Standards Measurement Study (LSMS) program. Informed by the power calculations based on the past rounds of the Tanzania National Panel Survey (TZNPS) and the Household Budget Survey (HBS), the experiment spanned 143 enumeration areas (EAs) across Mainland Tanzania and Zanzibar, including both urban and rural areas. In each sampled EA, 25 households were selected at random from a fresh household listing that was conducted, out of which five sampled households were assigned at random to one of five survey treatment arms.

We analyze three survey treatment arms that are most relevant for our study.<sup>3</sup> Treatment Arm 1 (TA 1) administered the standard TZNPS household questionnaire that provides observed consumption and poverty estimates and that permits the estimation of all imputation models presented in Dang _et al._ (forthcoming), whose Tanzania-specific portions of the research relied on the data from the previous rounds of the TZNPS. Table A.1 in Appendix A shows each of the models and their predictors. The TA 1 sample consists of 711 households.

Treatment Arm 2 (TA 2) administered a light questionnaire that includes:

- (1) “Core modules” that only include the questions necessary for computing the predictors for

a data-modest subset of models that are presented in Dang _et al._ (forthcoming) - specifically

> 3 The two additional treatment arms that are not discussed/used in this paper were (a) the sample that was subject to a 14-day diary for data collection on food consumption, following the HBS 2017/18 methodology, and otherwise identical non-food consumption expenditure modules vis-à-vis T1; and (b) the sample that was subject to a modified version of T1 questionnaire, specifically with a food consumption module that was set up to be aligned with the T1/TZNPS food consumption module but with the HBS food item list.

7
clearly too early to call this a sustained reversal. However, it is clear that projected success in achieving MDG1 depends critically on the level of the poverty line.

The relatively slower progress in reducing “$2-a-day poverty” reflects, of course, the rising numbers of people living between $1 and $2. That is not too surprising; those escaping extreme poverty will not be rapidly entering the global “middle-class.” A more positive perspective on these numbers is obtained if we note that there has been even more rapid expansion in the number of people living above $2 a day. Based on Tables 1 and 2, we can derive the population growth rates over this period for the three groups: those living under $1 a day, those living between $1 and $2, and those living over $2. The annual exponential growth rates (obtained by regressing the log population on time) for these three groups are -1.4%, 1.9% and 3.5% respectively (with standard errors of 0.2%, 0.4% and 0.1%).

Thirdly, China naturally carries the largest weight in these calculations. Tables 1 and 2 also give our estimates excluding China. The trend rates of decline in the headcount indices are roughly halved when one focuses on the developing world outside China.<sup>14</sup> If the trend rate of decline over 1981-2004 in the headcount index for $1 a day continues until 2015 then the index will have fallen to 16% — more than half its 1990 value.<sup>15</sup> When we exclude China, we find a fairly static picture in terms of the number of people living under $1 a day, with no clear trend, and a clear trend increase in the number of poor by the $2 a day poverty line, which shows little sign of the possible reversal after 2000 indicated by the series including China (Table 2).

The aggregate pattern of population growth rates across the three “income” groups — under $1, between $1 and $2 and over $2 — changes radically when we focus on the developing world outside China. We now find annual growth rates of 0.1% (s.e.=0.1%), 2.4% (0.2) and 2.5% (0.1) respectively.

> 14 The regression coefficients on time are -0.45 (s.e.=0.03) and -0.28 (s.e.=0.03) for $1 and $2 respectively.

> 15 The 2015 extrapolation based on a regression of the headcount index on time implies an index in 2015 of 16.00% with a standard error of 0.55%.  So the MDG goal of about 13.6% (outside China) implied by Table 1 is also outside the 95% confidence interval for this forecast.

10
Table 3: Estimation results: Drivers of firm growth in the Belarusian machine building industry

|||**LSDV est**|**imator**|||**Differ**|**ence GMM est**|**imator**||**Corrected LS**|**DV estimator**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Full sample|Private|State|Mixed|Full sample|Private|State|Mixed|Full sample|Private|State|Mixed|
|Lagged growth|-0.004|-0.103|-0.007|-0.009|-0.033|-0.160|0.011|-0.040|0.069**|0.036|0.107*|0.036|
||(0.017)|(0.173)|(0.041)|(0.018)|(0.032)|(0.139)|(0.074)|(0.035)|(0.028)|(0.171)|(0.056)|(0.034)|
|Lagged employment|-0.549***|0.706***|-0.437***|-0.494***|-0.842***|-0.851***|-0.711***|-0.813***|-0.553***|-0.661***|-0.478***|-0.489***|
||(0.071)|(0.185)|(0.086)|(0.079)|(0.087)|(0.144)|(0.109)|(0.083)|(0.037)|(0.216)|(0.060)|(0.061)|
|Lagged wages per employee|0.084*|-0.087|0.314***|0.013|0.048|-0.002|0.258***|0.009|0.084***|-0.074|0.314***|0.013|
||(0.045)|(0.144)|(0.077)|(0.014)|(0.030)|(0.102)|(0.066)|(0.011)|(0.018)|(0.112)|(0.036)|(0.020)|
|Lagged exports|-0.004**|0.003|-0.006**|-0.001|-0.004***|-0.010|-0.009***|-0.003|-0.003|0.003|-0.005|-0.001|
||(0.002)|(0.016)|(0.003)|(0.003)|(0.002)|(0.011)|(0.002)|(0.002)|(0.003)|(0.025)|(0.005)|(0.005)|
|Lagged investment|0.004**|0.018***|0.002|0.006**|0.003**|0.013***|0.002*|0.001|0.004*|0.016*|0.002|0.006**|
||(0.002)|(0.006)|(0.002)|(0.003)|(0.001)|(0.003)|(0.001)|(0.002)|(0.002)|(0.009)|(0.003)|(0.003)|
|Lagged capacity utilization|0.098**|0.089|-0.080|0.128**|0.114***|0.091|-0.020|0.126***|0.094**|0.077|-0.092|0.129**|
||(0.042)|(0.207)|(0.072)|(0.107)|(0.039)|(0.183)|(0.062)|(0.046)|(0.041)|(0.133)|(0.074)|(0.061)|
|Year fixed-effects|0.83***|2.39|1.55***|7.75**|36.82***|13.43***|40.74***|32.40***|75.61***|7.97**|126.53***|12.59***|
|Observations|563|60|239|264|420|45|171|204|563|60|239|264|

_Notes:_ Robust standard errors in parenthesis. *(*)[***] stands for significance at the 10%(5%)[1%] level. All specifications include firm fixed effects. Year fixed effect test based on chi-squared tests with 3 degrees of freedom.

12
# **1. Introduction**

It has been increasingly recognized that rapidly-growing Asia is becoming a driving force of economic growth in the world, but also a growing concern in the global food economy due to its large share of grain imports in the world.<sup>3</sup> Economic growth, urbanization and transformation of the economic structure in Asia have been the fastest in the world, which has induced increasing real wages not only in urban and non-agricultural sectors, but also in many parts of rural and agricultural sectors in Asia. Although the above transformation contributed to reducing poverty in the region, it also created a challenge to farming which depends on small-scale and family based operations. In this paper, I examine (i) how an increase in real wage induced a realization of scale economies and investments in machines substituting for labor, and (ii) potential differences between small and large farmers, using farm panel data from Indonesia.

Family labor tends to be more intensively used on smaller farms in the absence of efficient labor markets, which, in turn contributes to the inverse relationship between farm size and crop yield (Feder 1985; Berry and Cline 1979; Benjamin and Brandt 2002). In fact, Asian agriculture has been dominated by labor-intensive small farms mainly relying on family labor. However, such an inverse relationship could be altered with the fast economic growth, accompanying a rising wage rate, because labor-intensive production becomes costly. The wage growth may have significant effects on the efficiency of small-scale farming in Asia and potentially more generally in developing countries.

The following intuition shapes up the key hypothesis. An increase in real wages increases the production cost of the labor-intensive farming system and thereby decreases comparative advantage in agriculture based on the labor-intensive production methods widely observed in many parts of Asia. To restore comparative advantage, at least partially, farm size expansion and large-scale mechanization must

> 3 Low self-sufficiency is evident in Japan; the Republic of Korea; Taiwan, China; and more recently becoming a serious concern in China, where imports of certain commodities increased very rapidly. Though exporters such as Thailand, Brazil, and Argentina can enjoy such a trend, it is possible that the above trend observed in Asia will destabilize the supply-demand balance in the global food economy.

2
|**Measure**<br>|**M1**<br>**M3**|**M4**<br>**Sector**<br>**scope**|
|---|---|---|
|Interest rate restricted|x<br>x|SS|
|Regulated contractual interest rates|x<br>x|SS|
|Regulated banking fees<br>|x<br>x|SS|
|Early repayment conditions and fees subject to regulation|x<br>x|SS|
|Restrictions on the currency deposited<br>|x<br>x|SS|
|Size of the loan restricted|x<br>x|SS|
|Insurance premiums, fees or margins regulated<br>|x<br>x|SS<br>|
|Value of insurance policy restricted|x<br>x|SS|
|Term of insurance policy restricted|x<br>x|SS|
|Restrictions on writinginsurance contractsin foreigncurrency|x<br>x|SS|
|Rail rates are regulated|x|SS|
|Confidential contracts allowed|x|SS|
|Accessfees areregulated<br>|x|SS<br>|
|Interswitching rates are regulated|x|SS|
|Rates for terminal running rights regulated<br>|x|SS<br>|
|Retailprices of roadfreight services areregulated|x<br>x|x<br>SS|
|Government pricing guidelines for road freight companies<br>|x<br>x|x<br>SS<br>|
|Freight carriers required to file tariffs|x<br>x|x<br>SS|
|Advertising either prohibited or subject to restrictions|x<br>x|x<br>COM|
|Duration of stay initially allowed–Foreign workers||x<br>COM|
|Duration of stay initially allowed-CSS||x<br>COM|
|Duration of stay initially allowed-IP<br>||x<br>COM|
|Duration of stay initially allowed-ICT||x<br>COM|
|Limitation on a number of days a foreign driver may stay in a host country||x<br>SS|
|Minimum wage/salary requirement–Foreign workers||x<br>COM|
|Minimum wage/salary requirement–CSS<br>||x<br>COM|
|Minimum wage/salary requirement–IP||x<br>COM|
|Minimum wage/salary requirement–ICT<br>||x<br>COM<br>|
|Universalservice obligations|x|COM|
|Contracts for universal services obligations assigned through grandfathering<br>|x|SS|
|Contracts for universal service obligations awarded through competitive bidding|x|SS|
|Otherconditions onsupply ofservices|x<br>x|x<br>COM|
|Spectrum sharing|x|SS|
|VoIP operation allowed|x|SS|
|Labelling provisions beyondinformation requirements|x<br>x|SS|
|Zoning laws: Differential treatment|x|SS|
|Seasonal sales periods regulated|x<br>x|SS|

35
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these countries, followed by 26 percent going to the services sector. Energy and mining (7.4 percent), agriculture, forestry fishing (2.6 percent), account for a total of 10 percent of all Chinese FDI projects. Chen et. al. (2015) also provides a more detailed case study of Ethiopia which shows that recent flows of Chinese FDI has been focused in the manufacturing and construction sectors.

Table 4: G7’s total direct investment (in-stock) in millions of USD in selected African countries, 2001-2012

||Canada|France|Germany|Italy|Japan|United<br>Kingdom|United<br>States|Total|
|---|---|---|---|---|---|---|---|---|
|Angola||44605.0||779.7|||25336.0|70720.8|
|Botswana|83.2|8.6||22.0|||248.3|1754.8|
|Cameroon||9556.4|11.0|102.2|||1940.0|11609.7|
|Chad||1425.1||4.1||||1429.2|
|Congo, Rep.||25281.5|9.0|346.3|||1530.0|27166.6|
|Congo, Dem. Rep.||||75.3|||358.0|433.3|
|Equatorial Guinea||||277.4|||29189.0|29466.4|
|Ethiopia||||964.0|||188.0|1152.0|
|Gabon||15147.8||196.3|||2876.0|18220.2|
|Côte d’Ivoire||9384.2|319.5|214.1|||1459.0|11376.8|
|Kenya||2359.0|694.3|190.1||7266.1|2404.0|12913.6|
|Liberia||590.7|320.3|17.2|||4834.0|5762.2|
|Madagascar|4038.4|1736.0||515.8|2394.1|5021.1|513.7|12483.0|
|Tanzania|7998.9|525.5|821.1|760.1|916.4|8332.0|1457.2|20811.1|
|Uganda|||1184.6|||12966.5|3778.8|17929.8|
|Zimbabwe|203.5|||83.4||1081.4|585.0|1953.3|
|Total|12324.1|110619.8|3359.8|4548.0|3310.5|34667.1|76696.9|245182.7|

Source: Authors’ computation based on UNCTAD (2016) With reference to G7 countries’ direct investment; France, Italy and UK tend to have a large presence in countries where they have political, cultural or social ties, especially, in their former colonies or formerly occupied territories (Table 4). For example, Italy has a large presence in Ethiopia, while France has a strong presence in Chad, the Republic of Congo, Gabon, Côte 18
**Figure 11: Non-Traditional Exports by 6-digit subheadings: 1993-2009 (in US$ million)**

<!-- Start of picture text --> Other t-shirts, of cotton, for men or women<br>Other vegetables, fruits and other edible parts of ...Other plates, sheets, foil and strip of polymers of ...400 Asparagus, fresh or chilledCotton knit shirts with ribbed fabric cuff opening ...<br>Onions and shallots, fresh or chilled Artichokes<br>Crocheted fabrics of a width exceeding 30 cm, ... 350 Refined copper wire with the largest cross-...<br>Other wood sawn or chipped lengthwise, sliced ... 300 Fresh grapes<br>250 Asparagus, prepared or preserved, not frozen<br>Flat-rolled products of zinc thickness >0.65 mm 200 Other cuttlefish, bobtail, and squid, frozen, dried, ...<br>Undenatured ethyl alcohol volume > = 80% 150 Other women's or girls' shirts, cotton blouses, ...<br>100<br>Hydrogenorthophosphate calcium ("dicalcium ... Paprika (Capsicum annuum L) whole<br>50<br>Jewelry, other precious metals Mangoes and mangosteens, fresh or dried<br>0<br>Cocoa butter with an acid-oleic acid> 1% <= 165% Avocados, fresh or dried<br>Other fish meat fillets shelled, uncooked by steaming or boiling shrimp ...<br>Publuishing material Other plates of polymers of polypropylene<br>1993 2009<br>Sulfuric acid Other strips and friezes for parquet flooring, not ...<br>Other plates and floor tile, ceramic, painted or ... Scallops<br>Other knitted fabrics, cotton fabrics Evaporated milk without sugar or sweetener<br>Preforms bottles Other preparations used for animal feed<br>Other garments, knitted cotton Other cotton sweaters<br>Bar of iron or non-alloy steel with indentations or ...Zinc, not alloyed, with a lower zinc content of ... Bananas like "cavendish valery" freshOther molluscs and aquatic invertebrates, ...<br><!-- End of picture text --> _Source: SUNAT - MEF, Own Elaboration_ Finally, the analysis turns to the diversification of the destination export markets for which a HerfindahlHirschman Index can be calculated for total, traditional and non-traditional exports (See Figure 12). While the total index seems to be dominated by the evolution and value of the traditional grouping, it is significant to note that destination concentration tended to be higher during the bulk of the period; The index follows two phases, one of declining diversification until about 2004, and then a sharp reversal ending in 2009. However, when looking at non-traditional exports, a greater diversification trend is evident since 2004, which possibly can be related to the gradual maturation of market-friendly reforms and greater private sector expectations in a context of both a growing international and domestic, which promoted the rapid expansion of new products and destinations. By contrast, the index of traditional exports exhibits less dramatic swings between 1993 and 2009, as the only significant novelty has been the addition of only one new significant trading destination to the traditional list – i.e, the People‟s Republic of China, for which exports from Peru increased from 5.5% in 1993 to 19.1% in 2010.

18
**Figure 5: Returns Effects for Rural I-Rural LI Gaps, 2000 and 2005**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>Percentile<br>2000 2005<br>Difference in LRPCE<br><!-- End of picture text -->

**Figure 6: Returns Effects for Urban-Rural Gaps in Integrated Region, 2000 & 2005**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>-0.05<br>-0.1<br>Percentile<br>2000 2005<br>Difference in LRPCE<br><!-- End of picture text -->
|TV ownership|Share of men in delegation _d_ that own a TV|The 2014 Tunisia Census of Pop-<br>|
|---|---|---|
|(rate)||ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Unemployment|Share of men in delegation _d_ that are unemployed|The 2014 Tunisia Census of Pop-<br>|
|(rate)||ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|

_Notes:_ [a] The 2014 Tunisia Census of Population and Housing was conducted by L’Institut National de la Statistique (INS). [b] Tunisia administrative boundaries for delegations are obtained from The Humanitarian Data Exchange (HDX) and produced by The United Nations Office for the Coordination of Humanitarian Affairs (OCHA). Date of access: March 20, 2019 [link]. [c] The National List of Persons, Organizations and Entities Associated with Terrorist Crime is produced by La Commission Nationale de Lutte Contre le Terrorisme (CNLCT, 2021). Date of access: April 19, 2021 [link].[d] Elections in Tunisia are conducted by the Instance Sup ́erieure Ind ́ependante pour les Elections ISIE (2015). Date of access: September 20, 2021 [link]. [e] The Ansar al-Sharia in Tunisia’s Clerical Establishment dataset is described in Zelin (2020). Date of access: March 25, 2021 [link]. [f] The Mountains of the World 2002 dataset, produced by The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), is described in UNEP-WCMC (2002). Date of access: April 28, 2021 [link]. [g] The Armed Conflict Location and Event Data Project (ACLED) is described in Raleigh et al. (2010) [h] The precipitation data are produced by the Global Precipitation Climatology Centre (GPCC) and described in Schneider et al. (2015). Date of access: April 28, 2021 [link].

26