rafmacalaba/datause-encoder-data huggingface dataset:
**Tabel 3**

Percent of Women Workers by Labor Intensive<sup>Sectors and Size of Company, 1993</sup>

||Expor|t/ Produc|tion|||Total Wor<br> <br>|kers<br>|||||<br>|Total Wo<br>|men Wor<br>|kers<br>|W|W||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||o of Work|ers<br>g%||Distributi|on|No of|Women|orkers<br>|%<br>|istributi<br>|on<br>|o<br>|en or<br>|ers<br>|
||Small|Medium|Large|Small|Medium|Large|Small|Medium|Large|Small|Medium|Large|Small|Medium|Large|Small|Medium|Large|
|1 Textile|6.2|15.3|19.1|46,314|125,905|<br>409,978|9A4|13.4|<br>19.2|23.951|<br>61,920|223,274|12.5|15.9|<br>20.3<br>|51.7<br>|49.2<br>|54.5<br>|
|2 Garment|17.6|52.1|45.3|48,907|91,350|211,702|9.9|9.7|9.9|27,722|<br>66,889|174,041|14.5|17.2<br>|<br>15.8<br>|56.7<br>|73.2<br>|82.2<br>|
|3 Footwear|24.6|33.4|79.7|6,233|19,336|205,566|1.3|2.1|9.6|2,384|<br>12,930|<br>155,604|1.2|3.3|14.2|38.2|66.9|75.7|
|_Subtotal (t-3)_|_12.3_|_28.0_|37.7|_101,454_|_236,591_|<br>_827,246_|_20.5_|_25.2_|<br>38.6|54,057|_141,739_|<br>552,919|_28.3_|_36.4_|_50.3_|53.3|_59.9_|<br>_66.8_|
|4 Food|11.5|24.2|7.5|110.544|114,941|<br>291,255|22.4|12.2|<br>13.6|48,269|<br>48,368|105,102|25.3<br>|12.4<br>|<br>9.6<br>|43.7<br>|42.1<br>|36.1<br>|
|S Cigarettes|8.1|5.2|6.5|26,115|27,633|131,864|5.3|2.9|6.2|18,528|<br>22,115|106,558|9.7<br>|5.7<br>|9.7<br>|70,9<br>|80.0<br>|80.8<br>|
|<br>6 Electronics|13.0|27.5|38.3|8.436|29,582|69,242|1.7|3.1|3.2|2,821|<br>13,820|<br>38,985|1.5|3.5|3.5|33.4|46.7|56.3|
|_Subtotal_**_(I-_**_6)_|_13.0_|_27.5_|_22.5_|246,549|_408,747_|_1,319,607_|<br>_49.9_|43.5|<br>61.7|_123,675_|_226,042_|<br>_803,564_|_64.7_|_58.0_|73.2|50.2|55.3|_60.9_|
|Manufacturing|10.9|21.1|23.0|493,906|940,453|2.140,450|100.0|100.0|100.0|191.086|389,534|1,098,258|100.0|100.0|100.0|38.7|41.4|51.3|

**Notes: Small 20-100 workers, Medium 100-500**<sup>**workers, Large 500- workers**</sup> **Source:** **_OBPS IndusIrial_ SlatisUcs.** **_1993_**

**Un**
the guidelines are imperfect substitutes for trust; the guidelines can assist a researcher in handling the burden of that trust but they cannot eliminate ambiguities.

Among the ambiguities is the question: To whom is the researcher obligated? This question needs to be confronted, for example, in cases of conflicts of interests within a household or within a community – conflicts that seldom arise between participants in medical trials. They occur quite clearly in the situation of domestic violence illustrated above, but also in clusterbased studies where individuals are asked to speak for the larger community. They also arise between principles and their agents or between service providers and their clients.

Economists generally seek Pareto improvements where actions harm no one and help some. No harm, of course, also lies at the very core of research ethics. Economists know that Pareto improvements are rare outside a classroom discussion. Fortunately, no harm is the norm in research. But as we attempt to illustrate here, researchers in the field do have to be acutely aware that there are times when they need to go beyond existing protocols in order to judge whose welfare are they obligated to protect.

30
price change is rather permanent and/or if the income gain creates sufficient liquidity, it might give farmers an incentive to invest in production assets. On the other hand, if they perceive that the price shock is transitory, they will increase their savings for the future price fall, leaving their investment unaffected (Paxson, 1992; Rosenzweig and Wolpin, 1993; Kazianga and Udry, 2006). Which of the two effects dominates is ambiguous.

Did the positive food price shock increase investments in production capitals, creating a dynamic positive impact on agricultural productivity? Or, did farmers increase their savings (e.g., to cope with future negative income shocks) or production inputs (e.g., fertilizer and labor) to temporarily increase their outputs?

This question is related to “induced innovation” hypothesis which suggests that agricultural development is directed by the change in the conditions of factor and product markets (Hayami and Ruttan, 1985). In our context, if the food price spike increases real wage of hired labors, it might induce farmers to invest in productive assets to save the labor cost. Farmers may also substitute labors with fertilizer as the fertilizer price fell relative to agricultural real wages. As the output price of food items increases, farmers may also have stronger incentive to reallocate their resources from non-farming activities to farm investments in response to the price spike.<sup>6</sup> In this paper, we aim to answer the questions of (a) whether farmers increased farm investments to expand the production frontier and (b) whether any constraints bind farmers’ optimal strategies.

> 6 Using household panel data from Indonesia, Yamauchi (2012b) showed that (i) large farmers tend to acquire more lands by renting in land when non-agricultural real wages increase, and (ii) they install machines if agricultural real wages increase.  In contrast, small farmers seemed not to change their behavior. Since land size is significantly larger in non-Java islands than Java, the above observations imply regional differences in dynamic patterns of landholding distribution along with increasing real wages.

3
# **A Appendices**

## **A.1 Estimation of Livestock Value**

The price data for livestock are neither available at the provincial nor available at the national level. While it is possible to get some estimates for a couple of larger livestock types, we are not able to obtain reasonable prices for most other types. As such, we collect prices of livestock from three different sources to aggregate the wealth accumulated in the form of livestock. Table A.1 lists these prices.

Table A.1: Prices of Livestock Used In Estimating Livestock Wealth (2014)

|Income Source|US Dollars<br>(1)|
|---|---|
|Cattle|140|
|Oxen|150|
|Horses|1,500|
|Donkeys|500|
|Camels|1,000|
|Goats|100|
|Sheep|150|
|Chicken|20|
|Other Birds|10|

Sources: Cattle.com, BusinessInsider.Com, Ino.Com.

We note two concerns with the price data. First, we find the price of donkeys to be particularly high – more than three times that of price of cattle and oxen. Second, we find the price of chicken and other birds to be unreasonably higher as well. To check if these prices quantitatively affect our findings, we consider several specifications in which we reduce the price of these three livestock types by a factor of 3 – 5. We observe no major changes in our findings.

## **A.2 Household Income and Occupations**

To estimate the income uncertainty of households, we categorize them according to the occupations from which household income is derived. Table A.2 shows the complete list of occupations in our sample from the 2013-2014 round of the ALCS. As many of the household controls that we use are at the household head level, We exclude those households with heads who were under 18 or over 64 years old, or the household head was retired or unemployed. We also drop those 39
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infant mortality during these negative shocks to aggregate income is 7.4 deaths per 1000, that of boy mortality is 1.5 per 1,000. These simple back of the envelope calculations suggest that the magnitude of the effects of income volatility on infant mortality is large by any standard.

# **4. Conclusion**

Macroeconomic volatility is a fact for most developing countries. In recent decades, the standard deviation of income over time has been approximately twice as large in developing as developed countries (Aguiar and Gopinath 2007). A recent review stresses the welfare costs of volatility for developing countries in terms of their inability to smooth consumption (Loayza et al. 2007). In this paper we document another way in which aggregate economic fluctuations can have dramatic welfare consequences. In developing countries, infants, in particular girls, are more likely to die when there is a negative economic shock.

Our analysis adds to the existing literature on economic volatility and child health in several important ways. One strand of this literature has used unit data for a single country to investigate the effects of income shocks (for example, Paxson and Schady 2005; Miller and Urdinola 2007); the findings from these papers are mixed. Another strand has used available bidecennial aggregates of infant mortality and income (for example, Pritchett and Summers 1996; Jamison et al. 2004); with these data it is hard to convincingly control for changes in the composition of women giving birth, or to investigate whether the estimated effects vary with the characteristics of mothers and children. Moreover, bi-decennial averages are likely to have smoothed out some genuine variation in infant mortality. In this paper, we use the unit data for a large number of developing countries, fifty-nine, covering approximately 1.7 million births. This allows us to combine some attractive features of the country-specific approach—notably, to 26
relax shortly. However, this assumption is very useful because it allows us to simply illustrate the existence of a saving-based poverty trap. In particular we allow the saving rate to be an exogenous function of the capital stock per capita, s(k), and assume that the saving rate is constant at some low rate until a threshold value of the capital stock, k<sup>~</sup> , is reached, and then it jumps to a constant higher rate, i.e.

**Figure 1** shows the familiar Solow diagram for this economy. There are two steady states, indicated by kL and kH. The lower one can be thought of as a poverty trap, since if a country starts out below kL, it will grow until it reaches the low steady state, and then remains there forever. On the other hand, if a country starts out between _k_<sup>�</sup> and _kH_ , and therefore has a high saving rate, it will grow steadily until it reaches the high steady state. The basic intuition for the poverty trap is simple:  at low levels of saving, investment is so low that it can only sustain a very small capital stock per capita.

We have calibrated **Figure 1** so that it captures what we think are some key features of reality. We have drawn the low equilibrium at a capital stock per capita of $1500, which is approximately equal to the population-weighted average capital stock per capita in Sub-Saharan Africa. This is also roughly equal to output per capita in the region, implying a capital-output ratio of around one. The capital-output ratio in the low steady state is given by<sup>_s_</sup> _L_ /( _n_ + δ ) . For a depreciation rate of 6% and population growth rate of 4%, this implies that the saving rate in the low steady state must be 10%, which is actually just a bit higher than the saving rate of 8% for Sub-Saharan Africa as a whole.<sup>3</sup> We then choose the level of technology, A, to ensure that the 1/(1 −α ) steady state level of the capital stock at the low equilibrium, i.e. _kL_ =  _sL_ ⋅ _A_  is equal to  _n_ + δ  $1500. For a benchmark value of α =0.5, this implies that A=1.22. We have drawn the high steady state under the assumption that saving rates are twice that in the low steady state, but that there are no other changes in any of the parameters of the model. Since the ratio of the capital 3 Note that all calculations are done using PPP-adjusted data. Since the relative price of investment goods tends to be higher in poor countries than in rich countries, saving and investment rates are substantially lower when evaluated at PPP than in local currency terms.

9
about 35% and for men about 52%. The well-documented gender occupational segregation is also prevalent in the country. Specifically, women are underrepresented among professionals and skilled workers but overrepresented among clerks. Partly as a result of this gender occupational segregation, there are significant pay differences. Women’s pay is only about half of that of men (ETB500<sup>9</sup> versus ETB266) in urban areas of the country (CSA 2004). What is of interest here is to capture differences in the changes in the relative rate of women’s participation in occupations with more non-home based work, paid work and full-time work in the areas with and without the reform.

**Table 1** : **Ethiopian Regions and Chartered Cities**

|**Region/**<br>**Chartered**<br>**City**|**Population in**<br>**2007 **|**Annual**<br>**Pop.**<br>**Growth**<br>**(1994-2007)**|**%**<br>**Female**<br>**in 2007 **|**%**<br>**Urban**<br>**in 2007 **|**Pop.**<br>**Density**<br>**2007**<br>**(km**<sup>**2**</sup>**)**|
|---|---|---|---|---|---|
|Addis Ababa<sup>§</sup>|2,738,248|2.0%|52.4|100|5167|
|Afar|1,411,092|2.2%|44.3|13.4|15|
|Amhara|17,214,056|1.6%|49.8|12.6|108|
|Benishangul<br>Gumuz|670,847|3.0%|49.3|13.5|14|
|Dire Dawa<sup>§</sup>|342,827|2.4%|49.8|67.5|283|
|Gambella|306,916|4.6%|48|25.2|12|
|Harari|183,344|2.7%|49.7|50.5|590|
|SNNP<sup>‡</sup>|15,042,531|3.0%|50.3|10|134|
|Somali|4,439,147|2.6%|44.4|13.9|16|
|Oromiya|27,158,471|3.0%|49.6|12.2|77|
|Tigray|4,314,456|2.5%|50.8|19.5|86|

> ~~‡~~ SNNP stands for Southern Nations, Nationalities and People.

§City councils.

# **Revised Family Code**

Ethiopia’s new Revised Family Code (Federal Democratic Republic of Ethiopia 2000) came into effect in July 2000, updating the 1960 Family Code. The new law covers most areas of

> 9 ETB stands for Ethiopian Birr.

13
# **Table A4 continued**

|Element<br>category|Element|Scoring options|Notes|
|---|---|---|---|
|Openness|5. Terms of use|1 point if all published data have a terms of use<br>classified as open.<br>0.5 points if some published data have a terms of use<br>classified as open, or if some published data has a terms<br>of use classified as semi-restrictive or if all published<br>data has a terms of use classified as semi-restrictive.<br>0 points If no terms of use are found or if all published<br>data have a terms of use classified as restrictive.|Generally, terms of use (TOU) will apply to an<br>entire website or data portal (unless otherwise<br>specified). In these cases, all data found on the<br>same website and/or portal will receive the same<br>score. If a portal is located on the same domain as<br>the NSO website, the terms of use on the NSO site<br>will apply. If the data are located on a portal or<br>website on a different domain, another terms of<br>use will need to be present. For a policy/ license to<br>be accepted as a terms of use, it must clearly refer<br>to the data found on the website. Terms of use<br>that refer to nondata content (such as pictures,<br>logos, etc.) of the website are not considered. A<br>copyright symbol at the bottom of the page is not<br>sufficient. A sentence indicating a recommended<br>citation format is not sufficient.<br>Terms of use are classified the following ways: (1)<br>Not Available, (2) Restrictive, (3) Semi-Restrictive,<br>and (4) Open.|
|Source:<br>https://docs.<br>*Scoring opti<br>it is presente<br>For the elem|Authors<br>own<br>google.com/document/d/1<br>ons for coverage element 1<br>d in Annex Table A2.<br>ents of data openness, scori|compilation<br>from<br>ODIN<br>ubPL1l_3im9bjlCVZ6W2ICAy6UAiXl1hGeA1aXImkxI/edit<br>- Representative indicators are available and are disaggre<br>ng is calculated independent of the data coverage.|2018/19<br>Methodology<br>Report.<br>gated appropriately - varies by each indicator. Hence|

43
19

**Only Korea raised her investment ratio** during **adjustment, thereby showing a positive output gain.**

**Excluding Korea and** Zambia, the countries **fall** into three groups: **low,** medium, and high output **loss as** a percent of GDP. Colombia, Ghana, **Cote d'Ivoire and Pakistan lost,** on **average, less** than one percent of GDP per year. At the other extreme, Mexico, Malawi, Chile, and Kenya, on **average, lost close to four** percent of GDP **per year.** In interpreting this result, one must remember that for unstable countries (Cote d'Ivoire, Mexico, **Malawi,** and Chile), the estimate of the output loss may be biased in an unpredictable direction because of a change in the value of a during the adjustment period.

In spite of these caveats, our results suggest a sizeable output loss because of lower aggregate investment levels during the period of adjusti:nt under IMF-WB lending. Since an objective of these growthoriented programs was to r store growth by, among others, raising investment, one must ask what causes this sharp decline in investment and resulting output loss. While our analysis does not allow us to measure by how much private investment actually fell, the estimated fall in aggregate investment output ratios was large enough in most countries t'9 leave little doubt that private investment fell substantially during the adjustment. At least two factors must have contributed to the fall in private investment.

The first factor is that the expenditure-switching policies that accompanied the adjustment programs resulted in an increase in the relative price of imported capital goods. This cost increase was caused by the real exchange rate devaluation required to achieve a trade balance surplus to service the external debt. Furthermore, the interpretation of the statistical analysis of performance indicators in section 4 suggests that
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17
- **i.a2** -

S oL..1L AC **DLsaaregation** of Macroeconomic Projections. 1964-95

|Year|(000'r)<br>Populatioa|(000'r)<br>Labor<br>Force|(000's<br>675)<br>GDr/<br>Worker|Labor<br>Prod.<br>rarowLb/Yr|(10¢75)<br>GDP|(106¢75)<br>ri.<br>Cons.|2<br>GOP|(675)<br>Pri. Cons.<br>Per Capita|
|---|---|---|---|---|---|---|---|---|
|1964|12,323|6.228|.$2$2||5,158|3,917|.759|317.9|
|1985|12,737|6,433|.8425|1.73|5,420|4,161|.771|328.3|
|1986|13,095|6,645|.8581|1.85|5.702|4,321|.756|330.0|
|1967|13,506|6,664|.8706|1.46|5.976|4.269|.718|317.5|
|1988|13,900|7,091|8884|2.05|6.300|4,725|.750|338.0|
|1989|14,468|7,325|.9036|1.70|6,619|4,964|.750|343.1|
|1990|14,974|7,566|.9185|1.65|6,949|5,212|.750|346.1|
|l991|15,449|7.825|.9332|1.60|7,302|5,476|.750|354.5|
|1992|15,940|8,093|.9461|1.60|7,673|5.755|.750|361.0|
|1993|16,446|5,371|.9633|1.60|8.064|6,048|.750|367.7|
|1994|16947|6,657|.9767|1.60|6,473|6,355|.750|374.6|
|1995|17,506|6,954|.9944|1.60|6,904|6,678|.750|381.5|
|1996|18,031|9.268|1.0101|1.56|9.362|7,003|.748|388.4|
|1997|18,571|9,593|1.0259|1.56|9,841|7,341|.746|395.3|

|**Year**|(000o c)<br>Rural<br>_L.F._|Rurcl<br>LI<br>asI<br>**Total**|(000s)<br>AMr<br>L.F.<br>/|(000'.)<br>Urbn<br>L.F.|(000'° )<br>N. Rural<br>L.F.<br>!'|(000"' )<br>S. Rural<br>L<br>b/|(000")<br>X.Agr<br>.P.L_V_|(000's)<br>SA&r<br>L.F b/|
|---|---|---|---|---|---|---|---|---|
|1984|4,397|70.6|3,183|1,831|1,218|3,179|882|2.301|
|1985|4,503|70.0|3,260|1.930|1,247|3,256|903|2,357|
|1986|4,618|69.5|3,343|2.027|1,279|3,339|926|2,417|
|1967|4.736|69.0|3,429|2,128|1,312|3,424|950|2,479|
|1906|4,857|68.5|3,516|2,234|1,345|3.512|974|2,542|
|1989|4,981|68.0|3,806|2,344|1,380|3,601|999|2,607|
|1990|5,107|67.5|3,697|2.459|1.415|3,692|1,024|2,673|
|1991|5,243|67.0|3,796|2,582|1,452|3,791|1,051|2,745|
|1992|5,382|66.5|3.897|2,711|1,491|3,891|1,079|2,818|
|1993|5.525|66.0|4,000|2,646|1,530|3,995|1,108|2,692|
|1994|5,670|65.5|4*105|2,967|1,571|4,099|1,137|2.966|
|1995|5,620|65.0|4,214|3,134|1,612|4,206|1.167|3,047|
|1996|5,976|64.5|4,326|3,290|1,656|4,322|1.196|3 129|
|1997|6,140|64.0|4,445|3,453|1.701|4,439|1,231|3.214|

**a'** Computed **as** 27.72 of rural/agr labor<sup>**force**</sup>

g/ Com puted **as** 72.32 of rural/&gr labor **force**
# Regional Labour Markets During Deregulation in Indonesia Have the Outer Islands Been Left Behind?

Chris Manning Australian National University and Consultant, World Bank This paper was written as part of a broader labor market study on Indonesia undertaken by Country Department III, East Asia and Pacific Region. It was presented at a joint Ministry of Manpower-World Bank workshop entitled "Indonesian Workers in the 21st Century: Workshop for Economic Reforms and Labor Market Restructuring for Indonesia", Jakarta, Indonesia, April 2-4, 1996.
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