rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: Employment distribution: Manufacturing <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008) Ghana (2013)<br>Rwanda (2013) USA (2013)<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>50<br>50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50<br>40 50<br>30 40<br>30<br>20<br>20<br>Share of employment (%)<br>10<br>10<br>0 0<br>50<br>40<br>30<br>20<br>Share of employment (%)<br>10<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. For the U.S., the data comes from the 2019 BDS dataset. _Note:_ The figure is constructed based on data for establishments in the manufacturing sector irrespective of their state or foreign ownership status. Establishments with missing employment data are excluded.

Furthermore, figure 5 shows that most of the employment is concentrated in the lower and uppermost tails. Apart from Cameroon, where most of the workforce is employed in establishments with at least 100 workers, the employment share of the smallest size category (1-4 workers) is the highest for the manufacturing sectors of Burkina Faso, Ghana, and Rwanda. At the same time, the next largest employment share belongs to the uppermost tail of the distribution, the size class of 100 or more workers.

An in-depth assessment of the size distribution in manufacturing uncovers that the feature of the missing middle is not attributed to few yet large foreign-owned establishments or state-owned enterprises. This observed pattern holds for private (those that are not state-owned), domestic (in which local citizens are principal owners), or domestic, private manufacturing establishments. Moreover, the missing middle phenomenon does not emerge from a specific way we slice the data, rather it reflects the inherent feature of the size distribution in the sample countries. As a robustness check, we plot the size distribution based on broader size categories. The key takeaways remain the same (see figures A.3, A.4, and A.5 in the appendix).

14
Our data include information on quantities and values of the products produced, and raw inputs (domestic and imported) used by each plant. These are both defined at a highly granular level, namely nine digits _Klasifikasi Komoditi Indonesia_ (KKI), a more detailed classification based on KBLI. In our sample, each plant produces on average 2 products and 25% of the plants produce more than one product. On average, each plant uses four different varieties of raw inputs. The drawback of the high level of detail in materials’ data is that sometimes there are coding mistakes by the reporters. Appendix B.2 describes in detail the methodology we use to clean the data. After computing unit prices by dividing value with quantities, we use them to construct plant-level price deflators (see Appendix B.5).

One challenge of the _Statistik Industri_ data is the lack of complete series of capital stock. Earlier studies tried to re-construct capital stock series applying the _Perpetual Inventory Method_ (PIM) to the first year of capital stock data reported by the plant (Amiti and Konings, 2007; Javorcik and Poelhekke, 2017). However this imputation method crucially relies on the capital value self-reported by the plant the first year this data is available, which is not necessarily accurate.<sup>13</sup> One potential advantage of using PIM is that purchase and sales data might be more accurate relative to selfreported value of the stock, requiring an appropriate calculation of market values and depreciation. However, PIM needs to rely on measures of capital depreciation, which are difficult to accurately estimate. To mitigate such tradeoff, we have adopted a hybrid strategy, which is described in Appendix Appendix B.3.

# _2.5. Estimating Productivity and Markup Measures_

Our main dependent variables are estimates of plant-level productivity and markup. Unlike most existing studies using industry-level price indexes to deflate nominal variables, the granularity of our data allows us to calculate plant-specific output and input price indexes. This allows us to mitigate the bias deriving by inputs’ price heterogeneity across plants and to disentangle technical efficiency (TFPQ) and markups.<sup>14</sup> To estimate plant-level production function parameters, we follow the control function approach and timing assumptions of Ackerberg et al. (2015), which are useful to mitigate

> 13In particular, there is no a priori reason to believe that the quality of the self-reported capital stock the first year is necessarily better than the value in other years.

> 14Foster et al. (2008) discuss the bias arising when using plant revenue deflated by industry deflators. De Loecker et al. (2016) extend the analysis in the contest of unobserved variation in input prices.

12
allocation, project selection, and local government finance are crucial for the long-term sustainability and effectiveness of some of the underlying national and state-level projects. The analysis draws some lessons from China’s experience—for China and for other countries, both developed and developing. These lessons may be of particular relevance today, as the global economy appears to be on the precipice of another recession as a result of the ramifications of the debt crisis in Europe. The possibility of a sharp rise in oil prices as a result of geopolitical events in the oil-producing countries of the Middle East also looms on the immediate horizon. All of these events are likely to reduce the fiscal space (because of lower economic growth and automatic stabilizers) for a sizable fiscal stimulus in many countries. Policy makers must thus carefully assess their experience with the first round of countercyclical policies in response to the global crisis and draw lessons to ensure maximum efficiency and effectiveness in future efforts they may launch in response to another regional or global crisis.

# **China’s Response to the Global Crisis**

In the last quarter of 2008, global industrial production dropped 20 percent (23 percent in advanced economies and 15 percent in developing countries) (World Bank 2009b). Rising fiscal deficit and government debt in the advanced economies led to lower investment levels in developing countries, as a result of wider interest rate spreads and the tightening of international credit. Net private capital flows to emerging and developing economies plummeted from $700 billion in 2007 to around $270 billion in 2008 and 2009 before recovering to about $530 billion in 2010 . The capacity of firms to invest declined, as a result of the drop in access to financial resources and the sharp decline in international trade; their propensity to invest plunged, as a result of weak economic prospects, particularly in advanced economies. Limited profit opportunities and high risk reduced new flows of investment, reinforcing the vicious cycles of excess capacity, low demand, and excess capacity.

The Chinese economy was hit hard by the global crisis, mainly through trade and capital flows. GDP of almost all of China’s main trade partners fell after the onset of the crisis in the fourth quarter of 2008. China’s volume of exports of goods and services declined 10 percent in 2008/09 (China Statistics Yearbook 2011). Market-based investment and confidence declined severely, 4
children survive. Alternatively, children are switched from breastfeeding to supplementary milk and solid food at about six months after birth--this switch might be associated with higher child mortality (Barrera 1990; Olango and Aboud 1990), with higher chances of survival for stronger children. Doblhammer and Vaupel (2001) show that in Denmark, the death rate for infants born in June is 32 percent higher than the death rate of those born in January. Similar seasonal patterns are found by Breschi and Bacci (1998) for Switzerland and Belgium. In Gambia and Bangladesh, birth during the hungry season resulted in excess mortality during the first year of life (Moore _et al.,_ 2004).

The NFHS asks mothers to categorize the weight of their children at birth as large, average or small<sup>13</sup> . We use this subjective assessment as a proxy for health endowments at birth. The weight of a child at birth influences his/her health and prospects for survival (for example, Rosenzweig and Shultz 1982). The distribution of the answers to this question for three waves of NFHS is shown in Table 5. The weight of about 20 to 25 percent of children was smaller than average according to their mothers’ assessments.

Let *<sup>be a latent variable determining the survival of a child, which is a function</sup><sup>_S_</sup> _i_ of its health endowments at birth (weight _Wi_ ), as well as maternal and household characteristics. Let _Si_ be an observed event of child _i_ surviving longer than 12 months. Then: To test whether selective child mortality during the first months after birth explains the variation in health outcomes, we estimate the probability of a child surviving past age 3 as a function of the characteristics of a household, a mother, and a child; month-of-birth dummies; and the interaction between the month-of-birth and weight at birth. Under the assumption that<sup>ε</sup> _i_<sup>is distributed normally, the probability of survival (</sup><sup>_S=1_) can be</sup> estimated by the binary probit model. The significance of the coefficients on the

> **13** The direct measurement of children’s weight at birth is available in NFHS only for a small sub-sample of children. The weight at birth was reported for 2.9 percent of children in the 1992 sample, 3.2 percent in the 1998 sample, and 8.1 percent in the 2005 sample.

15
WPS3945 net reduction in gender inequalities in access to education.

We account for selection into migration using two strategies. We use a census measure of village migration prevalence rates and obtain within village variation in this measure by interacting it with the number of adult males in the household, which, in the context we study, is a strong predictor of migration. This allows us to clean out any unobserved community characteristics, such as local labor market conditions or school quality, which could affect the returns to schooling and the propensity to migrate. Our results indicate negative selection into migration.

Our second strategy is to con fi ne attention to migrant households and to use information on the year of initial migration and the child’s age on the survey date to examine differences in educational outcomes for siblings, differentiated by their attained age before the fi rst migration episode for the household. This allows us to exploit the fact that many schooling decisions are time sensitive and have sustained impacts on educational attainment. A comparison of siblings using this strategy further corroborates our results on school attainment and child labor activity.

Finally we ask whether the evidence we fi nd for the protective effects of migration, and its disproportionate bene fi t for girls, in particular, arises, at least in part, due to the substantial incidence of female headship among migrant households. It has been argued that a woman’s ability to make decisions regarding the disposal of household resources is a key determinant of child outcomes. It is reasonable to ask therefore if the migration induced absence of males provides mothers with an opportunity to more easily realize their preferences with regard to investments in their children’s education. Of course this must be balanced against the potential disruptive effect of migration on family life. We examine this issue by con fi ning attention to migrant households and asking whether schooling and labor market outcomes vary signi fi cantly by female headship, and whether we can discern any gender differentials in outcomes.

Our results are quite surprising. Female headship appears to protect boys at the cost of girls. Girls in such households are signi fi cantly more likely to dropout and they lose most of the bene fi ts of migration on accumulated schooling as well. We cannot fi nd direct support for this effect in the work burden of girls since both boys and girls work more in female headed migrant households and work about as much. However, we do not have data on domestic work or child care, which may place a much larger burden on girls in female headed households. In sum, our results suggest that female headship is not the source of the migration effects we observe in our data. Rather, it seems to put households 23
|||Means by Gro|up|Dife|rence in Means|(t-test)|
|---|---|---|---|---|---|---|
||(1)<br>Chair<br>mean/sd|(2)<br>Contender<br>mean/sd|(3)<br>Councillor<br>mean/sd|(4)<br>Chair v.<br>Contender<br>dif.|(5)<br>Councillor v.<br>Chair<br>dif.|(6)<br>Councillor v.<br>Contender<br>dif.|
|Integrity|-0.19|0.02|-0.29|-0.20|-0.10|-0.31***|
||(1.07)|(1.03)|(1.03)||||
|PSM|0.17|0.17|-0.05|-0.00|-0.22|-0.22***|
||(1.26)|(0.99)|(0.92)||||
|Big Five|0.19|0.15|0.03|0.04|-0.16|-0.12|
||(1.03)|(0.98)|(1.04)||||
|Other Non-Cognitive Traits|-0.01|-0.10|-0.28|0.09|-0.27**|-0.18**|
||(0.98)|(0.90)|(1.01)||||
|Non-Cognitive|0.13|0.06|-0.11|0.07|-0.24**|-0.17**|
||(1.00)|(0.97)|(1.03)||||
|Cognitive|5.63|5.41|5.12|0.22|-0.51***|-0.29***|
||(1.14)|(1.25)|(1.11)||||
|Choose dominated risk option|0.10|0.13|0.13|-0.03|0.04|0.01|
||(0.30)|(0.33)|(0.34)||||
|Altruism|0.34|0.32|0.32|0.01|-0.02|-0.00|
||(0.22)|(0.23)|(0.20)||||
|Risk-aversion (1-5)|3.92|3.65|4.01|0.27|0.09|0.36**|
||(1.44)|(1.54)|(1.36)||||
|Asset index - total|2.60|2.26|1.30|0.34***|-1.30***|-0.96***|
||(0.76)|(0.96)|(1.02)||||
|Less than Secondary|0.02|0.04|0.30|-0.01|0.28***|0.26***|
||(0.16)|(0.19)|(0.46)||||
|Less than College|0.16|0.24|0.33|-0.08|0.17***|0.09**|
||(0.37)|(0.43)|(0.47)||||
|Complete College|0.51|0.50|0.34|0.01|-0.17***|-0.16***|
||(0.50)|(0.50)|(0.47)||||
|Masters/PhD|0.30|0.23|0.03|0.08|-0.27***|-0.19***|
||(0.46)|(0.42)|(0.18)||||
|Observations|82|181|504|263|586|685|

Table 9: Selection Traits by Type of Politician _Note:_ This table compares average characteristics of elected Chairperson and Mayors; contenders for Chairperson/Mayor; and elected Councillors . Column (1) reports the average characteristic for all elected Chairperson/Mayors; column (2) does the same for non-elected Chairperson/Mayors; and column (3) for elected Councillors. Columns (4) presents the difference in average trait between Chairpersons and Contender; column (5) between Chairperson and Councillors; and column (6) between Councillors and Contenders. The starts in columns (4)-(6) represent level of p-values in a t-test of equality of means (* p<0.1), ** p<0.05, *** p<0.01) 60
WPS3945 labor market participation. In contrast, the decline in labor market activity is about 33% for boys. This gender differential in labor market participation is also visible when we use days worked instead of a dichotomous measure of work. Moreover, as with labor market participation, the decline is much larger for girls. Since days worked are more likely to be subject to measurement error as compared to the simpler dichotomous measure, and we have a much smaller sample, we use log of days worked when comparing siblings.

These results suggest that migration reduces the work burden of girls to a much greater degree than it does for boys. Of course these results may overstate the work bene fi ts of migration for girls. Our measures of work do not include housework or child care, activities in which girls are much more likely to be involved. Nonetheless, their weaker participation in income related activities clearly reduces their overall work burden and suggests that migration induced "male absence" increases the opportunity cost of time spent in school for boys to a greater degree than for girls? If so, this could explain some of the observed gender differential in schooling outcomes. An alternative hypothesis is that "male absence" generates larger gains for girls due to the more benevolent preferences of mother’s towards their daughters. Of course, both forces may simultaneously be at work. We now turn to this question.

# 4.4 Does Female Headship Matter?

Tables 7 and 8 present the estimation results. The main variables of interest are an indicator for whether the household is female headed and the interaction of this indicator with child gender. The fi rst speci fi cation is OLS with village fi xed effects. The second speci fi - cation treats female headship as endogenous. Although female headship emerges almost exclusively in the context of migration, unobserved household or community characteristics, such as family or community norms around female seclusion could well determine both schooling outcomes and female headship. Fortunately, our instruments for migration vary at the household level and, plausibly, also in fl uence the prospects of female headship in the event of migration. As before, we also difference out any unobserved village characteristics that could in fl uence schooling decisions.

The joint explanatory power of our instruments, conditional on the village fi xed effect and included household and child characteristics, is very high in all cases. The instruments also easily pass the overidenti fi cation test. (all statistics reported at the end of each table). All speci fi cations include the full set of child and household characteristics described earlier (see Appendix Table 4 for coefficient estimates for the IV speci fi cation and the fi rst stage 21
10 1990).

o At the household level, use of health Pervices does not seem to make any differencw. to mortality.

This is paradoxical, given tfat countries successful in reducing mortality, such as ;hina and Sri Lanka, have emphasized access to basic health services. Of course, health services may matter more for morbidity than for mortality; few household studies have examined morbidity. And at the household level, much use of services is in direct response to illness, whereas the key to low mortality and morbidity in successful countries has been emphasis not on curative services or even services per se but on preventive care through outreach. It is also worth noting that China, Sri Lanka, Cuba and other successful countries have had extensive and effective food distribution systems and relatively well-educated populations.

o In developing countries, use of health services does not appear to be particularly price-elastic, except for the poor.

For the average consumer and within certain ranges, the price of health services has little effect on their use by consumers; (see Akin et. al., 1985; Akin, Birdsall and de Ferranti, 1987). But at the same time, and not surprisingly, prices do matter for the poor, for whom a higher price represents, all other things the same, a bigger chunk of income (Gertler et. al., 198 ). On the one hand, insofar as use of health services does not matter for mortality (above), it may not seem important that prices discourage use of services by the poor. On the other hand, use even of simple curative services may have important welfare effects, i' it relieves pain and anxiety. And existing studies may not sufficiently differentiate between the effects of use of different kinds of services; if high prices discourage use of particularly effective services (e.g. pre-natal care), health costs (including
**Table 2**

**Global Balance of the 17 Major Fats and Oils, 2005/06‐2007/08 Average**

||**_Production_**<br>**_(000 tons)_**|**_Exports_**<br>**_(000 tons)_**|**_Stocks_**<br>**_(000 tons)_**|**_Traded_**<br>**_Share_**<br>**_(percent)_**|**_Production_**<br>**_Share_**<br>**_(percent)_**|
|---|---|---|---|---|---|
|Palm|38,657|30,232|6,177|78.2|25.3|
|Soybean|36,371|10,727|3,926|29.5|23.8|
|Rapeseed|18,676|2,085|1,256|11.2|12.2|
|Sunflower|10,764|4,151|990|38.6|7.0|
|Tallow|8,515|2,244|502|26.4|5.6|
|Lard|7,672|120|438|1.6|5.0|
|Butter|6,882|728|639|10.6|4.5|
|Cotton|5,003|154|364|3.1|3.3|
|Palm kernel|4,516|2,523|562|55.9|3.0|
|Groundnut|4,360|190|306|4.4|2.9|
|Coconut|3,141|2,030|375|64.7|2.1|
|Olive|2,851|741|937|26.0|1.9|
|Corn|2,311|795|227|34.4|1.5|
|Fish|1,043|727|157|69.7|0.7|
|Sesame|834|34|46|4.1|0.5|
|Linseed|682|108|78|15.9|0.4|
|Castor|543|335|59|61.7|0.4|
|**TOTAL**|**152,821**|**57,928**|**17,039**|**37.9**|**100.0**|

**Source:** _Oil World_ , Hamburg.

**Notes** _:_ Traded share is global trade as a percentage of global production. Production share is the contribution of each type of oil to global production of all 17 fats and oils. Lard (from pig fat), tallow (from beef fat), and butter (a dairy product) are classified as fats and the remainder as oils.

An econometric model similar to [1] was used to analyze the degree of co‐ movement between the prices of various edible oils and grains. Specifically, _log(Pt_<sup>_i_</sup> _) = μ + β1log(Pt_<sup>_j_</sup> _) + β2log(MUVt) + β3t + εt._

[3] _Pt_<sup>_j_</sup> and _Pt_<sup>_j_</sup> denote the prices of interest while the other parameters and variable are defined as before. The results, reported in Table 3, show a high degree of co‐ movement between the prices of three vegetable oils (cotton, soy, and palm) and the edible oil index (upper panel) as well as among the prices of the three oils (second panel). For example, all cross‐price effects are significant at the one per‐ cent level and very close to unity while the adjusted‐ _R_<sup>_2_</sup> s averaged 0.93. These es‐ timates indicate a high degree of substitutability among the various edible oils,

**_Page 12_**
# **Table 5: Organized Sector Log (Employment)**

|Hitl Elt Sill f|(1)|(2)<br>_Other-Sector Emp_|(3)<br>_loyment Spillove_|(4)<br>_rs_|
|---|---|---|---|---|
|orzona mpoymen povers rom<br>Unorganized Sector|0.1805***<br>(0.0202)|0.1631***<br>(0.0184)|0.1424***<br>(0.0164)|0.0512***<br>(0.0144)|
|Forward Employment Spillovers from<br>Unorganized Sector|0.2604++|0.2946+|0.0940|1.6027**|
||(0.1890)|(0.1831)|(0.1769)|(0.6885)|
|Backward Employment Spillovers from<br>Unorganized Sector|0.2309*|0.1668++|0.4742***|0.3246|
||(0.1206)|(0.1215)|(0.1145)|(0.8240)|
|||_Other-Sector Pro_|_ductivity Spillove_|_rs_|
|Average TFP of Horizontal-linked<br>Unorganized Plants||0.0249|0.0185|0.0118|
|||(0.0449)|(0.0435)|(0.0418)|
|Average TFP of Forward-linked<br>Unorganized Plants||0.2199*|0.0398|-0.9905**|
|||(0.1311)|(0.1332)|(0.4919)|
|Average TFP of Backward-linked<br>Unorganized Plants||0.4326***|0.1469+|-0.1833|
|||(0.0975)|(0.0938)|(0.4414)|
|||_Own-Sector Emp_|_loyment Spillover_|_s_|
|Forward Employment Spillovers from<br>Organized Sector|||1.4155***|-0.8653|
||||(0.2752)|(0.7876)|
|Backward Employment Spillovers from<br>Organized Sector|||1.2749***|0.4528|
||||(0.1559)|(0.5919)|
|Constant|-1.2768|-2.8268|-31.7320***|-9.3435|
||(2.5831)|(2.5669)|(4.4228)|(15.5799)|
|Observations|5888|5888|5888|5888|
|R-squared|0.0951|0.1042|0.1433|0.3373|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Standard errors are clustered at the state-industry level. Column 4 includes state-year and industry-year fixed effects. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 34