rafmacalaba/datause-encoder-data huggingface dataset:
## Labor force survey

The estimation of the parameters of the workers' problem requires panel data on workers' sector of employment and wages or non-employment status. We use the panel sample of the Encuesta Permanente de Hogares (EPH, Permanent Household Survey). The database contains information on individual wages, employment sector, non-employment and other standard variables in labor force surveys. Part of the EPH is a panel and we can use it to track employment decisions across sector pairs, across employment and non-employment, and average sector wages. We use the available panel from 1996 to 2006.

# A.2 Estimation of the technology parameters

## Production function parameters

We postulate a production function which is Leontief in materials _Mijt_ and a Cobb-Douglas index of capital _Kijt_ and labor _Lijt_ . To estimate the Cobb-Douglas production function coecients on labor and capital, we follow the regression approach of Ackerberg, Caves and Frazer (2015). It is important to note that the structural assumptions of the estimation method are compatible with our own structural model. We use value of production ( _Yijt_ ) on the left-hand side, capital ( _Kijt_ ) and labor ( _Lijt_ ) on the right-hand side, and following the Leontief specication of the production function we exclude materials from the regression at this stage. The regression equation for this stage takes the form where _ε_ is a combination of productivity shocks and measurement error in output. The equation reects the contribution of labor and capital to output and is therefore conceptually a value-added production function (given the Leontief specication), even though total value of production is on the left-hand side. See Ackerberg, Caves and Frazier (2015). We use the predetermined variables _Kijt_ , _Kijt−_ 1 and _Lijt−_ 1 as instruments. Standard errors are computed from 500 bootstrap replications. The labor coecient _αL_ is 0.619 and the capital coecient _αK_ is 0.283. Both are statistically signicant. These results are comparable to those obtained by Pavcnik (2002) for Chile.

We calibrate the unit input requirement for materials _vM_ by computing the mean of _Mijt/Yijt_ across rms. The estimated value for the unit input requirement is 0.41. As a robustness exercise we 41
economy, but transactions in an open economy such as capital transfer can be used for investment. Capital transfer includes the foreign direct investment (FDI), official development assistance (ODA), grants or loans, and remittances, among others. Examining the trends of each component of the Balance of Payment informs the source of investment and capital accumulation.

# **2.3. Total Factor Productivity**

Total Factor Productivity (TFP) conceptually measures how efficiently factors such as capital and labor are utilized in producing output. As widely discussed in the growth literature, TFP growth is often interchangeable with technological progress, and there are many contributing factors to its growth. The major determinants of TFP can be summarized as: macroeconomic factors such as managed inflation, debt, and government spending; knowledge spillovers through trade openness and FDI; quality labor; institutional factors such as political stability; structural transformation from lower to higher productivity sectors; and better utilization of labor through increased female labor force participation.<sup>21</sup>

# **3. Data**

The variables considered for growth accounting are described in Table 1 along with the source of data for the 32 countries analyzed here.<sup>22</sup> The major source is the World Bank’s _World Development Indicators_ (WDI). From WDI series, Gross Domestic Product, the size of population by age and employment status,<sup>23</sup> and their growth rates are obtained for the period of 1960 - 2010.

> 21 See Loko and Diouf (2009) and Isaksson  (2007) for a discussion of the determinants of productivity growth.

> 22 See Table A1 in Appendix for the full list of countries.

> 23 Employment ratios (share of the employed among working age population) in WDI are indeed coming from the KILM (Key Indicators of Labor Market) constructed by the ILO (International Labour Organization). The statistics the KILM are not based on micro data but estimated using a model. This approach gives a large geographic and time coverage of data, which is needed for the growth accounting exercise, although the accuracy of the statistics is often questioned.

9
meetings and was administered to a sample of active members, including individuals who were members at the time of the conversion as well as new clients who entered afterwards.<sup>18</sup> Third, we use social network data collected before the intervention and again one year later to examine the impact on social networks, as well as heterogeneous treatment effects for groups with different preexisting levels of social networks.

# _A.  Lender Costs: Activity-Based Costing Exercise_

It is important from a sustainability perspective to examine the complete impact on the lender of such a change from group to individual liability. If the lender spends more money on credit officer labor in order to screen, monitor and enforce loans then this is a necessary component of the analysis. In order to evaluate to what extent this was true, we conducted two activity-based costing exercises in which each credit officer kept a detailed diary of all activities for one week. We then attributed their activities to either repayment (preparing for center meetings plus collection and processing of repayments outside of the meetings), center meeting, monitoring, enforcement and/or re-loan activities. Table 6 reports these results. We find no statistically significant differences in the way credit officers allocated their time, and furthermore the point estimates are actually the opposite of what one may have expected on enforcement. On approval and processing of new loans (Column 7), credit officers do spend more time under individual liability, although again this result is not statistically significant.

# _B.  Center Activities_

The client follow-up survey asked questions about center penalties for missing meetings, leaving early and missing payments as well as various activities such as anniversary, Christmas and snacks during the meeting. Table 7 reports changes in penalties (columns 1 and 2) and activities between

> 18 Since meeting attendance is compulsory, we should not be concerned with having a bias sample of survey respondents.  In any event, we compared past repayment between respondents and non-respondents in converted and control centers and found no statistical differences across samples (largest t-stat is 0.82).

16
||years between 1997<br>&2003.|||||
|---|---|---|---|---|---|
|US exports|<br>Exports to the United<br>States|Average<br>exports to<br>the United<br>States<br>during the<br>period|1982-1986<br>(period 2)<br>1987-1991<br>(period 3)<br>1992-1996<br>(period 4)<br>1997-2001<br>(period 5)|Adjusted for<br>inflation<br>using US<br>PPI (all<br>commodities<br>). log.|US PPI: St. Louis<br>FRED. Export<br>Data: IMF<br>Direction of Trade<br>Statistics|
|R&D<br>expenditure|Expenditures on<br>R&D|Average<br>expenditure<br>during the<br>period<br>(constant<br>1995 USD)|1982-1986<br>(period 2)<br>1987-1991<br>(period 3)<br>1992-1996<br>(period 4)<br>1997-2001<br>(period 5)|Log**|Lederman and<br>Saenz (2003)|
|ln(GDP Per<br>Capita)|Natural log of real<br>GDP per capita (PPP)|2000 PPP<br>Constant<br>Prices|1995 (Table 1<br>uses all years)|log|World Bank WDI|
|Population|Population|Count|1995|log|WorldBankWDI|
|Net Export<br>Growth|Growth rate of<br>exports of that<br>Leamer commodity<br>group less average of<br>annual growth rates<br>of non-export GDP<br>1994 – 2003|decimal<br>form|1994-2003|None|COMTRADE &<br>World Bank WDI|
|ln(historical<br>booms)|Historical Boom<br>counts***|Total<br>counts<br>between<br>1984 &<br>1993|1984-1993|log**|COMTRADE|
|ln(historical<br>discoveries)|Historical Discovery<br>counts*|Total<br>counts<br>between<br>1984 &<br>1993|1984-1993|log**|COMTRADE|
|Factor<br>Endowments|Average value of net<br>exports per capita<br>between 1989 &<br>1993 per capita|Current<br>dollars per<br>capita|1989-1993|Net exports<br>for each year<br>divided by<br>that year’s<br>population.|COMTRADE &<br>World Bank WDI|
|Income Gini|Average value of<br>Gini|Index|1985-1995|None|Deininger &<br>Squire (1996)<br>(high-quality<br>national Ginis<br>only)|
|Poverty<br>Count|Average percentage<br>of the population<br>living on less than<br>$1/day. Calculated<br>using the GDP per<br>capita and income|Percentage|1985-1995|None|World Bank WDI|

27
Figure 1: Rankings of _MLD_ ( _XB_ ) vs. _MLD_ ( _X_ ) <!-- Start of picture text --> 45o<br>0 10 20<br>Rankings using Mean Log Deviation<br>(between type component)<br>20<br>10<br>Rankings using Mean Log Deviation<br>0<br><!-- End of picture text --> Source: Authors’ calculations based on Demographic Household Surveys circa 2008.

A promising path to address the problem of partial observability of circumstances is the use of panel and pseudo-panel data. Niehues and Peichl (2014) show that we can estimate an upper bound of inequality of opportunity through the fixed effects component of a fixed effects regression, addressing problems of measurement error. However, since panel data are scarce, we can use pseudo-panels in order to exploit the availability of household surveys (Verbeek and Vella, 2005). By using pseudo-panels we should be able to estimate the long-run upper bound of IOp _`a la_ Niehues and Peichl addressing (plausibly) both measurement error and comparability across countries. This is a path that has not been explored yet, and it would be worth exploring by the scientific community.

# **References**

- Aturupane, H., Deolalikar, A., and Gunewardena, D. (2008). The determinants of child weight and height in sri lanka: A quantile regression approach. Research paper/UNU-WIDER 2008.53.

- Barros, R., Molinas, J., and Saavedra, J. (2010). Measuring progress toward basic opportunities for all. _Brazilian Review of Econometrics_ , 30(2):335–367.

- Case, A. and Paxson, C. (2008). Stature and status: Height, ability, and labor market outcomes. _Journal of Political Economy_ , 116 (3):499–532.

- Checchi, D. and Peragine, V. (2010). Inequality of opportunity in italy. _Journal of Economic Inequality_ , 8(4):429–450.

7
_Table A2.3: Impacts on S/E Skills Index and GPAs in Short-Run, Balanced Sample (Z-Scores)_

|||Gender||Ethnicity||||Academic Year||Pre-Treatm|ent Achievem|ent|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Average|Male|Female|<br>Macedonian|Albanian|Roma|Other|Sixth Grade|Seventh Grade|Tercile 1|Tercile 2|Tercile 3|
||<br>(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|_Panel A: S/E Ski_|_lls Index_||||||||||||
|Treatment 1|0.060***|0.034|0.087***|0.069**|0.029|0.225|0.104|0.057*|0.121***|-0.007|0.047|0.122***|
|“Student Self-<br>Learning”|_(0.023)_|_(0.030)_|_(0.026)_|_(0.027)_|_(0.054)_|_(0.182)_|_(0.104)_|_(0.031)_|_(0.029)_|_(0.033)_|_(0.033)_|_(0.030)_|
|Treatment 2|0.149***|0.105***|0.192***|0.168***|0.068|0.525***|0.249**|0.138***|0.182***|0.017|0.168***|0.210***|
|“Teacher<br>Delivery”|_(0.023)_|_(0.031)_|_(0.027)_|_(0.028)_|_(0.048)_|_(0.145)_|_(0.110)_|_(0.032)_|_(0.027)_|_(0.034)_|_(0.031)_|_(0.033)_|
|N|16,575|8,059|8,516|11,562|4,038|318|657|7,943|8,632|4,167|5,808|6,600|
|N Control|6,606|3,282|3,324|4,493|1,754|113|246|3,192|3,414|1,615|2,366|2,625|
|N Treatment 1|4,979|2,394|2,585|3,604|1,037|93|245|2,419|2,560|1,329|1,741|1,909|
|N Treatment 2|4,990|2,383|2,607|3,465|1,247|112|166|2,332|2,658|1,223|1,701|2,066|
|_R2_|_0.336_|_0.303_|_0.348_|_0.353_|_0.205_|_0.360_|_0.439_|_0.331_|_0.385_|_0.228_|_0.282_|_0.356_|
|_Panel B: GPAs_|||||||||||||
|Treatment 1|0.023*|0.030**|0.016|0.028*|0.014|-0.025|-0.022|0.019|0.027*|0.030*|0.029*|0.006|
|“Student Self<br>Learning”|_(0.012)_|_(0.013)_|_(0.014)_|_(0.015)_|_(0.018)_|_(0.049)_|_(0.029)_|_(0.018)_|_(0.015)_|_(0.017)_|_(0.017)_|_(0.010)_|
|Treatment 2|0.019|0.024*|0.013|0.017|0.011|0.113*|-0.019|0.007|0.035**|0.027|0.013|0.014|
|“Teacher<br>Delivery”|_(0.014)_|_(0.015)_|_(0.015)_|_(0.018)_|_(0.018)_|_(0.060)_|_(0.030)_|_(0.019)_|_(0.017)_|_(0.018)_|_(0.020)_|_(0.009)_|
|N|16,575|8,059|8,516|11,562|4,038|318|657|7,943|8,632|5,550|5,569|5,456|
|N Control|6,606|3,282|3,324|4,493|1,754|113|246|3,192|3,414|2,152|2,242|2,212|
|N Treatment 1|4,979|2394|2,585|3,604|1,037|93|245|2,419|2,560|1,742|1,684|1,553|

35
- 19 -

certain internal characteristics of each country group appear to have an important influence on relative prices.

# V. Additional Evidence on the Extent of Discriminatory Pricing

While the previous analyses focussed solely on the pricing practices of French enterprises, a question of obvious importance is whether or not other industrial countries' firms have adopted similar policies. For a test of this proposition, f.o.b. unit values were computed for the United Kingdom's exports of major iron and steel products to former African colonies (Kenya, Uganda, Tanzania, Sudan, Nigeria, Gambia, Sierra Leone and Ghana) as well as to all other developing countries. Next, similar computations were made for Belgium (with Burundi, Rwanda and Zaire designated as colonies) and Portugal (Angola and Mozambique). These data were then used to compute the average premium or discount that the Belgium, Portugal or United Kingdom colonies paid over the 1962-1987 interval. These figures, as well as similar statistics for the French colonies, have been summarized in Table 5.

Over the full 1962-87 period the average premiums paid by the former Belgium and French colonies are remarkably close (23.7 and 23.2 percent, respectively) while the former United Kingdom associated paid a slightly lower premium of 20.0 percent. The same pricii.g pattern emerges during 1962-75 for Portugal's exports to former colonies, but from 1976 on the premiums more than tripled and averaged over 120 percent. It appears that the hostilities in Angola were a major factor behind this dramatic rise as domestic firms may have employed excess pricing as a means of transfering resources out of the country. However, as far as policy implications are concerned, the statistics in Table 5 are important since they show that the problem of "overpricing" of imports is widespread among African countries.
dissolution. These may or may not be tempered by remarriage so that the effects may be lasting. In unstructured, open-ended qualitative interviews conducted by two of the authors in Senegal in 2012, one of the dominant messages was that women who have the option not to remarry seized it eagerly.<sup>5</sup> Such women tend to talk about married life as an ordeal they are happy to be in the position to avoid. Echoed in the interviews were general preferences for non-co-residing husbands and/or mothers in law.

To date, there have been few studies of divorce or widowhood in Senegal. Antoine and Dial (2003) and Dial (2008) focus on a small selected sample of women with complex marital trajectories in Dakar only.<sup>6</sup> Another study using fertility surveys that estimated that 17% of all unions dissolve within the first 5 years is likely to be out of date (Smith _et al._ 1984).<sup>7</sup> The 2015 World Marriage Data reveal that percentages of divorced (or separated) and nonremarried women in the population have been increasing slightly since the mid-1980s for all but those below age 30. Rates were between 2% and 3% for ages 30 to 55 until the most recent period when they seem to have risen more for women over 40 than for younger ones. Current divorcees account for about 7% of women in the 45-55 age range and only 4% of those between ages 30 and 40 according to the 2014 Demographic and Health Survey (DHS) (Table A1). Given that the data do not record divorced and remarried women, this increase could be due to decreasing remarriage rates at older ages, either because age makes non-remarriage more socially acceptable nowadays or because it makes remarriage more difficult. For women aged 40-50 the percentage of non-remarried widows was higher than that for divorcees until the beginning of the 2000s. This has changed in recent years as the percentage of widows has remained fairly stable while that of divorced women has increased. The 2014 DHS indicates that the percentage of non-remarried widows is higher than that of divorcees only above age 50 when it reaches 12.5% (Table A2).

To our knowledge, this study is the first to directly examine the relationship between marriage dissolution and women’s well-being in Senegal. We focus on women because, as a result of large spousal age gaps, and the widespread practice of polygamy, ever-married men rarely, and then only temporarily, find themselves in a non-married state. In fact, although the share of ever-married men who are currently divorced is similar to that for women (2.5% for

> 5 Interviews transcribed in Lambert and van de Walle (2012).

> 6 Findings in Antoine and Dial (2003) that one-third of marriages end in divorce and that for the youngest generation, 25% of divorces occur within 7 years of marriage are likely to be highly specific to their sample. 7 The survey used was part of the World Fertility Surveys.

3
|**Table A26: World Bank Country Policy and Institutio**<br> <br>|**nal**|**Asse**|**ssmen**|**ts (**|**PIA)**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Data Provider**<br>The World Bank.||||||||||
|**Description**<br>Multilateral development bank headquartered in Wa|shing|ton,|United|Stat|es|||||
|**Website**<br>www.worldbank.org||||||||||
|**Data Source**<br>Country Policy and Institutional Assessments||||||||||
|**Type**<br>Expert Assessment||||||||||
|**Respondents**<br>World Bank country economists subject to centraliz|ed re|view|for co|mpar|ability|||||
|**Frequency**<br>Annually since 1978||||||||||
|**Coverage**<br>World Bank client countries||||||||||
|**Public Access**<br>Since 2005, only for countries eligible for conc<br>Development Association.|essio|nal le|nding|fro|m the|Inte|rnatio|nal||
|**Description**<br>Indicators on 16 dimensions of policy and instituti<br>on a 6-point scale. CPIA indicators are used<br>countries.|onal<br>to all|perfo<br>ocate|rmanc<br>conc|e. R<br>essi|espon<br>onal l|ses a<br>endin|re co<br>g acr|ded<br>oss||
|2008<br>**Voice and Accountability**<br>NA<br>..|2007<br>..|2006<br>..|2005<br><br>..|2004<br>..|2003<br>..|2002<br>..|2000<br>..|1998<br>..|1996<br>..|
|**Political Stability and Absence of Violence**||||||||||
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|Management of external debt<br>X<br>|X|X|X|X|X|X|X|X|X|
|Quality public administration<br>X|X|X|X|X|X|X|X|..|..|
|Budget management<br>X|X|X|X|X|X|X|X|X|X|
|Efficiency of revenue mobilization / public expenditures<br>X|X|X|X|X|X|X|X|X|X|
|**Regulatory Quality**||||||||||
|Business regulatory environment<br>X|X|X|X|X|X|X|X|X|..|
|Trade policy<br>X|X|X|X|X|X|X|X|X|X|
|**Rule of Law**||||||||||
|Property rights<br>X|X|X|X|X|X|X|X|X|..|
|**Control of Corruption**||||||||||
|Transparency, accountability and corruption in public sector<br>X|X|X|X|X|X|X|X|X|..|
|**Country coverage**<br>**142**|**140**|**136**|**134**|**135**|**136**|**136**|**136**|**136**|**131**|
|**Year of Publication**<br>**2008**|**2007**|**2006**|**2005**<br>|**2004**|**2003**|**2002 **|**2000**|**1998**|**1996**|

65
R2 0.17 0.18 0.19 0.20 0.19 0.20 0.67 0.67 _Note_ : The dependent variable is the log of rent (combined rent paid and self-estimated rent), measured in 2017 PPP US$by year. Robust standard errors are clustered at the enumeration area level. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
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**Table 1. Endogenous Variable Definitions for the** USAGMKTS **Model.**

# Variable Definition

# Feed Grain Supply Compoorient

- COMPFGA - Feed grain program participation of corn and grain sorghum in percent of acreage

- ACGSN - Acreage of corn and grain sorghum in million acres

- YLDCGS - Yield per planted acre of corn and grain sorghum in bushels per acre

- COSTCGS - Variable costs per acre for corn and sorghum in dollars (includes seed, chemicals & labor) weighted by the respective acreages and deflated by the GNP price deflator

- PRDFG - U.S. production of feed grains (corn, sorghum, oats, barley), million metric tons

# Soybean Supply Component

AS - Acreage of soybeans in million acres

- YLDS - Yield per planted acre of soybeans in bushels per acre

- RCOSTS - Variable costs per acre for soybeans in dollars (includes seed, chemicals & labor) deflated by the GNP price deflator

- PRDS'1 - U.S. production of soybeans, million metric tons

# Feed Grain Demand ComRonent

- DLVKFG - U.S. feed and residual of feed grains (corn, sorghum, oats, barley), million metric tons

- DINDFG - U.S. feed grain use by industry, million metric tons XFG - U.S. exports of feed grains (corn, sorghum, oats, barley), million metric tons

- KFORFGE - U.S. ending farmer owned reserve stocks of feed grains, million metric tons

- KGOVFGE - Ending government owned feed grain stocks (total CCC inventory), million metric tons

- KMKTFGE - Ending inventories controlled by market forces (privately held stocks pius stocks under CCC loan)

- RPAFC - U.S. average price of corn at farm in dollars per bushel deflated by the GNP price deflator

- RPAFSG - U.S. average price of grain sorghum at farm in dollars per bushel deflated by the GNP price deflator

# Soybean Demand Component

XSB - U S. exports of soybeans, million metric tons CRUSH - U.S. crushings of soybeans, million metric tons

- KPRISBE - U.S. ending free soybean stocks, million metric tons

- RPAFS - Soybeans, price at farm, U.S. average in dollars per bushel deflated by the GNP price deflator

23
about, and experiencing, food insecurity is due to these factors alone, then food prices should have no additional effect. Throughout the analysis, we use maize prices as our proxy for food prices. Maize accounts for more than 50 percent of households’ caloric consumption and so fluctuations in maize prices directly affect most of the population (Minot 2010). Panel weights are used to make the results representative of the national population of Malawi.

# _3.2 Results_

Table 4 shows the estimated average marginal effects (and conditional average marginal effects)<sup>14</sup> from predicting our three outcomes of experiential food insecurity: a dichotomous measure of worrying about food in the last 7 days, a dichotomous indicator of experiencing food insecurity in the last 12 months, and a count of the number of months experiencing food insecurity in the last 12 months. For all models, we present the fixed-effects OLS estimates. For the binary outcome models, we also present fixed-effects logit estimates and for the count model, we report estimates from a fixed-effects Poisson estimator. The Logit estimator has the advantageous attribute that it bounds the fitted values between zero and one, and the Poisson estimator similarly is meant to better account for the clumping associated with count variables.

Consistent with expectations, FCS is negatively associated with experiential food insecurity. A reduction in dietary diversity is associated with increased anxiety about, and experiences of, food insecurity at the national level (Table 4) and in rural areas of Malawi (Table 5). Given that FCS is intended to measure food security, it is reasonable to anticipate

> 14 For OLS, estimates are average marginal effects, and for the Logit and Poisson estimators the estimates are marginal effects averaged over the values of the observed controls but at a fixed point (zero) for the unobserved controls (i.e. individual-level fixed effects). We refer to the Logit and Poisson effects as conditional average marginal effects.

14