rafmacalaba/datause-encoder-data huggingface dataset:
> _ + + +

1 01 < 1 <!-- Start of picture text --> 2000 2005 2010 2016<br>Avg. 2001-2005 Avg. 2006-2010 Avg . 2011-2016 |<br>lo th bh<br>Late 2000s Early 2010s !<br>2000-2016 |<br>Source: Based on Moller and Wacker (2015). :<br><!-- End of picture text -->
<!-- Start of picture text --> 02 Perc. proc. firms Proc. premium<br>0.15sr ae<br>0.8<br><!-- End of picture text --> <!-- Start of picture text --> Reg. coef. (Yp) O4 Reg. coef. (%,)<br>eee Le<br>0.3 0.2<br><!-- End of picture text --> <!-- Start of picture text --> 0.1 0.6<br>0.7 0.8 0.9 0.56 0.6 0.64<br>No TM<br><!-- End of picture text --> <!-- Start of picture text --> 0.2 ;<br>01 02 03 O04 0.2 0.4 0.6<br>dp dg<br><!-- End of picture text -->
within an EA by clustering standard errors at the EA level.

The main concern our is that be en- regarding identification strategy polygyny may dogenous. Communes with a high proportion of polygynous households may have different characteristics (geographic, cultural, etc.) than less polygynous communes. This could affect For if the of communes are lo- crop yields. example, majority polygynous cated in fertile areas, households living in these areas will tend to have higher crop yields than households living in less polygynous communes. We could (wrongly) attribute the yield differential as the impact of polygyny. We address this potential threat to identification using EA fixed effects. These EA-level fixed effects, capture average differences in geographic, economic, and cultural factors that do not vary over time. Over a short period of time like that denoted by our data set (2014 and 2017), we can reasonably assume that the prevalence of polygyny in a commune is not affected by omitted time-variant variables.

# **_Spatial Correlation_**

A serious problem confronting our identification is the potential presence of spatial correlation in the error term, due to the tendency of both droughts and polygyny to be clustered in space. Although droughts can happen randomly over time, they often tend to be clustered across neighboring geographic units (Hsiang et al., 2011). Likewise, polygyny, as a cultural practice, is often clustered in geographic units that share similar livelihoods and cultural values (Lawson et al., 2015). To account for this problem, we adjust the standard errors using the method developped by Conley (1999) and Hsiang et al. (2011). In particular, we correct standard errors by allowing for both spatial correlation across the EAs within a radius of 52 km (the median distance across EAs) and infinite serial correlation.

Furthermore, there is the potential presence of spatial correlation due to omitted weather variables (Auffhammer et al., 2013). To account for this problem, we construct a measure of drought during the agricultural season, using the Standardized PrecipitationEvapotranspiration Index (Vicente-Serrano et al., 2010; Miao and Popp, 2014; Harari and Ferrara, 2018).

22
a specific country/sector, if the complete list of countries or sectors of origin is specified with the same code).<sup>7</sup> If the option `all` is not specified the share will be clearly equal to 100%. For example ``` .*Whatisthevalue-addedoriginatedinGermanyandabsorbedineachcountry?
.icio,origin(deu)destination(all) ``` ``` Value-Addedbyorigin/destination: Origin:DEU Destination:ALL Output:Value-Added ```

||`Millions of $`|`% of tot`|`al`|
|---|---|---|---|
|`AUS`|`12048.65`|_`0.`_|_`33`_|
|`AUT`|`36839.13`|_`1.`_|_`02`_|
|`BEL`|`20719.98`|_`0.`_|_`57`_|
|`BGR`|`3004.85`|_`0.`_|_`08`_|
|`BRA`|`15943.49`|_`0.`_|_`44`_|
|`CAN`|`15305.40`|_`0.`_|_`42`_|
|`CHE`|`37205.73`|_`1.`_|_`03`_|
|`CHN`|`101042.25`|_`2.`_|_`79`_|
|`CYP`|`768.14`|_`0.`_|_`02`_|
|`CZE`|`16171.75`|_`0.`_|_`45`_|
|`DEU`|`2446528.60`|_`67.`_|_`58`_|
|`DNK`|`12205.44`|_`0.`_|_`34`_|
|`ESP`|`33469.66`|_`0.`_|_`92`_|
|`EST`|`1219.86`|_`0.`_|_`03`_|
|`FIN`|`9049.83`|_`0.`_|_`25`_|
|`FRA`|`85684.36`|_`2.`_|_`37`_|
|`GBR`|`77158.04`|_`2.`_|_`13`_|
|`GRC`|`6543.15`|_`0.`_|_`18`_|
|`HRV`|`2473.71`|_`0.`_|_`07`_|
|`HUN`|`8782.43`|_`0.`_|_`24`_|
|`IDN`|`4689.61`|_`0.`_|_`13`_|
|`IND`|`12267.21`|_`0.`_|_`34`_|
|`IRL`|`5407.92`|_`0.`_|_`15`_|
|`ITA`|`52128.41`|_`1.`_|_`44`_|
|`JPN`|`23269.19`|_`0.`_|_`64`_|
|`KOR`|`17377.73`|_`0.`_|_`48`_|
|`LTU`|`1978.03`|_`0.`_|_`05`_|
|`LUX`|`3989.54`|_`0.`_|_`11`_|
|`LVA`|`1134.20`|_`0.`_|_`03`_|
|`MEX`|`10619.36`|_`0.`_|_`29`_|
|`MLT`|`321.40`|_`0.`_|_`01`_|
|`NLD`|`34154.59`|_`0.`_|_`94`_|
|`NOR`|`10257.55`|_`0.`_|_`28`_|
|`POL`|`33030.97`|_`0.`_|_`91`_|
|`PRT`|`6773.81`|_`0.`_|_`19`_|
|`ROU`|`9065.05`|_`0.`_|_`25`_|
|`ROW`|`235768.63`|_`6.`_|_`51`_|
|`RUS`|`38509.83`|_`1.`_|_`06`_|
|`SVK`|`6112.61`|_`0.`_|_`17`_|

`destination(all[,` _sector_ _~~c~~ ode_ `])` or `destination(` _country code_ `,all)` . 7This is the case if the complete list of countries or sectors of origin is specified, i.e. `origin(all[,` _sector code_ `])` or `origin(` _country code_ `,all)` , as well as a specific country/sector of destination, `destination(` _country code_ `[,` _sector_ _~~c~~ ode_ `])` .

8
# **1.  Household financial assets: how holdings vary across income groups and countries**

Our knowledge of the pattern of household financial asset holdings varies greatly by country. The US has the most comprehensive and apparently reliable data in the form of the triennial Survey of Consumer Finances, preliminary results of whose most recent wave, from 2004, have just been published by the Federal Reserve Board (Bucks et al., 2006, updating Aizcorbe et al., 2003). Earlier waves of the US survey are thoroughly compared with data for four other advanced economies, Germany, Italy, Netherlands and the UK, in Guiso et al. (2002). Other advanced countries for which data are available include Korea and Japan. Data for developing countries are, however, very sketchy and current data collection exercises in this area are focused more on measuring access or participation than on quantifying asset magnitudes. In this review we draw on much more limited data mainly on savings balances collected in general household surveys for Ghana, Jamaica and Vietnam, on a 2003 module on household assets and liabilities in India collected as part of the 59th National Sample Survey (NSSO, 2005), on a survey of investment in stock market assets in India (Kar et al. 2003) and on a household survey carried out in 2002 in rural and urban areas in China by CASS (Li and Zhao, 2006). They show some commonalities with other advanced economies, but also some differences which may be due to survey limitations.

Despite the sketchiness of data sources, some stylized facts can be adduced by reasonable extrapolation and interpolation from what we do have. One cannot, of course, rely wholly on the assumption that cross-sectional patterns within economies – variations in the pattern of asset allocation by such characteristics as age, education and wealth – can be extrapolated from the rich countries for which we do have cross-sectional data to the poor. Nevertheless, some of those patterns likely do hold up qualitatively (and we will show the extent to which some of them are confirmed in our handful of developing countries for which some data are available) and the patterns can be scaled to the aggregate magnitudes for which data are available in the poor countries.

7
Intelligence Unit of the Word Bank includes its “orderly transfers” indicator as part of its own “political stability” sub-index, the WGI authors assign it to VA instead of to PS. Similarly, indicators of “institutional permanence” from Global Insight and “institutional stability” from Bertelsman are assigned to VA instead of to PS. But an indicator on “government stability” from the International Country Risk Guide is assigned neither to PS nor to VA, but to GE. Some indicators on tax policy and administration are assigned to GE, but others are assigned to RQ. For several years they assigned the Heritage Foundation's "informal market activity" rating to RL. The extent of informal market activity is often used as a proxy for regulatory problems, including inefficient tax structure and weaknesses in tax administration, so conceptually it could belong in RQ or GE instead of in RL. However, Heritage "measures" its informal market activity indicator simply by re-scaling Transparency International’s corruption index. Presumably the WGI authors simply did not read the Heritage methodology paper, or they would not have included it in RL.<sup>2</sup> We ask, and try to answer, the following question: Are these really separate, causally related, concepts, or are they part of the general concept (or definition) of “good government?” Is “failed” government a syndrome that manifests itself in governments that are unaccountable, ineffective, ruled by “bandits”, instable, and perceived as “corrupt” in mass and elite surveys? Or, are these separable “causes” of corruption? The answer depends on the how these indicators relate to one another, and to the latent concept of “good government”. There are two basic stories of how the six concepts are related: one is causal, and one is a story of measurement. We first examine the support for these two contrasting models; then we consider a mixed model. Figure 1 shows the causal model.

> 2 In its most recent Index of Economic Freedom, Heritage re-named “informal market activity” to "freedom from corruption," reflecting more accurately the way the rating is produced.  The WGI authors no longer use it in their indexes.

8
affect male workc pattern the same way as male layoff affects female activities? The underlying reason for testing the effects in this manner is that there are reasons to expect there may be asymmetries in the adjustment of time allocation to unemployment shocks. If women have a greater tendency to smooth household consumption than do men, then they would react to the job loss of a male counterpart by increasing their participation in income-generating activities more than men would faced with a female job loss. It remains an open question what happens to the time of children (the subject of a companion paper) and to time allocated to housework (this paper).

How can we conduct this test in a consistent and clean manner? Ideally, we need an indicator for whether a member of the household was laid off from his/her job, or whether they experienced a wage cut. However, even quite sophisticated labor surveys do not make a distinction between voluntary and involuntary job losses. The distinction is extremely important for our purposes because the former will be endogenous and the latter exogenous. In the absence of this distinction in the Peru survey, we calculate unemployment shocks at the cluster level-we calculate separate unemployment rates for men and women. Gender-specific cluster unemployment rates are defined as proportion of men (women) in cluster not employed divided by total number of prime-age men (women) in cluster.'°<sup>1</sup> We do not use this analysis for urban Peru for the following reasons. Clusters in the urban sample are a lot more integrated in terms of labor demand than are rural ones. For instance it would be hard to interpret changes in employment in two adjacent clusters in Lima as employment shocks, since individuals in one cluster can work in the next one with little cost.

9 However, one difference between the approach in this paper and that study is that in the latter the data are cross-section. Pitt and Rosenzweig (1990) construct a pseudo panel by employing "household" fixed effects, i.e. fixed effects that are common across household members contemporaneously.

> **10** Prime age: between 18 and 60 years.

1 Aggregating the unemployment rate to the cluster level does not completely get rid of the voluntary vs. involuntary quits problem. However it does tend to disconnect from the individua **l** evel by indicating that general chamges in cluster level unemployment rates over time are indicative of changes in labor market situation.

8
In sum, the "coverage problem" stems from the fact that the high discount rate of low income workers and their reluctance to pay social security taxes may be quite rational, increased coverage may come at the expense of lower take-home pay, higher evasion and lower employment so a careful cost-benefit analysis must be made, and groups that are difficult to monitor predominate in developing countries. Thus, expanding coverage may not be administratively feasible and furthermore, it may not improve society's welfare unless accompanied by a heavy dose of redistribution-which in tum may not be politically acceptable.

Empirically, we observe that degree of coverage is closely tied to a country's per capita income (World Bank, 1994, p. 40). Until government's capacity to enforce tax collections increases, the structure of the economy changes to facilitate this, and the earning capacity of the bottom half of the labor force grows-all of which occurs with the process of economic development--it is difficult and not necessarily desirable to extend contributory social security coverage to the entire population.

Finally, while reform may draw a sharper line between the insured and uninsured, augmenting coverage in the absence of reform would simply aggravate an already nonsustainable fiscal situation, increase the implicit pension debt and make reform more difficult. It is essential to put social security on a financially sound basis, which includes a large funded defined contribution component, before expanding coverage.

# **Are voluntary contributions the answer to the coverage problem?**

Some analysts have urged that voluntary contributions be permitted to extend pension insurance to those who don't have covered jobs. In Japan, men are encouraged to make flat contributions for their wives, in exchange for the promise of a flat benefit. In China, workers in township and village enterprises are subject to social pressure to contribute to their individual accounts, on a "voluntary" basis. Making coverage voluntary avoids the evasion and unemployment problems discussed above. In several countries community organizations such as NGO's have tried to set up small-scale self-help schemes for informal sector workers (van Ginneken, 1999).

While the availability of voluntary mechanisms is probably utility-improving, I believe we shouldn't expect them to accomplish very much. After all, a basic reason for the existence of mandatory programs is that many people, especially low income people, will not save much voluntarily for their retirement and the basic reason for limited coverage is that, even when the program is mandatory, many workers and employers will evade making contributions because they believe that participation does not make them better off. In addition, the transactions costs of collecting many small voluntary contributions might be relatively high. Voluntarism and self-help schemes are more likely to succeed in programs when the costs are small and the benefits immediate (as for micro-finance or primary health care). It is likely to be only a small part of the solution to a large long term old age security problem.

12
Note that a strict interpretation of the empirical results according to the model developed in the conceptual framework section implies that the negative coe¢ cient on income at the extensive margin captures the cases where the teacher does not ask for bribes, but it does not re‡ect the cases where the household refuses after facing a bribe demand. This is because the refusal depends on part of the bargaining power that is uncorrelated with income. However, the income information available to the teacher in many cases may be less precise than the income estimate we have from the household survey. Thus the estimated e¤ect is likely to re‡ect, at least partly, the ‘refusal power’of the households after the bribe demand is made.

Another issue relevant for the interpretation of the evidence reported in this paper is that the magnitudes of the income coe¢ cients in the IV estimates for the propensity to bribe vary signi...cantly depending on the de...nition of the income variable. A reader might thus be unsure about the relevant magnitudes. To standardize the estimated e¤ects, we use the estimates from Tables (3) and (5) to produce elasticity estimates. When income variable is de...ned as per capita household income, the elasticity estimate implies that a one percent increase in income reduces the propensity to pay bribe by -0.73 percent. The corresponding elasticity estimate from the speci...cation with log of total household income is larger: -1.06. The e¤ects of income on propensity to pay bribe is thus substantial. The di¤erence in the elasticity estimates between per capita income and log of total income re‡ects the fact that the household size increases with the income in the data.

# (10) Conclusions

We exploit the negative e¤ects of heavy rainfall on the rural economy in Bangladesh for identi...cation of the e¤ects of the household income on the propensities to bribe for education services, and on the amounts paid as bribes. The IV estimates provide strong evidence that income has a substantial negative e¤ect on the probability that a household pays a bribe for its children’s education; a one percent lower household income increases the probability that parents need to pay bribes to teachers by 1.06 percent. This evidence is consistent with an interpretation that the poor face a higher probability of bribe demand and they cannot refuse to pay because of their weak bargaining power, while the rich, given their strong bargaining power, may not need to bribe for the schooling of their children. We ...nd no statistically signi...cant e¤ect of income on 35
particularly large, with over 160 employees at inception, appear to roughly double their size early on, then stabilize, meaning that in a few short years they have added nearly 180 jobs, more than tenfold the raw increase by firms that are born as micro.

Lastly, table 7 displays the same base models, with the (log) wage bill — that is the total remuneration cost, including wages but also social security payments — per worker. In line with several other instances in the literature on the wage-size-effect, the model controls for the proportion of workers with secondary-level education, which is positively and significantly associated with a higher wage bill per worker, though this coefficient loses significance when micro firms are excluded, pointing to a segmentation of the very smallest firms. Similarly, the correlation between the average wage bill and labor productivity is positive. However, the size story is less consistent. Though large firms do enjoy a “wage premium” relative to other firms, the story is not consistent (that is relative to the omitted micro category) across all increasing size groups.

# **V. Conclusion**

Taken broadly, Ghana has been experiencing sustained economic growth, which has been bolstered by recent oil production. What is more, evidence from macro-level data shows that recent growth has not been jobless, showing expansions of employment at pace with GDP growth. Despite this growth, the formal private sector measured through the WB ES 2013 data is small and in aggregate, the number of jobs in the formal sector is still negligible compared to the size of the labor force measured through the national household survey (GLSS6) in the same year.

Indeed, using micro-level evidence affords the opportunity to disaggregate these effects. One recent account argues that employment growth lagging GDP expansion had been the result of labor being absorbed into slow-growing industries (Aryeetey and Baah-Boateng 2015, World Bank 2016); the evidence presented here supports that finding, indicating that these sectors may be lagging behind in their absorption of labor relative to their capacity. This is notable in light of evidence of the firms operating in the formal private sector: while micro and small firms make up the majority of firms, large firms are the predominant employers. Likewise, these larger firms are responsible for the largest contributions for employment growth, on net.

Firm-level regression results provide further evidence of the prominence of already-large firms. While, encouragingly, there is a positive and significant relationship between firm productivity and size (an indication of market efficiency), there is also evidence that it is driven mostly by larger firms and that the magnitude is low. In addition, no such relationship between employment growth and labor productivity is observed. Yet larger firms remain those with higher labor productivity. This is a possible 18
<!-- Start of picture text --> Wealth distribution, 1998/1999-2005/2006<br>5 6 7 8 9 10<br>log per capita expenditure (adjusted to 1998 value)<br>1998/1999<br>2001/2002<br>2005/2006<br>kernel = epanechnikov, bandwidth = 0.0567<br>1<br>.8<br>density .6<br>.4<br>.2<br>0<br><!-- End of picture text --> Note: capita is adjusted with adult equivalence.

22
<!-- Start of picture text --> wo<br>=<br>i)<br>e = -_=<br>o nwa no<br>o ae<br>= a -<br>oO - aw<br>“ ant<br>s3wn Pal - a<br>= 0 -”<br>= - -<br>oO oad<br>3 oo"<br>oS a” “<br>3“<br>©Pha<br>o o“”<br>+1<br>°o<br>0 4 8 12 16<br>Years of Schooling (Fathers)<br><!-- End of picture text --> <!-- Start of picture text --> Ld<br>0 wo = “~ = a<br>= nw<br>£ me<br>P=rs]o tee a a SO ae aoe,<br>Ww nw<br>_— 7 a<br>G ty<br>£ Se oe a<br>e —>~ a<br>==a Se a, im ina<br>c<br>2<br>rs<br>_<br>oc<br>om<br>om<br>_<br>o<br>2<br>£<br>Oo<br>0 4 8 12 16<br>Years of Schooling (Fathers)<br>nos SONS = Daughters<br><!-- End of picture text -->