rafmacalaba/datause-encoder-data huggingface dataset:
disputes since 2008 has left most arbitration committees overwhelmed with cases such that most disputes take 3-6 months between filing and the first hearing; resolution can take one to two years. Local courts encountered similar problems of capacity and caseload. In Dongguan, Guangdong Province, a local court reported extremely heavy caseloads on judges, the majority of which were labor disputes (Zhao, 2008). The recent push toward more basic level mediation, at the firm, at the district and county levels, is one government strategy to reduce the number of cases proceeding to arbitration and litigation.

The China Urban Labor Survey asked a set of questions about labor disputes initiated by respondents. Summary statistics are provided in Table 8. Just less than one percent of both local resident and migrant workers have initiated a dispute, a frequency that is similar to those derived from aggregate data. Because of the small sample size (data on only 75 disputes), the results should be interpreted as being suggestive rather than definitive. Disputes by local residents are concentrated in the period after the Labor Law was implemented, but this is not the case for migrants. Most disputes are over wages (43% for local resident workers, 49% for migrants). Local residents appear much more likely than migrants to initiate disputes about changing or ending a labor contract. Most local residents are dissatisfied with the resolution of their dispute, while most migrants are satisfied. Some of the patterns for migrants may reflect the fact that migrants often initiate disputes because of unpaid wages, which may be more straightforward to resolve.

Using the CULS data, we also estimated probit models of the determinants of initiating a labor dispute (Table 9). The only significant determinant is level of education, especially for migrants. For migrants, relative to having primary education or less, having a junior high school education increases the likelihood of initiating a dispute by 27%, having a senior high education by 59%, and having a college degree by 78%. For local residents, middle school graduates are the most likely to initiate disputes, followed by those with more 21
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context of the two crises considered here. Though dual agency problems are likely (to some degree) behind market responses, this hypothesis is difficult to test empirically.

A second explanation lies in the information content of fundamentals during crises. More precisely, to the extent that fundamentals are not independent from systemic factors, rapidly changing systemic innovations may detract from the predictive power of bank fundamentals that are released at lower frequencies and with a delay. This second explanation is easier to test in the data. To see how systemic factors can influence standard fundamentals it is enough to consider the case of non-performing loan ratios, which are likely to deteriorate due to the increase in borrowing costs and the downturn in economic activity associated with a systemic crisis.

To illustrate this point, consider the case of a depositor that attempts to assess the future evolution of a bank’s portfolio (summarized here by its non-performing loan, NPL, ratio) over different time horizons, based on the most recently released ratio and readily available measures of systemic risk.<sup>17</sup> Assuming, as before, that balance sheet data are released with a four-month delay, this exercise amounts to predicting future values of the non-performing ratio using its four-month lag and systemic risk indicators that are currently available.

We replicate this exercise by estimating regressions of the type illustrated by the equation below, where _NPL_ refers to the non-performing loan ratio, _S_ is a matrix of systemic factors (country risk and exchange rate risk), α _i_ refer to the individual bank effects, and ε _t_ is the error term. _k (j)_ denotes the number of lags in the systemic indicators ( _NPL_ ) used for predicting the evolution of bank’s non-performing loan ratio.

> 17 We focus on the ratio of non-performing loans to total loans, since we expect this variable to reflect bank conditions better than bank capital (which can be increased at any time by bank shareholders) and return on assets (which tends to be a more volatile measure of bank performance).

20
: : : :

-

|—o||r||e|a|||
|---|---|---|---|---|---|---|---|
|<br>|<br>|<br>Lower~<br>Bound<br>|<br>|Lower-<br>Bound<br> <br>|Inter<br>Quartile<br>|Lower-<br>Bound<br><br>|Inter<br>Quartile<br>|
|mL«WooDWenNNGYo||SeaH||Teeee]|SRTe|]<br>saaswe||eawsae|]_<br>ee]|ws|
|<br>SPECIAL INDUSTRIAL MACHINERY &<br>EQUIPMENT||<br>148.61||<br>|<br>60.58|||
|OFFICE,<br>COMPUTING,<br>& ACCOUNTING<br>MACHINERY|||||25.78|0,08||
|e||||||||
|eM||||||||
|RADIO,<br>TV,<br>& COMMUNICATION<br>EQUIPMENT||732.25|638.96|660.59||||
|ELECTRICAL APPLIANCES &<br>HOUSEWARES||||117.99||||
|ELECTRICAL APPARATUS AND<br>SUPPLIES,<br>N.E.C.||414.90||a58.69|237.32|10.33||
|P||||||||
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# 5.3.2 Metals Intensities

As previously mentioned, metals pose a particularly serious problem because they bioaccumulate. The natural distribution of metals is progressively altered by industrial activity, giving rise to focal concentrations. The potential consequences for exposed populations were demonstrated by ; 49
Transactions Profile for the Pakistan Province of Sindh (FY 2016/17‐ Amount Ranges in Pakistan Rupees).

<!-- Start of picture text --> SINDH<br>PHR<br>Series1 Series2 2 per. Mov. Avg. (Series1) 2 per. Mov. Avg. (Series2)<br>Transactions Profile for the Pakistan Province of Khyber Pakhtunkhwa (FY 2016/17‐ Amount Ranges in<br>Pakistan Rupees).<br>KP<br>Series1 Series2 2 per. Mov. Avg. (Series1) 2 per. Mov. Avg. (Series2)<br>PKR<br>97.421 99.335 99.876 99.939 100.000 100.000<br>94.216<br>90.927<br>76.206<br>66.918 65.184<br>57.743<br>43.092<br>PERCENTAGE 39.034<br>26.486 23.609<br>14.953<br>10.832<br>0.253 0.674 2.050 3.270<br>94.221 96.758 99.050 99.670 99.912 99.954 100.000 100.000<br>84.805<br>70.638<br>54.286 57.255<br>49.171<br>41.182<br>PERCENTAGE 34.628<br>27.183<br>17.893<br>12.900<br>5.792<br>0.532 1.039 2.425<br><!-- End of picture text --> Transactions Profile for the Pakistan Province of Khyber Pakhtunkhwa (FY 2016/17‐ Amount Ranges in Pakistan Rupees).

27
This yielded a sample of 1,620 firms, comprising 852 firms in treatment blocks and 768 firms in control blocks. On average, 69 percent of firms in treatment blocks and 70 percent of firms in control blocks expressed interest in Agruppa, giving us samples of 586 interested firms in treatment blocks, 266 uninterested firms in treatment blocks, 536 interested firms in control blocks, and 232 uninterested firms in control blocks.

# **3.2 Characteristics of the Sample**

Table 1 summarizes key characteristics of the sample by treatment status, and by interest in Agruppa. The sample consists of small retail stores that sell fruit and vegetables, along with selected other products like grains (85 percent of stores), eggs (84%), dairy products (79%), bottled drinks (79%), household and personal cleaning products (each 79%), bread (77%), and occasionally other items such as meat (29%), and airtime (11%). Fruits and vegetables have the highest rotation rates and are purchased most regularly by customers. The average store sells 4.3 out of the 5 core products Agruppa began with (potatoes, onions, spring onions, plantains and tomatoes), and these products constituted just over half of the total value of sales for these firms. The prices of these products change very frequently, with 76 percent reporting that they change prices every day, and a further 22 percent changing prices at least weekly.

Firm owners range in age from 18 to 83, with an average age of 43, and one-third of the sample are female. They have been running their businesses for a mean (median) of 10.6 years (8 years). There is considerable variation in education levels, with 49 percent having completed no more than primary education, 43 percent having done some high school education, and only 8 percent with post-high school education. Many owners were reluctant to provide full financial information for several reasons. First, was fear of crime, with 56 percent viewing their neighborhood as unsafe or very unsafe and 10 percent being robbed in the last six months. Indeed, our enumerators were robbed several times. A second reason may be partial informality: 64 percent were registered for taxes, but those registered for taxes were less likely to report sales data. Finally, a third reason is that few owners keep financial records, with only 29 percent saying they record quantities purchased, and only 38 percent saying they record amounts sold. As a consequence, we are missing baseline household income data and profits and sales data for about half the firms in the sample. Of those who do report, mean (median) per-capita monthly household income is US$164 9
<!-- Start of picture text --> 0.6<br>0.5 predictions based on district market prices<br>observations based on trader survey<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 1 2 3 4 5 6 7<br>unit transaction costs in constant Malawi kwacha<br>12<br>10 prediction based on district market prices<br>observations based on trader survey<br>8<br>6<br>4<br>2<br>0<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%<br>% share of transaction costs in sales price<br>kernel density<br>kernel density<br><!-- End of picture text --> _Source_ : Author’s calculations;

* The figure shows kernel density estimations of predicted and observed unit transaction costs in Malawi kwacha per kilogram of maize (constant 2000 prices), in levels (upper panel) and expressed as a percentage of sales prices of the supplying district (lower panel). Predictions are based on district market prices, and observations are obtained from a trader survey (Fafchamps et al., 2005).

|**Table 2**<br>|**Comparison of **|**Predicted an**|**d Observed **|**Transaction **|**Costs* **||
|---|---|---|---|---|---|---|
|**Rural region,**<br>**urban areas**||**level**|||**Share**||
||predicted|observed|mean test|predicted|observed|mean test|
|rural north|1.547|2.298|2.4|0.221|0.213|0.2|
||(49; 1.064)|(19; 1.322)||(49; 0.204)|(17; 0.111)||
|rural central|1.829|1.794|0.2|0.098|0.208|8.1|
||(518; 1.170)|(52; 0.990)||(518; 0.092)|(49; 0.078)||
|rural south|1.279|1.462|1.7|0.059|0.205|19.7|
||(439; 1.036)|(116; 1.008)||(439; 0.053)|(112; 0.116)||
|urban areas|1.610|1.694|0.4|0.079|0.198|9.7|

21
# Table of Contents

|I.|Introduction .............................................|1|
|---|---|---|
|II.|The Background ............................................|2|
||A. Tunisia's Demographic Transition ..........................|2|
||B. The Family Planning Program ..............<br>...........|....<br>4|
|III.|The Structural Model and Statistical Methods<br>..|_5_|
||A. The Structural Model ....................................|_5_|
||B. Statistical Methods .....................................|8|
|Iv.|The Data jet and Descriptive Statistics ..............<br>.............|..<br>12|
|V.|Multivariate Results ......|13|
||A. Determinants of Current Contraceptive se ............|..........<br>'3|
||<br>B. Choice of Current Contraceptive Method .......<br>......|.......<br>17|
||<br>C. Recent Fertility Results .................................|19|
|VI.|Summary of the Findings and their Policy Implications ......<br>..|....<br>23|
||A. Importance of Access to Social Services .23||
||B. The Role and Determinants of Fertility Intentions .25||
||C. The Effects of the Family Planning Program<br>in Eliminating Socioeconomic Differentials .26||
||<br>D. In Summation .27||
|Bibli|ography .......|29|
|Appe|ndix Tables ..........|45|
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29
_-_ 17 -

# _4. Externaldes of Growth_

The above type of capital market failure invol,ved externalities on the saving side of the market. Externalities may also occur on the supply side. Recently several economists have stressed the possible importance of externalities in the process of economic growth. For example, Romer (1986) has argued that capital accumulation generates external benefits (e.g., technological improvements) to firms other than those undertaking the investment. The results hark back to those of the growth theories of the 1960s where the rate of technological change was made endogenous and dependent on the rate of investment.<sup>6</sup> They also bear some resemblance to the infant industry arguments of trade theory. In the context of growth theory, they are interesting because they can account for differences in the rate of growth of economies as well as differences in the levels of income achieved. To the extent that they are true we would expect to see a correlation between rates of investment and rates of growth in per capita incomes.

Scott (1989) examined the causes of the rate of growth of output in pooled data on 26 period averages for ten developed countries (with 19 out of 26 observations for UK, USA and Japan). He used a simple ordinary least squares regression with share of investment in output and the rate of growth of quality-adjusted employed labor force and the ratio of output per quality adjusted in non-residential output excluding agriculture in the country to that in the U.S. in a base year (considered as a "catch-up" factor) as the only explanatory variables. He found that, for the sample as a whole, nearly half of the growth in non-residential business output can be explained by changes in the share of investment. Scott further estimates that marginal social rates of return to investment in the U.K. (1951-73) and USA (1948-73) exceeded the marginal private retun by about seven percentage points with the former averaging 12.6% and the latter _5.3%._ A third of this gap is accounted for by taxation. Three other factors explain the rest of the gap: the "learning externality" (firms other than the investment undertaking firm benefit disproportionately from inceased opportunities); the "demand externality" (firms selling in imperfect markets receive lower marginal than average returns because of higher marginal selling costs) and "animal spirits" (a positive externality based on a tendency for firms to value increases in output by more than their value to shareholders) (see Scott (1989), p.xlvi).

Since it is the act of investment _per se_ which yields an externality, the appropriate would be to implemert measures which influence investment directly. Policies operating on the saving side will not be effective. This may be the strongest argument for investment incentives. Indeed, it might also be the argument for incentives for investment in human capital and R&D as well. For example, Lucas (1988) 6 Some key **references are** Kaldor and **Mirlee** (1962), Arrow (1962) and Kennedy (1961). **This** literature **i surveyed** in Hahn and Matthews (1965).
reported in Section 4 demonstrate that among women _τ_ is not equal to zero: making investment returns observable impacts individual choices. However, the magnitude of the reduced form impact does not identify _τ_ because the treatment effect depends on an individual’s level of risk aversion. Fortunately, random assignment should insure that the distribution of individual risk parameters in each of the six treatments is representative of the population distribution. We can therefore estimate the parameters of that distribution using data from the private treatments, and estimate _τ_ while controlling for heterogeneity in risk aversion in a structural framework.

We model decisions in a discrete choice setting, where each subject decides how many ten shilling coins to allocate to the business cup, and allocates the remainder to the savings cup. Subjects in the small endowment treatments have nine options (0 _,_ 10 _,_ 20 _, . . . ,_ 80), while those assigned to the large endowment treatments have nineteen (0 _,_ 10 _,_ 20 _, . . . ,_ 180).

In what follows, we index experimental treatments with _t_ , individual subjects with _i_ , and investment options with _j_ and, when necessary, _k_ . We assume that subject _i_ ’s expected utility of investing _bj_ is given by: where _EVij_ denotes the explicitly-modeled expected utility of investing _bj_ (what Train (2003) terms “representative utility”) and _εij_ is a Gumbel-distributed preference shock.<sup>36</sup> Behavior in the private treatments suggests that individuals have heterogeneous risk preferences. As in Section 3, we assume these can be represented by a CRRA utility function, _vi_ ( _·_ ), parameterized by _ρi_ , and that individual _ρi_ parameters are normally distributed with mean _μρ_ and variance _σρ_<sup>2.The</sup> probability that subject _i_ chooses to invest _bj_ can then be written in the form of a mixed logit model:

> where _σε_<sup>2is proportional to the variance of</sup><sup>_εij−εik_.37Let</sup><sup>_yij_be an indicator function equal to one</sup>

> 36Loomes (2005) refers to such error terms as “Fechner errors.” See Hey and Orme (1994) and Von Gaudecker, van Soest, and Wengstr ̈om (2011) for examples of their use in modeling stochastic choices in individual decision-making experiments.

> 37When _Vij_ = _X ′β_ , _β_ and _σε_ are not separately identified; _σε_ is identified in our framework because _EVij_ is a non-linear function of parameters.

28
**Figure 1 - Reservation and market wage distribution**

<!-- Start of picture text --> Employed (market wage)<br>Unemployed (reservation wage)<br>2 3 4 5 6 7<br>Log(wage per hour)<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._

**Table 2 – Basic statistics of wages**

|||**Confiden**<br>|**ce interval**<br>|<br>||
|---|---|---|---|---|---|
||Average|Lower<br>bound|Upper<br>bound|Median|Observations|
|**Market wage per hour**|93.30|92.74|93.87|79.55|28,995|
|**Reservation wage per hour**|90.54|89.90|91.18|79.55|10,195|
|**Ratio of reservation wages**<br>**to market wages**|0.97|0.97|0.97|1.00||
|_Source: Authors’ calculation_|_s based on_|_LFS. Popu _|_lation weig_|_hts accordi_|_ngly used._|

The same conclusion could be reached if reservation wages are contrasted with market wages at the skill level. **Figure 2** reveals that reservation wages grow with education, similarly as market wages do. In addition, tertiary-educated job seekers may be even more rigid in setting their expectations than what the market rewards them. What the figures do not reveal, though, is the potential differences – in observables and/or unobservables – of employed and unemployed, which is frequently documented in various studies.

6