rafmacalaba/datause-encoder-data huggingface dataset:
11 <!-- Start of picture text --> 1980 Chinese FRv3 1980 Chinese FIv1B<br>100 -- - 100<br>80 - - 80<br>60 -- 60<br>40 o 4<br>0<br>0<br>20 -020<br>0<br>0-<br>1980 1981 1982 1983 1984 1985 1986 1987 1988<br>_ Budget M Patient Fees<br>EH3 Insurance O Average, All Sources<br>Source Bunrgarner, ed. (forthccrning)<br><!-- End of picture text -->

**Figure 1.** Real Per Capita Recurrent Health Expenditure in China, by Source

of services has been skewed away from the majority of the population. Insurance reimbursements were the source of 44 percent of health finance in 1988, yet only about 20 percent of the population was covered under official insurance programs in that year. Figure 1 shows that, on a per capita basis, consumption of health care by insured persons has been much greater (and is rising) than by uninsured persons, suggesting some moral hazard on the part of the insured and possibly limited access for low income uninsured persons. Uninsured people are receiving less health care, and the rapid growth in fee payments during the 1980s has exacerbated the equity problem because these persons are using a greater share of their incomes for health services than are the insured.

Lessons **learned.** As in Brazil under the GIH system, China experienced rapid cost escalation resulting from third party fee-for-service reimbursement of 100 percent of health service charges. In a system where only some of the population has insurance coverage, there is clear evidence of differential utilization of services, suggesting moral hazard on the part of the insured and possibly limited access for the uninsured. China's experience also highlights the importance of price incentives for the use of particular medical technologies. The signals sent to providers have resulted in the expansion of profitable high-technology services and drugs. In addition, because investment decisions are based on existing signals, the long run consequences of today's distorted price signals are likely te be an even more inefficient mix of facilities and equipment. In short, although China's decentralized pricing system and insurance programs have been effective in generating revenues for
Table 6.

# OLS and 2SLS Estimation of Equation 3

||Dependent Variable:<br>Log of Sales in 1997|Dependen<br>Log of Exp|t Variable:<br>orts in 1997|
|---|---|---|---|
||OLS<br>2SLS|OLS|2SLS|
|Constant|6.10***<br>6.19***|-10.1***|-9.62***|
||(15.77)<br>(10.88)|(-11.0)|(-9.14)|
|_Private Ownership_|0.28***<br>2.51***|1.09***|3.87***|
||(3.24)<br>(4.49)|(5.28)|(3.73)|
|LnK|0.40***<br>0.37***|0.58***|0.50***|
||(12.26)<br>(7.41)|(7.38)|_(5.50)_|
|LnL|0.50***<br>0.57***|0.22**|0.33***|
||(10.56)<br>(7.97)|(1.999)|(2.49)|
|Subsidies in 1996|-0.000013***<br>-0.00001**|-0.000012|-0.00001|
||(-3.97)<br>(-1.96)|(-1.56)|(-0.80)|
|Market Concentration|-0.143<br>0.49|3.36***|3.93***|
||(-0.35)<br>(0.79)|(3.45)|(3.40)|
|Dummy97|0.035<br>0.06|0.08|0.36|
||(0.29)<br>(0.33)|(0.27)|(1.09)|
|Industry dummies included|Yes<br>Yes|Yes|Yes|
|Adjusted R-sq.|0.691<br>_0.515_|0.574|0.510|
|Number of Observations|618<br>618|612|612|
|**First Stage Regression**||||
|Adjusted R-sq.|0.30|0.|30|
|F-statistic for zero IVs|30.6***|30.|6***|
|OV version of Hausman Test|24.54***|5.5|6***|
|Critical Value for F-statistic|2.37|2.|37|

T-statistics in parenthesis. * ** and * indicate significance at 1, 5 and 10 percent confidence levels respectively.

36
|Informal charges||0.000||0.005|
|---|---|---|---|---|
|||(0.008)||(0.007)|
|Proactive||-0.002||-0.003|
|||(0.006)||(0.006)|
|Labor||0.004||0.004|
|||(0.006)||(0.006)|
|Legal||-0.004||0.005|
|||(0.009)||(0.008)|
|Constant|1.831***|1.914***|0.763***|0.827***|
||(0.160)|(0.171)|(0.138)|(0.156)|
|Observations|12,382|12,382|9,730|9,730|
|R-squared|0.412|0.412|0.237|0.237|
|Adjusted R-squared|0.392|0.392|0.204|0.204|
|N-cluster|63|63|63|63|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# _4.3 Which firm benefits from improvements in local governance?_

The individual PCI modules can have heterogeneous impacts on different types of firms. We explore whether firms of different productivity levels are affected by the different aspects of local governance. For example, we expect the PCI modules on entry and transparency have a stronger effect on less productive firms as a reduction in red tape will be more beneficial to less productive firms who may not have the resources to deal with the bureaucratic burden. To control for this possibility, we divide the sample of firms using their initial productivity levels in 2009. The regressions are conducted in the four sub-samples: firms above and below the 50<sup>th</sup> percentile, firms above the 75<sup>th</sup> percentile and firms below the 25<sup>th</sup> percentile. The results are presented in Table 6.

Improvements in the overall business environment tend to benefit more productive firms. The overall PCI has a significant and positive effect on all firms but this effect is not present in the sub-sample of low productivity firms, measured as those below the 50<sup>th</sup> or 25<sup>th</sup> percentile of the productivity distribution in 2009. These results suggest that more productive firms are better prepared and able to take advantage of the improvements in the business environment and increase their productivity levels. Similarly, when we examine the individual PCI modules, we find that high productivity firms enjoy the positive effect for lower land expropriation risks and lower informality. In particular, the effect of lower informality has a larger and more significant effect for the most productive firms among firms with high productivity levels. Conversely, the positive and significant effect of lower entry costs for all firms is only present for low productivity firms. High productivity firms did not benefit when entry costs became lower in the provinces. This result will be consistent with a scenario where lower entry barriers introduces more competition at the lower end of the productivity distribution, inducing previously less productive firm to compete through enhancing productivity.

**Table 6: Regression Results of Sub-samples based on Initial Productivity Levels**

|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>(9)<br>(10)|
|---|

14
# **_Table 1: Baseline Balance_**

||**Total s**|**ample**|**Control**|**Mo**|**vie**|**M**|**FB**|**Movie**|**+ MFB**|**Pure**|**control**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|N|Mean|Mean|Mean|P-value|Mean|P-value|Mean|P-value|Mean|P-value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|_Personal characteristics_||||||||||||
|Age of respondent|2314|37.76|37.90|37.52|0.553|37.89|0.996|37.31|0.339|38.44|0.427|
|Gender (male)|2358|0.29|0.26|0.30|0.173|0.30|0.220|0.29|0.371|0.31|0.138|
|Married|2357|0.84|0.85|0.82|0.211|0.86|0.557|0.82|0.206|0.86|0.845|
|Widowed|2357|0.02|0.02|0.03|**0.094***|0.01|0.284|0.02|0.984|0.03|0.264|
|Single|2357|0.14|0.13|0.15|0.551|0.13|0.795|0.16|0.190|0.12|0.494|
|Muslim|2356|0.36|0.35|0.40|0.136|0.35|0.793|0.36|0.717|0.33|0.421|
|Christian|2356|0.64|0.64|0.60|0.154|0.65|0.958|0.63|0.621|0.67|0.387|
|Can speak English|2346|0.70|0.70|0.67|0.321|0.72|0.450|0.71|0.636|0.73|0.382|
|Igbo|2356|0.20|0.17|0.17|0.925|0.21|0.141|0.22|0.104|0.24|**0.012****|
|Yoruba|2356|0.75|0.78|0.78|0.873|0.75|0.219|0.72|**0.035****|0.71|**0.025***|
|Other ethnicitiy|2356|0.05|0.05|0.05|0.635|0.04|0.777|0.06|0.242|0.04|0.839|
|_Education_||||||||||||
|No completed school education|2356|0.07|0.06|0.07|0.421|0.08|0.180|0.08|0.297|0.08|0.347|
|Primary school education|2356|0.22|0.24|0.24|0.968|0.21|0.164|0.21|0.209|0.19|**0.067***|
|High school diploma|2356|0.50|0.49|0.48|0.749|0.50|0.754|0.51|0.527|0.53|0.329|
|Diploma|2356|0.10|0.11|0.10|0.512|0.11|0.825|0.09|0.276|0.11|0.945|
|Graduate school|2356|0.10|0.09|0.10|0.866|0.10|0.626|0.11|0.425|0.09|0.916|
|_Household characteristics_||||||||||||
|Household (HH) size|2343|4.53|4.58|4.57|0.902|4.43|0.168|4.48|0.395|4.61|0.825|
|Number of children below 12 in HH|2311|1.33|1.38|1.29|0.230|1.30|0.311|1.25|**0.080***|1.44|0.524|
|Number of dependents in HH|2322|2.44|2.45|2.39|0.671|2.41|0.769|2.41|0.747|2.57|0.385|
|Number of dependents outside the|2213|1.55|1.50|1.53|0.843|1.53|0.827|1.54|0.784|1.66|0.330|
|_Business characteristics_||||||||||||
|Months in operation|2310|97.40|98.69|97.58|0.847|96.98|0.771|101.02|0.698|91.03|0.218|
|Has a savings account|2350|0.57|0.56|0.57|0.732|0.54|0.624|0.57|0.753|0.63|**0.035****|
|Keeps written financial records|2340|0.37|0.36|0.35|0.684|0.37|0.708|0.38|0.619|0.40|0.315|
|Operating inside main market|2324|0.25|0.24|0.26|0.500|0.24|0.985|0.26|0.535|0.27|0.287|
|Number of employees|2352|1.44|1.57|1.46|0.345|1.40|0.169|1.39|0.161|1.36|0.168|
|Business experience inyears|2350|10.75|10.84|10.77|0.892|10.78|0.907|10.48|0.497|10.97|0.834|

*** p<0.01, ** p<0.05, * p<0.1

26
faced by nonagricultural producers bidding from the same national pool of inter-sectorally mobile resources. More than seventy years ago Lerner (1936) provided his Symmetry Theorem to prove that, in a two-sector economy, an import tax has the same effects on production, consumption, trade and national economic welfare as an export tax. This carries over to a model that has many sectors, and is unaffected if there is imperfect competition domestically or internationally or if some of those sectors produce only nontradables (Vousden, 1990, pp. 46-47).

Figure 1 plots the time-series evolution of the RRA for each of the 14 large Sub-Saharan African countries in our sample (which accounts for more than three-quarters of the population and even more of the GDP of Sub-Saharan Africa excluding South Africa).<sup>8</sup> It shows that, for all of those Sub-Saharan African countries except Nigeria, the RRA is negative almost all of the time. Hence on average there has been a strong policy bias against agriculture over the past half-century. However, there is also substantial RRA variation across time and countries. For example, in Ethiopia, Mozambique, and Tanzania there was a continuous reduction in policy biases against agriculture, while in countries such as Zambia and Zimbabwe the strong bias against agriculture was firmly maintained.

We note here that the RRA measure we use has several important advantages over other existing measures of policy distortions available for Sub-Saharan African countries. First, the estimated nominal rates of assistance to agriculture and non-agriculture reported in Anderson and Valenzuela (2008) provide by far the longest and most consistent annual time-series data on policy distortions in Sub-Saharan African countries. Second, measures of just trade policy distortions typically use trade weights to obtain sectoral averages, whereas the new World Bank study uses more-appropriate weights based on production valued at undistorted prices. Third, most other estimates of agricultural trade policy distortions focus on just import tariffs (see, e.g., WTO 2010), thereby missing export distortions (as well as occasional food import subsidies) which turn out to

> 8 This is the largest possible sample and time-span given the availability of data on the relative rate of assistance estimates for the Sub-Saharan African region.

7
**Table 4.  Comparator Countries: Financial Soundness Indicators, 2007 (%)**

|**Country**|**CAR**|**NPLs Ratio**<sup>**4**</sup>|**Return on Assets**|**Return on Equity**|
|---|---|---|---|---|
|Brazil<sup>5</sup>|17.4|4.4|2.3|24.5|
|Indonesia<sup>6</sup>|19.2|16|2.6|28|
|Malaysia<sup>7</sup>|12.7|8.7|1.3|14.1|
|Nigeria|18.6|7.7|1.8|13.8|
|South Africa<sup>8</sup>|12.7|1.1|1.4|18.6|
|Kenya|18.0|10.9|3.0|27.5|

Sources: Central Bank of Kenya, Central Bank of Nigeria (CBN), and IMF, GFSR.

Stress tests confirm the resilience of Kenya’s banking system. Table 5 below shows stress tests conducted using individual bank data from December 2008 to assess the impact of credit risk using two shocks. The first evaluates a deteriorating loan portfolio that would result in an increase in provisions by 50% and 100%, while the second assesses the impact of migrating 25% and 50% of performing loans to the substandard category. The results indicate that on the whole, banks appear resilient to the shocks measured **.** However, the most significant impact would be felt if a riskier client base resulted in a 100% increase in provisions. Such a significant shock would result in 17 banks (accounting for 24 percent of assets) failing to meet minimum capital requirements. Four of these banks would be insolvent. These results could be seen as providing a justification for increasing minimum capital requirements.

> 4 NPLs to gross loans for Nigeria, Kenya and Malaysia. NPLs to total loans for all others.

> 5 CAR, NPL for 2005, ROA and ROE for March 2006.

> **6** CAR Sept. 2006; NPLs include compromised assets ratio, restructured loans, and foreclosed assets for the largest

> 16 banks. ROE is based on the largest 12 banks.

> 7 ROA is before tax.

> 8 All entries are for March 2007.

9
# **<mark>Table IA5 – Impact of FD Law on Bank Lending to Firms Given State Public Debt</mark>**

This table reports OLS estimates of the impact of the FD Law on employment and GDP of states above the ex-ante public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. I(DebtStates,16Q1)_ is an indicator variable that equals one if a state's measure of public indebtedness in the quarter prior to the FD Law (calculated as the ratio of public debt of a state over its net income) is above the median state and zero otherwise. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance <u>at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Volu|mef,b,m|Investm|entf,b,m|Working|Capitalf,b,m|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*I(DebtStates,16Q1)|0.011*|0.013*|0.054***|0.058***|0.008|0.012|
||(0.006)|(0.007)|(0.021)|(0.021)|(0.011)|(0.011)|
|Observations|1,252,105|1,252,105|206,666|206,655|1,131,483|1,131,483|
|R-squared|0.802|0.803|0.870|0.871|0.799|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

78
equal to or greater than the actual mean conditional income for immigrants of $564. Moreover, we get a subjective mean no more than 10 percent below the actual mean in only 10 out of the 10,000 draws, and a subjective mean no more than 20 percent below the actual mean in only 33 out of the 10,000 draws. Therefore it appears extremely unlikely that the large gap between expected and actual earnings can be attributed to the immigrants all receiving very good draws from their subjective earnings distributions.<sup>10</sup>

# **6.3 Comparison to the wrong reference group**

Our survey allows us to compare the expectations of PAC ballot losers to the realized outcomes of PAC ballot winners at the same point in time. However, although we have complete information on how PAC ballot winners fare in New Zealand, it appears unlikely that individuals in Tonga do. A third potential explanation for the understated employment probabilities and expected income is therefore that it arises from forming expectations on the basis of information coming from the wrong reference group. We consider two possible likely reference groups. The first is that PAC ballot losers have received some information about how the winners are doing, but have only heard about the experiences of individuals whose initial job offers fell through. The second is that PAC ballot losers base their expectations on the experiences of earlier cohorts of migrants.

# **_Feedback from migrants whose job offers fell through?_**

The Pacific Access Category requires individuals with successful ballots to obtain a job offer before immigrating to New Zealand. The job offer must be for “ongoing and sustainable employment” and should be a full-time job which pays salary or wages (not commission or self-employment) and which complies with employment law in New Zealand. Despite having a job offer, our survey found that only 39 percent of the Tongan immigrants coming through the PAC during our sample period actually worked in the job that they had the offer for upon arriving in New Zealand. Amongst those not taking up

> 10 Of course these calculations assume that draws from the subjective distributions are independent across individuals. In practice all individuals could receive a common positive shock. However, consider the very extreme case of perfectly correlated draws, so that if one individual draws from the 95<sup>th</sup> percentile of his or her subjective distribution, all other would-be emigrants also draw from this percentile of their distributions. Even in the case, the probability of getting a mean subjective income of $564 or higher is only 0.067.

15
workers in response to the intervention the opposite sign to what the learning-about-type theory would predict.

Column 3 of Table 8 examines heterogeneity with respect to baseline business practices. Consistent with the higher take-up seen in Table 3, we see that firm owners with more management ability were more likely to hire workers in response to the subsidy during the intervention. A one standard deviation improvement in baseline practices (5.9) is associated with a 0.19 additional worker increase during the intervention, which is significant at the five percent level. However, like the aggregate effect, this effect dissipates over time and there is no long-term effect. Our theory section noted that higher managerial ability firms might be more responsive to subsidies because of non-convexities in hiring labor. Our wage subsidy required firms to hire a full-time worker, so the margin for adjusting labor was lumpy. Better managed firms should be closer to the threshold of hiring a whole worker, so may have responded more. An alternative explanation is that firm owners with higher management ability could better understand the short-term gains possible through the subsidy.

Columns 4 and 5 of Table 8 then examine heterogeneity with respect to wealth. If firms need capital to make new workers productive, then the theory predicts that treatment effects should be higher for firms which have more access to capital. We see no heterogeneity with respect to either baseline capital stock, nor to household wealth at baseline.

Finally, in columns 6 and 7 of Table 8 we examine whether coupling the wage subsidy with additional treatments increases the impact. Column 6 considers the business training treatment. The specification here adds to equation (5) the pure treatment effects of business training in each of the time periods (not shown), and then the interactive treatment effect of receiving both the wage subsidy and business training. We cannot reject that the treatment effect is the same for those who get business training. Column 7 likewise shows there is no differential effect for those who get the savings treatment. These results are consistent with the interactions with baseline variables in showing that the lack of long-run effects do not seem to be due to lack of capital or lack of business skills.

# **6.2 Survey Evidence**

23
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may suffer from omitted variable bias and so should be interpreted with much caution and as mere correlations.

**Table 4: OLS: Effect of mobile use and activity on business enterprise**

|DV:**Business**|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Enterprise**||||||
|MOBILE use<br>activity|0.064**<br>(0.021)|0.067**(0.022)|0.069**(0.022)|0.070**(0.025)|0.082**(0.028)|
|**1**[Sex = female]||0.012(0.021)|0.012(0.021)|0.014(0.023)|-0.053(0.036)|
|**1**[Married = Yes]||0.044*(0.023)|0.044*(0.025)|0.044*(0.025)|0.009(0.034)|
|Mobile X**1**[Sex =<br>female]||||-0.007(0.044)|0.029(0.058)|
|Age in Quantiles|No|No|Yes|Yes|Yes|
|Any Education?|||||Yes|
|Constant|0.200***<br>(0.014)|0.182***(0.030)|0.172***(0.048)|0.172***(0.048)|0.392*(0.194)|
|R–squared (%)|1.05|1.44|1.70|1.70|2.50|
|# of Clusters/OBS|258<br>(1294)|258<br>(1294)|258<br>(1294)|258<br>(1294)|251<br>(1294)|
|Fixed Effects|Cluster+<br>Time|Cluster+Time|Cluster+Time|Cluster+Time|Cluster+Time|

_Notes_ : Table reports estimates from regressions of “engagement in non-agricultural business enterprises” on mobile use and activity. Columns differ based on the combination of models type and fixed effects included. All standard errors are clustered at the region level and shown in <u>parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%. Unit of Analysis:</u> **<u>cluster-level</u>** Next, results from 2SLS estimations that pertains to business enterprises are reported in Table 5.<sup>6</sup> We expect negative and sizable effects of the SIM mandate since the reform resulted in much less mobile use and activity over the period. Similarly, results displayed in the different columns correspond to the different models. The estimate on mobile activity MOBILE is negative and significant across all model specifications. The average 2SLS estimate across specifications is about -0.331, which is about 3.3 times the size of the average OLS estimate. This suggests that the bias of the OLS estimation is downward, an attenuation. The 2SLS results imply that, on average households affected by the SIM mandate have lower likelihoods of engaging in some form on business enterprise all things being equal. A 2SLS estimate of -0.331 implies that moving a household with mobile use activity to a no mobile use activity due to the SIM reform

> 6 Table 4 replicates OLS estimations reported in table 3 but with the unit of analysis now being cluster level. OLS results are similar across the two different unit of analysis. All 2SLS estimations are carried at the cluster level.

‐ 14 ‐