rafmacalaba/datause-encoder-data huggingface dataset:
However, there are many reasons why the actual BWRs on an antipoverty program may differ from the nominal rate.<sup>13</sup> While the State Council’s proclamations imply a BWR on DB of 100% there is scope for local discretion and innovation.<sup>14</sup> Our results suggest that the way the DB program operates in practice through its local-level implementation greatly attenuates the incentive effects implied by its formal design. Thus the official nominal rules appear to be highly deceptive about actual incidence. While in theory, DB imposes a 100% marginal tax rate on participants, the reality on the ground is a much lower rate. Using our data on DB participants and a matched comparison group of non-participants, we estimate that the BWR is only about 12-14% per annum. We find a higher BWR in richer cities, peaking at 27% for Beijing. It appears that (even in Beijing) the incentives built into the program as it works in practice are unlikely to create a poverty trap. Indeed, when viewed in the light of the literature on the optimal design of targeted programs, the program’s BWR appears to be too low.

The following section examines the problems of estimating the BWR and describes our solution. Section 3 describes the _Dibao_ program and our data. Section 4 presents our results, also comparing our estimate of the BWR with the non-behavioral method. We then offer some observations on the implications of our findings in Section 5. Section 6 concludes.

# **2. Theory and methods of estimating the benefit withdrawal rate**

One can define the “benefit incidence” of a specific set of transfers (or taxes) as the mapping from incomes in the absence to those transfers to the transfer payments received. With little loss of generality we can think of this mapping as some unknown smooth function giving the transfer to household _i_ , denoted<sup>_T_</sup> _i_<sup>, with income in the absence of transfers</sup><sup>_Y_</sup> _i_ *<sup>; let this</sup>

* * function be φ _i_ (<sup>_Y_</sup> _i_ ) . Note that the function varies, allowing transfer receipts to vary at a given<sup>_Y_</sup> _i_<sup>.</sup> (A special case identifies the benefit incidence as the conditional mean transfer received at given income, i.e., the regression function of<sup>_T_</sup> _i_<sup>on</sup><sup>_Y_</sup> _i_ *<sup>.) We can then define the BWR as the local</sup>

* − slope of the tangent to φ _i_ (<sup>_Y_</sup> _i_ ) , as given by<sup>β</sup> _i_ in the equation for the tangent:<sup>15</sup>

> 13 Moffitt (2002) makes this point in the context of welfare policies in the US.

> 14 This has been noted by Hammond (2009, 2011) and Duckett and Carrillo (2011).

> 15 Notice that there is no error term in this equation since it holds by definition.

6
Number of partners

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|Time|0.093|0.153|0.062|0.091|
||(31.30)***|(42.34)***|(40.07)***|(48.33)***|
|Time, squared|0.003|0.001|0.004|0.003|
||(46.90)***|(14.21)***|(129.70)***|(75.63)***|
|Post 2000||0.305<br>(29.10)***||0.147<br>(27.01)***|
|Constant|3.259<br>(111.72)***|2.917<br> (92.81)***|3.033<br> (196.73)***|2.871<br>(173.47)***|
|Observations|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared|0.09|0.09|0.29|0.29|
|Fixed effects a/|no|no|yes|yes|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1% a/ fixed effects by importer  product pair

# **2.3   The China effect**

Considering the rising importance of OECD trade with China over the last decade, we must control for the role that China may play in that re-concentration. Figure 3 shows the evolution of the Theil index for extra-OECD imports, both with and without China. The figures show that China is indeed driving the observed re-concentration. Further evidence is provided in Table 5, which shows that the coefficient on time squared loses its significance when China is excluded from the sample.

Figure 3 Theil index for OECD imports excluding China, 1963-2006 Extra-OECD imports Extra-OECD imports excluding China <!-- Start of picture text --> 3.3<br>3.2<br>3.1<br>3<br>2.9 Theil<br>2.8<br>2.7<br>1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> <!-- Start of picture text --> 3.3<br>3.2<br>Theil<br>3.1<br>3<br>2.9<br>2.8<br>2.7<br>1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003<br><!-- End of picture text --> 12
one. Clearly, measures which<sup>wouldfacilitate investment,suchas betteraccessto</sup> credit, carry a potential to boost<sup>employment.</sup> To conclude, main factors<sup>contributingtofirmemploymentgrowth include</sup> high productivity and low unit<sup>laborcosts, high capitalintensityandinvestment, and</sup> export orientation. Access<sup>to credit does not contributeto job creation,likely because</sup> credit is directed to ineffective<sup>firmsrather thanfirmswith growth potential. To the</sup> extent this is the case,<sup>inefficientallocationofresourcestranslates intolessjob</sup> creation than otherwise would<sup>bepossible.</sup> These findings have straightforward policy implications. Employment<sup>growthwillbefosteredbypoliciesthatpromote</sup> competitions and thereby force<sup>firmstoimproveproductivity,policies whichhelp</sup> firms to lower costs (e.g. through<sup>lower taxation),policieswhichimproveaccessto</sup> credit, facilitate investment<sup>and export.Inshort, a key for faster job creationis better</sup> investment climate.

Stagnant **labor market:**<sup>**job stability but no hiring**</sup> The stagnant nature of the<sup>Croatianlabor marketalsomanifestsitselfin</sup> limited hiring. In a low turnover labor market<sup>anunemployedperson haslittle</sup> chances of finding a job.<sup>Dataonemploymenttenure showanotheraspectof poor</sup> job opportunities in Croatia. A proportion of persons<sup>with shorttenureis16w,</sup> reflecting the unwillingness<sup>of employers to hire new workers.On theother hand,the</sup> proportion of workers with<sup>long jobtenureisveryhigh,reflectingthehigh levelof</sup> employment protection<sup>inCroatia.</sup> Poor chances of finding<sup>work for thejobless</sup> coexist with high job security<sup>of thosewhohavework.This pointstolabormarket</sup> duality. There are _insiders_<sup>withstable jobsand</sup><sup>_outsiders_with little chances to break</sup> into the formal labor market.<sup>Table13documents this division.</sup> _-39-_
**Table 2: Drought Impacts on Child Anthropometrics OLS with Cluster x Month Fixed Effects and Wealth Quintile Controls**

|**VARIABLES**|(1)<br>**Stunted**|(2)<br>**Underweight**|(3)<br>**Wasted**|(4)<br>**Stunted & Underwt**|(5)<br>**Any**|
|---|---|---|---|---|---|
|Drought: Birth Season|0.10***|0.04***|0.00|0.04***|0.09***|
||[8.16]|[4.01]|[0.01]|[5.11]|[7.20]|
|Drought: Season after Birth|0.10***|0.01|-0.03***|0.03***|0.07***|
||[7.41]|[1.26]|[-4.39]|[3.14]|[5.00]|
|Second Poorest|-0.03**|-0.03**|-0.01|-0.02**|-0.04***|
||[-2.31]|[-2.55]|[-0.90]|[-2.12]|[-2.99]|
|Middle|-0.03**|-0.05***|-0.01|-0.04***|-0.04***|
||[-2.20]|[-4.16]|[-0.81]|[-3.72]|[-2.63]|
|Second Richest|-0.08***|-0.07***|-0.01*|-0.07***|-0.09***|
||[-4.46]|[-5.22]|[-1.69]|[-5.25]|[-4.97]|
|Richest Quintile|-0.14***|-0.08***|-0.01|-0.07***|-0.14***|
||[-4.16]|[-4.04]|[-0.54]|[-4.11]|[-4.03]|
|Constant|0.39***|0.17***|0.06***|0.14***|0.45***|
||[41.12]|[24.47]|[15.17]|[21.39]|[46.53]|
|Observations|28,162|28,144|28,062|28,162|28,162|
|R-squared|0.43|0.40|0.40|0.40|0.42|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ cluster x month fixed effects. The weights used are person weights adjusted for the national populations. All regressions use all available data on rural households from 5 Southern African countries (Mozambique, Malawi, Tanzania, Zambia and Zimbabwe). All metrics are based on children being measured below -2 standard deviation thresholds. 41.0 percent of the children are stunted, 15.1 percent are underweight, 5.1 percent are wasted, 12.6 percent are stunted and underweight, and 45.2 percent are malnourished based on any of the three metrics.

24
48

# tabl a

Rstimates of Private ltetutrs la t Peret pa **vejer** Whisk Hen and Woan **larn** en Investims their TUNe<sup>toSbhelim.</sup> As Selected Labor Mark.ts and leara

|Country<br>and/er<br>Metropolitan<br>Area|Yearot<br>Survey|Int<br> <br>Age|ernal<br>Ra<br>tn Poreo<br>2*-44|te of**Ret**<br>ntper Yter<br>Age|**urn**<br> <br>45-65|
|---|---|---|---|---|---|
|Argentina.<br>.ueoesAires<br>**(asjimatee similar**in 1976)|b<br>1360|0.3|6.6|10.|11.|
|Bolivia,<br>LaPas<br>(estimates<br>similar<br>in 1976)|Il0|0.3|11.|3.6|6.7|
|Brazil,<br>SaoPaulo<br>(estimates higher in 10g0)|1071|5.4|6.3|6.0|6.1|
|Colombia<br>(estimates lower in 1930)|1073|is.|19.|16.|14|
|Paraguay.<br>Asuncion<br>(estimates<br>similar<br>In 1977)|1079|11.|6.0|10.|11.|
|Peru|1074|14.|14.|11.|19.|

aPrivtet returns refer to the estimated coefficsfit (times 100) on **the** veriable years of completed education ln a logarithmic hourly **wage rats regression** which *lso includes pot-scebooling experienee. eperience equared. and some regional or migration origin variables. Smples vry ftrom 21 to 3470, but all estimates *ae statistioslly significantly diffecont from soro at the .001 confidence level (t > 2.63). The solection of<sup>the sample of only workers in the</sup> labor force tot whom wage rate could be<sup>calculated Is not treated as a specif-</sup> ic source of bies in these estimatce.

bThe available **ago** groups in Argentina ar* 35-49<sup>and rver 49.</sup> Source: Sehults (l19OS).
# **Table 3   Stationarity of the domestic and the world prices**

Augmented Dickey-Fuller Test (without trend)<sup>1</sup>

||ADF s<br>|tatistic<br>|
|---|---|---|
||Price level|First differential|
|_ICO indicator prices_|||
|Brazilian naturals|-2.11|-6.42 ***|
|Colombian milds|-2.09|-6.52 ***|
|Other mild arabica|-2.26|-5.84 ***|
|Robustas|-1.61|-5.68 ***|
|_Producer prices_|||
|Brazil arabica|-2.48|-6.60 ***|
|Brazil robusta|-1.77|-5.80 ***|
|Ethiopia|-2.20|-6.78 ***|
|Kenya|-2.22|-8.02 ***|
|Tanzania arabica|-2.76 *|-5.75 ***|
|Tanzania robusta|-1.83|-5.82 ***|
|Colombia|-1.86|-5.21 ***|
|India arabica|-2.31|-5.72 ***|
|India robusta|1.57|5.09 ***|
|Mexico|-2.12|-6.08 ***|
|Uganda|-2.17|-7.46 ***|
|Madagascar|-1.09|-6.77 ***|
|Togo|-2.12|-5.33 ***|
|Angola|-0.05|-8.44 ***|
|Cameroon|-1.53|-6.61 ***|
|Central African Republic|-1.59|-5.70 ***|
|Ghana|-5.18 ***|-5.36 ***|

* Null of unit root rejected at 10% significance ** Null of unit root rejected at 5% significance

- ***  Null of unit root rejected at 1% significance

- 1 6 months lag length is used

23
levels of income hiding within the experiment are associated with lower rates of skilled and formal employment and lower wages, and are also associated with substantial reductions in the probability of using fertilizer.

Such cross-community results do not merit a causal interpretation. Nonetheless, these findings are of interest for two reasons. First, results are consistent with the view that pressure to share income may act as a drag on savings and investment, and may therefore slow development. An alternative, though not mutually exclusive, explanation is that in the poorest areas, enforced sharing norms remain an essential tool for handling risk, and thus the pressure to share is the most acute. In either case, the evidence suggests that social pressure to share income is greatest in the worst off areas, and should be viewed as an important social factor in models of poor, rural village economies. More generally, these results go some way toward addressing the concern that individual choices in lab experiments may not be meaningfully correlated with decisions and outcomes outside the lab (cf. Levitt and List 2007); in this case, they clearly are.

# **4.2 The Willingness-to-Pay to Hide Income**

We now examine the willingness-to-pay to avoid the public announcement using data from the price treatments. Subjects assigned to the price treatments were offered the option of paying a randomly chosen price — 10, 20, 30, 40, 50, or 60 shillings — to avoid announcing their investment income to the other subjects.<sup>35</sup> Of 690 subjects assigned to the price treatments, 627 subjects could afford to pay _p_ to avoid the public announcement, and 190 (30.3 percent of those able to pay) chose to do so. Subjects who chose to buy out paid an average price of 29.3 shillings — equivalent to an average of 15.3 percent of their gross earnings. Figure 6 plots the proportion of subjects paying to avoid the public announcement, broken down by gender, endowment size, and exit price. Subjects are generally more likely to buy out at lower prices, though this is not as evident among men receiving the large endowment.

Table 7 reports OLS regressions of the probability of paying to avoid the public announcement

> 35Though this randomization was not stratified, it was largely successful. Table 2 in Appendix III (online) reports the results of a balance check exercise in which subject characteristics were regressed on dummies for prices 20 through 60. The price variables are jointly significant at the five percent level in only four of the 68 F-tests.

26
# **VI. What is the impact of recent changes in taxes and benefits?**

As noted earlier, reforms introduced in 2015 and 2017 were aimed at improving the fairness of the tax system. In 2015/16 the government increased the threshold for the top rate, increased the tax allowance slightly and introduced PIT on interest income. We estimate these impacts using the 2014 data, by changing solely the parameters of tax design, thus abstracting from any other changes that would have affected the distribution. The results suggest that the effect of these changes was a decline in collections and a more progressive PIT (Figure 10A) in relative terms as poorer households spent a smaller share of their incomes on direct taxes. This is also seen by an increase in the Kakwani index from 0.3049 to 0.3315. In 2017, the brackets of the personal income tax (PIT) were reduced from three to two and the personal allowance was increased. In addition, a more progressive scale for expanding the personal allowance for dependent children was introduced and the narrowing of eligibility criteria for other supported family members. Pensioners pay Personal Income Taxes calculated as 50 percent of the amount calculated based on the general PIT schedule (but their basic personal allowance is no longer lower than for non-pensioners). The effect of these changes was a further decline in collections and a slightly less progressive PIT, thus reversing part of the improvement that had taken place in 2016 (the Kakwani fell from 0.3315 to 0.3260).

In terms of changes to indirect taxes, in 2017 the reduced VAT rate of 13 percent is now being applied to inputs in agriculture, electricity and utility services while food and drink services were moved from the reduced to the standard rate. The effect of these changes, largely driven by lower electricity and utility bills was a less regressive VAT system (Figure 10B) as the Kakwani index for VAT fell from -0.1586 to -0.1515.

<!-- Start of picture text --> Figure 10. Incidence of Taxes<br>C. Direct Taxes, 2014 -2017 D. Indirect taxes<br>16% 40%<br>2014 2016 2017<br>14% 35% 2016<br>12% 30% 2017<br>10% 25%<br>8% 20%<br>6% 15%<br>4% 10%<br>2% 5%<br>0% 0%<br>Deciles of Market income + Pension Deciles of market income + pensions<br>Source: World Bank estimates based on PIS and HBS (2014).<br>pensions<br>Share of market income plus Share of disposable income<br><!-- End of picture text --> 24
related to the youth skill development scheme, which is funded by a rule-bound formula, not unlike most other private and public programs that we examined earlier. Moreover, the results also show that the fund allocation-rules were strictly adhered to, and that our incentives did not negatively affect the funding in the control GPs.

The results are also robust to adjusting for multiple hypotheses testing. At the bottom of Table 3, we report the adjusted p-values calculated using the Westfall and Young (1993) step-down procedure for the family-wise error rate. The effect of the financial incentive on the evaluation score as well as on the private transfer and public investment indices is statistically significant at conventional level. The multiple-hypothesis-corrected _p_ -values are 0.062, 0.062, and 0.072, respectively. Similarly, the multiple-hypothesis-corrected _p_ -values for the estimated effect of the non-financial incentive on the two indices are 0.058 and 0.091.

# **6.2 Within-GP resource allocation**

Table 4 uses the hamlet-program level data to examine the impact on within-GP inequity across hamlets based on their physical proximity to the GP president,<sup>14</sup> and reports the estimated coefficients _π_ , _β_ , and _ψ_ from equation (2).

We find that among control GPs, pres-hamlets were 5.9 percentage points more likely to receive program-specific resources from the GP president compared to nonpres-hamlets in the GP. Nonfinancial incentives reduced this within-GP inequity in resource allocation between pres- and nonpres-hamlets by 8.7 percentage points. Both estimates are statistically significant at the 5 percent level.

In comparison, financial incentives reduced within-GP inequity in access to resources by only 4.4 percentage points, and the estimate is not statistically significant at conventional levels. Additionally, the equity effect of nonfinancial incentives on within-GP resource allocation is also statistically significantly larger from that of financial incentives: the _p_ -values of the _F_ -test of this difference is 0.097.

Overall, these results—in line with the theoretical predictions in section 4—show that resources are preferentially targeted to hamlets in which the GP president resides. More importantly, we find that our incentives reduced these within-GP inequities (that were prevalent in the status quo).

> 14This is in line with Chauchard (2017), who finds that GP presidents in the Indian state of Rajasthan build vote-buying structures with preferential targeting on different types of group identities, one of which is based on location.

21
bias, we match control countries to treatment countries on the following factors –average GDP per capita over the period 2002-2004, average real GDP growth over the period 2002-2004, and propensity to reform. We measure propensity to reform using the 3 year _change_ in the World Governance Indicator (WGI) Regulatory Quality Index over the same period.<sup>17</sup> This Index captures perceptions of the ability of the government to formulate and implement policies and regulations that promote private sector development. By matching on changes in regulatory quality, we are matching countries that show similar trends in propensity to reform.<sup>18</sup> In addition, we seek to keep the treatment and control countries similar on the level and rate of overall development by also matching on GDP per capita and real GDP growth.

We calculate propensity scores for each country using pre-reform values on these variables, and use nearest neighbor one-to-one propensity score matching (with replacement).<sup>19</sup> With a newly matched treatment and control countries, we subsequently estimate the differencein-difference specification of equation 2, taking into account the number of times a control country matches multiple treatment countries with frequency weights.

Second, we introduce within-country variation in dependence on external sources of finance across industries. This approach compares the impact of CB reform on employment growth across industries that are more versus less dependent on external sources of finance. Firms in different industries rely on external financing to a different extent, and we use the varying response of these industries to credit bureau reform as part of the empirical strategy. It

> 17 A three year timeframe provides a more stable regulatory direction of a countries policies that is more likely to smooth out annual jumps.

> 18 Using another measure of regulatory quality, the ICRG Legislative Strength index produces similar matching results.

> 19 We verified that the choice of matching method is not crucial to the overall results. Using Kernel density based matching, for example, produces similar matching and qualitatively similar regression results.

17
to the estimation of returns to self-employed labor in the farm sector (see Annex III for details). The reconciled survey data are used to compute the poverty elasticities discussed in Box 2, while the revised GTAP data are used to specify agricultural technology in the global CGE model. Other inputs to the global modeling exercise include: farm income sources by farm type for the US, the poverty headcounts, by region, for $1/day and $2/day, the estimated parameters for our consumer demand system, estimates of farm factor supply and demand elasticities from the OECD, as well as the trade reform scenarios (see Table 6). These inputs are combined with a modified version of the GTAP CGE model of the global economy.

With this overview in mind, a bit more needs to be said about some key aspects of the analytical framework. Our starting point for this analysis is the GTAP version 6.1 data base (Dimaranan, 2006). Virtually all contemporary analyses of the Doha Development Agenda start at this same point. Data availability is easily the most limiting resource for global analysis and GTAP version 6.1 represents the only data base covering global economic activities with bilateral trade and protection data that reflects tariff preferences. This also permits us to draw on the carefully constructed Doha reform scenarios developed and utilized in the recent books by Anderson and Martin (2006), and Hertel and Winters (2006).<sup>3</sup> These scenarios also involve a pre-experiment in which key trade policies are updated to 2005, and it is from that benchmark that the trade liberalization experiments proceed.

> 3 These tariff cutting scenarios are now available on the GTAP web site to those wishing to replicate this work. For purposes of this paper, we have used scenarios S0 (pre-simulation with China’s WTO accession, EU enlargement, etc.) and S8: the central Doha scenario used in the Hertel-Winters volume.

8