rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Identifying unreliable observations: 2002/03 and 2008/09**

In order to cope with measurement errors in consumption and with the ‘error-poverty line interaction’ (described in Section 3, below), we utilize a method that preserves most of the original information while correcting for only manifestly inaccurate observations. Our approach is to carefully screen all consumption data and define specific criteria by which to determine their degree of reliability.<sup>9</sup> In a second stage, the unreliable observations are re-estimated using out-of-sample predictions from the 1996/97 coefficients model (see Section 2.3, below). Since we also identified clear signs of measurement errors in 2002/03, after using this methodology for the 2008/09 data we apply it to the earlier survey as well.

The original consumption data are ranked and compared to a wealth index.<sup>10</sup> Observations that are below the poverty line (the 54th percentile of the income distribution), but above the 70th wealth-index percentile, or observations above the poverty line but below the 30th wealth-index percentile, are singled out as ‘suspicious’.<sup>11</sup> Figure 2 illustrates the definition of these two observation sets: one set of households (A) is considered poor based on the original consumption data, but is classified as non-poor based on the wealth index; the other set of households (B) is considered non-poor based on the original consumption data, but is classified as poor based on the wealth index.

**Figure 2: Observation Sets A and B**

<!-- Start of picture text --> B: Non-poor based on consumption<br>data/Poor based on wealth data<br>NON-POOR<br>Consumption Poverty line<br>POOR A : Poor based on consumption<br>data/Non-poor based on wealth data<br><!-- End of picture text -->

> 9 The first-best approach to identifying consumption measurement errors would be to administer a small consumption-based survey questionnaire in the areas where underreporting and over-reporting are deemed most serious, revisiting the same households sampled in the IOF 2008/09. A comparison with the original data would indicate the extent to which questionnaire design and fieldwork operations may have impacted consumption data quality, diet-composition and caloric intake indicators. This information could then be used to adjust the consumption data in the IOF 2008/9.

> 10 With this asset index, suggested by Filmer and Pritchett (2001), principal-components analysis is used to calculate the weights of the index. The first principal component, the linear combination capturing the greatest variation among the set of variables, can be converted into factor scores, which serve as weights. The rationale for using this index is that it captures the household’s permanent welfare dimension better than simple consumption data, and can provide more reliable rankings between households.

> 11 The use of the 30th and 70th percentiles is not arbitrary: choosing extreme and symmetrical values (+/- 20 percentiles from the median) of the wealth distribution allows us to screen consumption observations that were reasonably inconsistent with their corresponding wealth-index value.

6
# The Costs Come before the Benefits<sup>1</sup>

Why Donors Should Invest More in Refugee Autonomy in Uganda Aziz Atamanov, Johannes Hoogeveen and Benjamin Reese* JEL classification: I30, I38, O15 Keywords: Uganda, refugees, aid, self-reliance, inclusion

* All authors are with the World Bank. Corresponding author is Aziz Atamanov

(aatamanov@worldbank.org).

**Acknowledgments: The authors are thankful to Xavier Devictor, R. Mukami Kariuki and Pierella Paci for overall support and guidance.

> 1 Dutch expression (de cost gaet voor de baet uyt) displayed on the front of an 18th century former trade information office in Amsterdam conveying the idea that one must make investments first, whether in terms of time, effort, or finances, before one can reap the rewards.
# Table 1: Marginal Effective Corporate Tax Rates for a Profit-making Firm, 1983-87

||Pre-1989 B|udget Reform||
|---|---|---|---|
|Year|Debt Financing|Retained Earnings|Debt and Ret.Earn.|
|1983|-.0436|.0112|-.0223|
|1984|-.0433|.0098|-.0231|
|1985|-.0272|.0066|_-.0155_|
|1986|-.0277|.0054|-.0158|
|1987|-.0308|.0091|-.0118|

||Post-1989 B|udget Reform||
|---|---|---|---|
|Year|Debt Financing|Retained Earnings|Debt and Ret. Earn.|
|1983|-.0331|.0073|-.0173|
|1984|-.0329|.0064|.0179|
|1985|.0207|-0043|-.0121|
|1986|-.0211|.0035|-.0123|
|1937|-.0235|.0060|-.0095|

Source: Boadway, Chua, and Flatters 85
could receive old-age income as low as 14 percent of the average wage if the returns in the financial market were not higher than the average wage growth.

||**Table7 **|**.Old-agePensionMinimumBenefitsProv**|**isions**||
|---|---|---|---|---|
|**Country**|**Required**<br>**service**|**LevelwhenthenewPensionLaw was**<br>**adopted**|**Addedto**<br>**benefits?**|**Indexing**<br>**rules**|
|Kazakhstan|**25**|**25**percentoftheaverage wage|No|Prices|
|Kyrgyz<br>Republic|**5**|**0.6**percentperyearofservicefor women<br>**_0.5_**percentperyearofserviceformen<br>uptoamaximumof12percent ofaverage<br>wage|Yes|Wages|
|Latvia|**10**|**28**percentofaveragewage|No|**Ad**hoc|
|Moldova|20|0.45percentperyearofservice,uptoa<br>maximumof**16**percentofaverage wage,<br>increased**by**2%whenservicebeyond**35**<br>**1**yearsand/orretirementage.|No|Wages|

_Source:_ Country legislation.

4.15 In the four countries, low-income earners who contribute for many years will receive the minimum old-age benefit. However, workers who are in agriculture or the informal sector for long periods of time may not qualify for the minimum in Kazakhstan or Moldova. The Kyrgyz Republic is the country that offers the broadest coverage and the only country that still makes some gender differentiation when calculating the minimum old-age benefit, so it is most beneficial to women.

_Women in Transition: What is the Impact of the Reform on Different Age Cohorts of Women?_ 4.16 Since the new pension systems are introduced gradually, two types of transition issues must be considered. The first one, which usually attracts a great deal of attention, appears when funding is added. Some of workers contributions are directed to the new scheme, thereby producing a shortfall in income in the Public system. This transition cost associated with the creation of private pension funds has fiscal but not gender implications, and therefore it follows that this paper does not focus on it. The second type of transition issue, which does have a large gender dimension, is related to the gradual changes in eligibility criteria and pension formulas and their implication on different cohorts. In the

**25**
Table 6: Robustness checks

|||All coun|tries||
|---|---|---|---|---|
||||Spatia|l only|
||None|Factor only|Trade|Distance|
|_gt−_1|0.592|0.329|0.379|0.400|
||(43.024)|(24.666)|(29.583)|(31.170)|
|_Wgt_|||0.607|0.304|
||||(31.186)|(16.023)|
|_Wgt−_1|||0.003|0.177|
||||(0.124)|(8.084)|
|Pesaran CD statistic|49.889|-0.861|12.526|31.885|
|(p-value)|(0.000)|(0.195)|(0.000)|(0.000)|
|Exponent of CSD|0.910|0.462|0.670|0.827|
|_R_<sup>2</sup>|0.129|0.219|0.168|0.143|
| ̄_R_<sup>2</sup>|0.074|0.194|0.166|0.132|
|||Advanced c|ountries||
||||Spatia|l only|
||None|Factor only|Trade|Distance|
|_gt−_1|0.683|0.376|0.428|0.438|
||(16.645)|(11.983)|(14.219)|(14.418)|
|_Wgt_|||0.735|0.659|
||||(32.452)|(26.132)|
|_Wgt−_1|||-0.193|-0.147|
||||(-4.612)|(-3.440)|
|Pesaran CD statistic|40.773|2.166|3.080|7.673|
|(p-value)|(0.000)|(0.015)|(0.001)|(0.000)|
|Exponent of CSD|1.004|0.770|0.683|0.789|
|_R_<sup>2</sup>|0.163|0.503|0.461|0.424|
| ̄_R_<sup>2</sup>|0.082|0.462|0.458|0.423|

Notes: GDP growth is the first difference of the log of real GDP. ’Exponent of CSD’ is the exponent of cross-sectional dependence of Bailey, Kapetanios and Pesaran (2015). The sample period covers 1970-2016.

The last two columns of Table 6 report estimates including spatial effects, for each of the two versions of the spatial weight matrix we consider, but excluding the common factor. In all cases, the estimates of the parameter on the lagged dependent variable exceed the values shown in Table 3, likely exaggerating the persistence of growth. In turn, the spatial effects are strongly significant, except for the space-time lag under the trade matrix in the full sample. In fact, the magnitude and the significance of the spatial lag parameter appear substantially overstated relative to the results shown in 25
**Table 4**

# **DIF-GMM parameter estimates** **<u>(developed</u> countries)**

|_yit_|_mlit_|_celit_|_p_<br>_resit_|
|---|---|---|---|
|_yit−_1|0.063|0.424<sup>_∗∗∗_</sup><br>|-0.078<br>|
|_regit−_2<br>||0.012<sup>_∗∗∗_</sup><br>|0.002<br>|
|_corruptionit−_1<br>||0.036|-0.004<br>|
|_bureauit−_1<br>||0.069<br>|0.067|
|_lawit−_1||0.049<br>|0.038<br>|
|_expropriit−_1||-0.069<sup>_∗∗_</sup>|0.143<br>|
|_currencyit−_1<br>||0.012|-0.025<sup>_∗∗_</sup>|
|_corruptionit−_2<br>|0.006<br>|||
|_bureauit−_2<br>|-0.009|||
|_lawit−_2<br>|-0.005<br>|||
|_expropriit−_2|-0.007<sup>_∗_</sup><br>|||
|_currencyit−_2<br>|-0.001|||
|_checksit−_1<br>||0.019<br>||
|_privait_<br>|-0.014<sup>_∗_</sup><br>|0.033<br>|-0.017<br>|
|_comp_<br>_fixit_<br>|0.014<sup>_∗_</sup><br>|-0.043<br>|-0.016<br>|
|_comp_<br>_celit_|-0.004|0.000|-0.044|
|_m_1|-2.72<sup>_∗∗∗_</sup>|-3.45<sup>_∗∗∗_</sup><br>|-3.33<sup>_∗∗∗_</sup>|
|_m_2<br>|0.22<br>|-2.11<sup>_∗∗_</sup><br>|-0.96<br>|
|_J_|2.52|2.52|4.18|
|Time dummies<br>|4.06<sup>_∗∗∗_</sup><br>|5.00<sup>_∗∗∗_</sup><br>|43.94<sup>_∗∗∗_</sup><br>|
|Endogenous reforms<br>|Yes<br>|Yes<br>|No<br>|
|_L_<br>|2|2|2|
|Obs.|276|253|182|

_Note:_ The starting lag for the instruments is _L_ and ( _L −_ 1) for the equation in first differences and levels respectively.

**Table 5 Impact of political accountability on regulatory performance** **<u>(developing</u> countries)**

|Variables<br>local|accountability<br>_reg_|global acco<br>_institutional_|untability<br>_checks_|
|---|---|---|---|
|_ml_<br>_cel_<br>|+<br>NA<br>|NS<br>+|+<br>+<br>|
|_eff_|NA|+<br>|NA<br>|
|_p_<br>_res_|_−_|+|+|
|_p_<br>_cel_|_−_|_−_|NA|

_Note_ : NA ans NS stand for not applicable and not significant respectively.

25
> where starred variables represent potential or trend levels. B stands for the cyclically ty disturbed primary budget balance. The budget balance variable included in the preceding

> definition (Bty and B ∗ t ) can be expressed in terms of its defining revenue and expenditure categories where, in principle, each budgetary category may or may not be cyclically sensitive. After simple manipulations, the preceding semi-elasticity can be rewritten in terms of a weighted sum of elasticities associated with cyclically sensitive budgetary items alone:

where ε R i (ε E j ) is the elasticity of the corresponding budgetary category ( R i , i = 1,..., n , for revenues and E j , j = 1,..., m , for expenditures) with respect to its base and ηR i (η E j ) is the elasticity of the corresponding macroeconomic base with respect to (cyclical) economic activity. n revenue categories and m expenditure categories are found to be cyclically responsive. (R i Y)* ((E j Y) ∗ ) represents the average size of the corresponding budgetary category relative to the size of the economy (GDP). In the ensuing analysis, the second term on the right-hand side of the equation is dropped because cyclically-sensitive expenditures in the LAC region (mainly unemploymentrelated expenditures) are poorly designed or very small or time series data are lacking. This choice also reflects the concern here for the stabilizing role of the tax system and not that of the benefit system. For the sake of comparison, developed country sensitivities are calculated the same way.<sup>1</sup> Based on the definitions adopted in the IMF Government Finance Statistics database, five revenue categories are acknowledged to be dependent upon the cycle: 1) taxes on income, profits and capital gains; 2) taxes on goods and services; 3) taxes on international trade; 4) social security contributions and 5) other taxes.<sup>2</sup> Fiscal elasticities are obtained from country-by-country regressions for each revenue category i (in real terms) as:

> 1 Unemployment-related expenditures are small in developed countries. For instance, this budget category amounts to 1.3% of GDP in the Euro area and 1.4% in the EU-15 countries in 1999. However, it is highly responsive to cyclical variations in output (Bouthevillain et al., 2001).

> 2 Zero elasticity with respect to the business cycle is implicitly attached to other government revenue categories.

7
**Table A5: Base Regression Results for Petty Corruption Incidence**

|Dependent variable:_Employment Growth_|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Petty Corruption Incidence (CI|-0.168**|-0.180***|-0.175***|-0.173***|
|avg.)*Financially Constrained (CI avg.)|(0.066)|(0.064)|(0.065)|(0.065)|
|Petty Corruption Incidence (CI avg.)|0.097**|0.105***|0.107***|0.106***|
||(0.038)|(0.037)|(0.037)|(0.037)|
|Financially Constrained (CI avg.)|3.004|3.528*|3.538*|3.546*|
||(1.969)|(2.030)|(2.041)|(2.043)|
|Initial Employment (logs)|-5.763***|-5.622***|-5.889***|-5.889***|
||(0.322)|(0.311)|(0.310)|(0.314)|
|Age of firm (logs)||-2.061***|-1.950***|-1.950***|
|||(0.295)|(0.291)|(0.291)|
|Manager Experience (logs)||0.019|-0.083|-0.068|
|||(0.273)|(0.274)|(0.274)|
|Exports (proportion of sales)||1.608|1.402|1.406|
|||(1.804)|(1.830)|(1.842)|
|Foreign Ownership (% ownership)||0.022***|0.021**|0.021**|
|||(0.008)|(0.008)|(0.008)|
|Multi-firm (dummy)||1.905***|1.845***|1.833***|
|||(0.639)|(0.648)|(0.645)|
|Female Manager (dummy)|||-1.335***|-1.334***|
||||(0.436)|(0.435)|
|Power Outage (logs)|||0.201*|0.211*|
||||(0.120)|(0.120)|
|Informal Competition (dummy)|||-0.766*|-0.725*|
||||(0.428)|(0.421)|
|Firm Bought Assets (dummy)|||3.107***|3.135***|
||||(0.412)|(0.423)|
|Time Tax (%)||||0.005|
|||||(0.013)|
|Courts: Obstacle Severity||||-0.033|
|||||(0.198)|
|Crime Losses (% of sales)||||-0.024|
|||||(0.045)|
|Taxes: Obstacle Severity||||-0.172|
|||||(0.164)|
|Labor Laws: Obstacle Severity||||-0.004<br>(0.171)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Number of observations|41,117|41,117|41,117|41,117|

55
the south, however, annual precipitation ranges from 1000 to 1500 mm. In the north, in the Huaihe River and Yellow River Basins, annual precipitation is only 600-1000 mm. It is only 500-600 mm in the rest of northern China. Generally, it is also quite dry in Western China.

The seasonal patterns of precipitation also vary by region. In the north, more than 70 percent of each year’s precipitation is concentrated in the summer. Precipitation during the winter months is very low, less than 5 percent of the annual total (Ren, 2007). In contrast, in the south, precipitation is mainly concentrated in the spring as well as the summer. These regional differences in climate may be reflected in our results, where climate change has different effects on regions with different present climates.

Recent evidence indicates that global temperatures have been rising since 1750 and especially since 1950 (IPCC 2007). There is supporting evidence in China as well of temperature increases between 1950 and the present (Ren, 2007). Of much greater concern are projections that temperatures will rise even more quickly into the future (IPCC 2007). It is not yet clear how large these temperature changes will be, but climate research consistently predicts warming (IPCC 2007). The exact amount of warming across China is therefore not known, but scientists are confident warming will occur here. The climate models also all predict an increase in global precipitation but how these changes are distributed across different regions is not yet known. Individual locations across China may get more or less rainfall. The change in precipitation patterns is more uncertain than the change in temperature for China.

# **Relationship between Net Crop Revenue and Climate**

On average, in 2001 the crop net revenue in China was 10,146 Yuan per ha (1353 USD) (Table 1). The reliance on irrigation and the availability of ample rain in certain regions of China has led to relatively high net revenues compared to other countries (even developed countries such as the US) (Rozelle et al., 2007). The high levels of per hectare output in China offset the somewhat lower real prices. These net crop revenues differ by region. In general, net crop revenue in the south is higher than in the north and net revenues are higher in the east than in the west.

Just as significantly, if not more, net crop revenues also vary between villages that are irrigated and those that are rainfed (Table 1). The average net crop revenue in irrigated villages was 12319 Yuan per hectare (1643 USD), a rate that is more than 20 percent higher than average.

10
of business training on firm performance. This raises the question of whether the strong associations we see in our data are inconsistent with experimental results, and therefore suggest that we are not capturing a causal phenomenon.

To examine this, we use the fact that three of our datasets were collected as part of business training experiments. The first three rows of Table 5 show the effects on business practices of the International Labor Organization (ILO) Start-and-Improve-Your-Business and Get Ahead training programs in two of our Sri Lankan sub-samples and our Kenyan sample. The first column shows the measured effect of training on business practices. In all three experiments, the treatment effect on our business practices measure is between 0.04 and 0.06, implying the owners undertake an additional 1 to 1.5 practices after training. The next two columns present the 95 percent confidence intervals for the treatment effects on log sales and log profits one year after this training. Many of these contain zero, which is consistent with the general struggle in the literature to find significant impacts of training on sales and profits. In the final two columns we use the coefficients from columns 4 and 8 of Table 2 to predict changes in profits and sales, given the change in business practices that the training causes. Given the small change in business practices, the earlier regressions suggest we should see only a 3-4 percent increase in sales and profits in each study. These estimates lie well within the confidence intervals of the estimated treatment effects.

Although most other business training evaluations have not measured business practices in as comprehensive a way as our measures, the evidence summarized in McKenzie and Woodruff (2014) shows that other existing training programs have generally also only increased the use of good business practices of around 6 or 7 percentage points, or 0.1 to 0.2 standard deviations. This contrasts with the experimental evidence on management practices in large firms provided in Bloom et al. (2013), in which intensive consulting resulted in a 37.8 percentage point increase in management practice use.

The bottom two rows of Table 5 summarize two recent studies that have used more intensive training programs and also found larger impacts on business practices, using measurement tools based on our survey instrument. The control group data from Martínez et al. (2013) provides the Chile data used in our paper. In their experiment, they find an approximate 0.18 increase in the business practice score, which is about four times as large as in the ILO training experiments. This results in a statistically significant increase in business income. Our associations from Table 2 15
**<u>Appendix Table 3:  Labor allocation hours for household agriculture by men 10 years and older</u>**

||(1)|(2)|(3)|
|---|---|---|---|
||Overall|Less Biomass Areas|More Biomass Areas|
|Age inyears|0.113***|0.115***|0.116***|
||(0.010)<br>|(0.013)<br>|(0.017)<br>|
|Age Square|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|
|Head Education: Snr. Prim or more|-0.164***|-0.166**|-0.137|
||(0.056)|(0.069)|(0.093)|
|HH Size|-0.021**|-0.032**|-0.031*|
||(0.011)|(0.013)|(0.019)|
|Real Value of Assets '000|-0.003***|-0.003***|-0.004***|
||(0.001)|(0.001)|(0.001)|
|Wet Season & Wet Area Interaction|0.252***|0.259***|0.274***|
||(0.039)|(0.045)|(0.077)|
|DrySeason and Dimba Area Interaction|1.063***|0.982***|1.040***|
||(0.132)|(0.159)|(0.235)|
|Total hectares of land uncultivated|0.030|0.139*|-0.121*|
||(0.047)|(0.072)|(0.062)|
|Total hectares of trees|-0.233|-1.166*|1.047*|
||(0.463)|(0.692)|(0.607)|
|Predicted Real Wage for Women|-0.005***|-0.005***|-0.004***|
||(0.001)|(0.001)|(0.001)|
|Number of <5yr children|-0.114|-0.149|-0.004|
||(0.141)|(0.168)|(0.259)|
|Married|0.453***|0.434***|0.448***|
||(0.079)|(0.098)|(0.131)|
|Rural Centre|-0.530***|-0.767***|-0.038|
||(0.097)|(0.132)|(0.151)|
|Rural South|-0.487***|-0.684***|-0.380***|
||(0.095)|(0.135)|(0.138)|
|Wet Season Dummy|0.740***|0.616***|0.950***|
||(0.066)|(0.082)|(0.114)|
|ADMARC Market Distance KM|0.002***|0.001***|0.018***|
||(0.001)|(0.001)|(0.003)|
|Distance to Asphalt Road KM|0.009***|0.005***|0.011***|
||(0.001)|(0.001)|(0.001)|
|Elevation|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|
|Biomass20(m3/ha)|-0.006***|0.004|-0.004**|
||(0.001)|(0.006)|(0.002)|
|Price of Maize Grain MK/Kg|-0.001|0.056***|-0.088***|
||(0.013)|(0.017)|(0.023)|
|Recent illness/injury|-0.244***|-0.282***|-0.189*|
||(0.060)|(0.075)|(0.098)|
|Chronic illness|0.107|0.205**|-0.232*|
||(0.083)|(0.102)|(0.141)|
|Other Members ChronicallyIll|-0.075**|-0.068|-0.161***|
||<br>(0.035)|<br>(0.043)|<br>(0.059)|
|Productive Assets Index|11.334***|9.565***|19.574***|
||(0.854)|(0.974)|(1.828)|
|Constant|-0.193|-0.644*|0.080|
||(0.302)|(0.381)|(0.507)|
|Observations|10935|7186|3749|

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 50
# INTRODUCTION

Since the adoption of the _doi moi_ or renovation<sup>policy in 1986, Vietnam has been</sup> undergoing a transition from central planning to<sup>a socialist market oriented economy. Among</sup> other reforms, this has involved an expansion<sup>of the private sector and the gradual opening up</sup> of Vietnam's economy to international trade<sup>and foreign investment. These changes have</sup> made a significant contribution to economic<sup>growth. Over the period 1993-97, Vietnam has</sup> been one of the fastest growing economy on<sup>the planet, with GDP increasing at a rate of</sup> almost 9% per year. This growth was<sup>led by the industrial sector which expanded at 13% per</sup> annum. However, despite such impressive<sup>achievements, there is a concern among</sup> policymakers in Vietnam that growth has<sup>been "unbalanced" and that it has only had a modest</sup> impact on employment.

The first part of this paper examines this concern<sup>using new employment data from the</sup> second _Vietnam Living Standard Survey_ (VLSS<sup>2) which was completed in 1997-98.</sup> Information from this survey can be compared<sup>to the data from the first</sup><sup>_Vietnam Living_</sup> _Standard Survey_ (VLSS 1) conducted in<sup>1992-93. The paper shows that industrial</sup> employment growth has indeed been low compared<sup>to industrial GDP growth. Section I also</sup> documents Vietnam's export growth in recent years.<sup>It indicates that, prior to the Asian crisis,</sup> trade reforms had put the country on the road to export-led<sup>growth. Section II uses previous</sup> statistical evidence from Wood and Mayer<sup>(1998) to estimate the extent of Vietnam's</sup> untapped comparative advantage in manufacturing<sup>exports. It finds that there is scope for</sup> Vietnam to significantly increase such exports in<sup>the near future through a better allocation of</sup> its resources. Estimates based on factor content<sup>calculations show how labor would benefit</sup> from such export-led growth. Section III of<sup>the paper looks at whether Vietnam's labor</sup> regulations present any major constraint for the<sup>development of an export-oriented private</sup> sector.

3