rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Backward imputation (2009 HIES to 2006 LFS Imputation)**

||Direct esti<br>2009|mation<br>2006|Imputation<br>Variables only<br>from LFS|-**2006**<br>With wage<br>data|
|---|---|---|---|---|
|National|8.6|15.3|8.3|9.8|
||(0.5)|(0.7)|(0.8)|(0.8)|
|Urban|6.1|7.4|4.1|4.6|
||(1.1)|(1.3)|(1.5)|(1.6)|
|Rural|8.9|15.4|8.8|10.3|
||(0.6)|(0.8)|(0.9)|(1.0)|
|Estate|10.4|33.6|10.8|14.0|
||(1.7)|(3.0)|(3.5)|(3.6)|

Source: Authors’ estimations using HIES 2006/7 and 2009/10 data. Notes: Tables A3-1 to A3-3 show the consumption models with means of these variables included in the final models for urban areas, rural areas and estates. The figures in parentheses are cluster-robust standard errors for the direct estimations and the standard errors that include the modeling and sampling errors for imputation.

# V. **Two sources of failures in SSI**

There are potentially two underlying causes for the failure of SSI to accurately predict changes in poverty. First, the HIES and LFS surveys may not be compatible, because the questionnaires differ in important ways. Finally, the HIES to LFS imputation is limited to the set of variables that are available in both the HIES and LFS. If these variables do not closely track changes in consumption, the resulting models will not accurately project changes in poverty rates. In addition, a different sampling scheme can also affect how well the imputed results from the LFS match the HIES. This section investigates these three possibilities.

To see the effects of the two potential sources of error, we impute from the HIES into itself. In other words, we create a consumption model using one round of the HIES and use it to estimate consumption based on proxy indicators in the other round of the HIES. To make the imputed poverty rates comparable to those in the HIES to LFS imputations discussed above, we use the same survey period, geographic coverage, and consumption models.<sup>9</sup> The only difference is the source of the welfare proxies used to predict consumption, which are now taken from the HIES instead of the LFS. If using proxies from the HIES instead of the LFS substantially improves the accuracy of the estimates, then it is likely that the HIES and LFS surveys are using incompatible samples or questions.

Table 7 shows that for rural areas and estates, the estimated poverty rates change little when using the HIES instead of the LFS, implying that the effect of different survey designs is minimal in these areas. For urban areas, however, the poverty rates projected by the HIES to HIES imputation become much closer to the corresponding direct estimates. For example, for the backward imputation, the HIES to HIES imputation projects a poverty rate of 7.2 percent, which is just 0.2 percentage point different from the direct estimate of 2006 and inside its 95 percent confidence interval. For the forward projection, the HIES to HIES imputation projects 6.6 percent, which is just 0.5 percentage point higher than the direct estimate of 2009 and inside the 95 percent confidence interval.

> 9 Furthermore, all models do not include wage data.

13
_ceteris paribus_ (see Table 2 – Wages Only column).<sup>12</sup> When considering also the impact of inkind compensation and bonuses, the sign of the public sector dummy changes to negative (see Table 2 – Total Compensation column). Total compensation in the formal private sector is higher (1.06 Nuevos soles per hour, or 11 percent). This underscores the importance of considering non-monetary compensation when comparing salaries in the public and private sector.<sup>13</sup>

**Table 2. OLS Estimation Results**

|**Variables**|**Wages Only**|**Total Compensation**|
|---|---|---|
|**Public Sector Dummy**|**0.070*****|**-0.059****|
|Secondary Education|0.258***|0.286***|
|Non Completed Post-Secondary|0.198***|0.190**|
|Completed Post-Sec (non University)|0.169***|0.174***|
|Completed Post-Sec (University)|0.474***|0.466***|
|Postgraduate|0.357***|0.364***|
|Age|0.035***|0.038***|
|Age Squared (/100)|-0.027***|-0.029***|
|Female|-0.149***|-0.180***|
|Married|0.095***|0.116***|
|Urban|0.096***|0.119***|
|Observations|7702|7702|
|R-squared|0.320|0.292|
|*** p<0.01, ** p<0.05, * p<0.1|||

The coefficients estimated for the rest of explanatory variables show the expected sign and significance. Education has the expected positive sign on compensation. For example, a worker with secondary education is estimated to have a total compensation 1.33 Nuevos soles higher than a worker who achieved just primary education.<sup>14</sup> Given our modeling strategy, the impact of higher levels of education can be computed using the cumulative impact of all education levels achieved. This is because a worker that has completed post-secondary education would also have completed all lower levels of education.

In keeping with the literature, experience is included in the regression both in levels and squared to account for non-linearities. As expected, experience has a positive non-linear impact

> 12 The value of 1.07 Nuevos soles is obtained by taking the exponential of the coefficient estimated for Equation 1.

> 13 The robustness of results is tested by re-estimating the log-wage equation after dividing employees into three categories: professionals, technical experts and auxiliaries. The results of this robustness test are presented in Annex 2.

> 14 The marginal impact is computed by taking the exponential of the coefficient estimated for secondary education (0.286). See footnote 6 for details on the structure of education dummies.

10
WPS3773

# **Global Logistics Indicators, Supply Chain Metrics, and Bilateral Trade Patterns**

Warren H. Hausman Hau L. Lee Uma Subramanian

## **_Abstract_**

Past research into determinants of international trade highlighted the importance of the basic spatial gravity model augmented by additional variables representing sources of friction. Studies modeled many sources of friction using various proxies, including indices based on expert judgment in some cases. This paper focuses on logistics friction and draws on a data set recently compiled by the World Bank with specific quantitative metrics of logistics performance in terms of time, cost, and variability in time. It finds that the new variables that relate directly to logistics performance have a statistically significant relationship with the level of bilateral trade. It also finds that a single logistics index can capture virtually all of the explanatory power of multiple logistics indicators. The findings should spur public and private agencies that have direct or indirect power over logistics performance to focus attention on reducing sources of friction so as to improve their country’s ability to compete in today’s global economy. Moreover, since the logistics metrics are directly related to operational performance, countries can use these metrics to target actions to improve logistics and monitor their progress.

### World Bank Policy Research Working Paper 3773, November 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _______________ Warren H. Hausman and Hau L. Lee are at Stanford University, and Uma Subramanian is at the World Bank. The authors graciously acknowledge support provided by the World Bank for this research. They also gratefully acknowledge research assistance from Kihoon Lee of the World Bank and Luis Blancas of Stanford University.

Corresponding author: Uma Subramanian, World Bank; Usubramanian@worldbank.org 1
total sample of 18344 households<sup>10</sup> . We drop these households from the current analysis since we are unable to map them with the remote sensing satellite data, and this exclusion may result in a selected sample. In table A.1, we present results from a series of weighted regressions of a dummy variable for bad geocodes (households dropped out of analysis) on demographic and location covariates. We find that households with bad geocodes were highly likely to be interviewed using paper surveys. While only 5 percent of households interviewed with tablets had bad geographical locations, 88 percent of households interviewed with paper questionnaires had incorrectly mapped locations. Households in rural areas, and Taliban-controlled and contested-regions were also more likely to have incorrect location data. Moreover, households with bad geocodes were also slightly more likely to be poor. With our sample skewed along the mentioned characteristics, our results should be interpreted as a lower bound on the impact of drought on welfare, as the analysis may be excluding poor households in the most remote areas.

With these caveats, Table 1 describes the household sample. Expectedly, households in our sample are predominantly rural, have male heads with less than 50 percent of the heads being literate. Half of the sample was collected after the impositions of COVID-19 restrictions. Over 50 percent of the sampled households resided in government-controlled districts, whereas 8 percent were in Taliban-controlled districts.

In Appendix B, we detail the methodology used to construct consumption aggregates and poverty rates based on cost of basic needs methods. The consumption expenditures per person per adult equivalent and poverty lines are estimated for each of the 14 regions.<sup>11</sup> Table 2 show the description of consumption aggregates and poverty line constructed with this socioeconomic data. Forty-seven percent of the Afghan people are poor, with urban poverty only slightly lower than rural poverty.

# **3.2 Weather Data**

As discussed earlier, we expect temperature and NDVI deviations to be better measures of weather shock in the country. In addition, we also use variations in precipitation to confirm if this hypothesis is valid in Afghanistan. Below we detail the data sources for each of these measures.

_Precipitation (CHIRPS):_ The daily rainfall data is available at a 5-kilometer resolution

> 10Incorrect locations are characterized by missing household latitude or longitude, point outside the specified district, etc.

> 11Afghanistan has 14 poverty lines for rural and urban areas separately in eight regions across the country: North, Northeast, West, West-Central (only rural), South (only rural), Southwest, East, Central regions.

7
The squared malnutrition gap takes into account not only the distance between the malnourished children and the reference line (the malnutrition gap), but also the inequality among the malnourished children. That is, a higher weight is placed on those who are further away from the reference line.

# **3. Data**

We reanalyzed all DHS and MICS, phases 3 onwards, and calculated individual Z-scores for all children with available anthropometric data according to the WHO standard approach. As of today, we obtained estimates for 168 DHS from 1993 to 2014 and 70 MICS from 2005 to 2014. These surveys combined cover 94 countries. Annex 1 lists all the surveys included in this data set.

The recalculation of Z-scores was based on the WHO child growth standards and prevalence estimates were generated following standard analysis as per available Stata macro (http://www.who.int/childgrowth/software/en/). The recalculated Z-scores may generate slightly different prevalence estimates from those published by DHS and MICS reports, mainly due to the use of the WHO standard approach, which (i) uses all valid Z-scores for each child, and (ii) imputes the missing day of birth as 15.

Each of our surveys is representative for the data collection areas, and most are nationally representative for the country. Therefore, we use survey weights in our analysis to ensure that we have a representative estimation for the country or the areas where data were collected.

# **4. Results**

6
(2013), we assume a minor degree of learning-by-doing externality in the traded good sector ( _ρYT_ = _ρzT_ = 0 _._ 10). Also as in Berg _et al._ (2010), investment adjustment costs are set to _κN_ = _κT_ = 25.

- _Households preferences._ The coefficient of risk aversion _σ_ = 2 _._ 94 implies an intertemporal elasticity of substitution of 0.34, which is the average LIC estimate according to Ogaki _et al._ (1996). We assume a low Frisch labor elasticity of 0 _._ 10 ( _ψ_ = 10), similar to the estimate of wage elasticity of working in rural Malawi - see Goldberg (2013). The labor mobility parameter _ρ_ is set to 1 as in Horvath (2000), and the elasticity of substitution between traded and nontraded goods is _χ_ = 0 _._ 44, following Stockman and Tesar (1995). To capture limited access to international capital markets, we set _η_ = 1 as in Buffie _et al._ (2012).

- _Measure of intertemporal optimizing households_ . Since a large proportion of households in LICs are liquidity constrained, we pick _ω_ = 0 _._ 40, implying that 60 percent of households are rule-of-thumb. Depending on the degree of financial development of a country, the measure of intertemporal optimizing households can be lower than 40 percent in some SSA countries. Based on data collected in 2011, Demirguc-Kunt and Klapper (2012) report that on average only 24 percent of the adults in SSA countries have an account in a formal institution.

- _Mining_ . The royalty tax rate _τ_<sup>_O_</sup> is made time-varying to match the projections of natural resource revenues for Kenya in Subsection 5.1.

- _Tax rates_ . Consistently with data collected by the International Bureau of Fiscal Documentation in 2005-06, the steady-state taxes on consumption, labor and capital are chosen so that _τ_<sup>_C_</sup> = 0 _._ 10, _τ_<sup>_L_</sup> = 0 _._ 15, and _τ_<sup>_K_</sup> = 0 _._ 20, respectively.

- _Fiscal rules_ . In this application we use only the consumption tax rate as the instrument that stabilizes government debt. We impose a non-negativity constraint for the stabilization fund by setting _ffloor_ = 0. In the baseline calibration, the fiscal instrument does not have a ceiling. This translates in setting _τceiling_<sup>_C_=100</sup><sup>_,_000.</sup> The baseline calibration also implies that the whole fiscal adjustment takes place through changes in external commercial borrowing and consumption taxes. This is

19
|**Appendix 2: Definitions and Sources o**<br>**Variable**|**f Variables Used in the Regression Analysis**<br>**Definition and Construction**|**Source**|
|---|---|---|
|GDP per capita|Ratio of total GDP to total population. GDP is in1995 PPP-adjusted US$|World Development Indicators (WDI), The World Bank.|
|GDP per capita growth|Log difference or real GDP per capita|Author's calculation using WDI.|
|Initial GDP per capita|Initial value of ratio of total GDP to total population. GDP is in 1995 PPP-<br>adjusted US$|Author's calculation using WDI.|
|Trade openness|Log of ratio of exports and imports (in 1995 US$) to GDP (in 1995 US$)|WDI, The World Bank.|
|Government consumption|Log of the ratio og government consumption to GDP.|WDI, The World Bank.|
|Inflation rate|Annual change in CPI.|WDI, The World Bank.|
|Changes in Terms of trade|Log differences of the net barter terms of trade index (1995=100)|Author's calculation using WDI.|
|Human capital investment|Ratio of the total secondary enrollment, regardless of age, to the population<br>of the age group that offically correspond to that level of education.|WDI, The World Bank.|
|Stockmarket turnover ratio|Ratio of the value of total shares traded and average real market capitalization,<br>the denominator is deflated using the following method:<br>Tt/P_at/{(0.5)*[Mt/P_et+ Mt-1/P_et-1]<br>where T is total value traded, M is stock market capitalization, P_e is end-of period CPI<br>P_a is average annual CPI|Financial Structure Database, Thorsten Beck.<br>Standard and Poor's Emerging Market Database (and<br>Emerging Stock Markets Factbook).Data on the deflators<br>(line 64M..ZF or , if not available, 64Q..ZFand annual<br>CPI (line 64..ZF)) are taken from the electronic version ofthe IFS.|
|Life insurance penetration|Life insurance premium volume as a share of GDP|Financial Structure Database, Thorsten Beck.<br>Premium data is taken from various issues of Sigma. Data on GDP<br>in local currency (lines 99B..ZF or, if not available, line 99B.CZF),|
|Non-life insurance penetration|Nonlife insurance premium volume as a share of GDP|Financial Structure Database, Thorsten Beck.<br>Premium data is taken from various issues of Sigma. Data on GDP<br>in local currency (lines 99B..ZF or, if not available, line 99B.CZF),|
|Private credit by deposit money banks to GDP|Private credit by deposit money banks to GDP, calculated using the following<br>deflation method:<br>{(0.5)*[Ft/P_et+ Ft-1/P_et-1]}/[GDPt/P_at]<br>where F is credit to the private sector, P_e is end-of period CPI, and P_a is<br>is average annual CPI.|Financial Structure Database, Thorsten Beck.<br>Raw data are from the electronic version of the IMF's_International Financial Statistics_(IFS lines<br>22d). Data on GDP in local currency (lines 99B..ZF or, if not available, line 99B.CZF), end-of<br>period CPI (line 64M..ZF or , if not available, 64Q..ZF), and annual CPI (line 64..ZF) are from the<br>electronic version of the IFS.|

20
**Table 3: Dependent variable: Inflow FDI (Random-effect)**

||1|II|III|IV|V|VI|
|---|---|---|---|---|---|---|
|CFDI|-0.420<br>(0.347)|0.200<br>(0.174)|0.095<br>(0.221)|0.387<br>(0.499)|0.114<br>(0.230)|0.395<br>(0.523)|
|FDIt-1|0.649***<br>(0.038)|0.235***<br>(0.037)|0.378***<br>(0.059)|0.373***<br>(0.129)|0.370***<br>(0.061)|0.391***<br>(0.133)|
|GPCGR|-0.053*|-0.006|-0.005|-0.031|-0.006|-0.034|
||(0.031)|(0.016)|(0.019)|(0.032)|(0.019)|(0.034)|
|GDP||0.792***|0.775***|0.936***|0.718***|0.849***|
|||(0.102)|(0.124)|(0.217)|(0.132)|(0.232)|
|WFDI|0.404|0.571***|0.259|0.073|0.342*|0.121|
||(0.281)|(0.147)|(0.173)|(0.244)|(0.191)|(0.276)|
|WAGE|0.195|-0.041|0.092|-0.130|0.141|-0.095|
||(0.202)|(0.116)|(0.148)|(0.172)|(0.162)|(0.195)|
|ILLIT||-0.022|0.009|-0.886**|-0.029|-0.891**|
|||(0.027)|(0.028)|(0.345)|(0.044)|(0.359)|
|OPEN|-0.005|0.004|0.009***|0.013***|0.010***|0.013**|
||(0.004)|(0.003)|(0.003)|(0.005)|(0.004)|(0.005)|
|TARIFF||||0.027||0.024|
|||||(0.048)||(0.049)|
|INCTAX||||-0.021||-0.020|
|||||(0.017)||(0.018)|
|PSR|||0.014|0.034|0.006|0.025|
||||(0.017)|(0.021)|(0.018)|(0.023)|
|TELE|||||-0.321|-0.182|
||||||(0.308)|(0.457)|
|CRISIS||0.001|-0.010|-0.110**|-0.023|-0.117**|
|||(0.016)|(0.018)|(0.044)|(0.021)|(0.047)|
|ACCESS||0.263|0.285|0.276|0.260|0.260|
|||(0.172)|(0.197)|(0.224)|(0.200)|(0.226)|
|Obs.|157|132|101|62|99|61|
|R-squared|0.672|0.669|0.625|0.587|0.619|0.583|

# Notes:

(1). Standard errors are reported in parentheses. ***, ** and * indicate coefficient significant at 1, 5 and 10%, respectively.

35
empowerment (growth drivers and enablers), environmental sustainability, and social re-engineering (essentially improving the well-being of the people and social cohesion).

This paper simulates Malaysia’s long-run growth prospects using the World Bank Long-Term Growth Model (LTGM), a suite of spreadsheet-based tools building on the celebrated Solow-Swan growth model (Loayza and Pennings 2018, and Hevia and Loayza 2012). The Long-Term Growth Model – Public Capital Extension (LTGM-PC) is used as the base model (Devadas and Pennings 2018), which allows for private and public investment to have different effects on growth. Human capital and TFP growth are endogenized using LTGM’s Human Capital extension (LTGM-HC) and TFP extension (LTGM-TFP), respectively. The LTGM-HC combines average years of schooling by age cohort with the quality of education and health components to determine human capital. In the LTGM-TFP (Kim and Loayza 2018), the TFP growth rate is calculated as the composite effect of TFP determinants: innovation, education, market efficiency, infrastructure, and institutions. The models are described in more detail in Appendix 1 and most are available for download at www.worldbank.org/LTGM.

Our first result concerns a Malaysia’s business-as-usual baseline growth path, where the growth drivers ─public and private investment-to-GDP ratios, total factor productivity (TFP), human capital, and labor force participation rates ─ follow their historical or recent trends (future demographic projections taken from the United Nations (UN). We find trend GDP growth in Malaysia is likely to fall from around 4.5 percent in 2020 to 2.0 percent in 2050, driven mostly by demographics, falling private investment effectiveness and declining TFP growth. Declining growth is common among peer countries as they transition to high-income status.

However, this decline is growth is not destiny, and our second finding is that it can be partially offset by economic reforms. Specifically, we simulate weak, moderate, and strong reforms for each growth driver, with targets set with reference to the distribution of those values among high-income (HI) countries. While weak reforms have little effect (relative to the baseline), moderate and strong reforms generate growth in 2050 that is 1.5 to 1.8 times that in the business-as-usual baseline (respectively).

The rest of the paper is organized as follows. Section 1 presents the historical developments for each growth driver. Section 2 discusses the assumptions regarding growth drivers and parameters used in the baseline simulation. Section 3 shows the baseline GDP growth trajectory over the next three decades and analyzes the contribution of each growth driver to both current growth rates and changes in the growth rate over 2020-50. Section 4 presents the impact on GDP growth of the different levels of reforms for each determinant of growth: “weak” reform benchmarking at the 25th percentile among high-income countries; 3
policies (Keefer 2011) and rely on clientelist interactions with the public administration to ensure that these policies are carried out.

The results here also extend the existing literature on the political economy of public administration to non-democracies. Autocrats vary in the degree to which they choose to fend off potential challengers by providing benefits to large groups of citizens. Those that choose to provide broadly distributed benefits to citizens must make credible commitments to reward members of the ruling party, who implement these policies. As Gehlbach and Keefer (2010) argue, the credibility of ruler commitments to party members depends on whether party members can act collectively to limit the scope for ruler shirking. However, party members able to act collectively are also more likely to demand such administrative reforms to facilitate oversight of the ruler’s pursuit of party goals. Public sector reforms are therefore also more likely to succeed in non-democracies with programmatic parties.

# **Conclusion**

This paper develops a new argument that relates public sector organization to the organization of political parties: the better able are politicians to act collectively to prevent shirking by party leaders, the more likely are voters to hold them accountable for public sector failures and the greater are political incentives to enforce rules governing public sector performance. Such organizational arrangements are essential for the credibility of parties’ programmatic commitments. Using data on whether political parties are programmatic and new evidence on World Bank public sector loans, we find strong and robust evidence that public sector reforms are least likely to succeed in countries in which no trace of programmatic tendencies can be detected in their largest political parties.

44