rafmacalaba/data-use-mentions-extended huggingface dataset:
files/publications/RISE_WP 009_Bruns_0.pdf</u>

- Berlinski, Samuel, and Norbert Schady. 2015. _The Early Years: Child Well-Being and the Role of Public Policy_ . New York, Palgrave Macmillan.

- Bold, Tessa, Deon P. Filmer, Ezequiel Molina, and Jakob Svensson. 2019. The Lost Human Capital: Teacher Knowledge and Student Achievement in Africa” World Bank Policy Research Working Paper No. 8849. The World Bank.

- Bold, Tessa, Deon Filmer, Gayle Martin, Ezequiel Molina, Brian Stacy, Christophe Rockmore, Jakob Svensson, and Waly Wane. 2017. “Enrollment without learning: Teacher effort, knowledge, and skill in primary schools in Africa.” _Journal of Economic Perspectives_ 31(4): 185-204.

- Chang, Mae Chu, Sheldon Shaeffer, Samer Al-Samarrai, Andrew B. Ragatz, Joppe de Ree, and Ritchie Stevenson. 2014. _Teacher Reform in Indonesia: The Role of Politics and Evidence in Policy Making_ . Directions in Development. Washington, DC: World Bank

- Chamberlain, G. (1982). Multivariate regression models for panel data. _Journal of Econometrics_ 18(1), 5– 46.

- Chaudhury, Nazmul, Jeffrey Hammer, Michael Kremer, Karthik Muralidharan, and F. Halsey Rogers. 2006. “Missing in Action: Teacher and Health Worker Absence in Developing Countries.” _Journal of Economic Perspectives_ 20(1): 91–116.

- Coflan, Andrew Moore, Andy Ragatz, Amer Hasan, and Yilin Pan. 2018. “Understanding Effective Teaching Practices in Chinese Classrooms: Evidence from a Pilot Study of Primary and Junior Secondary Schools in Guangdong, China.” World Bank Policy Research Working Paper No. 8396. Washington, DC.

32
|0.093***|0.053***|0.050***|0.094**|0.063**|0.064*|0.040|
||(0.043)|(0.027)|(0.018)|(0.013)|(0.042)|(0.030)|(0.033)|(0.026)|
|Unadjusted p-value|0.001|0.001|0.003|0.000|0.026|0.035|0.053|0.120|
|Adjusted p-value|0.003|0.003|0.010|0.000|0.083|0.111|0.167|0.348|
|Test T1 >= T2 (unadjusted p-value)|0.562|0.470|0.536|0.437|0.539|0.604|0.453|0.694|
|Test T1 >= T2 (adjusted p-value)|0.921|0.858|0.919|0.847|0.922|0.953|0.867|0.981|
|Endline control mean|0.7|40|0.0|16|0.|204|0.|150|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.034|0.321|0.056|0.330|0.011|0.145|0.007|0.118|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|2,382|2,382|

_Source:_ Authors’ analyses based on baseline and endline survey data
Bank, 2018). _Notes:_ Numbers are percentages of households who received advice on indicated topic, conditional on having received in-person advice (i.e., excluding advice received through media).<sup>_a_</sup> Exact wording differs between countries.

extension programs seem to be less prevalent, farmers report receiving advice relatively more often from private extension services and from other farmers.

**Shocks.** Data on shocks are available at the household level and include detailed information on whether households have been negatively affected by any shocks over the past year and the type of each shock.<sup>25</sup> The latter is collected based on a list of around 20 different events, which are very similar across countries. To test the predictions of the model, we classify shocks as either directly related to agriculture ( _AgShock_ ) or not directly related to agriculture ( _NonAgShock_ ). Table 3 provides a complete list of the shock items that appear in the data and the assigned categories for our baseline regressions.<sup>26</sup> As can be seen in the table, agricultural shocks include events such as “irregular rains”, “unusual high level of crop pests”, and “livestock disease”. Non-agricultural shocks include events such as “illness of a household member”, “theft of money”, and “end of regular assistance/remittances”.<sup>27</sup> Of course, any classification of the shock items listed in Table 3 into two distinct groups related to agriculture and not directly related to agriculture

> 25As described above, there are several years between panel rounds, and these are inconsistently spaced over time and countries. Therefore, we do not work with lagged shocks, as these are (i) several years, and therefore, in our view, too far in the past, and (ii) irregularly spaced.

> 26The shock items “other” and “increase in the prices for food”
Panels B and C present averages from the daily diaries. The average woman makes almost 700 Kenyan shillings (Ksh) from sex work, which was equivalent to about $10 during the sample period and which is roughly 4-5 times that of other female daily income earners in the area (Dupas and Robinson, 2009). Also of note from Panel B is that women see a client on 76 percent of days and see about 1.5 clients per day. Roughly one-third of these clients are regulars. Despite the substantial risk of HIV in this area, unprotected sex is also common: women have unprotected sex on 18 percent of all days and have an average of 0.42 unprotected sex acts per day (including the 24 percent of days in which they do not participate in the transactional sex market).

Finally, Panel C highlights how vulnerable women are to health and other shocks. Women report having a sick household member (where sickness is an indicator equal to one if a person experiences cough, fever, malaria, typhoid, diarrhea, cuts or burns, or other illness) on 37 percent of days, own sickness on 34 percent of days, and report having an STI on 3 percent of days. Women are also frequently asked for money by friends or relatives (on 7 percent of days). Finally, the biggest shock of all is also unfortunately quite common: women report the death of a friend or relative on 4 percent of days.

To examine shocks over a somewhat longer time period, Column 2 shows the probability of experiencing a shock over a week. Own and household sickness are experienced on 60 percent of weeks, and the death of a friend or relative occurs on 14 percent of weeks. Similarly, Column 3 shows the percentage of women that experienced these shocks at least once over the three month sample period – as expected, the percentages are quite high for all shocks. This is an environment in which access to insurance is likely to be important.

18
Policy Research Working Paper 8910

# **Abstract**

As one of the most pivotal ways that labor markets adjust to changing economic conditions, international migration is never far from the center of the national discussion in Uzbekistan. This paper summarizes the relationship between recent international migration trends and household well-being using a combination of administrative records and unique panel survey data from the Listening to the Citizens of Uzbekistan study. The panel design provides data on changes in well-being leading up to and following a migrants’ departure. This feature enables analysis that controls for unobserved time-invariant respondent and location characteristics. The findings show that weak local labor markets drive labor migration from Uzbekistan. Beginning to consider migration is associated with low life satisfaction, job loss, and unemployment. In contrast, actually migrating is associated with a remarkable improvement in labor market outcomes, alongside strong recovery in subjective and monetary measures of household welfare. The results further show that current migrants are more likely to send remittance payments when household members have deteriorating life satisfaction and/or subjective reports of worsening economic conditions at home. In the absence of remittance income, the poverty rate in Uzbekistan (measured at $3.2/day purchasing power parity) would be expected to rise from 9.6 to 16.8 percent, or to about 12.2 percent assuming (implausibly) that all current migrants were to find formal employment at the local prevailing median wage.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at wseitz@worldbank.org.

_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 reallocation of tourism expenditure corresponds to a relocation of demands for output between sectors. To maintain a balanced SAM a final correction was needed to ensure that the value of output equals total demand in each sector. Capital earnings are increased in sectors where demand is increased, and decreased in sectors where demand is decreased.

The SAM includes 12 household accounts. 6 accounts represent rural households and 6 accounts represent urban households. 2 accounts in each group represent households below the food poverty line and between the food and basic needs poverty lines. The other accounts are grouped according to the education of the head of the household.17 The SAM contains nine types of labor: Adults are grouped both according to gender and to one of four levels of education. All child labor (age 10 to 14) is the 9th and final category. Capital and agricultural land and a factor called a subsistence factor are the three remaining primary factors of production.

The subsistence factor is a composite of land, labor and capital used in the production for home (own) consumption by households.18 The subsistence factor is used in the agricultural and food-producing sectors. In each sector the SAM shows the value of output allocated for home consumption and of output allocated to the market, both of which are coming from the same activity.

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database. The regions of our model are Tanzania, the European Union, the East African Customs Union plus SADC and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In appendix A, we calculate and report data for the East African Customs Union and

> 17 Average per capita income in the SAM is calculated as USD 2.2 per day. This calculation is based on income data in the SAM, the number of heads in each
# Figure 18. SAR: Output growth and drivers of potential growth

<!-- Start of picture text --> A. GDP growth B. Contributions to potential growth<br>Percent SAR Median Percent TFP Capital Labor Potential growth<br>10 8<br>8 6<br>6 4<br>4 2<br>0<br>2<br>0<br>-2<br>2000-21 2000-10 2011-21 2022-23 EMDEs SAR<br>C. Investment growth D. Potential TFP growth<br>Percent Percent<br>SAR Median SAR Median<br>14 4<br>12<br>3<br>10<br>8<br>2<br>6<br>4<br>1<br>2<br>0 0<br>2000-21 2000-10 2011-21 2000-2021 2000-2010 2011-2021<br>E. Secondary education attainment F. Working-age population growth<br>Percent Percent<br>100 SAR Median 3 SAR Median<br>80<br>2<br>60<br>40<br>1<br>20<br>0 0<br>2000-20 2011-20 2000-2021 2000-2010 2011-2021<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Population Prospects; World Bank; World Development Indicators database.

Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates).

A.C.-F. Bars show period averages of annual GDP-weighted averages. Markers show median of GDP-weighted averages of the six EMDE regions. Orange whiskers show max-min range.

B. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 3 economies are from SAR (Bangladesh, India, and Pakistan)
construct a _Principal Component of KYC Requirements._ We expect more extensive documentation requirements to be negatively related to the use of accounts.

As the number of KYC requirements has increased in recent years, the Financial Action Task Force (FATF), recognizing that overly cautious Anti-Money Laundering and Terrorist Financing (AML/CFT) safeguards can have the unintended consequence of excluding legitimate businesses and consumers from the financial system, has emphasized the need to ensure that such safeguards also support financial inclusion (FATF, 2011). We indicate countries that have made exemptions with a dummy variable, _Exception from KYC Requirements_ . All data come from CGAP (2009). We expect exemptions to have a positive relationship with account use.

Proxies for distance barriers (or indicators of proximity to and accessibility of financial service providers) are measured by _Branch Penetration_ and _ATM Penetration_ , which denote the average number of commercial bank branches and automated teller machines (ATMs) per 1,000 square kilometers in 2011, respectively. These data come from the International Monetary Fund’s annual Financial Access Survey (IMF, 2012). We expect higher penetration to be positively related to account use.

Proximity to bank outlets is meaningless if there is limited or no interoperability between ATMs or points of sale (POSs) across different banks (that is, if account holders of any given bank cannot use the ATMs or POSs closest to them). We include a measure of the interoperability of POSs from the World Bank Global Payment Systems Survey (World Bank, 2010). This variable measures the degree to which payment cards issued by banks in the country can be used seamlessly at any national POS terminal. This variable ranges from 1 to 3, where lower numbers mean more interoperability; therefore, we expect a negative relationship with account use.

21
the customs duties collection (which is primarily of interest to customs) but indirectly in the whole port clearance processes.

In this section we intend to make use of explanatory statistics to analyze shipment level data collected through ASYCUDA by customs administration and test the assumptions and findings presented in previous sections and recalled in table 12 below. Different models are tested and areas for further research are identified.

|**Factor**|**Type **|**Impact**|
|---|---|---|
|**Fiscal regime**|Shipment specific|High fiscal pressure leads to high dwell time|
|**Bulking of containers**|Shipment specific|LCL containers stay longer in the port|
|**Density of value**|Shipment specific|Higher value leads to higher dwell time|
|**Commodity type**|Shipment specific|Commodity category is a crucial determinant|
|**Concentration of C&F market**|External Factor|Dominant C&F players have a low<br>performance|
|**Low volume per operation**|External Factor|Lack of regularity leads to poor performance|
|**Concentration of shipping flows**|External Factor|Disruption in ship arrivals leads to discrete<br>behaviors|

**Table 12 – Early assumptions about determinants of long dwell times**

# **Parametric fit using continuous distributions**

We first attempt to fit the distribution of container dwell times using parametric asymmetric distributions of continuous data with positive values. The analysis of cargo dwell time qualifies as survival analysis since the research output is the expected time at which cargo will exit the port (continuous positive values with right-censoring patterns<sup>30</sup> ). Such methods have not been successful however at this stage.

Univariate analysis shows for example that standard parametric distributions (Gamma, Lognormal, and Weibull) are not fitting the dwell time data well. Data
whole index. Furthermore, as the impact of a given disaster on the economy of a country clearly depends on the disaster intensity relative to the overall size of the country, the authors scale all respective disaster variables by land area, as suggested in Skidmore and Toya (2002).

The rest of the data comes from the World Bank WDI. The exceptions are the Polity index which is taken from the Polity IV Project and gross government debt-to-GDP ratios from the IMF Historical Public Debt Database. The final dataset covers an unbalanced panel of 108 economies over the period between 1979 and 2010.

> 1The unbalanced panel covers 108 countries over the period 1979-2010: Albania, Algeria, Angola, Argentina, Armenia, Australia, Azerbaijan, Bangladesh, Belarus, Belgium, Bolivia, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cameroon, Canada, Chad, China, Colombia, Congo, Rep., Costa Rica, Cˆote d’Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Ethiopia, Finland, France, Gabon, Germany, Greece, Guatemala, Haiti, Honduras, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kenya, Korea, Rep., Kuwait, Kyrgyz Republic, Lao PDR, Liberia, Madagascar, Malaysia, Mali, Mauritania, Mexico, Mongolia, Morocco, Mozambique, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, North Macedonia, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Rwanda, Senegal, Serbia, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Uganda, Ukraine, United Kingdom, United States, Uruguay, Vietnam, Yemen, Rep. and Zambia.

5
_Table 2: Estimated Results of Different Models_

|Dependent Variable: Female-to-Male Labor<br>Force Participation Ratio|(1)|(2)|(3)|
|---|---|---|---|
|Method|GLS|GLS|2SLS|
|Main Independent Variable|Internet<br>Inclusive<br>Index|Gender Gap<br>in accessing<br>internet|Gender Gap<br>in accessing<br>internet|
|Inclusive Internet Index|0.781***|||
||(0.139)|||
|Gender Gap in Internet Access||-0.0176|-0.501*|
|||(0.0440)|(0.297)|
|Population Growth|0.243|-0.894**|-1.290|
||(0.467)|(0.423)|(1.325)|
|Educational Attainment (Years of||||
|education)|-2.049***|-2.026***|-4.044***|
||(0.299)|(0.379)|(1.032)|
|Gender Parity Index in||||
|<br>Tertiary Education||||
|Unemployment Rate (%)|-0.180|-0.0571|-0.735|
||(0.177)|(0.173)|(0.704)|
|Share of Industry Value Added in GDP (%)|0.624***|0.818***|0.240|
||(0.0798)|(0.0963)|(0.456)|
|Share of Service Value Added in GDP (%)|0.528***|
out of every thousand), this direct effect is likely to be negligible. Second, agricultural labor force participation could fall indirectly due to measures to contain the virus, such as travel restrictions, and quarantines, but primarily due to the increased fear of congregating in work teams as discussed in the previous paragraph. Lower availability of labor, especially for land preparation and planting, would reduce area devoted to rice and potentially lower yields on rice land that is planted.

Subjective recall data from the 2016 HIES on how Ebola affected rice cultivation in the last completed season lends support to the importance of farm labor availability. A year after the onset of the Ebola outbreak (during the agricultural season 2015/16) many farmers in Liberia were unable to grow or sell their rice. The primary constraint was lack of farm labor for clearing, planting and harvesting. Specifically, 9 percent of rural farming households report not planting rice due to the presence of Ebola in their community. Another 14 percent planted rice, but encountered difficulties during cultivation because of EVD. Of the 48 percent of rural farming households planting rice during the course of the epidemic, 22 percent reported that land clearing and planting were affected, whereas 1 percent did not harvest any of the planted rice and 3 percent did not complete their harvest because of Ebola sickness in their community. While these recall data provide some insights, they are, after all, subjective impressions of farmers. The remainder of this study, therefore, focuses on objective recall data, which are less prone to ex-post rationalization and related biases.

One limitation of the HIES questionnaire is that it does not distinguish the use of _kuu_ labor from that of (conventional) hired labor, although we suspect that the former institution is far more widespread than the latter in Liberia’s traditional rice farming 10
FSU. Two frames were used (as per the 62nd round survey): List frame and Area frame. List frame was used for urban manufacturing enterprises only. For unorganized manufacturing enterprises, a list of about 8000 large non-ASI manufacturing units in the urban sector prepared on the basis of the data of the census of manufacturing enterprises conducted by Development Commissioners of Small Scale Industries in 2003 was used as the list frame for surveys in 2005-06 and 2010-11. Area frame was adopted for both rural and urban sectors. The list of villages as per census 2001 was used as the frame for the rural sector, and the latest available list of UFS blocks was used as the frame in the urban sector. The relevant year’s economic census was used as the frame for towns with population 10 lakhs or more (as per Census 2001).

30 The categories listed for the ownership and premises fields are taken from the 1994 survey instrument. Later surveys expanded the listed categories to include additional ownership categories that do not overlap with the primary male/female proprietary categories used in this work. See Ghani, Kerr and O'Connell (2014) for more details.

31 The sampling design followed in in most ASI surveys is a stratified circular systematic one. All factories in the updated frame (universe) are divided into two sectors: Census and Sample. The Census Sector is comprised of industrial units that belong to the six less-industrially developed states or UTs: Manipur, Meghalaya, Nagaland, Tripura, Sikkim, and the Andaman & Nicobar (A&N) Islands. For the rest of the states/UTs, the Census Sector includes (i) units having 100 or more workers and (ii) all factories covered under Joint Returns. All other units belonging to the strata (state x four-digit industry of the NIC-04 framework) having four or fewer units are also considered as Census Sector units. Finally, the remaining units, excluding those of Census Sector, called the Sample Sector, are arranged in order of their number
# **Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax-Office Data**<sup>**1**</sup>

Fujin Zhou<sup>2</sup> Remco Oostendorp<sup>_3_</sup>

**Key words** : MIMIC, underreporting, investment climate, tax compliance model

**JEL** : C39, D24, E26, H26

1 The authors gratefully acknowledge the valuable comments from Eric Bartelsman, Chris Elbers, and Erik Plug.

2 Tinbergen Institute Amsterdam, and VU University Amsterdam. Email: _f.zhou@ vu.nl_ .

3 VU University Amsterdam , Tinbergen Institute Amsterdam, and Amsterdam Institute for International Development. Email: _r.oostendorp@ vu.nl_ .
import protection evaluated using the sample data; and we then introduce (one at a time) a one standard deviation shock to each of the macroeconomic determinants of interest, holding everything else constant, and regenerate the model’s predictions of import protection.

The grey bars of Figure 2 present estimates based on specification (1) of Table 2, whereas the black bars illustrate impacts based on specification (2). First note from Table 1 that a one standard deviation increase in the percent change of the bilateral real exchange rate is roughly a 15 percent appreciation. Figure 2 indicates this 15 percent appreciation is associated with a 33 (34) percent increase in imported products subject to TTB protection per trading partner per quarter according to specification (1) (alternatively, specification (2)). Next, the figure illustrates a sizeable estimated impact of domestic macroeconomic shocks. A one standard deviation increase to the change in the domestic unemployment rate – i.e., 0.86 or nearly one full percentage point (see again Table 1) - is associated with, per trading partner, a 52 percent increase in import protection. In specification (2), 12