rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7527

# **Abstract**

The Government of Rwanda has introduced several reforms since 2009 that have significantly improved the domestic business environment. One of the reforms is the one-stop shop for business registration. Making use of newly available administrative data sources such as tax and company registrations records, this paper sheds light on the effects of the introduction of the one-stop shop on company registrations, taxes, employment, and exports between 2008 and 2012. The paper finds sizable benefits to Rwanda’s efforts, which are mostly driven by the registration of large and mid-sized companies, with a limited contribution from small and micro-sized ones. The use of a newly available data source highlights the potential uses of growing bodies of administrative data and their possible shortfalls.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at msavinizangrandi@worldbank.org and mmogollon@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 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 4. Daily Wages (in birr) by Employment Type and OSD Severitya**

||_Per_|_manent_|_Tem_|_porary_|
|---|---|---|---|---|
||N|Daily Wage|N|Daily Wage|
|Non-OSDb|58|5.9<br>(0.29)|99|_3.5_<br>(0.07)|
|Severe-OSDb|63|5.2<br>(0.18)|33|3.6<br>(0.12)|
|Differenceb'||0.7*<br>(0.34)||-0.1<br>(0.14)|
|Wilcoxon Rank-Sum<br>Statisticd||2.068*||-1.432|

a. Daily wages equal average monthly income over average days worked per month for latest 12 months.

b. Standard errors in parentheses.

c. "*" represents statistical significance at 5% based on a t-test with unequal variances.

d. "*" represents statistical significance at 5% based on the Wilcoxon rank-sum test for equal medians.

As reported in Tables 3 and 4, we see that severe-OSD permanent employees earn a lower monthly income not only because they work fewer days, but also because they are less productive on the days that they work. This can be seen in the statistically significant difference in daily wages earned. However, again we see that severe-OSD temporary employees do not show lower productivity in terms of daily wages earned.

Table 5 disaggregates the data for temporarv workers by gender and age in order to see if OSD-related productivity differences may be revealed among certain subgroups. We examine two age categories: those below the mean age of 25 ("young") and those above 25 ("experienced").

7
|
|_Girl-boy enrollment rate 14-17_ * _Female_||0.145<br>(0.226)|
|_Girl-boy enrollment rate 14-17_ * _Female_ * _Firstborn girl_||-0.384<br>(0.251)|
|N|182,287|179,799|

NOTES: Estimates of specification (1) with additional controls for son preference. Ideal fraction of sons = _idealboys_ + _ideal_ <u>(0</u> _._ 5 _kids∗idealeither_ <u>)</u> if _idealkids >_ 0, where _idealkids_ is the ideal number of total children; _idealboys_ is the ideal number of boys; and _idealeither_ is the number of children that parents are indifferent over the gender of, as reported by a woman. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

57
Despite declining poverty rates and rising living standards, the Philippines continues to struggle with food insecurity and undernutrition. Around 65 percent of individuals across the country struggle with chronic food insecurity. This breaks down to 39 percent of individuals experiencing mild food insecurity, 17 percent of individuals experiencing moderate food insecurity, and 8 percent of individuals experiencing severe food insecurity (IPC, n.d.). Further, according to Mbuya et al. (2021), there has been almost no reduction in the prevalence of undernutrition for 30 years. Nearly 30 percent of children younger than five suffer from stunting, with some regions suffering from rates greater than 40 percent. Micronutrient deficiency is also prevalent, including anemia and vitamin A deficiency (Mbuya et al., 2021).

Undernutrition and food insecurity tend to most affect vulnerable peoples, including landless poor households, indigenous peoples, and individuals engaged in irregular income livelihoods (IPC, n.d.). Additionally, many individuals living in the Philippines are subject to regular and severe climate shocks, including flood, landslides, drought, and typhoons, all of which cause compounding stresses on ensuring food and nutrition security for households. The Philippines’ rapid growth and urbanization further threatens the tenuous food security which exists. As the Philippines continues to grow, good nutrition will be imperative for ensuring continued economic prosperity. Addressing undernutrition will increase human capital and will improve the lives of individuals throughout the nation.

# _2.2 National Nutrition Survey_

As we are interested in household nutrition and intra-household consumption, our primary analysis is based on the National Nutrition Survey (NNS) of the Philippines. The NNS is a nationwide survey on nutritional status, diet, and other factors related to noncommunicable disease. The NNS data are nationally representative within each cross-section collected (Patalen et al., 2020).

The NNS in 2013 is a unique data set, due to the availability and richness of the individual-level consumption data. Despite the value of food consumption and/or dietary data, there are very
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Tsang, M. (1997). The costs of vocational training. International Journal of Manpower, 18(1/2): 63-89.

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World Bank (2014) Doing Business Survey Ethiopia.

World Bank (2015a
household consumption or income in survey round _j_ , j= 1 or 2. The linear projection of household consumption (or income) on household characteristics for each survey round is given by yy ′ i i i (C1) Let be the poverty line in period _j_ . We are interested in knowing the unconditional measures ii yy = ββ xx + εε of poverty mobility such as ii zz 1 2) (C2) which represents the percentage of households that are poor in the first survey round (year) but ii1 ii2 nonpoor in the second survey round, or the conditional measures such as PP(yy < zz aai yy > zz 2 1) (C3) which represents the percentage of poor households in the first round that escape poverty in the ii2 ii1 second round. PP(yy > zz | yy < zz

If true panel data are available, we can straightforwardly estimate the quantities in (C2) and (C3); but in the absence of such data, we can use synthetic panels to study mobility. To operationalize the framework, we make two standard assumptions. First, we assume that the underlying population being sampled in survey rounds 1 and 2 are identical such that their timeinvariant characteristics remain the same over time. More specifically, coupled with equation (C1), this implies the conditional distribution of expenditure in a given period is identical whether it is conditional on the given household characteristics in period 1 or period 2 (i.e., implies and have identical distributions) (Assumption 1). Second, we assume that i1 and ii1 ii2 i2 have a bivariate normal distribution with positive correlation coefficient ρ and standard xx = xx ii1 ii1 ii1 ii2 yydeviations |xx yy1 and |xx σ 2 respectively (Assumption 2). Quantity (2) can be estimated by εε εε εε εε σσ

> 17 We suppress the index for countries and FHH types to make notation less cluttered in this appendix.

> 18 While household heads may still increase their educational achievement in theory, this
i.e., rotating panels or split panels) and protocols for incorporating individuals not previously involved in the study to capture population dynamics. Based on tracking rules to follow the movers, the data collection stage should be designed to retrieve essential information on individuals or households that have dropped out of the sample. The use of flexible modes of data collection (e.g., telephone interviews) makes it possible to reach those who may not respond to traditional techniques. Additionally, collecting a minimal set of variables on dropouts (e.g., by proxy or doorstep interview) can improve the quality of the survey estimates. The estimation stage considers dropouts, movers, new entrants, and the dynamics of the target population over time. This is achieved by defining a weighting procedure that updates the direct sampling weights to account for new individuals in panel households and by using calibration estimators with upto-date known population totals.

This work proposes to improve the quality of panel surveys by considering the three aspects of survey design outlined above. We consider the estimation at the current time of cross-sectional and longitudinal parameters of a target population. Estimates are computed from sample data collected at the current time on individuals who entered the sample previously and were subsequently followed according to the rules of longitudinal observation considered in the survey. This approach clarifies many aspects of the representativeness of data collected in panel studies. It applies to the many cases that characterize current large-scale household panel surveys (e.g., EU SILC, LSMS, etc.) and can be easily extended to more complex issues in actual survey situations.

The paper is structured as follows: Section 2 gives a formal definition of longitudinal and crosssectional populations and the target parameters to be estimated in longitudinal studies. Section 3 presents the sampling framework and the estimator based on multisource (Singh and Mecatti 2011) and indirect sampling (Lavallée 2007; Falorsi, Righi and Lavallée 2019), which addresses 2
for urban programs; and (b) the potential for external‐domestic partnerships to respond effectively to the urbanization challenge.

# 1. <u>Urban aid and aid effectiveness</u>

Figuring out how much aid goes to fund projects in cities turns out to be difficult, since readily available aid data do not permit a simple identification of urban aid. Kharas et al. (2010) have combed through the available data and with some clever assumptions have calculated that about US$ 1 billion per year or less than 1 percent of overall official development assistance (ODA, equal to about US$120 billion per year) goes to urban projects.<sup>19</sup> This is a quarter of what has been estimated is needed to meet the urban slum reduction target under the MDGs (US$4 billion) and

> 18 This section is based on Kharas et al. (2010)

> 19 Note that ODA numbers do not include the lending by the multilateral development banks (the World Bank and regional development banks) for urban projects in middle‐income countries. No estimate of how much this amounts to is available at this time.

19
collaborative institutions built between regional governments and businesses that can serve to alert authorities to problems (McCubbins and Schwartz 1984; McNollGast 1987). Examples of such “fire alarm” institutions can include regular meeting between regional governors and business representatives, consultative groups attached to regional government institutions, etc.

While a great deal of work has been done on such institutions in the context of developed countries, analysis of existing institutions of this nature in the Russian context, their roles, and their ultimate effect on the reduction of uncertainty in the business environment is a potentially valuable area for future research. Our results also suggest that generally greater oversight of local officials by higher level authorities might also be helpful, although centralizing power may not restrain lower level bureaucrats if the logic of such centralization is mainly political ( _c.f._ Magaloni 2008; Reuter 2016).

Secondly, there is still large variation in the intensity of raiders’ attacks on businesses in Russia’s regions. Since law enforcement agencies are directly subordinate to the central Federal Government, however, this problem cannot be resolved at the regional administrative level. Despite the nominal primacy of the federal government, however, the problem of raider attacks has resisted efforts by the federal government and the President since at least 2009. The failed experiences in resolving this problem suggest that simple solutions, such as amending existing laws or establishing new control bodies, are ineffective. We believe that the problem can only be solved though deeper institutional changes to the incentive structures of law enforcement agencies themselves.

One promising change would be to modify the ways in which law enforcement officials are evaluated for promotions to better incentivize them to protect the property rights of businesses. For example, data on raider’s attacks could be used to define “problematic” and “best practice” regions for investment. Using this data as a guideline, human resources policy could be modified to reward officials in the “best practices” regions and punish those in the “problematic” ones, as well as to reward
**Figure 3: Probability to die between 1975 and 1979, by birth cohort and gender. From siblings' mortality data, DHS Cambodia 2000.**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>Males<br>Females<br>0.1<br>0.05<br>0<br>30-34 25-29 20-24 15-19 10 to 14 5 to 9 0 to 4 Not yet born<br>Age in January 1975<br>Probability to die<br><!-- End of picture text --> 23
enough sample of comparable data, we consider improved sanitation facilities as adequate even if they might be shared between households.<sup>3</sup> Facilities such as pit latrines without a slab or platform, hanging latrines or bucket latrines are considered unimproved (JMP 2020, Annex 1).

With regards to adequate living space (3), we follow the widely used definition of over-crowding as a <mark>condition where the number of occupants exceeds the capacity of the housing unit’s space.</mark> We adhere to a rather broad operationalization of over-crowding to ensure comparability across countries and regions, where different norms on e.g., children of opposite genders occupying the same room might exist.<sup>4</sup> We consider housing units to be over-crowded if there are <mark>less than 9 square meters per person or if more than three people occupy a room (</mark> cf. Blau et al. 2019; Gove et al. 1983; UN-Habitat 2007).

Regarding durable material and good structural quality (4), we consider the type of material used to build walls, roofs, and floors. While the categorization of material varies largely by region, country, and year in HH surveys, the various materials can be harmonized into durable or nondurable materials. We categorize housing material to be adequate if durable or finished materials such as burnt bricks, tiles, shingles, iron sheets, or polished wood are used. Unfinished, rudimentary, or natural materials include cane, straw, grass, or bamboo. We assume that an HH does live in an adequate house if all three materials for wall, roof, and floor are durable.

With respect to security of tenure (5), approaches to operationalize and measure are manifold, particularly across regions. This diversity is also reflected in the various coding schemes applied across various data sources. While there have been recent calls for a globally comparable (land) tenure module to be integrated into
Table 1 – Descriptive Statistics for Main Variables in Manufacturing Panel Data

||**N (Obs.)**|**Mean**|**s.d.**|
|---|---|---|---|
|**Avg # of Firms in Woreda**|1,916|10|40|
|**Avg Capitalper worker in Woreda**|1,916|860,107|3,698,632|
|**Avg Sales in Woreda**|1,916|252,053,444|968,001,530|
|**Avg Value of Production in Woreda**|1,916|249,461,183|954,237,179|
|**Avg Employment at in Woreda**|1,916|877|3,746|
|**Avg Book Value of Fixed Assets in Woreda**|1,916|94,437,524|331,522,005|
|**Avg Materials in Woreda**|1,916|127,692,617|542,969,363|
|**Avg Export Sales Value in Woreda**|1,916|15,636,992|105,203,671|
|**Avg Transport Cost in Woreda**|1,916|4,091,011|15,846,122|
|**Avg # of Exporter Firms in Woreda**|1,916|0.5|2.0|
|**Avg Age of Firms in Woreda**|1,912|19.8|10.7|
|**Avg Labor Productivity in Woreda**|1,916|228,403|335,585|
|**Avg Markup Materials ACF Cobb-Douglas**|1,916|2.0|1.0|
|*
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224||0.297||-0.744||-0.137||-0.946||-0.534||
||(0.577)||(0.566)||(0.697)||(0.747)||(0.606)||(0.595)||
|N|615||615||583||490||615||615||
|Pseudo-R2|0.108||0.127||0.331||0.331||0.085||0.096||

_Notes:_ Robust, clustered standard errors in parenthesis. * p<0.10; ** p<0.05; *** p<0.01. _Source:_ VUP household survey, 2009, 2011, 2014.

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Overall, the results presented in this section lend support to the efficiency focus among finance training recipients and the growth focus among marketing training recipients.

# **5.4. Which Business Training and for Whom?**

The analysis next presents statistical tests for Hypotheses 3 and 4 developed in Section 2. Specifically, we test whether the growth focus of marketing training is more valuable for firms with narrow exposure, and whether the efficiency focus of finance training is more valuable for larger, more established firms. Business exposure and establishment are composite variables that were constructed based on questions in the baseline survey. The full set of these questions and their summary statistics are reported in Table 1 and discussed in Section 4.1.

Table 11 presents the regression results. Columns (1) and (2) present heterogeneous treatment effects based on business establishment, and Columns (3) and (4) present results based on business exposure. The results show that the finance training did not improve business profits for firms with a below median score on the composite measure of business establishment. However, in contrast, the interaction term is strongly positive and statistically significant, showing that businesses with above median scores benefited significantly from the finance training. In terms of magnitude, the treatment effect for more established firms is equivalent to a 68% increase in business profits over the control group or a 0.35 standard deviation improvement.

In line with an efficiency focus explanation, these results show that small firm owners who have been running more established businesses prior to training tend to achieve greater profit gains when they receive the finance and accounting training program. Building finance skills is particularly helpful for firms who have reached a minimum threshold in terms of scale, and so the finance and accounting skills developed by the owner can actually be put into practice to reduce costs and increase efficiencies in the business, thereby increasing profits and improving bottom line performance.

In terms of business exposure, Column (4) of Table 11 shows that firms with narrow exposure
sample reported telling stories to children during the past week at baseline, and less than half took children out for a walk, though other activities such as singing (63.6%) and playing (73.1%) were more common. Overall, of 12 included activities, the households reported pursuing only 2.6 on average with their two youngest children in the past week.

# **5.2 Balance across Arms**

Table 1a describes the sample and provides full balance statistics, while Table 1b provides balance statistics for the population sampled for the MDAT (test of socio-cognitive development). Note the generally balanced means across the evaluation groups, indicating that random assignment was successful.

> 19 We have used Ordinary Least Squares (OLS) estimates in all cases, preferring a linear probability model in the case of binary outcomes for simplicity of interpretation.

> 20 Pre-analysis plan registered at the American Economic Association and available at

> <u>https://www.socialscienceregistry.org/trials/957/history/6111.</u> 21 Rice consumption and storage is a salient feature of rural Malagasy society, one reason for the difficulty of changing nutrition practices (Michel & Randriamanampisoa 2017).

> 22 In addition, in most households agricultural production is consumed by the household rather than sold. People generally tend to consume all items of production (on average 3 items) but only sell surplus for 1 item on average.

11