rafmacalaba/data-use-mentions-extended huggingface dataset:
30 post-crisis periods. During the pre-crisis period, policy adjustments may “undershoot”, since institutions constrain policy adjustments in response to pressures for change. Conversely, during the post-crisis period, policy adjustments may “overshoot.” The latter phenomenon seems to have occurred, for example, in the transition countries of Eastern Europe, where extreme liberalization of trade policies in the early 1990s—in the wake of the dramatic changes in institutions and politics—largely eliminated agricultural market and trade distortions. As the future unfolded, however, in the mid-1990s, protection in this region gradually increased toward moderate levels (Anderson and Swinnen 2010).

Recent analysis from the World Bank data set on NRAs, as well as the OECD reporting of PSEs, has revealed declining trends for high-income countries. These recent trends for this group of countries do not necessarily reflect actual changes in their distorting policy instruments. Instead, higher world food prices largely explain these outcomes. This result is entirely consistent with the “countercyclical bias” that we have assessed in Subsection 3.1. Regardless, the more recent biofuel and crop-insurance policies in the United States and other high-income countries have reversed the historical coupled-subsidy agricultural policies and their ultimate world price–depressing effects (Tyers and Anderson 1992; Rausser and de Gorter 2012).<sup>41</sup> The recent shift in agricultural policies focusing on renewable energy has major implications for world food prices and security. The trade-offs between food and fuel continue to be examined (e.g., Rajagopal et al. 2009; Chakravorty, Hubert, and Nøstbakken 2009). These policy shifts also have implications for the subsidies that result from current farminsurance programs.<sup>42</sup> However, unraveling these recent trends and the higher world food prices also establishes a causal connection with recent focus on biofuel and corn-ethanol energy production (Rausser and de Gorter 2012). In the case of at least the world corn commodity market, the small-country assumption invoked in the calculation of NRAs in the
_achievement of the millennium development goals (MDGs) and other internationally-agreed development goals_ [Technical report]. https://digitallibrary.un.org/record/840707?ln=en

- ---. <mark>(2022). Human Development Report 2021-22.</mark> _<mark>Human Development Reports</mark>_ <mark>.</mark>

- ---. (2023). Sustainable Development Goals no. 17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development. <u>https://sdgs.un.org/goals/goal17</u>

- Visser, H., Bruin, S., Martens, A., Knoop, J., & Ligtvoet, W. (2020). What users of global risk indicators should know. _Global Environmental Change_ , _62_ , 102068.

- <mark>Wolf, M. J., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A.,</mark> _<mark>et al.</mark>_ <mark>(2022).</mark> _<mark>2022 Environmental Performance Index.</mark>_ <mark>New Haven, CT: Yale Center for Environmental Law & Policy.</mark>

- Wollburg, P., Contreras, I., Carletto, C., Morales, L. G., Perucci, F., & Zezza, A. (2022). The uneven effects of the COVID-19 pandemic on national statistical offices. _Statistical Journal of the IAOS_ , _38_ (3), 785–803.

World Bank. (2020). _Statistical capacity indicators_ .

<u>https://datatopics.worldbank.org/statisticalcapacity/</u>

- ---. (2021). _World development report 2021: Data for better
# **1 Introduction**

The collapse of the Rana Plaza factory building in Bangladesh in April 2013 killed over a thousand workers and injured about 2,500 others. It is widely considered the worst accident in the history of the global garment industry, and the world’s worst industrial disaster since the 1984 Bhopal gas tragedy in India (Reuters, 2017). In its aftermath, local and international attention on the ready-made garment (RMG) sector intensified, resulting in a series of new policies and changes to the behavior of retailers. Direct reforms included an increase in the minimum wage and highprofile but voluntary audits examining safety. Indirectly, retailers may have pressured factories to improve working conditions or have become hesitant to buy from factories that subcontract or that appear to be skirting labor regulations. While the intended goal was to improve working conditions and raise wages, a concern arose that reforms which raise costs among garment factories could ultimately hurt workers.

In this paper, we evaluate the net effects of these post-Rana Plaza changes on workers in the RMG sector in Bangladesh. We use six rounds of the Bangladesh Labor Force Survey, spanning from 2003 to 2016, and employ a triple-difference estimation strategy that compares workers in the garment sector to workers in other manufacturing industries, in four “treated” districts whose garment factories comprise 99% of garment exports versus the rest of Bangladesh, pre versus – post Rana Plaza. We examine four key outcomes wages, an index of working conditions, hours – of work, and whether the worker receives a written contract and test parallel trends for each outcome. While we generally fail to reject the null hypothesis of divergent trends among garment workers in treated districts (compared to non-treated districts) pre Rana Plaza, we caution that the results for contracts should be viewed as suggestive because the test there is lower-powered and some of the point estimates are large. Each survey wave was collected in all four quarters of the year, allowing us to separately examine short-term effects (in the remainder of 2013)
been only one study to our knowledge (Gosavi, 2015) that has utilized firm-level data that has been representative of the private sector and allows for cross-country comparisons. However that study explores the adoption of mobile money and does not explore the relationship between mobile money use and firm outcomes.

The goal of this study is to empirically investigate the relationship between the use of mobile money and private investment using firm-level data in three countries in East Africa –Kenya, Tanzania and Uganda. This study adds to the literature in the following ways: (i) it is one of the first studies to explore mobile money use by firms and the likelihood of investments, (ii) it uses nationally-representative firm-level data that follow a consistent methodology across countries allowing for cross-country comparisons, which is a rarity in the literature, and (iii) it establishes a positive relationship between mobile money use and investment that is robust across a number of specifications as well as different measures of mobile money use. The study finds that an increase in the adoption of mobile money leads to a 16 percent increase in the probability of investment by the firm. A further in-depth analysis is conducted of the different purposes of mobile money use and their relationship with the likelihood of investment. The intensity of mobile money use by each type of purpose and the link with firms’ decision to invest is also explored. The findings have important implications for policy makers aiming to improve private investment in East Africa.

The paper is organized as follows. The conceptual framework, including theoretical underpinnings of mobile money use and the state of the empirical evidence is presented in section 2. Section 3 describes the empirical strategy, section 4 presents the main results and section 5 concludes.

# **2. Conceptual Framework**

From a theoretical standpoint the relevance of mobile money and its effect on investment can only be explained by relaxing the assumption of perfect capital markets in traditional investment models. In the neoclassical investment model, for example, in equilibrium investment equals the rate of depreciation, and
dest. region and year_||_yes_||
|_Controls HS-2, Laoprovince, dest. country and month_|||_yes_|
|N|13986|9881|8454|

_* p<0.10 ** p<0.05 *** p<0.01. Source: Own calculations based on Lao PDR customs data. Model (2) includes controls for sector, Lao region, world region and year. Model (3) includes controls for HS-2_ 23
Payton (2010). ―Export Promotion Agencies Revisited,‖ _Journal of Development Economics_ 91, 257-265.

Lederman, D., Rodríguez-Clare, A., and D. Xu (2011). ―Entrepreneurship and the extensive margin in export growth: a microeconomic accounting of Costa Rica's export growth during 1997-2007,‖ _World Bank Economic Review_ 25, 543-561.

Lopez-Acevedo, G. and M. Tinajero (2010). ―Mexico: Impact Evaluation of SME Programs using Panel Firm Data,‖ World Bank Policy Research Working Paper 5186.

Manski, C. (1997). ―The Mixing Problem in Programme Evaluation,‖ _Review of Economic Studies_ 64, 537-553.

McKenzie, D. (2010). ―Impact Assessments in Finance and Private-Sector Development: What Have We Learned and What Should We Learn?‖ _World Bank Research Observer_ 25, 209-233.

41
potential and less contribution to the economy. Our findings provide several ways for policy makers to reduce the productivity gap and improve the labor productivity of female-owned firms. That is, increasing the human capital of women entrepreneurs in the informal sector may help their businesses catch up with businesses owned by men. Enabling women-owned informal businesses to sell under contract, reducing the degree of informality, may further raise their productivity. Furthermore, we find that most women-owned businesses do wish to register. This implies that there is room to reduce informality if governments embrace policies that provide the right incentives to formalize.

To summarize, this is the first study we are aware of that decomposes the gender labor productivity gap across informal firms. Several contributors to these gaps are identified, with the magnitude of these contributions estimated. These findings provide concrete policy recommendations to aid women-owned informal businesses. The rest of the paper is structured as follows. Section 2 describes the data and variables used. Section 3 provides the decomposition methodology. Difference in the level of resources used by women- and men-owned firms is provided in section 4 along with the difference in the impact of the resources on labor productivity. Section 5 contains our main decomposition results based on the Oaxaca-Blinder methodology. Section 6 summarizes the main findings and suggests scope for policy intervention and future research in the area.

# **2. Data and Main Variables**

Our data source is the Informal Enterprise Surveys (IES) that is a specialized survey for informal businesses defined as those that are unregistered with the relevant authorities in the country. The data we use consists of a random sample of 2,759 informal or unregistered firms collected by the World Bank’s Enterprise Surveys team in 55 cities or regions in 14 countries in Africa, Asia, and 8
Table 1: Dependent Variables

|**Variable**|**Acronym**|**Unit**|**Expected Impact of**<br>**Renewable Energy Policies**|
|---|---|---|---|
|Fossil fuel energy (oil, gas & coal)<br>consumption|FCC|Percent of total|Negative|
|Electricity production from fossil<br>fuel(oil,gas & coal)sources|EFF|Percent of total electricity<br>output|Negative|
|Electricity production from oil<br>sources|EOS|Percent of total electricity<br>output|Negative|
|Renewable energy consumption|REC|Percent of total final<br>energyconsumption|Positive|
|Renewable electricity output|REO|Percent of total electricity<br>output|Positive|

_Source: World Bank World Development Indicators (WDI) database, 2020._

# 3.2 EXPLANATORY VARIABLES

Our explanatory variables come from the 2018 disaggregated policy instrument data behind the Regulatory Indicators for Sustainable Energy (RISE) renewable energy "traffic light" indicators that the Energy Sector Management Assistance Program (ESMAP) at the World Bank has published since 2010. Concretely, our independent variables are indexes that represent seven energy policy packages. Each energy policy package comprises the disaggregated policy instruments in the RISE data. In this section, we describe the primary RISE dataset and the three alternative methods we designed to reduce the dimensionality of the dataset and create our independent variables indexes.

In the primary RISE dataset, each policy is coded in two variables: one indicating whether the policy exists through yes/no answer, and the second specifying the year of the first instance of the policy, if applicable.<sup>3</sup> When restructuring the dataset, we combine the two variables for each policy instrument into one. Its value changed from 0 to 1 when the first policy was put into place,
Brazil’s level of economic development (World Bank, 2007; Deaton and Dupriez, 2011). The largest differences in terms of urban-rural as well as regional differences in prices are observed in the cost of rent.

Finally, using the spatial and temporal deflated welfare aggregate, we use the population in the bottom 40 percent as the reference group for the estimation of the food poverty line. This group is selected based on the expectation that it includes the poor and vulnerable sectors of the Brazilian population. By using this group and its consumption patterns, our approach aims to reflect how much necessities consumed by the less well-off cost. Incidentally, it is worth noting that the calorie mapping described above yields a projected average daily caloric intake of 2,368kcal per capita. The estimated average for the bottom 40 percent is 1,243kcal per capita per day. This further reflects the level of destitution of the reference group chosen.<sup>15</sup>

> 14 To calculate the rent price Paasche index, we used the same approximation as in the calculation of the food price index (equation 4).

> 15 This back of the envelope calculation is done as follows. Daily caloric intake is calculated based on the caloric intake assignment to quantities of food items purchased, using our main calorie mapping specification. We complement this with an extrapolation of the calorie intake to account for unassigned expenditures (coming from a small share of food consumed at home and away from home).

13
in the country at the commune-month level, between January 2018 and August 2020.<sup>9</sup> Each household business is responsible to file and pay one annual lump-sum tax within a fiscal year. We use the (change in) tax filing numbers as a proxy for the (changes in) number of tax-registered household businesses each month. For brevity, we refer to this data set as the “household business tax census” (“HBTC”). Beyond the regular annual filings reported in January each year, the tax portal also keeps separate retrievable records of several types of irregular filings. We focus on two such indicators directly related to firm exit: the number of tax fillings for (i) permanent business closure, and (ii) temporary suspension. While the former provides a measure of firm survival

> 6See Article 1, Decree 92/2015/ND-CP.

> 7The license tax is a fixed proportion based on revenue brackets. VAT and PIT rates are set by the central government and vary by revenue and industry. See Le et al. (2020) for further details on household business tax information.

> 8The link to the online portal is `http://www.gdt.gov.vn/wps/portal/home/hct` (accessed January 2022). The records can be retrieved by querying an administrative location up to the commune level (a third-tier administrative unit).

> 9While several provincial tax offices have published household businesses tax records until the end of 2021, there are currently missing records for many provinces after August 2020. To our knowledge, each provincial tax office is responsible for updating the database with monthly tax filings in their province. This results in a heterogeneous lagged reporting times across provinces.

4
country analysis."<sup>6</sup> But it is also an obstacle to econometric estimation of the impact of liberalization on a cross-country basis, as knowledge of the timing is all but indispensable.

An alternative is to try to infer the timing of key aspects of liberalization from the statistical properties of the interest rate data themselves."<sup>7</sup> Two classic approaches to measuring the degree of external capital account liberalization by the looking at interest rate and other macroeconomic time series have been proposed by Edwards (1985) and Edwards and Khan (1985), where interest rate data is available, and by Haque and Montiel (1991) where interest rate data is not available. Each of these approaches assumes that the effective interest rate is a weighted average of that which would prevail in fully controlled and uncontrolled regimes respectively; the estimated weiglht then becomes an indicator variable representing the _degree to_ which the domestic money market is open and uncontrolled. They could be adapted to allow some tiime-variation in the indicator, and hence in principle to identify a liberalization date. While both approaches thus offer elegant solutions to the problem at hand, they have the important shortcoming that each assumes that uncovered interest parity UIP prevails in an uncontrolled market. The well-known fact, that UIP is empirically questionable even for countries without any form of foreign exchange control, mars the use of this as an identifying assumption. There are also difficulties with the specification of equilibrium in the controlled market. Further commentary on these approaches, together with some discussion of possible alternatives, is included as Appendix 1.

A simpler approach that does not rely on UIP depends instead on the assumption that the short-run dynamic behavior of interest rates changes with liberalization.

'5 Thus it is not surprising to find, for example, that a recent study's table presenting just 27 liberalization dates had to be accompanied by two-and-a-half pages of qualifying notes (Galbis, 1993).

> 16 As illustrated by several contributions to the present research (Cho
“shifted” coefficients are smaller than the “true” coefficient, plotted at 0‐3, and are all statistically insignificant.

The second test involves replacing the upstream pollution variable with a falsified value using pollution data from the nearest off‐river region farther downstream—a location that is disconnected from river flow dynamics and from where the pollution cannot flow (upstream) to areas where the health outcomes are measured. In the case that the ‘falsified’ upstream pollution variable shows a significant impact on health, then it would be likely that our baseline results are capturing spurious spatial correlations. Table 4, however, reveals otherwise. There is no significant impact of the falsified value on health, suggesting that the upstream variable utilized in the analysis is indeed isolating quasi‐random variation in pollution.

In Table 5, in addition to the district fixed effect, we also include district time trends to address the concern that broad secular trends at the district level might be influencing our results. The results are of the same sign and magnitude as our baseline estimates, and remain significant at the 5 percent level.

In Table 6, we cluster the standard errors by state in DHS, as well as survey cluster in DHS, instead of district. Standard errors are more or less similar using either of these alternatives, and the results remain significant and unchanged.

# 6. Evidence from Other Regions

In Vietnam, home to one of the fastest‐growing and urbanizing societies in the world, agricultural growth and intensification have played significant roles in spurring development. But in parts of the country, the environmental footprint of the agricultural sector is deepening. In intensively farmed areas, agriculture has become a significant contributor to water pollution. This is particularly so in the intensively farmed Mekong delta region (Cassou, Jaffee, and Ru 2018; Chea, Grenouillet, and Lek 2016). To measure the consequences of nitrogen pollution, the analysis focuses on children aged four to twelve years surveyed in the latest Living Standards Survey of 1997–98
19 Could greater supply assurances from food-surplus countries, in the form of stronger disciplines on export restrictions, provide a Doha breakthrough? Potentially it could reduce the need for an SSM, which has been one of the more contentious issues in the Doha talks and the one that triggered their suspension in mid-2008. But more than that, it could reduce the concerns food-deficit countries have over relying on food imports in general, thereby increasing the chances of lowering not only the variance of but also the mean NRAs of those countries.

# **References**

- Aksoy, M.A. and B. Hoekman (eds.) (2010), _Food Prices and Rural Poverty,_ London: Centre for Economic Policy Research for the World Bank.

- Anderson, K. (ed.) (2009), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

- Anderson, K. (ed.) (2010), _The Political Economy of Agricultural Price Distortions_ ,

- Cambridge and New York: Cambridge University Press.

- Anderson, K., J. Cockburn and W. Martin (eds.) (2010), _Agricultural Price Distortions,_

- _Inequality and Poverty,_ Washington DC: World Bank.

- Anderson, K. and J.L. Croser (2009), _National and Global Agricultural Trade and Welfare Reduction Indexes, 1955 to 2007,_ database at <u>www.worldbank.org/agdistortions.</u>

- Anderson, K. and E. Valenzuela (2008), _Global Estimates of Distortions to Agricultural_

- _Incentives, 1955 to 2007_ , data spreadsheets at <u>www.worldbank.org/agdistortions</u>

- Bevan, D., P. Collier and J.W. Gunning (1990), _Controlled Open Economies: A Neoclassical_

- _Approach to Structuralism,_ Oxford: Clarendon Press.
Table 2. Estimating the effect of percentage changes in domestic food prices on changes in the $1.25/day and $2/day poverty headcounts in robust and IV GMM models

|Regression Number|1|2|3|4|
|---|---|---|---|---|
|Poverty line for headcount measure|$1.25/day|$1.25/day|$2/day|$2/day|
|Estimator|OLS|IV GMM|OLS|IV GMM|
|Change in log of domestic food prices|-19.27**|-81.67**|-17.65***|-71.87**|
||(9.24)|(40.10)|(7.48)|(36.09)|
|N|302|300|313|310|
|Tests|||||
|Kleibergen-Paap rk Wald F statistic||12.20||13.68|
|Kleibergen-Paap rk LM statistic||0.01||0.01|
|Stock-Wright LM statistic||0.04||0.04|

_Source_ : Author’s estimates from World Bank (2013) poverty data and ILO (2013) food price data.

_Notes_ : See text for definitions of the variables. *, **, and *** indicate significance at the 10%, 5% and 1% levels, respectively. Standard errors clustered at the country level are reported in parentheses. For the IV GMM, errors are clustered at the country level and the excluded instrument is the change in international prices (see section III for details). All regressions control for time dummies. The null hypothesis of the Hausman tests is that the variable under consideration can be treated as exogenous. The Kleibergen-Paap rk Wald F statistic measures weak instruments, with critical values varying between
4.2<br>Eswatini Namibia 4<br>Angola Cote d'Ivoire Ghana Cabo Verde 3.8<br>Nigeria Mauritania Kenya<br>ComorosGuinea-BissauSudan ZimbabweCameroonCongo, Rep.Guinea MaliEthiopiaZambiaTogoSao Tome and PrincipeBeninBurkina FasoTanzaniaLesothoGambia, TheSenegalUga... Rwanda 3.63.4<br>Somalia South Sudan Congo, Dem. Rep.Eritrea Chad LiberiaMozambiqueMadagascarSierra Leone Niger Malawi 3.2<br>Central African Republic 3<br>Burundi<br>2.8<br>-2.40 -1.90 -1.40 -0.90 Rule of law -0.40 0.10 0.60<br>log GDP pc PPP<br>log GDP pc PPP<br><!-- End of picture text --> <mark>Note: The x-axis represents the World Governance Indicator on rule of law. Oil rich economies are represented in yellow; non-oil but resourcerich economies in green; non-resource rich countries in blue. The data is the latest available (2021). Source: WGI database for rule of law indicator and WDI database for database for GDP per capita.</mark> 3