rafmacalaba/data-use-mentions-extended huggingface dataset:
To calculate a cost per calorie for each food item, we assign caloric intake values to the quantities purchased, as reported in POF. The TBCA contains the caloric information for a myriad of items, but there are no common food item identifiers with the POF 2017/18 that would allow directly matching the information across the two data sources. Thus, we use the food item description in POF to look up corresponding items and calorie intake per kilogram (kg) in TBCA. Given the large amount of individual food items in POF (4,549) and the fact that their contribution to total average consumption differs, we opted for a simplified approach to assign calorie intake values from TBCA to food consumption in POF. This approach can be divided into two main steps. In the first step, we identify “key food items” in the Brazilian consumption basket. These items are identified as having the highest share in average total expenditures on food consumed at home. We rank all food items in POF 2017/18 and find 350 food items that together account for about three-quarters (73.3 percent) of average total expenditures on food consumed at home and about half (49.2 percent) of all expenditures on food. For each of these food items we obtain calorie intake values from TBCA that are based on the descriptions of the food item in POF and TBCA. TBCA has calorie intake values for different preparation forms of the food item. We use calorie intake for the raw state, given that it is not clear how households will prepare the purchased food and that they buy it in the raw form.<sup>9</sup> A second step is applied in order to increase the list of food items that have calorie intake assigned to them—and thus contribute to the cost per calorie estimation. In this step, we create food groups by aggregating POF’s original 7-digit item code to a 5-digit item code. Based on this, we impute calorie intake to items that were
# **1. Introduction**

Most firms in developing countries are informal, that is they operate without registering with the government (OECD, 2009, and data from IFC, 2010). This can pose disadvantages to firms since they may be subject to government penalties and they may not have access to low cost sources of financing, government contracts, and public contract enforcement (Jansson and Chalmers, 2001). It can also limit firms’ market size since they cannot issue formal receipts to customers (McKenzie and Sakho, 2010). From the government’s perspective, informality may result in lower tax collection, restricting the government’s ability to finance public services (Levy, 2008).

Historically, different views have been put forward to explain why many firms operate informally. One view, associated with De Soto (1989), argues that informal business owners are viable entrepreneurs who are being held back from registering their firm due to complex regulation. This regulation includes the initial procedures for obtaining an operating license, as well as ongoing compliance costs for registered firms, such as taxes and labor contributions. Another view, expressed for example by Tokman (1992), sees informal business owners as individuals who are trying to make a living while they search for a wage job.

Several papers have developed theoretical models supporting either view<sup>2</sup> . Other papers examine empirically which view is correct, leading to different conclusions (Maloney, 1999 and 2004, and La Porta and Shleifer, 2008 and 2011). Recently, some have emphasized that informal firms are heterogeneous and that a mix of both views may be correct (OECD, 2009). Selfreported statistics support this mixed view. In World Bank Enterprise Survey data on informal firms in Madagascar, Côte d’Ivoire, and Mauritius, about 62 percent of business owners report that they started their firm to take advantage of a business opportunity, while the remainder says they were not able to find a satisfactory job elsewhere (Amin
we code exports as zero for firms that are closed, closure effectively results in replacing 0 exports for an open firm with 0 exports for a closed firm.

Panel B of Table E2 provides treatment estimates on monthly firm employment using the PILA. Column 1 and 2 show an impact on the level of employment of 0.49 workers relative to a control mean of 81 workers in 2019, and of 0.44 workers relative to a control mean of 79 workers. This effect is small in magnitude (0.6% of the control mean), and not statistically significant. Column 3 restricts to the sample of surviving firms, and again finds a small impact. The 95 percent confidence interval for the treatment impact in 2020 is (-3.5 workers, +4.4 workers), which includes changes of up to 5 percent in employment. Employment changes may take longer to manifest, but at least in the first two years, the program did not result in large changes in employment.

**Impact on Sales, Profits and Labor Productivity Using the RUES** After our pre-registration, we became aware of an additional source of administrative data on firms, which is the _Registro Único Empresarial_ (RUES), which contains data from firms’ annual renewal in the _Registro Mercantil_ (Mercantile Registry).<sup>13</sup> Colombia Productiva provided

> 13Our pre-analysis plan also said we would attempt to link the firms to the Encuesta Anual Manufacturera (Annual Manufacturing Survey, or EAM). These data are released with a considerable lag, and can only be accessed in a data lab in Colombia, with the pandemic making access to the lab more limited. We had a consultant work with these data, with only data up to the end of 2019 available. 181 firms were matched in the 2017 data, and 182 firms in the 2018 and 2019 data. The point estimates suggest a

58
Policy Research Working Paper 9498

# **Abstract**

This paper presents new high-frequency data on trade policy changes targeting medical and food products since the beginning of the COVID-19 pandemic, documenting how countries used trade policy instruments in response to the health crisis on a week-by-week basis. The data set reveals a rapid increase in trade policy activism in February and March 2020 in tandem with the rise in COVID-19 cases, but also uncovers extensive heterogeneity across countries in their use of trade policy and the types of measures used. Some countries acted to restrict exports and facilitate imports, others targeted only one of these margins, and many did not use trade policy at all. The observed heterogeneity suggests numerous research questions on the drivers of trade policy responses to COVID-19, the effects of these measures on trade and prices of critical products, and the role of trade agreements in influencing trade activism.

This paper is a product of the Macroeconomics, Trade and Investment 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 authors may be contacted at mruta@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
Sovacool, B.K. 2009. "The intermittency of wind, solar, and renewable electricity generators: Technical barrier or rhetorical excuse?" Utilities Policy 17: 288-96.

Tenenbaum, Bernard and Chris Greacen “On-Grid and Off-Grid Small Power Producers in Africa: Key Implementation Questions for Electricity Regulators” Washington DC: The World Bank, Africa Electrification Initiative (AEI).

Vagliasindi, Maria (2012) Revisiting Policy Options on Public Private Partnerships in the Power Sector, World Bank Study, forthcoming.

World Bank and E3 2010. "Creating Renewable Energy Ready Transmission Networks: A Survey of 14 Jurisdictions Highlights Emerging Lessons " Washington, DC: The World Bank and Energy and Environmental Economics, Inc.

25
the slope coefficient is equal to _σεj_<sup>2.The insightthattherandomwalkcomponentinincome</sup> implies a linearly increasing income dispersion over time is the basis of the estimation method used by several authors. For example, Carroll and Samwick (1997) estimate _σε_<sup>2by performing</sup> OLS regressions of the left-hand-side of (9) on _n_ . While the preceding example, with timeinvariant parameters, serves to illustrate the intuition underlying the estimation procedure, it should be clear that our exercise is more general in the sense that it allows for arbitrary time-variation in the income risk parameters.

# _II.4. Estimation using ENEU Data_

The preceding section provided a detailed description of a general econometric methodology that may be used to estimate time-variant income risk parameters given longitudinal data on individual incomes. We note here some additional issues that arise in applying this methodology to our data, with particular emphasis on the type of income risk accounted for by our estimation procedure.

In forming the sample analogs to the moment conditions (5), we use information on all individuals who are present in a given manufacturing industry in both time periods _t_ and _t_ + _n_ (with _n ≤_ 5) regardless of their employment status in any intermediate period. In doing so, we pick up shocks to workers who retain their jobs but experience income changes due to changes in their wage rates or the number of hours worked. Moreover, we also account for changes in income experienced by workers who have lost their job in period _t_ , but are re-employed in the same industry in some subsequent period _t_ + _n_ (with _n ≤_ 5), and this is true even if these workers are unemployed in any intermediate period. In particular, we do account for the long-term earnings losses of a large fraction of displaced workers, namely all those displaced workers who are re-employed in the same industry but have lost firm13
WPS3784

**Table 9. Impact of Quality-Driven Price Premium**

|Simulation: doubling<br>coffee price:|Headcount<br>P0|% change<br>in P0|Poverty<br>gap (P1)|Change in<br>P1|% change in<br>expenditure|% change<br>in income|
|---|---|---|---|---|---|---|
|**_baseline coffee farmers_**|**_41.88_**|**_0.00%_**|**_0.135_**|**_0.00%_**|**_0.00%_**|**_0.00%_**|
|50% of all coffee farmers|39.86|-4.82%|0.124|-8.25%|2.49%|4.85%|
|all large coffee farmers|41.15|-1.74%|0.131|-2.93%|1.34%|3.15%|
|all medium coffee farmers|<br>40.77|-2.65%|0.132|-2.79%|0.87%|1.92%|
|all medium and large<br>coffee farmers|40.05|-4.37%|0.128|-5.72%|2.21%|5.07%|
|small coffee farmers|39.23|-6.33%|0.120|-11.08%|2.93%|4.85%|

Source: Computed by authors from EICV data

**Table 10. Determinants** **<u>of the Probability of Being a Coffee Farmer (propensity
**7 Conclusion**

This paper discusses the identification-robust many and possibly weak moments estimation of the parameters of the New Keynesian Phillips curve, and applies this method to Moroccan data. The diagnostic criteria for weak identification, such as the concentration parameter, indicate that existing econometrics techniques perform poorly. The approach in this paper addresses several important econometrics issues. First, we use a regularization schema (TJIVE) to solve the problem of many weak instruments when we include more variables in the moments set. The use of many moments increases bias, but when regularization is introduced, the bias shrinks. Second, we implement a new _J_ test developed by Chao et al. (2014) to test the null hypothesis that the model is correctly specified. While our focus is on obtaining consistent coefficient estimates of the New Keynesian Phillips curve and testing the hypothesis that the model is well specified, our model provides a solid framework for developing near-term inflation projections.

Nevertheless, our analysis could be improved. Because of data limitations, we are forced to take the output gap as a proxy for the real marginal cost. An important extension would be to develop efficient algorithms for simulating the Bayesian posterior to correct for the proxy of the real marginal cost.

17
- 4.7 Support for gender equality in EAP is consistent with lower rates of domestic violence against women and legal contexts

There is a clear correlation between officials’ support for gender equality in schools and the average rate of domestic violence against women using WHO figures. This suggests that officials’ preferences for gender equality are consistent with lesser violence toward women. As can be seen in Figure 12, countries where officials tend to agree with the promotion of gender equality in schools also display lower rates of women reporting violence from their partner over their lifetime.

All EAP countries but Mongolia are in the upper-left corner, where support for gender equality is strong and domestic violence rates are lower than average. Mongolia is the only country where support for gender equality in schools is not as high among surveyed officials.

_Figure 12: Violence toward women and promotion of gender equality_ <!-- Start of picture text --> 4.8 Philippines<br>Lao PDR<br>4.7<br>4.6 Vietnam Peru<br>DRC<br>4.5<br>Ind...<br>4.4<br>Ghana Nigeria<br>4.3<br>Bangladesh<br>4.2 Mongolia<br>4.1 Pakistan<br>Uganda<br>4<br>0 10 20 30 40 50 60 70<br>Share of women 15-49 reporting violence from intimate partner over lifetime<br>"Strongly agrees, 1=strongly disagrees)<br>School should promote gender equality (5<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey and WHO, Global Database on the Prevalence of Violence Against Women.

There is also a strong correlation between officials’ level of support for gender equality in school and the overall legal and business contexts in the surveyed countries. In countries where the legal and business context is more favorable to women, officials are more
# **2.4.5 Other control variables**

A vector of control variables **zi** _,_ **t** includes several variables. First, we use the lagged domestic official sector holdings ratio _푑표푖,푡_ −1 as a control variable. Major central banks in advanced economies have been important players in the government bond markets, purchasing government bonds financed by the creation of central bank reserves through quantitative easing — which central banks have implemented to put downward pressure on interest rates when policy rates were at or near zero. The portfolio balance channel operates when the central bank’s bond purchases, which change the relative supply of assets held by the private sector, induce equilibrating changes in the relative yields.<sup>16</sup> In the United States, Gagnon et al. (2011) show a cumulative decline in the 10-year Treasury yields by about 91 basis points after announcements of quantitative easing. According to Joyce et al. (2011), the corresponding impacts in the United Kingdom is estimated to be 100 basis points.

Second, we add the lagged foreign official holdings ratio _푓표푖,푡_ −1 using data from Arslanalp and Tsuda (2014a) and Arslanalp and Tsuda (2014b). The U.S. long-term interest rates remained low in the mid-2000s despite increases in the federal funds rate, a phenomenon Alan Greenspan labeled as a “conundrum” (Greenspan (2005)). Bernanke (2005) hypothesized that a global saving glut – driven by net savings in Asia and oilexporting countries – lowered long-term interest rates through an accumulation of foreign exchange reserves. Warnock and Warnock (2009) show that a 12-month total of foreign flows of 1 percentage point of GDP is associated with a 40 basis point reduction. Beltran et al. (2013) find that a 10 percentage point increase in foreign official flows into and out of Treasuries lowers the five-year term premium by 135 basis points.

Third, following Gruber and Kamin (2012), we
# WPS3577

## **Testing Genuine Saving**

### Kirk Hamilton<sup>1</sup>

**Abstract.** The World Bank has been publishing estimates of adjusted net or ‘genuine’ saving since 1999. This measure of saving treats depletion of natural resources as a type of economic depreciation. Recent theoretical results relating growth in saving to growth in future consumption are used to provide a test of genuine saving using historical data – did measured genuine saving in 1976, for example, ‘predict’ the observed changes in consumption over subsequent decades? Four alternative measures of saving are tested econometrically. The worst measure, in terms of explained variation, is traditional net saving. Genuine saving adjusted to reflect population growth exhibits the worst fit with theory. Both gross saving and genuine saving perform better, with good concordance with theory, while genuine saving exhibits a moderate advantage in terms of goodness of fit.

### World Bank Policy Research Working Paper 3577, April 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._ 1 Environment Department, World Bank. The comments of Susana Ferreira are acknowledged with thanks.
aversion’. Many formal firms do not even _decide_ not to invest in fragile states: they know not to pose the question.

This uncertainty also affects the investors that a firm needs to attract. In the absence of an established industry, it is difficult for investors to evaluate the risk profile of an investment. Investors like to model risk based on data. But at the point at which pioneering firms need to invest, there are little or no data to guide their investment decisions. Again, the activities of the pioneering firms that do go ahead benefit the firms entering later because the risk premium of investors can be better calibrated by looking at the experiences of the pioneer firms.

# **_Who pioneers in fragile states?_**

The World Bank Enterprise Surveys (WBES) is a unique source of economic data to address this question. The WBES uses a pool of 131,000 firms from 139 developing countries, including many that are listed as fragile and conflict-affected. The data have been collected since 2006 using a harmonized methodology to ensure cross-country comparability.<sup>12</sup> Pioneering firms in the survey are identified as those that have innovated by introducing new products or services to the fragile state over the last three years. These include new products or services for an establishment’s main market, or a new or significantly improved process during the last three years. The new firms among the pioneers, which are of particular interest because of their financial needs, can be identified as firms that are less than five years old.

Since pioneer firms play such an important role in economic development, it is unsurprising that there is a clear relationship between the proportion of firms that are pioneering, and the income level that the country has reached. In states that are not fragile, this pattern emerges strongly: the proportion of firms that are pioneers is highest in low-income countries, where the opportunities for harnessing scale and specialization in production abound. Conversely, pioneers decline as income rises, and countries
spending at sub-national level.) A similar situation appears in government expenditure on education at both prefecture and sub-prefecture levels (Annex A figure A4).

At household level, the availability of public resources for health also differs across population groups according to their participation under the different health security schemes available in China. Figure 7 shows that since 2002, whilst China has introduced several major new schemes to benefit previously excluded population groups and make the allocation of public resources more equitable, the size of the allocations per capita is, as yet, far from equal. Similarly, in other social sectors, including old-age pensions and education, China has been introducing programs to target the poor trying to overcome the overall bias toward better off population groups. (10) Finally, catastrophic and out-of-pocket health expenditures continue to demonstrate considerable inequity in China. During 2003-08, as National Health Service Survey reports (11) show, incidence of catastrophic health spending among low-income households remained about 10 percent in rural areas and increased from 4 to 6 percent in urban areas (figure 8). Average out-ofpocket payment (after insurance reimbursement) for a single inpatient stay in 2008 was 50-70% of annual income per capita in rural areas, about twice the levels common in urban areas (figure 9). Moreover, to some extent, the funding increases for rural health care through the restored 3
or below appears to have lessened as a result of the intervention.

# **7.2 Qualitative Research**

We also collected qualitative data from 10 villages for the purposes of better understanding mechanisms of change. Because our main goal for this research was mechanism analysis, we selected 10 villages for qualitative research that were “on the regression line” in the sense that there was little improvement in

> 25We would expect civil society leaders to have higher levels of communication with superordinate political leaders, at least until a new equilibrium is established, and recall that our survey was only three months after the intervention ended.

34
while the joint effect of roads and electricity is positive for both groups, its magnitude is larger for women.

The findings imply that employment gains from investment in road and electricity infrastructure in Africa accrue to a greater degree among women than among men. However, in certain countries, the effects might be larger for men, as shown in the LSMS. Africa is undergoing a spectacular transition away from low-paid jobs towards skilled and better-paid occupations; our results show that this transition differs by gender.

# **6.4 Age group**

To analyze the differential employment effects of roads and electricity by age, we categorize individuals in age groups: 20-45 and 46-66 years old. Because of differences in occupational choices made by individuals in different age groups, the employment effect of infrastructure expansion might differ by age.

The IV results are reported in columns 7 and 8 of Table 7. For both samples, the results show greater net employment gains for the 20-45 age group than for the 45-66 age group. In locations with high access to electricity, brining road networks 10 km closer increases the likelihood of working to a greater degree among those in the younger group than those in the older group (+25.6 vs. +20 in the DHS sample, and +5.0 vs. -2.3 in the LSMS). In locations that have high access to roads, increasing proximity to 28