rafmacalaba/data-use-mentions-extended huggingface dataset:
(2014) found that when forecast errors are adjusted for the significant cross-country variations in average GDP growth rates and GDP growth volatility, differences in forecasting performance among OECD countries tend to diminish. From the late 1970s through the middle of the 2000s, the G7 economies' average absolute forecast error dropped. This is due in part to the moderation of GDP growth volatility. Shorter-term declines may also be a result of better data availability, particularly for near-term developments, and better forecasting methods. These findings are supported by Turner (2016), who also noted that the decrease in GDP growth volatility and possibly improved accessibility and timeliness of national accounts data and other hard indicators are likely to contribute to the trend improvement in current-year forecasting performance in OECD countries over the period 1971–2012. Therefore, we run an additional set of estimations including GDP growth volatility as a covariate, which is measured as the lagged rolling 10-year standard deviation of realized GDP growth rates. Growth volatility is correlated with a number of covariates, and therefore we include it in a separate sect of regressions to uncover what factors may be affecting forecast errors through the growth volatility channel. Additionally, equation 1 is re-estimated with forecast errors as the outcome variable ( F F ) to analyze forecast optimism. An additional adjustment is that the lagged log of change in export commodity prices ( ∆L ) is used in equation 2 instead of its absolute value in equation 1, to capture the direction AAF of the price shock (see equation 2). LLFFL LLFFppFFppAA F F 1 L L 2 L L L 3 ∆L + 4 I I 5 L L L 6 (2) ii,tt ii,tt−1 ii,tt−1 ii,tt AAF = αα+ ββ LLII + ββ LLFF + ββ LLFFL LLFFppFFppAA ii,tt ii,tt−1 ii,tt−1 tt ii,tt ββ FFppFFI IIL IIppF + ββ + ββ BBL mm + ττ + εε

# IV. Main Findings: Growth Volatility, Data Transparency and
0.101]||

Note: The value displayed for t-tests are the differences in the means across the groups. High-performing IPAs are identified based on the sectoral gravity model with country interaction effects (see section 4.1). IPA = Investment Promotion Agency. CRM = Customer relationship management. Green-highlighted fields indicate statistically significant differences between highperforming IPAs and other IPAs. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

# **6. Conclusion**

This paper brings together new data and analysis on sectoral FDI and IPA characteristics to analyze what characteristics make IPAs more or less effective in attracting foreign direct investment (FDI) into their home country. It does so by first creating a new data set on bilateral, sectoral FDI positions, using data from the US Bureau of Economic Analysis (BEA) and EUROSTAT’s BPM6 database for the years 2013 to 2018. This is combined with a recent WB-WAIPA survey to explore the effect of IPA sectoral targeting on inward FDI stocks for a sample of 36 middle- and high-income countries around the world.

Using a structural gravity model framework, the study finds that IPA sectoral targeting generally provides a significant positive effect on the sector’s FDI stock in that country. The most conversative, and preferred, estimate is from a PPML model that includes country-pair fixed effects, source-, host-, and sector-year fixed effects, and host-sector fixed effects. This suggests that an IPA’s targeting of a specific sector is 23
sector and encourages seed companies to develop a profitable portfolio of products—maize hybrids with different traits such as yield, time to maturity, and resistance to biotic and abiotic stresses—and then market them directly to farmers.

A related explanation for the differences in crop-specific outcomes stems from the historical evolution of DSM in Ethiopia. Even before the introduction of DSM, there was considerable accumulated experience in maize seed production and marketing, along with clear evidence of farmers’ preference for high-quality traits and seeds. While similar experience in wheat seed production also existed in Ethiopia alongside farmer demand for similarly high-quality traits and seeds, willingness to pay for such attributes may have been significantly lower due to farmers’ ability to save seed across multiple years without significant genetic deterioration, their access to traditional farmer-to-farmer exchange mechanisms, and other such factors. This has an intrinsic influence on how private firms view the opportunities in seed markets beyond hybrid maize and may explain the differences in our findings.

In fact, data on the DSM approach indicate that the initial emphasis was placed exclusively on maize beginning in 2011, with the expansion to wheat following in 2013 (Figure 4). The resulting supply response for maize occurred more rapidly than for wheat: whereas maize seed production increased steadily across the nine years for which we have data, wheat seed supply remained low until an inflection point emerged in 2018. Several factors might explain these different supply responses.

This delay may be attributable to the adjustment time required for private firms—most of which were likely more experienced with the production and marketing of maize than with wheat seed, or had fixed investments in land, capital, and technical expertise more suited to maize than wheat— to assess and respond to increased demand and changes in demand for specific traits. Adjustment times in seed production are particularly challenging because seed multiplication and bulking processes can take several years for a new variety and depend on the availability and quality of early-generation seed. This challenge is especially pronounced in Ethiopia, where the limited availability and use of irrigation
coverage, and then validated it against the standard aggregated public finance data source in Brazil.

We then illustrated the potential uses of this new data in two applications, uncovering new facts about local public finances in Brazil that are only measurable using the granular data we provide. First, we show that, on average, municipalities purchase 15%-25% of their goods and materials from suppliers located in the same municipality. Furthermore, we document wide variation behind that average - several municipalities purchase close to 50% from local suppliers. This average share is not meaningfully different when local officers use noncompetitive, discretionary tender methods. But it is systematically correlated with the size of municipalities – those with larger populations tend to buy more locally, perhaps reflecting the existence of a larger pool of suppliers.

Second, we produce new descriptive evidence on delays in payments to government suppliers. We show that approximately 15% of payments are delayed, meaning they are paid over the maximum allowed limit of 30 days. Furthermore, 20% of municipality-year observations have an average delay above 30 days – suggesting they are systematically late payers. Payment timeliness is also correlated with local per capita GDP, with higher-income municipalities paying their suppliers faster. These findings open several additional research questions. For example, if suppliers know that some municipalities tend to be late payers, they might include that "financial risk" of mismatched assets and liabilities in their decisions when selling to these governments and increase prices. Some firms, particularly smaller and liquidityconstrained ones, might also decide not to sell to these governments in order to avoid that financial risk. Other possible questions are whether these delays are systematically different depending on government functions (are health expenses paid faster than education for example?); whether they systematically vary with the business and/or political cycles<sup>28</sup> ; and whether some suppliers benefit from better payment terms than others (Ricca, 2019). All of these have important implications for competition and value-for-money in the public sector and
|0.95|0.96|0.96|**0.88**|**0.10**|
|Zambia|0.31|0.33|0.36|0.38|0.40|0.46|0.59|0.69|0.76|0.89|**0.52**|**0.28**|

**Note** : _Adequate Housing Index_ by income decile (Source: Authors’ calculation based on countries’ available household consumption and expenditure survey).

# 4.2.2 AHI – Disaggregated by Location, Gender, Education, and Occupation

Grouping HHs by age, education level, employment status, or location can also reveal key differences in housing adequacy. Within the sample, Brazil, India, Indonesia, and Nigeria are the most populous countries within their respective. To understand the _AHI_ at the subnational level
85

# **D Estimation Details**

## **D.A Preference Parameters**

**Engel Elasticity.** To estimate the Engel elasticity ( _η_ ), we start from model equation (13). Rearranging, taking logs, and adding household ( _h_ ) subscripts, we obtain the following equation:

We then collect relevant terms into a region fixed effect and add a household size control variable and an error term, obtaining equation (31).<sup>112</sup> As explained in Section VI.A.1, equation (31) is estimated using Two-Stage Least Squares (2SLS) with two IVs: income obtained from lottery (and other _ad hoc_ gains) and income obtained from disasters and other relief payments. We use Weighted 2SLS estimation, weighing observations with the sampling weights available in the HIES data, and with standard errors clustered at the Primary Sampling Unit level. Complete estimation results can be seen in Column (1) of Appendix Table A7, including the estimated coefficients, standard errors, and first-stage results.

**Cross-crop Elasticity of Substitution.** To estimate _σA_ , we first take logs of equation (17) and add household ( _h_ ) subscripts, obtaining:

We then collect relevant terms into crop- and household-specific fixed effects.<sup>113</sup> Finally, we add an error term to obtain equation (32), which is then estimated using Weighted 2SLS with standard errors clustered at the household level, with crop price ln( _Pnhk_ ) instrumented by a dummy variable _Dnk_<sup>_GAEZ_</sup> that indicates whether region _n_ has any growing potential for crop _k_ (according to the FAO-GAEZ data). The crops included in the estimation sample are: rice, maize, groundnut, onion, potato, coconut, and tea. See Column (2) of Appendix Table A7 for the complete estimation results. **Cross-origin Elasticity of Substitution.** As explained in the text,
Gender Perceptions - Violence The standardized total score of five questions regarding norms for inti(IPV) mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree? ).

**Financial Well-being** Savings Response to the question “How much money do you currently have in savings?” During the collection surveys (midlines) this question instead asked “How much money did you save in the past week?” Borrowing Total amount of money the household has borrowed. **Economic Decision Making** Risk Preference Measured using incentivized responses to the multiple price list decisions adapted from Holt-Laury and Sprenger (2002). TIme Preference Measured by adapting Andreoni and Sprenger’s (2011) convex time budget method following Gin ́e et al. (2018). **Other Outcomes** Cognitive Ability A standardized weighted index of the number of correct responses to i) a digit span (forward and backward) memory test and ii) basic arithmetic problems including addition, subtraction, multiplication, and division. Only the arithmetic problems were included in midline. Physical Health Answer to the question “In the past 30 days, how many days were you sick?”. For the collection surveys (midline), this question was modified to ask ”How many of the last 7 days did you feel sick?”

65
To this end, we perform our estimations using the Global Findex country-level data. We measure the gender gap in access to financial services by considering the ratio of the percentage of women having a bank account to the percentage of men with access to formal account ( _Female to male ratio_ ). The higher the ratio, the lower the gender gap in account ownership. We alternatively test the correlation of each of the eight WBL indicators on this aggregate financial inclusion measure. We include the three country-specific control variables formerly used in the individual-level estimations: the logarithmic value of the GDP per capita ( _lnGDPpercapita_ ), the share of domestic credit to the private sector ( _Domesticcredit_ ) and the natural logarithm of inflation ( _lnInflation_ ).

The results of the country-level regressions are reported in **Table 10** . While our sample size, and, therefore, our statistical power, is much reduced, we find that a greater egality in 18
s older person’s grant. Working Paper.

- Amare, Mulubrhan, Kibrom A. Abay, Luca Tiberti, and Jordan Chamberlin. 2021. Impacts of COVID-19 on food security: Panel data evidence from Nigeria. _Food Policy_ 101(May): 102099.

- Andam, Kwaw S.; Edeh, Hyacinth; Oboh, Victor; Pauw, Karl; and Thurlow, James. 2020. _Estimating the economic costs of COVID-19 in Nigeria._ NSSP Working Paper 63. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.133846

- Andersson, Camilla, Alemu Mekonnen, and Jesper Stage. 2011. Impacts of the Productive Safety Net Program in Ethiopia on Livestock and Tree Holdings of Rural Households. _Journal of Development Economics_ 94 (1): 119–26.

- Ardington, Cally, Anne Case, and Victoria Hosegood. 2009. Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa. _American Economic Journal Applied Economics_ 1 (1): 22–48.

- Arndt, C., Davies, R., Gabriel, S., Harris, L., Makrelov, K., Robinson, S., Levy, S., Simbanegavi, W., van Seventer, D., Anderson, L., 2020. Covid-19 lockdowns, income distribution, and food security: an analysis for South Africa. _Global Food Secur_ ity, 26, 100410 <u>https://doi.org/10.1016/j.gfs.2020.100410.</u>

- Baird, Sarah J., Craig T. McIntosh, and Berk Özler. 2011. Cash or Condition? Evidence from a Cash Transfer Experiment. _Quarterly Journal of Economics_ 126 (4): 1709–53.

- Banerjee, Abhijit, Michael Faye, Alan Krueger, Paul Niehaus, Tavneet Suri. 2020. Effects of a Universal Basic Income during the pandemic. Working
Figure 1: Poland’s trade openness and inward FDI from Germany, 1995-2013 <!-- Start of picture text --> 1994: Association<br>agreement<br>1995 1996 1997 1998<br><!-- End of picture text --> <!-- Start of picture text --> 2001: All<br>tariffs<br>removed<br>1999 2000 2001<br><!-- End of picture text --> <!-- Start of picture text --> 2004<br><!-- End of picture text --> Notes: The grey line depicts the evolution of Poland’s degree of trade openness relative to Germany (defined as the sum of bilateral exports and imports with Germany over GDP). The dashed line depicts the share of Poland in Germany’s total outward foreign direct investment in the years 1995 to 2012. The sources of the data are UN COMTRADE and the World Development Indicators of the World Bank for trade openness, and OECD for FDI position.

Figure 2: Event study: the effect of German acquisition on firm-level employment: common treatment <!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>Coefficient<br>-.1<br>-.2<br>-.3<br>-.4<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The plot is created by regressing the log of employment on a full set of event time indicators interacted with the a dummy variable indicating whether a firm was acquired by a German shareholder after 2004 and controlling for firm and year fixed effects (equation (1)). The omitted baseline
4,000), Mid (CFA 2,000 or 1,000), and Low (CFA 150 or Free). SARC prices were cross-randomized at two price levels with equal probability: Full price (CFA 1,250 for the injectable and 1,500 for the pill) or for free.<sup>5</sup> Both short-acting methods need to be renewed every three months and the clients can do so at the same price offered at the initial consultation, which is valid for a period of one year, representing three renewals.

Two details about implementation matter. First, the prices for all modern methods were revealed at once and _after_ the client had chosen a method they wanted to adopt, unless they inquired about prices earlier on in the counseling session.<sup>6</sup> This decision was made after careful deliberation with the healthcare providers at the hospital and based on established best-practices and ethical norms.<sup>7</sup> Second, the reader might worry about the clients (alone or in collaboration with the providers) trying to ‘game’ the system to receive higher discounts - for example, by asking the counselor to start a new _app_ session or by returning later. Carefully developed study protocols for return clients, monitoring the data from the tablets (including meta- and para-data), and monthly audits of HGOPY’s own administrative data allows us to rule out the possibility of clients redrawing a new set of prices.

# **2.2.2 Counseling approach**

The structure of the counseling session guided by the _app_ is not fundamentally different than the accepted best practices used around the world. The main innovation that it brings to FP counseling is a small but important paradigm shift with respect to shared decision-making: the app uses an algorithm to internally rank methods from most to least suitable for the client’s preferences. In the established, or _status quo_ , approach to counseling, the client is given information about all
# **SECTION I: Introduction**

This paper specifies a model of household demand for electricity services and demand in the Indian state of Rajasthan using a combination of survey data, conducted in two districts of Rajasthan, and administrative data, comprising of billing cycle level consumption and charges. This model incorporates customer-level demographic characteristics and weather variables. The model also accounts for the fact that households face increasing block price schedules to recover the parameters of the household’s indirect utility function, which is subsequently used to evaluate the welfare impacts of alternative retail pricing structures.

Our model of the household-level electricity demand embodies the fact that electricity consumption is the result of a household’s demand for hours of service from all electricity-consuming capital goods that it owns. For example, a household does not directly consume electricity, but instead consumes lighting services, which combines electricity use with an electricity-consuming lighting device. The amount of electricity that an hour of lighting service consumption depends on the number of lighting devices a household owns. Combining household-level survey data on electricity appliance holdings and the monthly hours of use of each appliance with the household’s billing cycle electricity consumption and total bill, allows us to recover an estimate of the household’s demand for the following electricity services: (1) lighting, (2) general appliances for domestic end-uses, (3) heating and cooling and (4) business end-uses of electricity.

A two-step estimation procedure is employed that first recovers a household-level “electricity production function” which characterizes the relationship between the household’s billing cycle electricity consumption in kilowatt-hours (kWh) and hours of use of each of four electricity services during that billing cycle. The partial derivative of this “electricity production function” with respect to an electricity service times the marginal price of electricity faced by the household is the effective rupees per hour of use price that the household pays for the last hour consumed of that electricity service. These effective prices and the hours of electricity services consumed along with information on the customer’s
based on results from the phone survey, and iii) impact channels that we were not able to assess quantitatively such as gender-based violence (GBV), health and education but that deserve consideration. Section 4 concludes the paper with a summary of the main findings.

# 2. Methodology

## 2.1. CGE Simulation

The model used for the CGE analysis is the “Mitigation, Adaptation, and New Technologies Applied General Equilibrium” (MANAGE) model (van der Mensbrugghe, 2020). MANAGE is a recursive dynamic, single-country CGE model based on neoclassical theories of the firm and household behavior, assuming firms are profit maximizers and households are utility maximizers. In the context of COVID-19, where individuals across all income levels and sectors of the economy are affected simultaneously, CGE modelbased simulations are well-suited for a comprehensive evaluation of ex-ante impact, capturing direct and indirect effects through a wide variety of transmission channels along several dimensions. One caveat of using CGE models for gender-based analysis is that these models use a social accounting matrix (SAM) as data input. However, most SAMs (like the one used in this study) do not account for unpaid domestic work and care labor in the economy, which is largely provided by women, due to lack of data as they are not covered by the system of national accounts and by most household surveys.

The 2017 Chad SAM<sup>5</sup> that we use to calibrate the CGE model is updated from the 2016 SAM using the 2017 Supply and Use Table (SUT), the Government Financial Operations Table (TOFE), and the 2010 Table of Integrated Economic Accounts (TCEI).<sup>6</sup> Furthermore, to better keep track of distributional and labor market impacts by gender dimensions, the household and labor accounts in the SAM are disaggregated using the Harmonized Household Living Standards Survey 2018/2019 (ECOSIT 4),<sup>7</sup> which was conducted by the National Institute of Statistics, Economic and
# **Figures and Tables**

Figure 1. Volatility of Aggregate Price Indices using IMF Commodity Indices <!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>import manufactures export manufactures all primary<br>non-fuel primary energy<br>standard deviations<br><!-- End of picture text --> Note: The figure shows the evolution of the annualized standard deviations of Hodrick-Prescott filtered price series. The aggregate price indices for all primary, non-fuel primary and energy goods are from IMF Primary Commodity Price Tables (2005=100). The aggregate price indices for import and export manufactured goods are from the Bureau of Labor Statistics (2000=100). The latter data is available using the Standard International Trade Classification from 1993 to 2005 and available using North American Industry Classification System from 2005 to 2010. We constructed an extended series throughout the period 1993 to 2010 by setting the same index value for December 2005 in those two available series.

17