rafmacalaba/data-use-mentions-extended huggingface dataset:
</mark>

- FAO. (2023). Agricultural Stress Index System (ASIS): Historic Agricultural Drought Frequency (Global - 1 Km). http://www.fao.org/giews/earthobservation/

Foster, J., Smallcombe, J. W., Hodder, S., Jay, O., Flouris, A. D., Nybo, L., & Havenith, G. (2021). An advanced empirical model for quantifying the impact of heat and climate change on human physical work capacity. _International Journal of Biometeorology_ , 65, 1215-1229. https://doi.org/10.1007/s00484-021-02105-0

- Gallardo, M. (2018). Identifying vulnerability to poverty: A Critical Survey. _Journal of Economic Surveys_ , 32(4), 1074-1105. https://doi.org/10.1111/joes.12216

- Gascoigne, J., Baquie, S., Vinha, K., Skoufias, E., Calcutt, E., Kshirsagar, V., Meenan, C., & Hill, R. Forthcoming. The Welfare Cost of Drought in Sub-Saharan Africa. Washington, D.C.: The World Bank.

- Gehrke, E. (2019). An Employment Guarantee as Risk Insurance? Assessing the Effects of the NREGS on Agricultural Production Decisions. The World Bank Economic Review, World Bank Group, vol. 33(2), pages 413-435.

- Gelb, A., Giri, A., Mukherjee, A., Rautela, R., Thapliyal, M., & Webster, B. (2022). Beyond India’s Lockdown: PMGKY Benefits During the COVID-19 Crisis and the State of Digital Payments. CGD Policy Paper 257. Washington, DC: Center for Global Development. https://www.cgdev.org/publication/ beyond-indias-lockdown-pmgky-benefits-during-covid-19-crisis-and-state-digital-payments

- Gentilini et al. 2022. Social Protection and Jobs Responses to COVID-19: A Real-Time Review of Country
(6) − −1 = + [1−g + 1α ] −1<sup>,</sup> rr where r is the real interest rate given by ddtt ddtt ppddtt +gg +GG ddtt 1 −1 and _i_ is given by equation (3) above. Solving for from 1+ +ii the expression for (1+z) given in the second bullet above, substituting into (6) and rearranging terms ππLL αα gives: (7) − −1 = + 1−g −1 + (1 1+ $� $−1 + (1+ LL$−)(1$ ) $−1 , rr zz ππ ππ <mark>where</mark> ddtt <mark>d</mark> $dd−1tt <mark>= αd</mark> ppdd−1tt<sup><mark>is the ratio of $ debt expressed in local currency at the end of year (t-1) to GDP in</mark></sup> +gg ddtt +gg)� ππ ddtt ππ +GG ddtt <mark>local currency for year (t-1).</mark> tt tt <mark>Equation (7) is the same as the ‘final equation’ used in the MAC SRDSF shown at the top of Box 3 on page 31 of IMF (2022), noting that ∆d ≡ d − d</mark> −1<sup><mark>, -pb=pd,</mark></sup> <mark>d</mark> −1 <mark>= d</mark> $−1 <mark>, and ignoring the stock-flow adjustment term, sfa.</mark><sup>4</sup> <mark>This completes the derivation.</mark> ff tt tt tt tt

**<mark>Note 4:</mark>** <mark>
conditional on having an idea and trying to realize it, the success and failure rates are not statistically different between the treatment and control groups.<sup>18</sup> As such, the increase in the probability of having set up a project at some point is driven by additional attempts, not a higher success rate conditional on attempting to launch a new project.

Table 4 in part explains why the projects were not more successful. Respondents who attempted to set up projects or were in the process of doing so by the time they took the medium-term follow-up survey were asked about the steps they had taken to realize their project. This helps assessing whether participation in the entrepreneurship track led to better-prepared and ultimately better-quality projects. It also helps understanding at what point in the process of trying to set up projects participants faced obstacles. Results indicate that individuals in the treatment group were more likely to prepare a business plan for their projects. However, they were not more likely to have undertaken other actions, such as doing extra studies, working to gain experience, requesting loans to fund the project, enrolling in training, talking to parents, friends, and relatives, asking help from university/ANETI, or using their savings. As such, the entrepreneurship track only affected a relatively narrow set of preparatory actions and did not trigger broader pro-activity in setting up projects.

Some indicators also shed light on whether the program led to an increase in ‘entrepreneurial culture’, entrepreneurial intentions, or ‘latent entrepreneurship’, which we proxy by having more ideas or anticipating to attempt to create projects beyond the medium-term follow-up survey. Impacts on latent entrepreneurship could indicate that impacts on final outcomes may take even more time than 4 years. As indicated above, the increase in active projects among program participants seemed to be driven by a one-off increase in project ideas and attempts to set-up projects. However, there was not a sustained effect on the generation of new ideas or attempts to set up projects. Overall, it does not
# **Table 25: Loss analysis—number of firms covered**

||Full data<br>(firms)|Data on<br>ownership<br>form|Private<br>sector|Net<br>turnover ><br>100 000|Profit data<br>present|Employee<br>info<br>present &<br>matched|Present<br>before &<br>after<br>reform|Only<br>calendar<br>reporting|
|---|---|---|---|---|---|---|---|---|
|2004|426 141|371 632|369 703|335 276|314 001|192 918|143 358|88 234|
|2005|429 287|378 047|376 146|341 911|335 038|204 969|155 288|99 244|
|2006|437 960|387 290|385 286|350 523|346 596|210 817|170 100|107 377|
|2007|444 987|394 362|392 320|366 976|361 871|216 154|165 795|102 288|
|2008|457 788|403 508|401 462|373 944|370 431|219 820|167 416|103 675|
|2009|463 133|407 062|404 963|371 724|366 789|217 278|152 780|92 235|
|2010|476 458|425 439|423 341|391 842|385 070|228 897|144 841|87 764|
|2011|527 983|469 719|467 641|413 851|407 317|237 887|136 001|82 458|
|2012|535 130|475 433|473 352|415 777|411 027|243 621|127 443|77 335|
|Percent
This equation in levels includes country fixed effects, _αc_ , which are differenced away in the first-difference transformation (1). Identification of the parameters is hence based on variation within countries over the different time periods.

Causal identification of all the model parameters in such a macroeconomic crosscountry panel data set appears futile. Subtle identification strategies with external instruments can sometimes be found for individual parameters, but they offer no promising chance for the wide range of model parameters in which we are interested. Some researchers have reverted to internal instruments (system generalized method of moments (GMM)), but the results by Kraay (2015) suggest that this approach frequently provides weak identification in the first stage.

For our preferred baseline results, we rely on identification with least squares dummy variable (LSDV) estimation of equation (2). In our view, this provides the most transparent estimation approach and avoids common pitfalls such as unobserved cross-country heterogeneity (through inclusion of fixed effects) and weak or overfitted first stages (which are common in System-GMM estimation). This is particularly true since we are not interested in deep structural identification of the ultimate growth drivers but simply want to detect variables that are correlated with economic growth in a wide range of countries. The downward bias for estimates of the autoregressive coefficient θ in small-T samples with fixed effects is also increasingly neglected in the empirical growth literature (e.g., Brueckner and Lederman 2018).

We additionally provide results from System-GMM estimation, which allows using “internal instruments” (that is, lagged level values and differences) for potentially endogenous series.<sup>16</sup> We treat period dummies as strictly exogenous variables (ivstyle) and use “collapsed” lags 1 to 5 for most of the variables as instruments in the difference equation, except inflation and changes in the terms of trade. Since those variables are less persistent, we use (non-collapsed) lags 1 and 2 in both equations (the difference and levels equations). For the lagged dependent variable,
|33.5|25.8|22.0|
|General government balance (percent of GDP)|0.9|-0.7|-3.1|-1.5|-1.2|-1.3|
|Central government fiscal balance (percent of GDP)|6.1|6.1|6.1|6.6|—|—|
|Local government fiscal balance (percent of GDP)|–5.5|–6.5|–8.3|–8.3|—|—|
|Total investment in fixed assets (percent of GDP)|51.6|54.8|65.8|69.0|—|—|

_Source_ : Authors’ compilation based on data from IMF World Economic Outlook April 2012, IMF Article IV Consultation with China July 2012, and China Statistical Yearbooks of recent years. IMF estimates of GDP growth, inflation, and general government gross debt start after 2011; current account balance estimates start after 2010. _Note_ : — Not available.

# **Effects of China’s Fiscal Stimulus**

China’s fiscal stimulus helped prevent a drastic slowdown of its economy, which had been growing by double digits for three decades. Had the economy suddenly slowed significantly, latent issues could have surfaced, such as a sharp increase in unemployment which would have undermining the “harmoniousness” of the society Several recent studies provide a range of estimates of the effects of China’s fiscal stimulus. The IMF estimates that it raised China’s GDP by 1.5–2.25 percentage points in the first half of 2009 7
# **Consumption Module Structure**

Expenditure surveys do not have a fully homogeneous structure across countries. Table B3 presents information on their structure and we provide a summary below:

# _•_ **Number and frequency of modules**

- The number of consumption modules ranges from 1 to 17 across countries in the All have a module which is a of over some

- sample. surveys diary consumption short to medium period of time and some countries complement these with recall modules for more infrequent purchases. For example, Costa Rica has a single consumption module, while Morocco has 17, with modules specialized by frequency and products. Surveys with multiple modules typically asked for consumption linked to the frequency of expenditures (e.g. weekly diary, quarterly recall).

- **Durables**

Durable items, which are not purchased frequently are included whenever available, but their inclusion is more probable in surveys which have recall modules.

- **Self-production**

- Self production is included as a “place of purchase” for all countries but Chile where it was not available. In some countries, it was pre-coded as an option for the place of purchase, while in other cases we added it as a place of purchase based on other variables, such as “mode of acquisition,” which had “purchased or “self/home production.” Self-production values are typically asked as value if you had purchased(or sold) this item at a market place.

# _•_ **Product codes**

All surveys have product codes for each consumption item, which typically follow the United Nations Classification of Individual Consumption According to Purpose (COICOP) or which we could matheced to the COICOP with a cross-walk. For a few countries the COICOP classification was not available and we could not find a product crosswalk. We used the nationally-specific product classification scheme for these countries (Brazil, Chad, Peru and Tunisia).

76
<!-- Start of picture text --> Machinery /<br>Mineral fuel / Crude materials Transport Animal / veg Chemicals / Manufactured Beverages and Food and Live Miscellaneous Commodities<br>lubricants excluding fuel Equipment oil / fat / wax Products n.e.s. goods tobacco Animals Manuf. Articles n.e.s.<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>-60 Total<br>-70<br><!-- End of picture text --> Note: Data is from UN Comtrade database. Export growth is in nominal USD. Years are calendar years. The bars denote the difference between the average growth rate between 2003 and 2008 and the average growth rate between 2012 and 2016.

- India’s export destinations are diversified too. The largest share of exports from India is destined for the Middle East (approximately 20 percent in 2016), and within it the largest share is exported to the United Arab Emirates. The United States is the second largest destination, accounting for 16 percent of India’s exports, followed by China (including Hong Kong SAR, China), Sub-Saharan Africa and Europe. We disaggregate Indian exports across its 8 main trading partners and the rest of the world for 2003 to 2017.<sup>48</sup> We note that the export slowdown experienced by India after 2013 was across destinations, as exports slowed to most of India’s main trading partners in the Middle East, the UAE and Saudi Arabia, but also to the US, China and Singapore.

**Figure 28: Destinations of Indian Exports**

28 A: Contribution to Export Growth 28 B: Contribution to Export Growth: Middle East and the USA

> 48 India’s main trading partners, in order of share in total exports, are the USA, the
fund and these funds have different objectives such as value, growth, or blend strategies. An important benefit of the exercises performed here is that they do not need to identify the exact stocks held by different mutual funds within a mutual fund family at every point in time. The only information needed is mutual fund returns over time and the investment scope. This allows us to extend the time horizon of the data to start in the late 1980s.

The summary statistics of the simulated and actual global funds are shown in two tables. Table 10 reports the results for simulated global funds constructed with the largest number of available specialized funds (the largest cross-section), called the “largest number of funds” simulation. This simulation includes all possible specialized funds for each family and adjusts the time series accordingly to use the sample available for all funds included in the simulation; however, it does not necessarily entail a very long time span due to data availability on mutual fund returns. Table 11 reports results that use instead only specialized funds that allow for estimations of simulated global funds with relatively long time series, called the “longest available sample” simulation. In particular, specialized funds are excluded from the simulations if they reduce the sample size by at least six months. Naturally, this longer time series come at the cost of having fewer specialized funds in the comparisons. On average, three specialized funds are excluded from these simulations if compared to the largest number of funds simulations, which leads to an increase on average of 44 months in the time span of the simulations.

Tables 10 and 11 present the following statistics based on daily and weekly data: the average annualized returns of the actual global funds and the simulated global funds, the average annualized difference in accumulated returns between each simulated global fund and the corresponding global fund, the standard deviation of returns, and the number of comparisons.<sup>33</sup> Annualized differences in accumulated returns are calculated over the entire sample for each simulation. Averages across simulations
|Rural Non-Poor|17.7%|18.5%|32.7%|
|---|---|---|---|
|Total|100.0%|100.0%|100.0%|
|Poor|19.6%|42.8%|19.4%|
|Non-Poor|80.4%|57.2%|80.6%|
|Urban|67.9%|50.8%|54.1%|
|Rural|32.1%|49.2%|45.9%|

_Source:_ Panama Semi-Input-Output model simulations.

Because of data uncertainties and simplifying assumptions used, the above analysis of the effects of increases in agricultural output on incomes in the Panama economy illustrates only the broad order of magnitude of the effects. Nonetheless, the broad structure of production, and the structure of household incomes and demand are reflected in the analysis.

It is important to note, however, that the simulations imply an even distribution of the gains to returns to labor and capital across all owners of these factors. This assumption is valid if labor markets function well so that increases in labor demand are reflected in general rises in wage rates that benefit all workers. Yet, location of activities matters a lot for local labor markets and especially for returns to capital and backward linkages to agriculture. The implication is that including much of the rural poor (and minorities) in the benefits of growth in tourism will require that these households are well integrated in product and factor markets where the investments take place.

Some of the assumptions underlying this model cannot be verified without destination level data. For example, it is necessary to ascertain such underlying basic facts as whether certain household groups tend to be precluded from employment in all-inclusive resorts; whether local agricultural products are deemed as suitable for purchase by hotels and restaurants; or whether any obstacles exist to development of handicrafts and local
# **4. Conclusion and policy implications**

The paper builds a new _de facto_ index of public investment quality based on World Bank data on investment project evaluations for the period 2000-21. The index allows to assess public investment quality for a large number of EMDEs and to track the evolution of public investment quality over time. We show that low-income countries and commodity-exporting EMDEs have much lower public investment quality in comparison with other EMDEs and that this gap has widened over time. This is especially alarming in light of the importance of public investment quality for growth-inducing effects of public investment.

The paper also shows that public investment quality has important implications for sovereign risk. Specifically, we show that scaling up of public investment leads to lower sovereign risk for countries with high public investment quality and increases sovereign risk for countries with low public investment quality. The results indicate that this effect is particularly important for countries with low levels of creditworthiness. We also show that in countries with high public investment quality, scaling up of public investment pays for itself. Debt-to-GDP ratios do not increase after a positive public investment shock. The opposite is true for low-public-investment-quality countries, where scaling up of public investment leads to an increase in public debt in the following years.

The results of this paper help to inform policy debates by providing empirical evidence on the macroeconomic implications of public investment and its quality. Many EMDEs face debt sustainability issues, particularly in the aftermath of the COVID-19 pandemic and the associated global economic recession. We show that the quality of public investment has non-trivial consequences for the cost of capital and debt sustainability. In this regard, scaling up of public investment when public spending is not efficient may be detrimental to macroeconomic stability.

These issues are especially critical for LICs that face significant infrastructure gaps but have limited capacity to address them given high sovereign spreads, limited fiscal space, and intermittent access to the international capital market. For these countries it is vital to step up technical and financial support from the global community with a multipronged strategy integrating
-0.027|
|Fla|Unpaid agricultural family workers|0.008**|-0.007|0.008**|-0.007|
|Lan|Log of cultivated area|-0.487***|-0.008|0.185***|-0.011|
|Mec|Mechanization|0.007***|-0.005|0.006***|0.001|
|HHh|Household head|0.022**|0.02|0.022**|0.013|
|NFI|Share of nonfarm labor income|-0.099***|-0.012|-0.098***|-0.011|
|NFn|Share of nonlabor income|0.066***|-0.005|0.065***|-0.004|
|SID|Simpson Index of diversification|0.032***|0.004|0.032***|0.000|
|Sub|Sum of Subsidy|-0.039***|-0.08|-0.037***|-0.072|
||Incidence of crop subsidy|-0.154***|-0.063|-0.159***|-0.054|
||Log of crop subsidy|0.115***|-0.017|0.122***|-0.018*|
|XIN|Export Orientation Index|-0.134***|0.03|-0.135***|0.029|

Source: Authors’ estimation using HIES 2016. Notes: *, **, and *** indicate that the coefficients are significant at the 10, 5 and 1% level,
Policy Research Working Paper 8810

# **Abstract**

China has quickly become the largest e-commerce market in the world. By matching a nationally representative China Family Panel Studies survey with county-level e-commerce information obtained from Alibaba, this paper examines how e-commerce development has shaped household consumption growth in China. The paper presents three major findings. First, e-commerce development is associated with higher consumption growth. Second, the relationship is stronger for the rural sample, inland regions, and poor households, suggesting that e-commerce development helps reduce spatial inequality in consumption. Third, the consumption of durable goods and in-style goods has grown faster than the consumption of local services.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at xluo@worldbank.org, yw548@cornell.edu, and x.zhang@nsd.pku.edu.cn.

_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
**|**-39.4**|**-39.4**|**-41.6**|**-11.1**|
||(26.12)|(26.12)|(29.00)|(29.03)|
|Intercept|505.9***|505.9***|543.6***|1004.0***|
||(17.95)|(17.95)|(19.75)|(26.13)|
|No. of Children||||-178.0***<br>(7.963)|
|No. Observations|27810|27810|24443|24443|

Notes: (1) The data used are IHDS 2005 and NSS 1995 respectively. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
significant, and if they are not well managed, the impacts can be long lasting. A central question is how development organizations can support host countries to promote positive impacts and mitigate negative ones. The welfare of host communities also plays a critical role in the political economy of asylum and integration.

# **Socio-economic data on displaced populations, returnees and host communities**

Data from socio-economic assessments of displacement crises can usefully inform the design of recovery and development policies and associated assistance programs. However, currently there is little comprehensive data available on the socio-economic vulnerabilities and needs of displaced populations or on the social and economic impacts of displacement on host communities. Data on refugees are rarely captured in poverty statistics, which rely on national household surveys usually covering the resident population only, although there are some examples of displacement related questions on IDPs and migrant populations being incorporated into household surveys. Rather, data on the socio-economic impacts of this kind tend to be collected for specific displacement crises (see Box 5).

# **Box 5: Bank-Supported Socio-Economic Surveys and Assessments of Displaced and Host Populations**

- <mark>The “Socio-Economic Impact of the Crisis in North</mark> **<mark>Mali</mark>** <mark>on Displaced People” [completed in 2015] analyzes</mark> the impact of the 2012 crisis on IDPs in Bamako, refugees in camps in Mauritania and Niger, and returnees in the regional capitals of Gao, Timbuktu and Kidal, using data from a face-to-face household survey and follow-up interviews using mobile phones.

- In **Kenya** , a Bank study [ongoing] on the dynamics of the Kakuma refugee camp investigates price effects in food, labor, cattle and housing markets, relying on an original survey across Kakuma and another town<sup>98</sup> to collect data on household demographics, remittances, employment opportunities and trends, wages and prices.

- The Bank with UNHCR is undertaking a regional assessment of social