rafmacalaba/data-use-mentions-extended huggingface dataset:
After July, rainfall was below normal in all of the study municipalities, affecting the critical period for plant development.<sup>41</sup> Appendix Table A8 shows that while the rainfall during the initial growing stages (before August) was similar in 2014, 2015 and 2016, total precipitation was significantly lower in 2015 compared to either 2014 or 2016. In both 2015 and 2016, we asked farmers whether they had suffered a drought and while in 2015, 73% reported that they did, only 32% of the same respondents reported a drought in 2016. Finally, we collected yield expectations data in July and November of 2015. In Appendix Table A9 we regress the change in expected yields from July to November against an indicator for whether the respondent reported a drought. The results show that reporting a drought is associated with a decline of 170 Kg/ha in expected yields. Given that the sample is smaller than the core sample, we also

> 38See e.g. Desiere and Jolliffe (2017) for a discussion.

> 39For farmers with a plot area of less than 1 ha, the research team measured how much they had and adjusted the denominator appropriately. Results are robust to excluding these plots.

> 40For 75% of farmers, the crop was still standing at the time of the QFD visit, and the maize was harvested by QFD using a mechanical harvester. After harvesting, the team threshed the maize and transported it to be weighted. The remaining farmers had already harvested at the time of the visit but the production from the registered plot could be identified. In this case, after drying the maize the team visited the farmer with a mechanical sheller to collect the grain and transport it to be weighted. See Online Appendix D for details.

> 41See e.g. Sinclair and Rawlins (1993).

21
<!-- Start of picture text -->
-.4 -.2 0 .2 .4<br>ln(NO2)<br>.5<br>0<br>ln(Real GDP)<br>-.5<br><!-- End of picture text -->

Figure 6: Raw Data, Net of Country and Year Fixed Effects _Notes._ � Annual data for 178 countries, 2005-2020. Unbalanced panel: total sample is 2,752. Slope is _ψ_ = 0 _._ 348 and robust standard error, clustered by-country, is 0.061 (statistically significant at 1% level). Here and in what follows “NO2” continues to mean per area.

16
income by 28.6%. Their results show that electricity disruptions have clear economic implications for households. Similar results are found by Rao (2013) focusing on household enterprises in India. He uses a subset of 8,125 urban and rural households that manage a non-farm enterprise. While he primarily finds strong evidence for a positive income effect of connectivity to the grid, he also identifies suggestive evidence of a positive effect of better supply availability. He estimates that the benefit from the income effect of improving supply to 16 hours a day or more would reflect 0.1% of GDP on an aggregate level. In Pakistan, a study finds that the middle class pays the highest cost of electricity outages in relation to its income (Pasha & Saleem, 2013).

As explored in Zhang (2019), the benefits from electricity access are magnified if the access is reliable. In the three countries included in the study, Bangladesh, India, and Pakistan, access to electricity is associated with higher income and better social outcomes. But the impact is much stronger if the electricity is reliable (Figure 2). In some places and for some metrics, for instance the effect of electricity access on women employment in India and Bangladesh, benefits are two or three times larger if access is reliable.

**Figure 2: Impacts of access to electricity and network reliability on income and social outcomes in South Asia**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Increase in Increase in Increase in Increase in Increase in Increase in Increase in Increase in<br>per girl's women's per girl's women's per women's<br>capita income study time labor force capita income study time employment capita income labor force<br>participation participation<br>Bangladesh India Pakistan
# **Appendix C : Estimates of the Dixit-Stiglitz Elasticities of Substitution for Goods**

It was necessary for us to obtain estimates of the Dixit-Stiglitz product variety elasticities of substitution for the imperfectly competitive sectors in our model. Christian Broda, Joshua Greenfield and David Weinstein (2006) estimated Dixit-Stiglitz product variety elasticities of substitution at the 3 digit level in 73 countries. There were no Commonwealth of Independent States (CIS) in their sample, but they did estimate elasticities for Lithuania. As a former Soviet Union country of about the same population as Armenia, we choose Lithuania as our proxy for Armenia.

Broda et al., estimate 3 digit elasticities for 130 goods sectors, but there is only one manufacturing sector in our model, It was necessary to map the sectors estimated by Broda et al. into the sectors of our model. In table C1 of this appendix, we show the mapping for the imperfectly competitive sector. (These elasticities are not relevant in our model for perfectly competitive sectors.) Next, since there are multiple sectors from Broda et al. mapped into a single sector in our model, it was necessary to determine a method of weighting the Broda et al. elasticities. There are reasons to use both export shares as well as import shares. A larger share of a subcategory in imports reflects more imports, and more likely there are more varieties of imports. So weighting by the import share of a subcategory is better than an un-weighted measure. Domestic varieties are also important. Since we do not have production data for the subcategories, we use export shares as a proxy for domestic production by subcategory. Analogously, weighting subcategories by export shares is better than unweighted categories. Since both import shares and export shares are useful in the weighting, we take one half the shares of both exports and imports as the weights. We obtained the data for the import and export shares from the COMTRADE database. The resulting elasticities are reported in table C1. XXX For the sensitivity analysis, we must take
. This could be a concern, given that both the cost of manure application on a given plot and tenancy status might vary by distance to the homestead. However, in our sample, 92% of all cultivated plots are located within the village (93% of owned plots compared to 90% of leased plots). Given the size of most villages in Pakistan, the vast majority of plots are therefore, at most, only a kilometer or two from the farmer’s home.<sup>22</sup> Within this radius, transport cost differentials are likely to be minimal. More-

> 19 We choose k so that it is at least an order of magnitude less than the minimum value of the dependent variable. Maximum likelihood estimation of k would fail, since the log Jacobian of the transformation of y is − log(y + k). At y = 0, the optimization algorithm would always try to set k as close to zero as possible.

> 20 For example, the t-value for the leasing coefficient in row (3) of Table 4 is 5.91. The corresponding t-value is 5.92 when k = 0.01 and 5.93 when k = 0.001, even though the associated coefficients vary widely in magnitude.

> 21 Both owners and tenant cultivators are asked to report plot value in the survey. One might worry that these values partly re fl ect the extent of past manuring, although this seems quite unlikely given that the effects of FYM are far from permanent. In any event, the exclusion of this variable from the regressions has a negligible impact on the estimates of interest.

22 We can be more precise by examining data from the 1991 IFPRI survey, which did collect information on distance to plot (but not on whether the plot was outside the village). Virtually all of the households in this survey were included in the 2001 PRHS sample as part of a panel. Among the 914 plots recorded in the 1991 survey
_Figure 13: Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP) and growth based on GDP current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>42%<br>40% 35%<br>28% 28% 28%<br>22% 23% 21%<br>19%<br>17%<br>20% 15% 15% 14%<br>12% 12% 12%<br>8%<br>2% 2% 6%<br>0% 5% 9% 10% 10% 11% 9% 8% 8% 11%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Unit Passthrough Clonflict Adjusted Passthrough Passthrough=0.7<br>Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data _Figure 14: Poverty Projections at LMIC international poverty line ($3.65, 2017 PPP) and growth based on GDP current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates_ <!-- Start of picture text --> 100%<br>80%<br>71%<br>65%<br>58% 58% 57%<br>60%<br>51% 51%<br>49%<br>47%<br>43%<br>40% 40% 40%<br>40% 36% 36% 35%<br>29%<br>24% 31% 33% 32% 33% 34%<br>29% 29% 28%<br>20%<br>13% 23%<br>15
<mark>First, while agriculture continues to become more knowledge intensive and high tech, the demand for more precise and timely information rises rapidly. Some of the world’s newest industries have started to put money and talent into farming—the world’s oldest industry. Digital soil maps, remote sensing, and GPS guidance are critical tools for modern farmers. Big data for precision agriculture increases yields and efficiency. These high tech tools mostly benefit big farms that can make large investments in technology. Smaller farms will often not have the capital to keep up. This may change as information access and delivery continue to become cheaper. But even in the short term there are innovative ways in which smallholder farmers with limited human and financial capital investments use digital technologies such as basic mobile phones and increasingly the internet to maximize returns.</mark> <mark>For this to have a widespread impact in rural areas of developing countries requires the closing of the remaining digital divide. While mobile phones have spread quickly even in low-income countries and among poorer population groups, access is by no means universal. And internet access remains very low in many countries. For example, the digital divide is large between and within 12 countries in Sub-Saharan Africa for which reliable data are available (Table 2 and Table 3). Mobile phone access is as high as 84 percent in South Africa, and as low as 18 percent in Ethiopia. Internet access is 34 and 3 percent, respectively, in the same two countries. Access is significantly lower in rural versus urban areas. Residents of rural areas in Ethiopia are only about one-fifth as likely to have access to a mobile phone as urban residents. On average, these disparities are as high as those between the bottom 40 percent and the upper 60 percent of the income distribution, and much greater than those between women and men, or between the old and the young.</mark> 22
<br>0<br>0.6<br>-0.5<br>0.4<br>-1<br>0.2<br>-1.5 0<br>0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10<br>Years Years<br><!-- End of picture text -->

**Note:** Unbalanced panel VAR model with four lags and fixed effects is estimated using the annual data from 1987 to 2017 for six South Asian countries (Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka). The dashed lines show +/- two standard errors. **Sources:** ADB, IMF, World Bank, and own calculations.

# **6. Differences for current and capital expenditure**

As mentioned above, it has been shown that government consumption and government investment affect economic activity differently (Ilzetzki et al. 2012). We therefore analyze current and capital expenditure separately. First, we compare their cyclicality and, second, their impact on economic activity.

We re-estimate the error-correction (2) described above separately for current and capital expenditure. Especially in economic downturns, the former is typically harder to adjust due to political policy reasons and we hence expect it to be less pro-cyclical. In addition, some components like social security spending or work programs may be inversely related to economic growth. And indeed, the short-run coefficient for current expenditure is only 0.69 and not statistically significant at the 10 percent level.

11
the expected discounted value of utility over an infinite horizon by making decisions on consumption, savings, and on whether to take advantage of the investment opportunity, subject to borrowing constraints. Kaboski and Townsend (2007) derive a steady state solution to the model.

The behavioral model is estimated using five years of ”pre-program” data, in particular, information from households and local businesses on their consumption, income, investment, credit, liquid assets, and interest income from these assets in addition to village population data. The validity of the estimated model is assessed by comparing the model‟s predictions of the effects of the Thai Million Baht program on consumption, investment and the probability of investing to the actual effects observed under the social experiment.<sup>16</sup> Impact estimates obtained using the simulated model are close and not statistically different from impact estimates obtained from reduced form regressions based on post-experiment data, lending support to the model.

One of the notable predictions of the model that is borne out in the data is that the impact of the program on consumption exceeded one million baht and that consumption increased more than credit did because the credit was used toward productive activities. Kaboski and Townsend (2007) use the estimated model to compare the costs of the microfinance program to the costs of a direct cash transfer program that would have provided the same utility benefit. They find that the cost of the microfinance program was 33 percent less, due to the fact that the microfinance program relaxed borrowing constraints that the transfer program did not.<sup>17</sup> In summary, Kaboski and Townsend (2007) demonstrate both theoretically and empirical that microfinance programs are an effective means of increasing the liquidity of credit constrained households and that they positively impact both investment and consumption.

# **4.2  Relaxing Consumer Credit in South Africa**

Many microcredit programs focus on providing credit for entrepreneurs rather than consumers. Providing credit to consumers is a more controversial policy initiative, because of concerns that behavioral
11 Two standard indicators to gauge the overall use of NTMs across countries are the coverage ratio and the frequency index. The first captures the percentage of trade subject to non-tariff measures, while the latter shows the percentage of products to which one or more NTMs apply.<sup>9</sup> While the use of non-tariff measures tends to vary considerably across countries, these simple inventory indexes show the importance of NTMs in terms of trade and sectors affected, justifying the growing interest of economists in this area. Based on NTM data from TRAINS, coverage ratios for the countries in the sample vary between 30 and 100 percent, with the median country (Brazil) having 71.7 percent of trade covered by NTMs. Frequency indexes have a higher variability (between 5 and 100 percent), while the share of products subject to at least one NTM is 66.9 percent for the median country (China).<sup>10</sup> Key questions on NTMs are: what countries are more likely to use them, in what sectors are they employed more frequently, what specific NTMs are more common, and how have they behaved over time. In what follows, we use the different sets of data discussed above to shed some light on these issues.

First, we look at the question of what countries are the larger users of non-tariff measures. In Figure 2a, we plot the coverage ratio calculated using TRAINS data against the log of GDP per capita. The size of the bubble represents the percent of imports under NTMs of the country. The regression line shows a positive correlation between the level of development and the use of NTMs, suggesting that developed countries tend to be larger users of NTMs relative to developing countries. This evidence is consistent with the findings in WTO (2012) based on specific trade concerns and the results of business surveys. This finding could be consistent with two types of explanations. One possibility is that more advanced economies have substituted declining tariff

> 9 The frequency index
.

The earnings dimension measures whether a job pays sufficiently to lift a family out of poverty and into the lower-middle class. Labor earnings are the main source of material well-being among most individuals, especially in developing countries. This dimension not only captures whether a job generates income, but also whether labor income can support a minimum standard of living among workers and their families (World Bank 2012). As a measure of minimum living standards, this paper adopts the World Bank international poverty line of US$1.90 a day per capita (2011 purchasing power parity [PPP] US dollars). Focusing only on the well-being of individual workers, however, is not sufficient for understanding the welfare implications of employment among the population at large. A poverty headcount around the world is measured based on household and not individual income, and it is thus imperative to consider the needs of workers as well as their dependents, especially in developing countries where labor earnings are the main source of household income. For the harmonization exercise, the dependency ratio is 7
to detention in Libya? A microlevel study of the determinants of detention_ . MMC Research Report, December.

- Mixed Migration Hub (2015). _Detained Youth: The Fate of Young Migrants, Asylum-Seekers, and Refugees in Libya Today._

- Murphy-Teixidor, A., A. A. Bonfiglio, and V. Leigh (2020). Migration and risks: smuggling networks and dynamics on the central mediterranean route. In F. Fargues and M. Rango (Eds.), _Migration in West and North Africa and across the Mediterranean: Trends, risks, development and governance_ . Berlin: IOM GMDAC.

- Pesando, L. M., V. Rotondi, M. Stranges, R. Kashyap, and F. C. Billari (2021). The internetization of international migration. _Population and Development Review 47_ (1), 79–111.

- Rahman, A. and M. Di Maio (2020). _The private sector amid conflict: The case of Libya_ . The World Bank. Washington D.C. (USA).

- Raleigh, C., A. Linke, H. Hegre, and J. Karlsen (2010). Introducing acled: an armed conflict location and event dataset: special data feature. _Journal of peace research 47_ (5), 651–660.

- Ribeiro, F. N., M. Ara ́ujo, P. Gon ̧calves, M. Andr ́e Gon ̧calves, and F. Benevenuto (2016). Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. _EPJ Data Science 5_ , 1–29.

22
Delhi, as shown in Appendix Figure C1.<sup>29</sup> Appendix Table D5 provides some summary statistics of the survey sample. Compared to non-users, bus users (both new and continuous) are predominantly aged between 31 and 40, more often homemakers, more likely to reside in households with a monthly income exceeding |20,000, and less inclined to travel for work. Given these differences, we constructed a comparable sample of nonusers, new users, and continuous users using propensity score matching. We match women on the following variables: age, occupation,<sup>30</sup> total average monthly household income and expenditure on travel, ownership of private vehicles, and driving knowledge.

Our study sample of the Delhi primary survey is the matched sample ( _n_ = 1, 290) consisting of 184 non-users, 182 new users, and 924 continuous users. Compared to the treated sample in the CPHS data, the matched sample is characterized by higher levels of education and annual household income. Approximately 68 % of the bus users in the matched sample hold a graduate degree, and 85 % of them report an annual household income of over |240,000.

**Perceptions of Buses** In Table A1, we compare perceptions of buses between non-users and users (panel A), as well as between new users and continuous users (panel B) in the post-scheme period. We examine the differences in their perceptions of five aspects: 1) affordability and availability of bus transit; 2) safety regarding accidents, crashes, threats, and thefts; 3) connectivity; 4) bus frequency, waiting time, travel duration, and unnecessary stops; 5) accessibility to bus stops. In panel A, we can see that non-users consistently give lower ratings across all five perspectives; they find bus travel less satisfactory across all five dimensions compared to users. They are particularly concerned about safety issues. The average rating concerning safety among non-users is 1.63,
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2302 VVhy Liberalization Alone Has Not<br>Improved Agricultural Productivity<br>irn Zambia: The Role of Asset<br>C)wnership and Working Capital<br>Constraints|Klaus Deininger<br>Pedro Olinto|March 2000|M. Fernandez<br>33766|
|WPS2303 Mlalaria and Growth|F. Desmond McC(arthy<br>Holger Wolf<br>Yi Wu|March 2000|H. Sladovich<br>37698|
|WPS2304 [)isinflation and the Supply Side|Pierre-Richard Agenor<br>Lodovico Pizzati|March 2000|T. Loftus<br>36317|
|WPS2'305 1The Impact of Banking Crises on<br>Motney Demand and Price Stability|Maria Soledad Mvlartine<br>Peria|z March 2000|A. Yaptemco<br>31823|
|WPS2306 International Contagion: Implications<br>for Policy|Roberto Chang<br>Giovanni Majnoni|March 2000|E. Mekhova<br>85984|
|\VPS2:307 Surveying Surveys and Questioning<br>Questions: Learning from World Bank<br>Experience|Francesca Racanatini<br>Scott J. Wallsteri<br>Lixin Coiin Xu|March 2000|P. Sintim-Aboagye<br>37644|
|VVPS2:308How Small Should an Economy's<br>Fiscal Deficit Be? A Monetary<br>F'rogramming<br>Approach|Paul Beckermar|March 2000|H. Vargas<br>38546|
|\NPS2:309 What Drives Private Saving around<br>the World?|Norman Loayza<br>Klaus Schmidt-Hebbel<br>Luis Serven|March 2000|E. Khine<br>37471|
To construct the total exposure index at the district level in Egypt, we utilize several databases. Initially, we gather data on export value from the UNCOMTRADE database. In order to account for the demand generated in other sectors as a result of exports and calculate the overall exposure index, we incorporate the 2008 input-output (I-O) tables from Global Trade Analysis Project (GTAP).

We begin by computing the input-output coefficients from the GTAP I-O tables which capture the interdependencies between sectors in an economy. We match these coefficients with trade data from the United Nations Commodity Trade Statistics database (UNCOMTRADE) to compute the total export value for each sector, accounting for indirect changes in export demand through input-output linkages. Annex 2 of the study provides a detailed explanation of how these coefficients are computed and merged with UNCOMTRADE data.

The next step is to link these total export data with labor force surveys. To this effect, we utilize concordance tables available online which provide mappings between International Standard classification (ISIC) rev 3.1. codes and HS codes. By leveraging this concordance, we merge the microdata on labor force variables at the industry and area level in Egypt with total export data. Once the integrated labor and trade data is prepared, we are able to calculate the total trade exposure index based on districts, as previously explained. However, it is worth mentioning that all workers below the age of 15 from the sample were excluded from the analysis to ensure the accuracy and integrity of the findings.

# **4. Methodology**

The goal of the current empirical strategy is to understand the impact of rising export expansion on real wages, informality and female labor force participating, exploiting the cross-regional exposure to total exports in Egypt between 2007 and 2018.<sup>2</sup> To this effect, we consider the following simple linear regression model: is the dependent variable, 0is the intercept, 1 is the coefficient of our trade exposure variable, and aa 2 is the coefficient for the set control