rafmacalaba/datause-encoder-data huggingface dataset:
Atlas provides aquifer productivity<sup>3</sup> and aquifer-type information from 51 African countries. Additionally, it has information on the recharge rates of groundwater, as well as groundwater depth maps across the African continent.

We merge the groundwater datasets with the Global Agro-Ecological Zones (GAEZ) dataset to impute water-supply information at the GAEZ grid level.

# **3.2 Agricultural Productivity**

We use the GAEZ dataset to measure agricultural productivity in SSA. The GAEZ dataset is curated by FAO and IIASA (2020) and provides information on agricultural productivity and estimates production gaps across the world for the years 2000 and 2010 at a granular level of approximately a 9x9 kilometer (km) grid. For our analysis, we consider the data for 2010 only, because they are the latest data.<sup>4</sup> The data are also disaggregated by crop type and irrigation type: rain-fed or irrigated using water from sources such as rivers, lakes, and/or groundwater. We have data on 291,798 GAEZ grids, and our subsequent analysis is conducted at the GAEZ grid level.

In addition to productivity data, the GAEZ dataset offers insights into agro-climatic conditions such as soil conditions, temperature, precipitation during the agricultural growing season, water availability, and land type in the area. We focused our study on croplands to determine whether shifting toward groundwater irrigation could lead to significant gains in agricultural productivity. To answer this question, we examined yield and potential yield at the crop–grid cell–year level to identify productivity gaps by irrigation type.

We restrict our analysis to cereals, namely, barley, millet, maize, rice, sorghum, and wheat. We focus on cereals for our analysis, because they are the primary source of energy for most households and the demand for cereals is expected to grow in the coming decades (OECD, 2016).

Table 1 provides descriptive statistics for the GAEZ dataset, showing that despite the average harvested area of rain-fed croplands being higher than that of irrigated croplands (100 hectares per grid cell versus 20 hectares per grid cell), the mean production and yield of irrigated croplands are relatively higher. This finding suggests production and productivity gains are possible through irrigation, which is also reflected in larger production gaps for rain-fed croplands than for irrigated croplands.

> 3Aquifer productivity, measured in liters per second, refers to the ability of an aquifer to sustain a certain level of groundwater extraction through a well or a bore. It can be thought of as the water yielded from a well or bore that sits on a certain aquifer.

> 4Note the irrigation landscapes of 2000 and 2010 are very similar for SSA, and thus, we opt to work with cross-sectional data for the latest year, which has a slightly higher rate of irrigation.

6
**Table 5: Unconditional Correlation between Tariffs, Other Key Provisions, and Labor Provisions**

||Tariff<br>Preference<br>Margins|Anti-<br>dumping|Competition<br>Policy|Subsidies|TBT A|TBT B|TBT C|TBT D|
|---|---|---|---|---|---|---|---|---|
|Antidumping|-0.065*||||||||
|Competition Policy|-0.030*|-0.0890*|||||||
|Subsidies|0.120*|-0.1174*|0.2321*||||||
|TBT A|0.063*|0.7650*|-0.1080*|0.1316*|||||
|TBT B|-0.054*|0.7383*|-0.0996*|0.1794*|0.6622*||||
|TBT C|-0.038*|0.7133*|-0.1115*|0.1853*|0.6324*|0.9340*|||
|TBT D|0.017*|0.0212*|-0.0077*|0.0144*|-0.0178*|-0.0188*|0.0336*||
|Aspirations|-0.119*|-0.2236*|0.2521*|0.0806*|-0.2641*|-0.2496*|-0.2672*|-0.0097*|
|Substance|-0.083*|-0.1168*|-0.0080*|-0.0100*|-0.1340*|-0.1404*|-0.1501*|-0.0166*|
|Substance: Invest|-0.102*|-0.1027*|-0.0781*|-0.0994*|-0.1180*|-0.1260*|-0.1321*|-0.0064*|
|Cooperation|-0.022*|0.4464*|-0.1963*|-0.0961*|0.2957*|0.2630*|0.2854*|-0.0302*|
|Institutions|-0.038*|0.5557*|0.0105*|-0.0294*|0.4042*|0.3725*|0.3705*|-0.0138*|

**Notes:** The * indicates significant at the 5 per cent level.  TBT represents Technical Barriers to Trade as described in Table 11.

pg. 28
(Core NTMs) for each HS six-digit tariff line item estimated by Kee, Nicita and Olarreaga.<sup>24</sup> AVEs of core NTMs<sup>25</sup> (price and quantity control measures, technical regulations, as well as monopolistic measures, such as single channel for imports) are computed by Kee et al. using Leamer’s comparative advantage approach.<sup>26</sup> The second is an all-encompassing measure of protection, Overall Protection, which is the sum of all three possible sources of protection: tariffs, NTMs and domestic subsidies in agriculture.<sup>27</sup> Since they are measured as ad valorem equivalents, they are summed to yield an overall measure of protection.

The third is a complete tariff measure, All Tariff, which combines ad valorem tariffs with (ad valorem equivalents of) specific tariffs.<sup>28</sup> Both measures have been historically prevalent in agriculture, and since agriculture was untouched by the multilateral rounds of negotiations 24 Kee, Nicita and Olarreaga (2004b).

25 Core NTMs include quantitative restrictions, price control measures, monopolistic measures and technical regulations corresponding to codes 6000, 8000, 7000 and 8100 in the UNCTAD TRAINS database, which is the source of the NTB indicators.

> 26 Leamer’s (1990) method is as follows. First, imports are predicted using factor endowments. Then the impact of NTBs on imports is measured as deviations of imports, in the presence of NTMs, from these predicted values. Finally, this quantity impact of NTMs on imports is converted into a price equivalent (or AVE) by using the import demand elasticities estimated in Kee, Nicita and Olarreaga (2004).

27 The source of the dollar value of agriculture domestic subsidies is Ng and Olarreaga (2004), obtained from WTO members’ notifications during 1999. The method of computing the AVE of domestic support is to treat them as NTMs and apply the same method as for Core NTMs. Production subsidies vary widely in their uses and aims. They include direct payments to producers (such as deficiency payments), input subsidies (on irrigation water, for example), policies that distort market prices (market price supports), and interest subsidies on commodity loan programs. Also included in these measures are the “boxed” policies demarcated in the Uruguay round. These include direct payments and price support benefits that directly influence production (“amber box”), payments tied to limits on production (“blue box” policies), research, domestic food aid, environmental programs, and certain crop insurance and income safety net programs (“green box” policies).

28 The main source of the ad valorem tariff data is the WTO Integrated Data Base (IDB). Specific (dollar/unit) tariffs can be converted to their ad valorem equivalents by dividing the specific tariff by the unit value of the imported product. The International Trade Commission (ITC) provides the data to accomplish this at the tariff line level.

13
# **Contents**

|**1**<br>**Intr**|**oduction**|**3**|
|---|---|---|
|**2**<br>**Mo**|**del description**|**4**|
|2.1|Base models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . .<br>4|
||2.1.1<br>Abatement investment model . . . . . . . . . . . . . . . . . . . . . . . . . .|. . .<br>4|
||2.1.2<br>“Strawman” model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . .<br>6|
|2.2|Adding delayed learning to the INV and MAC models . . . . . . . . . . . . . . . .|. . .<br>6|
||2.2.1<br>Abatement investment model with delayed learning<br>. . . . . . . . . . . . .|. . .<br>6|
||2.2.2<br>“Strawman” model with delayed learning<br>. . . . . . . . . . . . . . . . . . .|. . .<br>8|
|2.3|Calibration<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . .<br>8|
||2.3.1<br>Global parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . .<br>8|
||2.3.2<br>Marginal abatement and investment costs . . . . . . . . . . . . . . . . . . .|. . .<br>10|
|**3**<br>**Res**|**ults**|**12**|
|3.1|Impact of delayed learning on aggregate policy cost . . . . . . . . . . . . . . . . . .|. . .<br>12|
|3.2|Impact of delayed learning on the temporal distribution of policy cost<br>. . . . . . .|. . .<br>14|
|3.3|Impact of delayed learning on the sectoral allocation of abatement investment . . .|. . .<br>15|
|**4**<br>**Dis**|**cussion**|**17**|

2
businesses in the dominant sectors reported<sup>little or no capital, inventories,</sup> or operating expenses.

How much credit do informals use<sup>and obtain? The Peru Living Standards</sup> Survey provided information on<sup>the current debt position of each household, and</sup> on the source and termns of loans obtained in the past year.

Only 10 percent of the households<sup>that operated businesses reported that</sup> they received loans or were in debt.<sup>This is not surprising for several reasons.</sup> First, the PLSS was conducted when<sup>inflation rates were extremely high (June</sup> 1985-July 1986). (During the first<sup>half of 1985 the annual inflationwas200</sup> percent, and in the first half of<sup>1986 monthly inflation was 4 to 5 percent.</sup> Such high rates of inflation are unlikely<sup>to foster a willingness to lend, except</sup> at interest rates so high that few<sup>households would choose to borrow. Second,</sup> given a sensitivity to questions about<sup>indebtedness, the Peru Living Standards</sup> Survey probably underestimated the<sup>debt among respondents. (To preserve good</sup> will, questions on debt and credit<sup>were last in a long questionnaire".</sup> Third, given the uncertain legal position<sup>of family businesses, their ability to obtain</sup> credit from formal lending institutions is very restricted at best. As Carbonetto (1984), Mescher (1985), and Kafka (1984)<sup>document, only a minute fraction of</sup> informal sector firms in Lima borrow<sup>from financial intermediaries</sup> in the formal sector. Households that need to<sup>borrow must depend on loan sharks and</sup> pawnbrokers, or resort to a "pandeiro". This is a revolving fund<sup>to which</sup> members make a weekly contribution. A lottery determines the winner of the<sup>week's</sup> contributions .9 9 See Mescher(1985), World Bank (1989).

7
# **5.1 What Do Firms Say the Consequences of Formalizing Are?**

The August 2010 survey asked the firms that formalized as a result of our intervention if they had benefited from being formal. The most common response, given by 36 percent of firms, was that they had yet to see any benefit from registering. The next most common response, coming from 20 percent of firms, was an answer related to improvements in the image of the business. This encompasses answers like ―it is good publicity‖, ―customers trust the business more‖, and ―social validity‖. Other common responses refer to feeling more secure and protected (12 percent), and to fact that the business registration could be used in the future to help obtain business loans (10 percent). Very few firms claimed to have obtained a loan, or to have received a government contract as a result of formalizing.

# **5.2 Econometric Estimation of the Consequences of Formalizing**

We use the follow-up data to estimate the impact of formalizing on firm outcomes, intermediate channels, and attitudes of firm owners. For outcome _Y_ and firm _i_ in randomization strata _s,_ we estimate: where are randomization strata fixed effects, is the baseline value of the dependent variable, are survey wave effects. Our main object of interest is in estimating , the causal impact of becoming formal (defined in terms of being registered with the DS) on the outcome of interest. The inclusion of the lagged dependent variable increases power, and helps control for any selective attrition based on the outcome of interest.<sup>9</sup> We pool together all rounds of follow-up data to increase power (McKenzie, 2011); Appendix Table 3 shows we cannot reject equality of impacts on profits across our three follow-up rounds (p>0.90). In addition, for the key outcomes of firm profits and firm sales, the March and December 2011 survey asked for each of the past 3 month’s data, enabling us to run the specification in (2) with 7 months of observations per firm for these outcomes. Standard errors are clustered at the firm level. We instrument for formalization with three variables indicating assignment to the 10,000 LKR, 20,000 LKR, and 40,000 LKR treatments respectively. Since the information and reimbursement only treatment did not lead to any change in registration, it does not serve as an instrument and we drop this treatment group from this part of the analysis.

The treatments are valid instruments for being formal under the assumption that they affect the outcomes of interest only through changing registration status, and not through any other channel. An obvious concern with this assumption is that the grants given to the firm owner may have had

> 9 Recall that we can not reject that attrition is unrelated to treatment status.

16
# **The Dynamics of High-Growth Firms:**

# **Evidence from Tunisia**<sup>**1**</sup>

Marcio Cruz<sup>2</sup> , Leila Baghdadi<sup>3</sup> , and Hassen Arouri<sup>4</sup> JEL classification: D22 Keywords: High-Growth Firms, Entrepreneurship, Tunisia

> 1 The analysis for this paper was originally produced as background material for the World Bank Group’s report _High‐ Growth Firms: Facts, Fiction, and Policy Options for Emerging Economies_ (Grover, Medvedev, and Olafsen, 2019). The authors are grateful for the helpful comments received from Denis Medvedev, participants of the HGF report authors’ seminar, and participants of the Economics Research Seminar Series at the American University in Cairo. 2 The World Bank. Email: marciocruz@worldbank.org. Corresponding author.

> 3 Tunis Business School.

> 4 National Institute of Statistics, Tunisia.
# **5.2 Robustness**

There were several modeling choices made in the estimation of the baseline results. This section briefly explores the robustness of the results to these choices. The discussion above already showed that the use of a model in levels or differences does not importantly affect the results. In what follows we explore the role of the number of lags, the measure of disasters, the measure of output, and the order of the variables in the VAR.

The results of each of these exercises, reported in figures 3 to 7 show that the findings discussed above are not crucially driven by these modeling choices. Adding a third lag turns positive the point estimate for the GDP impact of climatic disasters, but as in the previous case, the impact is not statistically significant (Figure 3). The conclusions regarding deficits, expenditures, and revenues are largely unaffected.

We use two different indicators of the occurrence of disasters. First, a simple index that takes the value 1 if at least one disaster of each category took place in a given year (Figure 4, Panel A). Second, a more complex index that takes into account the month when the disaster occurs, thus reporting the impact of a disaster occurring January 1<sup>st</sup> (Figure 4, Panel B). In both cases, the output and fiscal impacts of disasters are similar to those reported in the baseline results. Similar results are also obtained when using the Penn World Tables’ measure of real per capita GDP that adjusts for purchasing power parity instead of the measure in constant dollars (Figure 5).

As explained in section 3, the baseline estimation in levels included the changes in the (log) interest rate instead of the level of this variable because in some cases its impulse responses suggested non-stationary behavior. While this choice makes a difference for the estimated responses of this variable, it does not importantly affect the estimated responses of output and the fiscal variables to disasters, as shown in Figure 6.

Finally, changing the order of variables in the VAR, so that expenditures are located after output inflation, and interest rates, and just before revenues, does not change the main results either (Figure 7).

18
Policy Research Working Paper 0

# **Abstract**

The vast majority of randomized experiments in economics rely on a single baseline and single follow-up survey. If multiple follow-ups are conducted, the reason is typically to examine the trajectory of impact effects, so that in effect only one follow-up round is being used to estimate each treatment effect of interest. While such a design is suitable for study of highly autocorrelated and relatively precisely measured outcomes in the health and education domains, this paper makes the case that it is unlikely to be optimal for measuring noisy and relatively less autocorrelated outcomes such as business profits, household incomes and expenditures, and episodic health outcomes. Taking multiple measurements of such outcomes at relatively short intervals allows the researcher to average out noise, increasing power. When the outcomes have low autocorrelation, it can make sense to do no baseline at all. Moreover, the author shows how for such outcomes, more power can be achieved with multiple follow-ups than allocating the same total sample size over a single follow-up and baseline. The analysis highlights the large gains in power from ANCOVA rather than difference-in-differences when autocorrelations are low and a baseline is taken. The paper discusses the issues involved in multiple measurements, and makes recommendations for the design of experiments and related non-experimental impact evaluations.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at dmckenzie@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
_40_

# _WATER WHEN IT COUNTS_

_mental Modelling and Software_ , 37(C): 68–77.

- **Global Facility for Disaster Reduction and Recovery (GFDRR).** 2017. “Country Profile: Mozambique.” _Accessed online February 9, 2017 at: https://www.gfdrr.org/sites/gfdrr/files/region/MZ.pdf_ .

- **Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein.** 2014. “Learning through noticing: Theory and evidence from a field experiment.” _The Quarterly Journal of Economics_ , 129(3): 1311–1353.

- **Kondylis, F., V. Mueller, and J. Zhu.** 2017. “Seeing is Believing? Evidence from an Extension Network Experiment.” _Journal of Development Economics_ , 125: 1–20.

- **Little, K., M. Hayashi, and S. Liang.** 2016. “Community-Based Groundwater Monitoring Network Using a Citizen-Science Approach.” _Groundwater_ , 54(3): 317–324.

- **Lowry, C., and N. Fienen.** 2013. “CrowdHydrology: Crowdsourcing Hydrologic Data and Engaging Citizen Scientists’.” _Groundwater_ , 51(1): 151–156.

- **Meinzen-Dick, R.** 2007. “Beyond Panaceas in Water Institutions.” _Proceedings of the National Academy of Sciences_ , 104(39): 15200–15205.

- **Ostrom, E.** 1990. _Governing the Commons: The Evolution of Institutions for Collective Action._ Cambridge:Cambridge University Press.

- **Ostrom, E., and E. Schlager.** 1992. “Property-Rights Regimes and Natural Resources: A Conceptual Analysis.” _Land Economics_ , 68(3): 249–262.

- **Plusquellec, H.** 2002. “Is the Daunting Challenge of Irrigation Achievable?” _Irrigation and Drainage_ , 51(3): 185–198.

- **Varzi, M. M.** 2016. “Crop Water Production Functions–A Review of Available Mathematical Method.” _Journal of Agricultural Science_ , 8(4): 76–83.

- **Vidal, John.** 2016. “As Mozambique’s Rivers Dry Up, the Hopes of a Harvest Evaporate
<!-- Start of picture text --> Limited reconstruction<br>capacity<br>Reduced economic Long reconstruction<br>Amplifying<br>development period after disasters<br>feedback<br>loop<br>Reduced accumulation of Large economic cost of<br>natural disasters<br>capital and infrastructure<br><!-- End of picture text --> _Figure 3: Amplifying feedback loop that illustrates how natural disasters could become responsible for macro‐level poverty traps._ This type of feedback can be amplified by other long term mechanisms, like changes in risk perception that reduces investments in the affected regions or reduced services that make qualified workers leave the regions. Because of these mechanisms, the consequences of a disaster can last much longer than what is considered the recovery and reconstruction period.

# 4. Assessment methodologies

From this description of mechanisms and determinants of indirect losses, many scholars have attempted to assess these losses for real or potential events. To do so, they have mobilized various methodologies.

Higher‐order losses are sometimes measured using firm‐ or household‐level surveys (e.g., Kroll et al., 1991; Tierney, 1997; and Boarnet, 1998; Smith and McCarty, 2006). More frequently, they are estimated using economic models, including (i) microeconomic models at the household level (e.g., Dercon, 2004); (ii) econometrics models at the local level (e.g., Strobl, 2008) or the national level (Albala‐Bertrand, 1993; Skidmore and Toya; 2002; Noy and Nualsri 2007); (iii) input‐output (IO) models at the regional or national level (e.g., Gordon et al., 1998; Okuyama and Chang, 2004; Hallegatte, 2008); (iv) Computable General Equilibrium (CGE) models at the regional or national level (e.g., Rose et al., 1997; Rose and Liao, 2005, 2007; Tsuchiya et al., 2007); or (v) network‐production system model, even though this line of research has not been operationalized yet (Haimes and Jiang, 2001; Henriet and Hallegatte, 2008). Available source of data for loss assessment are summarized in Annex 1.

# _Data collection on past events_

A first line of assessment consists of data collection on past events. This approach considers a single event in a single location. For instance, using firm‐level surveys, Kroll et al. (1991), Tierney (1997), and Boarnet (1998) investigate the consequences of lifeline and transportation interruption of firm activity and survival for the Loma Prieta earthquake in 1989 and the Northridge earthquake in 1994. This line of 16