rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2. MENA Public Education Spending as a Percentage of Government Spending (FY05–FY15 averages)**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Saudi Djibouti Iran Syrian Morocco Malta Kuwait Qatar Egypt Algeria Oman Bahrain Lebanon<br>Arabia (Islamic Arab<br>RepublicRepublic<br>of)<br>Percent<br><!-- End of picture text --> _Source:_ UNESCO 2017.

# **Health (in-kind transfer)**

Similar to education, with the changes taking place in the country since 2011, spending on health has become a priority for the GoE in the past three or four years. Article 18 of the 2014 Constitution mandated that 3 percent of gross national product be spent on health by FY17. Also similar to education, the target has not yet been reached. However, in absolute terms, spending on health doubled from FY09 to FY15, reaching LE 37.2 billion in FY15. As a percent of GDP, spending on health has been stable over the same period at 1.4–1.5 percent. It has, however, increased as a share of total government spending to 5.1 percent in FY15 from 4.5 percent in FY09. Per capita public health spending has also increased in nominal terms, reaching LE 418 in FY15 from LE 400 in FY09. This represented a 4.6 percent increase while the population grew at 15.6 percent during the same period.

Compared to its MENA neighbors, Egypt is on the lower end of the spectrum in terms of spending on health as a percent of GDP. Egypt fairs better than the lower middle income average (Figure 3), but as a share of total government spending on health, it is lower than the average spending of a lower middle income country (Figure 4).

**Figure 3. MENA Public Health Spending as a Percentage of GDP (FY05–FY14 averages)**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Percent<br><!-- End of picture text --> _Source:_ WDI 2017.

11
indices of practices weighted by these correlations between adoption and maize production.

We additionally explore patterns of knowledge and adoption specic to individual techniques. We use responses from the same knowledge exam to quantify farmer knowledge of individual SLM techniques, categorizing questions by technique. The knowledge of a specic technique is a [0,1] continuous variable that depicts the share of questions pertaining to the practice that the respondent has answered accurately. The adoption of a technique is captured by a binary variable that indicates whether the farmer adopted the technique on at least one of his plots.

Knowledge, adoption, and perception of the SLM techniques were collected at the individual level from the household questionnaire. Two respondents were interviewed: typically, the household head and the head's partner or spouse.<sup>23</sup> Our sample of CFs and other farmers consists of those who reported their personal information, participated in an agricultural knowledge exam with questions related to each specic SLM practice, and self-reported their SLM adoption rates. Our nal regressions samples consist of 347 CF-year observations and 10,955 person-year observations.<sup>24</sup> Selective sample attrition is of concern, and we address it in the next section.

# 3.8 Empirical Strategy

We causally estimate the intent-to-treat (ITT) eects of a community being assigned to a direct CF training (relative to a status quo CF modality) on the SLM knowledge and adoption of CFs<sup>25</sup> and other farmers in the community, Y, using a simple reduced-form specication:

> 23In the case of polygamous households, the main spouse was interviewed. Only 2.7 percent of our sampled households are polygamous.

> 24The number of villages that were administered the CF survey were 179 and 172 in 2012 and 2013, respectively. The number of farmers interviewed in 2012 were 6,252, and 5,290 in 2013. Sample sizes vary in descriptive statistic tables and some regression tables, due to the addition of variables excluded from the main analysis.

> 25CF-level regressions control for community-level CF outcomes and characteristics. In those communities where we (randomly) assigned an additional woman farmer to be trained, we measure increased village-level exposure by regressing the maximum value of CF outcomes within the village on the maximum (mean) value of binary (continuous) covariates. Switching to mean of outcomes, and controlling for mean and max of all covariates does not aect our conclusions.

15
15 immigration is believed to have positive macroeconomic and labor market effects, promoting the return of refugees at considerable financial and human cost only to have them replaced by economic migrants may not make sense. Policies relating to refugees need to account for the fact that ‐ depending on the human capital they bring and that they build during the time they reside in the host country – refugees can be substitutes or complements to both native workers and other migrants. Thus, while refugee policies are highly specific, (they must account for the special legal status of refugees, and for the humanitarian dimension of accepting refugees) the economic implications of integrating refugees cannot be simply separated from those of other migrants.

The macroeconomic impact and labor market impact of refugees will only be significant where the flow of refugee is large enough – which, in advanced countries, is the case of Sweden and Germany in recent years or of Israel in the early 1990s (arrival of Soviet Jews). In the first instance, the spending dedicated to absorbing a large flow of refugees can be a source of economic stimulus to demand. For example, recent OECD and IMF reports estimate that the demand increase related to the refugee inflow in Europe was about 0.1% of GDP in 2016 (OECD 2015; Aiyar et al. 2016).

Where the economy is already at full employment, the effect of spending on refugees could be inflationary at first but – assuming refugees are allowed to work – they eventually expand the economy’s productive capacity. While the demand‐expanding effect of refugee inflow may be temporary and depends on its intensity over short periods and the stage of the business cycle, the supply‐expanding effect are expected to be long‐lasting, and to cumulate if the refugee inflow is sustained over many years.

As refugees find jobs, they have effects like those of economic migrants, which is to stimulate increased capital formation. Investment is needed to equip the new workers with machines, as well as to house them. Refugees are likely to induce expansion of sectors such as construction and utilities (electricity, water, etc.), which are among the most capital intensive (See, for example, UK Office for National Statistics, 2013 and 2016). The increased capital formation can be financed from increased domestic savings or from increased capital flows from abroad, as in the United States in the second half of the 19<sup>th</sup> century (Hatton & Williamson 1998) and Israel in the 1990s (Cohen & Hsieh 2001).

In today’s Germany and Sweden, which are near full employment and run large structural current account surpluses, the increased domestic capital formation is most likely to be financed by domestic savings, implying reduced current account surpluses or, equivalently, reduced capital outflow. To give a sense of the magnitudes involved, if the new refugee flow adds 1% to the labor force and – assuming that in the long run, the capital/labor ratio is recognized feature of migration to the United States as far back as the 19<sup>th</sup> century. (Hatton and Williamson 1998)
Croke et al. (2016)

- vi. In the event of apparent textual contradictions about key parameter values in a trial (for example, language in the study text which reports significant effects versus reported standard deviation values which imply non-significant results), we first try to obtain the original microdata to perform the estimation ourselves. Where this is not possible, we assess which statistics were the primary focus of reporting in the text and contact the original authors for clarification.

- vii. When possible, treatment effect estimates are extracted based on an Analysis of Covariance (ANCOVA) model, rather than estimates based on difference-indifference estimators. The _Cochrane Handbook_ states that since ANCOVA estimates “give the most precise and least biased estimates of treatment effects they should be included in the analysis when they are available” (Higgins and Green (2011), section 9.4.5.2). Properly including baseline weight measures in the analysis is also critical in contexts with baseline imbalance (Kerwin, 2015).

The full sample includes 22 estimates from 20 studies, twice as many as TMSDG. In particular, the full sample includes four studies not identified in TMSDG or unpublished when their review was compiled (Gateff, Lemarinier and Labusquiere 1972; Ostwald et al. 1984; Joseph et al. 2015; Liu et al. 2016); data extracted from six studies discussed by TMSDG but not included in their meta-analysis for the MDA child weight outcome (Willett, Kilama and Kihamia 1979; Miguel and Kremer 2004; Ndibazza et al. 2012; Wiria et al. 2013; Stephenson et al. 1993; Gupta and Urrutia 1982); and improved estimates from three studies that are included in the TMSDG meta-analysis (Sur et al. 2005; Hall et al. 2006; Awasthi and Pande 2001). Note that we classify Stephenson et al. (1993) as an MDA trial; the Cochrane authors classify it in this way in their 2012 Review but change the 19
**Figure 1: Large numbers of SEZs exist in all of the world’s regions**

<!-- Start of picture text --> 1000<br>Private Zones<br>Public Zones<br>800<br>600<br>400<br>200<br>0<br>Asia and the Latin America Eastern Europe Middle East and Sub-Saharan<br>Pacific and the and Central Asia North Africa Africa<br>Caribbean<br>Number of SEZs<br><!-- End of picture text --> _Source_ : Akinci and Crittle, 2008.

# **1.2 The multilateral legal framework for SEZs**

SEZs are not specifically mentioned by name in any WTO agreement. However, a footnote to GATT Article XVI and the Agreement on Subsidies and Countervailing Measures excludes from the definition of “subsidy” the core fiscal benefit typically provided by SEZs – an exemption from import duties and taxes on goods exported from SEZs. SEZs as such have not been the subject of any GATT/WTO dispute settlement proceeding, although of course subsidies have and can provide some guidance, and SEZ programs have not been criticized as WTO inconsistent in WTO trade policy reviews.<sup>9</sup> In at least one recent instance, however, SEZ measures were raised in an accession.<sup>10</sup> Each SEZ comprises a unique configuration of individual measures and as such must be analyzed at the level of these measures. In this regard, certain measures imposed by some Members in connection with SEZ programs may be in conflict with WTO disciplines. Of greatest concern are export subsidies and requirements to use domestic over imported goods.

> 9   See, e.g., Trade Policy Review Report by the Secretariat, China, WT/TPR/S/199 (16 April 2008), pp. 56,80,87 and Table A.III.5.

> 10   See,  Report of the Working Party on the Accession of Ukraine, WT/ACC/UKR/152 (January 2008), para 338. Compare, Report of the Working Party on the Accession of Viet Nam, WT/ACC/VNM/48 (27 October 2006), pp. 34-35, 86-87.  Reportedly, SEZ measures are also an issue in the accession of the Russian Federation to the WTO.

9
What these results suggest is that climate change impacts will vary substantially across different agro-ecological zones. In the third regression, even though aggregate estimate indicates damage from increased rainfall, farms in most AEZs will get benefits from more rainfall. It is the harmful effects of increased rainfall on several distinct AEZs that turn the overall aggregate negative.

# 5. Predictions

In this section, we simulate the impact of future climate change scenarios on African agriculture using the results from the estimated coefficients in the previous section. Note that in these simulations only climate changes, all other factors remain the same. Clearly, this will not be the case over time. Technology, capital, consumption, and access will all change over time and these factors will have an enormous impact on future farm net revenues. The purpose of this exercise is not to predict the future but simply to see what role climate may play in that future.

In order to examine a wide range of climate outcomes, we rely on two Atmospheric-Oceanic Global Circulations Models (AOGMC’s): CCC (Canadian Climate Centre) (Boer et al. 2000) and PCM (Parallel Climate Model) (Washington et al. 2000). We use the A2 emission scenario from the SRES report (IPCC 2000). Given these emission trajectories, each of these models generates a future climate scenario. These scenarios were chosen because they bracket the range of outcomes predicted in the most recent IPCC (Intergovernmental Panel on Climate Change) report (IPCC 2007). In each of these scenarios, climate changes at the grid cell level were summed with population weights to predict climate changes by country. We then examined the consequences of these country level climate change scenarios for 2020, 2060, and 2100.

To obtain district level climate predictions for each scenario, we added the predicted change in temperature from the climate model to the baseline temperature for each season in each district. For precipitation, we multiplied the predicted percentage change in precipitation from the climate models by the baseline precipitation for each season in each district. Table 4 presents the African mean temperature and rainfall predicted by the two models for each season for the years 2020, 2060 and 2100. In Africa in 2100, PCM predicts a 2°C increase and CCC a 6.5°C increase in annual mean temperature. Although temperature predictions vary in its magnitude of change by the models, rainfall predictions vary also in its direction of change by the models. PCM predicts a 10% increase in annual mean rainfall in Africa and CCC a 15% decrease. Even though the annual mean rainfall in Africa is predicted to increase/decrease depending on the scenario, 10
provided by FINCA DRC agents could therefore be viewed as a better fit for the urban poor than for other groups. We derive similar results when we replace the cash-in variables with the cashout variables on the left-hand-side of our sub-sample regressions, and so we do not report those results in the paper.

# **_6b. Monitoring of FINCA Agents_**

FINCA agents receive unannounced visits from agent network officers to assess how well they are following FINCA protocols and to suggest areas for performance improvement. Agents are supposed to be visited on a monthly basis, though the monitoring reports that we have indicate that visits are less frequent than that for a large number of them. Agents are rated in four broad categories: liquidity management, client service, transactions performance, and branding. Within each category, agents receive scores in multiple sub-categories.<sup>32</sup> For example, for liquidity management agents receive a score for the management of “e-float,” that is, money in the agent’s account that can be transferred to a client’s account in exchange for cash, and “cash float” (which can be exchanged for e-float). For client service, agents receive scores for state of shop, connectivity/service availability, and operator availability. For branding, scores are given for agent visibility, posting of FINCA images, and the quality of FINCA signage outside the shop.

We sum the scores from the sub-categories to derive a score in each of the four broad categories, which we include in Table 7 in the regressions that we presented earlier.<sup>33</sup> Although we lose a large number of observations because most agents did not receive visits in all months for which we have transactions data, the total number of observations in the regressions still

> 32 It should be noted that this assessment of the agent is not in any way linked to the agent’s performance in terms of number of transactions or volumes transacted. These assessments are carried out in the field by personnel that does not have access to the headquarters database on transactions.

> 33 Because the transactions performance category and its sub-components were not significantly associated with transaction numbers or volumes, we do not include it among the explanatory variables in the models presented in Table 7.

25
- 13 -

# III. 1985 TAX STUDY AND TAX REFORM PROPOSALS

15. None of the tax and tariff changes in the early 1980s were a part of any long-run plan for the development of Malawi's revenue<sup>system. On</sup> the contrary, these changes evolved into a revenue system with a number<sup>of</sup> worrying features that could be ignored only as long as they were not binding. However, in 1984 and 1985 both the IMF and the<sup>World Bank</sup> recommended that the government create a more liberal economic environment by dismantling the foreign exchange allocation system and the prevailing system of administered prices, including a restructuring of SOEs. However, once the obstacles to a freely functioning price system were removed,<sup>the</sup> currently non-binding distortions of the new ad hoc revenue measures would become binding.

16. In the spring of 1984 a World Bank mission outlined some of the difficulties that the existing tax structure could pose for future growth of trade and investment. In the same year, another mission from the<sup>World</sup> Bank, this time dealing with a structural adjustment loan, expressed some concerns about the impact of existing taxes on production incentives<sup>in</sup> both agriculture and manufacturing.

17. By 1985 it had became increasingly clear to government officials -- following the Bank studies which had identified preliminary problems -- that recent revenue measures were not desirable in the long run and that reform of the overall tax system was necessary. The stage had been set for a restructuring of the Malawian tax system.

18. In January of 1985, the Government of Malawi asked the World Bank for assistance in reviewing its whole tax system (not just selected<sup>issues)</sup> and suggesting recommendations for reform. In response to this request, the Bank assembled a study team which went to Malawi to investigate<sup>its tax</sup>
Lewis, A. 1954. “Development with Unlimited Supplies of Labour,” The Manchester School, vol. 22, May, pp.1‐32.

McMillan, M. and K Harttgen. 2014. “What is Driving the ‘African Growth Miracle’?” National Bureau of Economic Research, NBER working paper no. 20077, April.

McMillan, M. and D. Rodrik. 2011. “Globalization, Structural Change and Productivity Growth,” NBER Working Paper # 17143, June.

Melitz, M. and S. Polanec. 2012. “Dynamic Olley‐Pakes Productivity Decomposition with Entry and Exit,” NBER Working Paper # 18182, June.

Moll, B. Forthcoming. “Productivity Losses from Financial Frictions: Can Self‐Financing Undo Capital Misallocation?” _American Economic Review_ Mortensen, D. and C. Pissarides. 1994. “Job‐Creation and Job‐Destruction in the Theory of Unemployment,” _Review of Economic Studies_ , 61, pp. 397‐415.

Ngai, L. and C. Pissarides. 2007. “Structural Change in a Multisector Growth Model,” American Economic Review, vlo. 97, no. 1, pp. 429‐443.

Olley, S. and A. Pakes. 1996.” The Dynamics of Productivity in the Telecommunications Industry,” _Econometrica,_ 64(6), pp.1263‐1298.

Parente, s. and E. Prescott. 1994. “Barriers to Technology Adoption and Development,” Journal of Political Economy, 102 (2), pp. 298‐321.

Ranis, G. and J. Fei. 1961. “A Theory of Economic Development,” The American Economic Review, Vol. 51, no.4, pp. 533‐565.

Republic of Uganda. 2007. _The Uganda National Export Strategy: 2008‐2012_ . Ministry of Tourism, Trade and Industry. October.

______. 2013. _Uganda: Diagnostic Trade Integration Study (DTIS) Update_ . April.

Resnick, D. and J. Thurlow, 2014. “The political Economy of Zambia’s Recovery: Structural Change without Transformation?” International Food Policy Research Institute, Discussion Paper 1320, February.

Rodrik, D. 2014. “An African Growth Miracle?” Centre for Economic Policy Research, CEPR Discussion Paper # 10005. June.

Romalis, J. 2004. ‘Factor Proportions and the Structure of Commodity trade,” _American Economic Review_ , vol. 94, no. 1, pp. 67‐97.

34
_Table 1.3: Additional Employment Probability for the DTS, General Average HS grade - Probit Estimate on Employment Right after TVET_

|VARIABLES|(1)<br>Total|(2)<br>Total|(3)<br>Total|(4)<br>Total|(5)<br>Total|
|---|---|---|---|---|---|
|DTS dummy (=1, RP=0)||0.073**|0.062*|0.078**|0.058|
|||(2.079)|(1.845)|(2.086)|(1.597)|
|Standardized HS grade|0.008|0.008|0.010|0.007|0.119|
||(0.481)|(0.485)|(0.641)|(0.386)|(1.402)|
|DTS * std. HS grade|||||−0.030<br>(−0.864)|
|Years of schooling|−0.005|−0.004|−0.021**|−0.007|−0.014|
||(−0.521)|(−0.404)|(−2.251)|(−0.628)|(−1.340)|
|Work experience|−0.016***|−0.015***|−0.014***|−0.014***|−0.016***|
||(−4.908)|(−4.269)|(−4.168)|(−3.785)|(−4.156)|
|Work experience (squared)|0.000***|0.000***|0.000***|0.000***|0.000***|
||(3.069)|(2.840)|(2.758)|(2.598)|(2.655)|
|Female|−0.010|−0.047|−0.050|−0.059|−0.042|
||(−0.308)|(−1.344)|(−1.558)|(−1.541)|(−1.131)|
|Mother's education level|−0.001|−0.001|−0.001|−0.001|−0.001|
||(−1.257)|(−1.301)|(−1.407)|(−1.391)|(−1.233)|
|Observations|840|745|830|745|742|
|Enrollment Year dummy|NO|NO|YES|NO|YES|
|TVI dummy|NO|NO|NO|YES|YES|

_Source: DTS Tracking Survey, 2015, World Bank. World Bank calculations. Note: z-statistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01._ 34
# **Table 4. Main Results**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent variable||Ratio of out|-province mig|rant workers||
|Landline phone|0.021**|0.022**|0.021**|0.020**|0.019**|
||(0.009)|(0.009)|(0.009)|(0.008)|(0.008)|
|_Year fixed effect_|Y|Y|Y|Y|Y|
|_Village fixed effect_|Y|Y|Y|Y|Y|
|_Other time-varying controls_|N|N|N|Y|Y|
|_Province time dummies_|N|N|N|N|Y|
|Time polynomial interactions with:||||||
|_Log income in 1991_|N|Y|Y|Y|Y|
|_% arable land in 1991_|N|N|Y|Y|Y|
|_Mountains area (0/1)_|N|N|Y|Y|Y|
|Observations|403|397|397|397|397|
|R-squared|0.064|0.094|0.106|0.139|0.304|
|Number of villages|58|57|57|57|57|

Note. Robust standard errors, clustered at the village level, in parentheses: *** p<0.01, ** p<0.05, * p<0.1 33