rafmacalaba/data-use-mentions-extended huggingface dataset:
fixed vegetable oils|4239|157859|0.4||0.0||0.1||
|Weaving, knitting mach. for preparing yarns, parts|7245|155098|0.4||0.1||0.0||
|Digital central storage units, separatelyconsigned|7524|154536|0.4||||0.0||
|Other non-electricparts & accessories of machiner|7499|153353|04|00|00|00|00||
|y<br>Transmission shafts cranks bearing housings etc|7493|153350|.<br>04|.<br>00|.<br>00|.<br>00|.<br>00||
|, , .<br>Medical instruments and appliances|8720|148094|.<br>0.3|.<br>0.0|.<br>3.3|.<br>0.0|.<br>0.0||
|Sheets & plates, rolled >4.75mm of iron/steel|6744|144164|0.3|0.0|||0.0||
|<br>Radiotelegraphic & radiotelephonic transmitters|7643|143939|0.3||||0.0||
|Machinery for extruding man-made textiles and parts|7244|139461|0.3|0.0|0.2||0.0||
|Eltil ihkiliitt|8748|136689|03||00||00||
|ecrca measurng, cecng, anayzngnsrumens|||.||.||.||
|Copper ores & concentrates;copper matte/cement|2871|135642|0.3||||||

_Source_ : Calculated using UN COMTRADE Database _Notes_ : A value of 0.0 represents less than US$100 in exports; blanks represent zero exports.
that good policies package such as private property rights, fiscal discipline, macroeconomic stability, and open to trade on average increase the income of the poor. Furthermore, there are some studies that found ambiguous or mixed relationship between foreign aid and growth. Bourguignon _et al._ (2007) argue that the empirical literature on aid effectiveness has yielded unclear and ambiguous results. The “black box”, which is the linkage from donors to policy makers, from policymakers to policies and from policies to outcomes, makes it difficult for researchers to quantify and open it. Rajan _et al._ (2005) find no robust positive relationship between aid and growth by using cross-country panel data. Ekanayake _et al._ (2010) find mixed effect of foreign aid on economic growth in developing countries. Werker _et al._ (2008) argue that foreign aid affects most components of GDP, but it has no statistically identifiable impact on prices or economic growth. Inanga _et al._ (2008) conclude that foreign aid finance can generate economic growth if effectively utilized in a stable macroeconomic environment. Finally, Holder (2004) argues that the relationship between foreign aid and growth turns out to be an inverted-U shaped under reasonable policy assumption, which is an Aid Laffer Curve. Positive relationship between foreign aid and growth is located in the upward sloped side of the Aid Laffer Curve, while the negative relationship is located at the downward sloped side of the Aid Laffer Curve. The importance of AfT for the low income countries can be related also to the relationship between trade and economic growth. We would find a large number of theoretical and empirical studies that have examined this relationship. Main survey studies are Anderson _et al._ (1996) and Greenaway _et al._ (1994). Winters _et al._ (2000) carry out a theoretical analysis of the relationship between trade measures and their impact on poverty using both simple forms of static, and short and long term dynamic analysis. He identified a number of
Figure 6. International trade policy, border processes, and logistics A. Tariff rates in AEs and EMDEs

# B. Tariff rates by different sectors

<!-- Start of picture text --> Percent<br>Advanced economies EMDEs<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 2000 2005 2010 2015 2020<br>C. Trade uncertainty for AEs and EMDEs<br>Percent Percent<br>8 40<br>6<br>30<br>4<br>20<br>2 Advanced economies EMDEs (RHS)<br>10<br>0<br>2 0<br>1995 1999 2003 2007 2011 2015 2019<br>E. Liner shipping connectivity index<br>Index, 100 = maximum in 2004 2006<br>60 2020<br>50<br>40<br>30<br>20<br>10<br>0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> Percent Agriculture Manufacturing<br>18<br>15<br>12<br>9<br>6<br>3<br>0<br>1995 2020 1995 2020<br>Advanced economies EMDEs<br>D. Logistics performance index<br>Score 2007 2018<br>4<br>3<br>2<br>1<br>0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# F. Regional trade agreement participation

<!-- Start of picture text --> Number of RTAs 2000 2020<br>AEs<br
outside the affected zone.

We estimate the impact of _Formosa_ by employing a series of difference-in-differences estimations (DiD) using individual-worker data, and show that the disaster sharply reduced average fishery income by as much as 42 percent in the rest of 2016. Utilizing high-resolution satellite’s boat-detection data at the monthly interval, we additionally estimate a direct impact of _Formosa_ on fishing activities, showing an average decline in fishing intensity of as much as 36 percent in the immediate period following the pollution breakout. Interestingly, we find that the negative impact of the disaster did not distribute evenly across locations; the

> 1As later discussed, we refer to the disaster as _Formosa_ following the name of the polluter that caused this marine pollution.

1
Overall, we see that male-headed households have higher agricultural productivity (measured as the value of harvested crops over the last 12 months per hectare), this advantage being significant for food crops only. In terms of explanatory variables, we see a large difference between female- and male-headed households for total land area cultivated, with male-headed households cultivating nearly twice as much. Though the total land area cultivated is substantially lower for women, the number of fields managed are only slightly lower. This indicates that the average size of plots managed by female-headed households is lower than that for male-headed households.

Female household heads also tend to be older than their male counterparts, be less educated, and have a higher incidence of disability. The dependency ratio is 32 percent higher in female-headed households. Female-headed households also have a lower number of household laborers – mostly deriving from lower household male labor. All labor variables are defined as the number of workers employed on household land during the last 12 months. Moreover, the prevalence of chemical fertilizer use and pesticide use is less than half that of male-headed households. Male-headed households are also 36 percentage points likelier to cultivate an export crop (cashew, cacao, coffee, cotton, rubber or palm oil).

Next, we investigate these same differences between male- and female-headed households in 2016, using data from the Enquête Nationale sur la Situation de l'Emploi et le Secteur Informel (ENSESI). Identically to the 2008 ENV, the sample is representative at the national, regional and urban/rural levels. The sample contains 10,392 households overall, of which 5,910 were agricultural households, and 4,529 households report some production and have information available for covariates included in the analysis. We implement the same procedure of excluding observations with productivity above the 99th percentile or below the 1st percentile, leading to a 3,936 households data set of which 517 are headed by women (13.1 percent), while 3,419 (86.9 percent) are headed
br>5|959.20|

_Source_ : Authors’ calculation using 2009 NBHS data. *** p<0.01, ** p<0.05, * p<0.1 Table 2: Test of Means between Households Affected by Shocks and Those Not Affected, 2014/15

|**Variables**|**No Shock**|**Mean**|**Shock**|**Mean**|**Mean Difference**|
|---|---|---|---|---|---|
|Urban (=1)|5,840|1.645|6,113|1.752|−0.107***|
|Poor (=1)|5,840|0.305|6,113|0.357|−0.052***|
|Extremely poor (=1)|5,840|0.21|6,113|0.257|−0.047***|
|Per capita education expenditure (in SDGs)|5,840|84.07|6,113|74.054|10.016***|
|Per capita health expenditure (in SDGs)|5,840|355.398|6,113|390.416|−35.018**|
|Per capita clothing expenditure (in SDGs)|5,840|175.497|6,113|178.322|−2.825|
|Per capita utilities expenditure (in SDGs)|5,840|126.527|6,113|124.198|2.328|
|Per capita transport expenditure (in SDGs)|5,840|393.269|6,113|361.97|31.299***|
|Per capita personal care expenditure
or two percentage points. Among infrastructure constraints, firms in low-income countries consider electricity as one of the top obstacles for their expansion. In particular, the World Bank Enterprise Surveys Indicators data show that 70 and 50 percent of firms in low-income and lower-middle-income countries, respectively, report electricity as a major constraint for growth.<sup>5</sup> In low-income countries, electricity constraints can be a combination of supply-side factors, such as low electrification rates and low generation capacity, and demand-side challenges, such as high tariffs and connection costs, as mentioned above. Surprisingly, the share of firms reporting electricity as a major constraint to growth was relatively

> 2 The World Bank Group. World Development Indicators database.

> 3 The World Bank Group. 2019. Electricity Access in Sub-Saharan Africa: Uptake, Reliability, and Complementarity Factors for Economic Impact.

> 4 The World Bank. 2010. Africa’s Infrastructure: A Time for Transformation.

> 5 Authors’ calculations using the World Bank Enterprise Surveys data.

2
negative effects of volatility are primarily due to the uncertainty it causes, which leads to planning errors by firms. Badinger (2010) contributes to the discourse by introducing a novel instrument to identify the causal effect of output volatility on growth, using exogenous volatility spillovers from abroad. The cross-sectional analysis of 128 countries confirms the negative relationship between volatility and growth, even when institutional quality is controlled for. The link between policy volatility and growth is shown in Fatas and Mihov (2013) who show that an increase in government spending volatility reduces long-term economic growth. More recently, Mumtaz and Ruch (2023), show that fiscal policy uncertainty decreases output, private consumption, fixed investment, and raises prices.

To test the hypothesis that tax revenue levels may affect government consumption volatility and hence output growth volatility, we identify time-varying volatility using quarterly data on real GDP growth and real government consumption (both seasonally adjusted) from Haver Analytics and calculate 10-year ahead standard deviations of the quarter-on-quarter (annualized) growth rate. We then convert these quarterly observations into annual observations (using the first quarter observation of each year). Figure 8A provides a binned scatterplot (where observations along the vertical and horizontal axis are partitioned and then binned into one single point) of the relationship between 10-year ahead average government consumption volatility to the level of tax to GDP. Higher levels of tax revenue are associated with lower levels of government spending volatility. Similarly, figure 8B shows that higher government spending growth volatility is associated with higher real GDP growth volatility.

We use the same methodology as above to identify the potential of a tax threshold on government consumption volatility. Figure 8C shows the coefficient on the tax threshold dummy for various values of tax to GDP. The coefficient on the tax threshold reaches a nadir at 13.5 percent of GDP and is negative (decreasing volatility above the threshold) between 13 and 23 percent.

ten-year-ahead government consumption volatility as the dependent variable. The predicted volatility is shown in figure 8D. The predicted future standard deviation of government consumption
# **1. Introduction**

The recent acceleration of natural resource discoveries in some of the world’s poorest countries ought to engender real prospects for sustainable growth and development. By right, Mozambicans should expect newly discovered gas reserves (estimated to be worth around $400 billion in revenue over the next four decades) to create a genuine opportunity for shared prosperity in one of the most impoverished countries in the world, just as Ghanaians, Ugandans and Tanzanians should harbor hope over recent oil and gas discoveries.

In reality, however, these opportunities are frequently squandered in countries that lack well-governed public institutions. Building on Sachs and Warner (1995, 2001), crosscountry observations by Mehlum et al. (2006) show that, in a sample of countries with weak initial governance, economic growth is negatively associated with resourceabundance.

Initial explanations for this relationship centered largely on economic channels, wherein resource windfalls undermine the competitiveness of the non-resource sector through currency appreciation and the misallocation of productive assets (van Wijnbergen 1984; Krugman 1987; Corden and Neary 1982; Torvik 2002). Of late, however, theoretical and empirical analyses have highlighted the primacy of political mechanisms underlining the resource “curse”. Robinson et al. (2006) submit that, in the absence of strong political constraints, resource boons lead to dysfunctional political behavior by raising the value of being in power and providing leaders with the means to influence election outcomes. Ross (2001) presents empirical evidence to support the claim that natural resource abundance – characterized in this instance as oil exports – impedes democracy, a finding later supported by Tsui (2011), McGuirk (2013) and Jensen and Wantchekon (2004). Vicente (2010) and Brollo et al. (2013) find respectively that oil discoveries and other exogenous government windfalls increase corruption, while Dube and Vargas (2013), Ross (2006) and Collier and Hoeffler (2004) find evidence that natural resources can increase the likelihood of civil conflict.

2
managed by the Carolina Population Center, University of North Carolina, and Russia’s National Research University Higher School of Economics. The ongoing panel survey started in 1994, and has been implemented every year since then, except for a break in 1997 and 1999. The RLMS collects nationally representative data on various topics including household demographics, income and consumption, occupation characteristics, and others. The sample size is between 4,000 and 6,000 households, capturing between 8,000 and 17,000 individuals for each year, which have been replenished several times due to panel attrition over time. Hardly any middle-income countries can offer such long-running and nationally representative panel data as the RLMS.

However, one data challenge with the RLMS is the considerable attrition rate over time. For example, out of the original 11,290 individuals in the 1994 round, the proportion that remains in the survey drops to 44 percent (4,917) in the 2005 round and 24 percent (2,702) in the 2015 round. We use a three-pronged approach to address attrition issues. First, we offer estimates for time periods of varying lengths. The attrition rate is far lower for shorter panels. For example, out of the original 11,290 individuals in the 1994 round, 63 percent remain in the 1998 round; the corresponding figure between the 2000 and 2004 rounds is 76 percent. Since these shorter panels and longer panels have different sample sizes due to different attrition rates, if estimation results are consistent, it will provide robustness checks on our findings. Second, we offer robustness checks that utilize econometric techniques that adjust for attrition bias (Fitzgerald _et al._ , 1998; Wooldridge, 2002). Finally, to keep reasonable sample sizes, we restrict our analysis of mobility patterns to three income categories only. To avoid any potential bias with the panel data attrition, we define these income categories using the cross-sectional data, which are nationally representative in each year. We mostly use the RLMS’s panel
<!-- Start of picture text --> All studies Studies with cost effectiveness data<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>LAYS Gained<br>Giving information on educationbenefits, costs, and quality Interventions to target teaching instruction by learninglevel not grade (in or out of school) (TaRL) Structured lesson plans w/ linked materialsand ongoing teacher monitoring & training Community involvement in school management Health products:sanitation, deworming, malaria Teacher accountability and incentive reforms Targeted intervention to reduce traveltime to schools Giving merit-based scholarships to disadvantagedchildren and youth Early childhood development (ECD) School Choice and PPP Cash transfers (as a tool to improve learning) Additional inputs alone (textbooks, class size,laptops/tablets, grants, libraries, etc) General-skills teacher training (in-service) Nutrition Incentive<br><!-- End of picture text --> _Notes: Each category of education intervention shows the learning-adjusted years of school (LAYS) gained from a given intervention or policy across over 150 impact estimates in 46 countries. The boxplot describes the 25th and 75th percentile. The boxplot is ordered in the same order as Figure 3 to provide a direct analogy, with the exception of the “nutrition” category which has no cost-effectiveness data and does not appear in Figure 3._ Figure 4: Learning-Adjusted Years of School (LAYS) Gained by Intervention Category vouchers for mind-spark adaptive learning software in India (0.36 LAYS); preschool attendance in Uruguay (0.35 LAYS); merit scholarships for girls in Kenya (0.34 LAYS); and ability grouping using extra teachers in Kenya (0.29 LAYS). By contrast, about half of all interventions produce no significant effects; those interventions are not included in the figure.

These findings point to a few overall lessons. In this sample of studies, the most effective programs are
microfinance operational data analysis shows that MFI lending, loan outstanding, loan recovery, and savings mobilized are lower in lean seasons than in non-lean seasons.

Seasonal patterns of financial intermediation of the MFIs or seasonality of household borrowing in response to agricultural seasonality does not necessarily mean inefficiency in the resource allocation of either MFIs or households in a rural setting. Inefficiency in resource allocation for the MFIs may occur if MFI lending is affected by seasonality, which in turn influences the MFI income portfolio such as loan outstanding and loan recovery. That means, we need to test if the seasonality of lending affects MFI loan outstanding beyond and above what is affected by agricultural seasonality. Similarly, inefficiency in resource allocation for rural households emerges if seasonality of income induced by agricultural seasonality affects borrowing in support of household consumption or investment. That is, we need to test if income seasonality affects borrowing beyond and above what is normally affected by agricultural seasonality.

Econometric analysis using panel household data involves model estimation where seasonal borrowing is regressed on income seasonality after controlling for agricultural seasonality plus a host of household and community factors affecting household demand for and supply of credit. Results show that despite the outreach of MFIs and their innovative measures to deal with agricultural seasonality, seasonal income induced by agricultural seasonality affects seasonal borrowing of rural households. This means borrowing is sensitive to seasonality of income, meaning supply of credit is not forthcoming when the demand for credit is high, possibly causing inefficiency in the production and consumption of rural households.

Monthly operational data on lending, loan outstanding, loan recovery, and savings mobilized by MFIs are used to test if seasonality affects MFI investment decision beyond and above what is affected normally by agricultural seasonality. We estimated the lagged dependent variable (LDV) method to control for joint dependence of MFI portfolio management behavior. Results confirm that despite innovative measures to deal with seasonality, asymmetric information and lack of enforcement of loan contracts 25
-3-

program from the 1987 perspective without devoting much space to the initial heterodox shock which was described in various other papers. 11 From the point of view of this objective the data which summarize the developments in 1987, which have now become available, are particularly useful. This is so since this yas the first year in which the economy can be said to have recovered from the heterodox shock of July 1985 and adjusted to the low inflation environment.

A related topic with which we shall be concerned is the evolution of the disinflationary policies in the course of the program. Here again the 1987 data will help us in diaentangling the transitory from the more lasting elements of the Israeli disinflation strategy.

In the final part of the paper we shall deal with the policy dilemmas which face the government at the present stage of disinflation. In spite of the rosy picture of 1987 one cannot ignore some problematic, features of the program. One disturbing fact is that the fixed exchange rate policy followed in the past year and a half has not succeeded in reducing the level of the crawling inflation and consequently a process of real appreciation has been taking place continuouely. This may have contributed to the recessionary symptoms in the first half of 1988.

One of the main merits of the Israeli stabilization was its ability to avoid a heavy cost in terms of unemployment by adopting a well-coordinated heterodox program based on a great degree of cooperation with organized labor. However, while this approach proved to be effective in administering the shock treatment it is not intended to be used in the same way in normal times. It is especially after the big achievement of bringing down inflation from a very high level that the public suspects that the government will not fight to eliminate a low inflation step. This, however, may set an expectational pattern which may lead to unstable developments.
at destination and export less – a selection effect of deep trade agreements. On average, the inclusion of an additional legally enforceable provision in RTAs stimulates the exports of large firms by 0.4%-0.6%, while it reduces the exports of small firms by 0.5%-0.6%.<sup>3</sup> Interestingly, high-productivity firms participating in Global Value Chains (GVC), i.e. firms that export and import to/from the same country, benefit the most from the signature of deep RTAs. For these firms, an additional legally enforceable provision in RTAs stimulates exports by 0.8%. The selection effect of deep trade agreements shows that using firm-level data is key to understand the welfare implications of RTAs. Specifically, by favoring exports of large and high-productivity firms and reducing exports of small and less productive firms, deep RTAs promote a reallocation of resources from the latter to the first which entails adjustment costs but also leads to an overall increase in the average productivity of firms.

We next investigate how deep trade agreements affect the margin of trade at the firm level. Deep RTAs could reduce both the variable and the fixed trade costs between member countries. This in turn impacts on the margin of trade that is affected. Previous work by Baier et al. (2014) uses aggregate trade data to study how different types of RTAs affect the intensive and the extensive margin of trade. Here, we use a decomposition approach à la Berman et al. (2012), to disentangle the average export sales effect of deep RTAs into intensive _versus_ extensive (firm-based) margin component. Namely, we use the firm-specific export behavior to impute the extensive _versus_ intensive margin effect of deep RTAs. We show that the extensive margin channel has only a slightly larger contribution (60%) than the intensive margin channel (40%). This evidence, in light of the theoretical predictions in Chaney (2008), suggests that indeed deep RTAs represent