rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

Remittances to Sub-Saharan Africa (SSA) have increased steadily in recent decades and are estimated to have reached about $32 billion in 2013.<sup>1</sup> Although smaller than the official development assistance and foreign direct investment inflows, remittances constitute important resource inflows to the region. In 2012 remittances accounted for about 4 percent of the GDP of a typical country in Sub-Saharan Africa with consistent data on official remittances inflows, and accounted for over 5 percent of the GDP in about ten of the countries in the region. The magnitude is sizeable even in some of the larger countries. For instance, recorded remittances to Nigeria amounted to about 8 percent of GDP in 2012, the corresponding figure being about 10 percent for Senegal. The importance of remittances is particularly evident in some of the smaller countries of the region. In Lesotho and Liberia, for instance, remittances account for over 20 percent of the GDP. The true size of remittances is believed to be substantially higher given that officially reported figures ignore remittances transferred through informal channels.

Though studies have shown that remittances can affect aggregate financial development in SSA - as measured by the share of deposits or M2 to GDP (Gupta et al. 2009), to our knowledge there is no evidence for this region on the impact of remittances on household financial inclusion defined as the use of financial services.<sup>2</sup> This question is important because there is growing evidence that financial inclusion can have significant beneficial effects for households and individuals. In particular, the literature has found that providing individuals access to savings instruments increases savings (Aportela, 1999, Ashraf et al., 2014), female

> 1 All country-level aggregate remittance inflows data are from the World Bank. See Eigen-Zucchi et al. (2013) for the latest data.

> 2 There is a vast literature examining the impact of remittance on various outcome measures, such as poverty (Acosta et al., 2007; Yang 2008; Anyanwu and Erhijakpor, 2010), health (Lu, forthcoming; De and Ratha, 2012), education (Yang, 2008; Edwards and Ureta, 2003), labor supply (Rodriguez and Tiongson, 2001; Kim, 2007), entrepreneurship (Yang, 2008; Amuedo-Dorantes and Pozo, 2006b), etc. Adams (2011) provides a review of some of the key literature on the household level impact of remittances.
values, therefore, the estimated effect of PSG conditions on the quality of governance is positive. A possible explanation for the negative relationship estimated for the upper tail is selection bias: countries with the most severe governance problems or those most at risk of deterioration may receive loans with more stringent conditionality. If our GMM procedure fully corrected for such a bias, then few if any observations should lie above the turning point. If it is even somewhat effective in correcting for it, however, we should observe a lower turning point using OLS than GMM. In fact, that is what we observed above: the estimated turning point in OLS was 55 conditions, and 11 % of observations lie beyond that point, compared to only 5 % lying above the turning point estimated using GMM.

# _4.3. Sample restrictions and additional controls_

The remaining regressions in table 5 report results of several robustness tests. First, we follow Easterly (2005) and limit the sample to include only countries that have received at least one DPL with PSG-related conditions over the period 1980-2010. As shown in equation 2, results remain significant, and quantitatively slightly larger than in equation 1. At 88 conditions, the turning point, the predicted CPIA cluster D score is 0.31 points higher on average than in the case of zero conditions.

As a second sample restriction, we drop all observations for a country after the last DPL with PSG conditions to that country has closed.<sup>14</sup> If reforms associated with DPLs are often not sustained following completion of the loan, then the estimated effects should be larger when the years following loan closing are dropped (about one-seventh of all observations). However, the effects of cumulative conditions in equation 3, where those years are dropped, are very similar to those in the base model.

As a third sample restriction we test our model excluding Argentina from the sample. With 210 PSG conditions, Argentina can be considered an outlying observation.<sup>15</sup> Equa-

> 14Data on closing years were extracted from a less comprehensive dataset.

> 15However, based on a visual inspection one could argue that the data on cumulative PSG lending are 16
# **Tables and Figures**

Figure 1: Total Killings and Insecure Districts in Afghanistan (2012-2016) <!-- Start of picture text --> 2012m1 2013m7 2015m1 2016m7<br>Month<br>Killings (#) Insecure Districts (#)<br>120<br>800<br>600 100<br>400<br>Killings (#)<br>80<br>Insecure Districts (#)<br>200<br>60<br>0<br><!-- End of picture text --> _Notes:_ Killings reflect total confirmed fatalities in Global Terrorism Database (GTD) and Insecure Districts reflect internal security tracking data from a national survey firm. See text for details.

27
variables are included together with income, income losses its significance because the institutional quality variables can already capture the impact of income. The age dependency ratio has the correct sign, but its estimated coefficients are not significant for the sub period of 1994-2001.

When we compare these new results with the ones reported in Table 1 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it can be seen that the number of observations increased from 884 (covering the period of 1994-2003) to 1322 (covering the period of 1994-2009); corresponding to an almost 50 percent increase. The R-squared are almost 0.15-0.20 points higher in the new results, indicating a better fit of empirical specifications. One interesting difference between these two sets of results is that the significance and the magnitude of the income variable (GDP per capita) drop in the new set of results. It is true especially in the specifications where institutional variables are included as determinants of taxes. While the estimated coefficient of income was around 2.2 in Table 1 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it is only 1.11 in the traditional tax specification; and it gets even lower and insignificant (0.503 and 0.002 with bureaucracy index and corruption index, respectively) when the institutional variables are included in the specifications. It means that with the recent improvements in taxation in developing countries and on-going effort by highincome countries to rationalize their tax systems toward greater efficiency and lower tax burden, particularly in direct income taxes, the income level becomes less important now in determining their tax revenues. On the other hand, the institutional quality of countries is more important (even more than income levels) in determining their tax revenues. The higher significance and magnitude of the estimated coefficients of the institutional variables in the new results support this argument. For example, while the estimated coefficient of the corruption index was only - 0.560 in Table 1 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it is now -1.273 and statistically more significant. This observation is also true for the bureaucracy quality index. Another difference between the old and new estimation results is that the population growth rate has almost 2/3 lower estimated coefficients now, indicating a lower impact of population growth on tax collection.

Table 2 presents similar results: for total fiscal revenues, all estimation results are as expected, except GDP per capita and population growth rates in some specifications. When we compare these new results with the ones reported in Table 2 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it can be seen again that GDP per capita is less significant and has lower estimated coefficients now. It is also true for the population growth rate. On the other hand, the institutional quality indicators have higher estimated coefficients, as well as higher statistical significance.

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|Head education: incomplete secondary school<br>0.04|0.08|0.05|0.06|0.04|
|---|---|---|---|---|
|Head education: complete secondary school<br>0.01|0.00|0.00|0.00|0.01|
|Head education:post-secondaryschool<br>0.02|0.05|0.03|0.03|0.03|
|**SWIFT model Variable**<br>**Urban**|**s:**||||
|Region==SNNP<br>0.15<br>|0.11|0.11|0.11|0.15|
|Reduced Consumption Preferred Foods Past 7|||||
|<br>Days<br>0.21|0.19|0.21|0.22|0.21|
|Reduce number of meals eaten in a day<br>0.16|0.13|0.15|0.16|0.15|
|Anyone in HHs is in agriculture last week<br>0.15|0.14|0.16|0.17|0.15|
|Anyone in HHs is in non-farm business last week<br>0.26|0.27|0.26|0.25|0.25|
|Household Size<br>3.65|3.70|3.71|3.70|3.71|
|Household size squared<br>1.75|1.75|1.77|1.76|1.80|
|Head can read and write<br>0.75|0.82|0.76|0.74|0.76|
|Number of rooms(excludin kitchen toilet and|||||
|g ,<br>bath<br>1.97|2.08|2.06|2.03|2.04|
|Floor==cement creed<br>0.42|0.44|0.41|0.39|0.41|
|Floor==tiles<br>0.06|0.07|0.06|0.06|0.06|
|Floor==other<br>0.01|0.01|0.01|0.01|0.01|
|**Rural**|||||
|Region==TIGRAY<br>0.07|0.07|0.06|0.06|0.07|
|Region==AMHARA<br>0.27|0.25|0.27|0.27|0.26|
|Region==SNNP<br>0.21|0.10|0.09|0.09|0.20|
|Reduced Consumption Preferred Foods Past 7|||||
|<br>Days<br>0.24|0.22|0.22|0.22|0.24|
|Reduce number of meals eaten in a day<br>0.17|0.14|0.15|0.15|0.16|
|Anyone in HHs is in agriculture last week<br>0.85|0.83|0.84|0.84|0.82|
|Anyone in HHs is in non-farm business last week<br>0.08|0.10|0.09|0.09|0.10|
|Household Size<br>4.88|5.07|5.07|5.03|4.87|
|Household size squared<br>2.86|3.07|3.07|3.02|2.86|
|Number of rooms(excludin kitchen toilet and|||||
|g ,<br>bath<br>1.85|2.05|2.02|2.03|2.04|
|Floor==cement creed<br>0.02|0.05|0.04|0.04|0.04|
|Floor==tiles<br>0.01|0.01|0.01|0.01|0.01|
|Floor==other<br>0.02|0.02|0.02|0.02|0.02|
|**Other variables:**|||||
|**Urban**|||||
|Consumed wheat<br>0.32|0.34|0.33|0.32|0.32|
|Consumed pasta/maccaroni<br>0.35|0.38|0.35|0.34|0.36|
|Consumed tomato<br>0.61|0.64|0.60|0.58|0.60|
|Consumed sugar<br>0.73|0.76|0.73|0.72|0.73|
|Consumed Lentils<br>0.43|0.49|0.46|0.44|0.44|
|Consumed banana<br>0.33|0.35|0.34|0.32|0.35|
|Consumed milk<br>0.24|0.23|0.21|0.20|0.23|
|Consumed tea<br>0.54|0.56|0.54|0.53|0.54|
|Consumed green chili pepper<br>0.68|0.72|0.71|0.70|0.69|

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**Table 6: Productivity Convergence and Regulation: sector level analysis for 2003-08 period with openness to trade data at sectoral level (robust standard errors in parenthesis)**

||1|2|3|4|5|
|---|---|---|---|---|---|
|∆lnLPit<br>leader|0.0278***<br>(0.009)|0.0336***<br>(0.009)|0.0296***<br>(0.009)|0.0315***<br>(0.009)|0.0311***<br>(0.009)|
|prodgapijt−1|-0.0735***|-0.0762***|-0.1073***|-0.0693***|-0.034|
||(0.009)|(0.018)|(0.028)|(0.016)|(0.024)|
|humanKit−1|0.0005|-0.0002|-0.0001|-0.0001|0.0006|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Open_tradeijt−1|0.0001**|0.0001**|0.0001**|0.0001**|0.0001**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Regit|0.1184**<br>(0.048)|||||
|(Reg∗prodgap)it−1|-0.0044**<br>(0.002)|||||
|All_DBit||0.0049**||||
|||(0.002)||||
|(All_DB∗prodgap)ijt−1||-0.002**<br>(0.000)||||
|Start−up_DBit|||0.0069**<br>(0.003)|||
|(Start−up_DB∗prodgap)ijt−1|||0.0003<br>(0.000)|||
|Bus. Operations_DBit||||0.0030**<br>(0.001)||
|(BusOperations_DB∗prodgap)ijt−1||||-0.0001<br>(0.000)||
|Inst−Env_DBit|||||0.0019*<br>(0.001)|
|(Inst−Env_DB∗prodgap)ijt−1|||||-0.0006*<br>(0.000)|
|_cons|-0.6108***|-0.7523***|-0.9707***|-0.6276***|-0.5760**|
||(0.146)|(0.186)|(0.273)|(0.168)|(0.263)|
|R-squared (within)|0.1259|0.1262|0.1249|0.1257|0.1252|
|N. obs|3155|3155|3155|3155|3155|

_Note: Country, sector and time dummies were included but not reported .* significant at 10%; ** significant at 5%; *** significant at 1%_ The computation of the marginal effect of trade openness on productivity convergence by sector allows identifying the industries for which productivity growth is most responsive to a variation in trade intensity. Figure 6 displays the effects on productivity growth due to a one standard deviation increasing of trade openness by sector over the 2003-08 period.<sup>28</sup> Only the sectors for which the estimated effect was statistically significant are presented. Manufacture of basic metals is the activity with the highest productivity dividend: a one standard deviation increase in the sector’s openness to trade over the 2003-08 period would lead to a productivity growth almost 34 percent higher (yearly). The average value of trade openness for this sector in Romania is largely inferior to the sector average of all countries considered in the analysis; this

> 28 The presented effects of trade openness on productivity growth refer to the model that includes _Reg_ as the regulatory proxy used. The trade effects considering alternative regulatory proxies were also computed. Results are available upon request.

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Table 1: Market Makers' Touch aJ Before and After the Big Bang (percentage of share price & £l.000s)

||Ca<br>Alpha|tegory ofSt<br>Beta|ock<br>b/<br>Gamma|
|---|---|---|---|
|Pre-Big Bang:||||
|Average touch at normal size (%)|0.8|1.8|3.4|
|Value of average quotation (f'000)|320.8|58.9|15.3|
|Post-Big Bang:||||
|Average touch at 1,000 share (0)|0.6|1.4|2.8|
|Value of average quotation (f'000)|4.8|2.5|1.9|
|Average touch at largest||||
|SEAQ ci quote (%)|0.8|1.7|3.2|
|Value of average quotation (E'000)|279.1|83.1|13.9|

Notes:

- a/ The touch is the difference between a market maker's bid and offer prices.

- b/ UK stocks are classified according to their liquidity: alpha stocks are the most frequently traded, betas less frequently, and so on.

- c/ SEAQ is the stock exchange's automatic quotation system, whereby bid and offer prices are circulated. Market makers must execute orders at their quoted prices. Prices are allowed to vary depending on size of the order.

Source: Bank of England Quarterly Bulletin, February 1987.

The table shows cwo important things. First, the average touch fell as a direct result of increased competition among market makers. Second, the liquidity of the market improved. Generally, the touch will widen for less-liquid stocks and for large orders, because the market maker faces a greater risk of unbalancing his book and being unable to reverse a position quickly. The data in the table show the greater liquidity in the beta and gamma stocks and the small increment in the touch. Of course, it remains to be seen whether these gains will be
This paper applies a reduced-form price-determination model to maize, wheat, rice, soybeans, and palm oil, using annual data for 1960-2012 (see figure 1 for the nominal and real food price index). The price determinants include one supply-side variable (energy prices), three macroeconomic indicators (exchange rate, interest rate, and inflation), income on the demand side, and a driver reflecting market fundamentals (stocks and consumption expressed as a single stocks-to-use ratio variable).

The paper finds that food prices respond strongly to stocks-to-use ( _S/U_ ) ratios (except rice), crude oil prices (all commodities), and exchange rate movements (in a mixed manner). With a few exceptions, interest rate and income growth do not matter. Crude oil prices matter the most because they experienced the largest increase after 2004. The remaining portion of the paper proceeds as follows. The next section discusses the model, data, and estimation procedure. The third section identifies and elaborates on the relative impact of each driver. The fourth section applies a number of robustness checks to assess the performance of the model, especially the post-2004 price movements. The last section concludes and identifies likely directions for future research.

# **Explaining Long-Term Price Trends**

To identify the relative impact of various drivers on long-term food price trends, we use a reduced-form price-determination model. The model is based on equating aggregate demand to the supply of a commodity, and then expressing the equilibrium price as a function of sectoral and macroeconomic fundamentals. The theoretical underpinning of the model can be found in Turnovsky (1983), Stein (1986), Holtham (1988), and Deaton and Laroque (1992). Empirical applications include Gilbert (1989) who looked at the effect of developing country debt on commodity prices; Pindyck and Rotemberg (1990), who examined comovement among various commodity prices; Reinhart (1991) and Borensztein and Reinhart (1994) who analysed the factors behind the weakness of commodity prices during the late 1980s and early 1990s; Baffes (1997) who examined the long term determinants of metal prices; and Frankel and Rose (2010) who analyzed the effects of various macroeconomic variables on agricultural and mineral commodities.

# Model

The model takes the following form: where _Pt_<sup>_i_</sup> denotes the nominal price of commodity _i_ ( _i_ = maize, wheat, rice, soybeans, and palm oil). _S/Ut-1_ denotes the lagged stock-to-use ratio (use includes human, animal, _Pt_<sup>_OIL_</sup> _XRt Rt_ and industrial consumption), is the price of crude oil, is the exchange rate, — 3 —
- 9 -

Following the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the translog cost function is considered: where _C_ denotes the amount of total operating cost, _Y_ is an output proxy, and _Wi_ is _i_ th input price. _Z k_ represents the _k_ th measurement of infrastructure quality.

Seven inputs are considered: labor ( _L_ ), electricity ( _E_ ), fuel ( _F_ ), water ( _W_ ), transport ( _T_ ), communications ( _C_ ), and the rest of costs ( _M_ ). Conceptually, the last can be referred to as capital or equipment. The prices of these production factors are denoted by _Wi_ . For _WL_ , it is defined by the average wage of each firm. For other factor prices, such as _WE_ , _WF_ , _WW_ , _WT_ , _WC_ , they are calculated by the firm’s spending on each infrastructure service divided by the replacement cost of the firm’s assets (e.g., machinery, buildings, and land). Finally, _WM_ is defined by the rest of the operating costs (such as annual depreciation and rental fees) divided by the asset replacement cost.

Output is measured by total sales in U.S. dollars. Ideally, this should be measured by some variable in real terms, but this is only the usable common proxy for output in our BEEPS data.<sup>4</sup> To control for sector heterogeneity, the empirical model incorporates the sector-

> 4 No physical output variable is available in the database, because firms in the sample engage in various businesses.
finds that democracies tend to exhibit higher environmental commitment. Pertianing to internatonal rivers, Gleditsch (1998: 389) suggests that democratic countries (as opposed to nondemocratic countries), which share a river basin, should be more peaceful in their hydropolitical relations. Brochmann and Hensel (2009) who consider conflictual river claims and their subsequent settlement, find that river claims are less likely to begin, and more likely to experience peaceful settlement attempts (when those claims do begin), between two democracies than between other pairs of states with less democratic forms of givernment. Tir and Ackerman (2009) make a similar conjecture and find that dyads with joint democracies, are more likely to engage in international hydropolitical cooperation (measured by treaty signature).

We follow a similar rational and expect that, all things being equal, country dyads which comprise of two democracies as opposed to two non-democracies or a mixed dyad (democratic state and non-democratic state), should exhibit more cooperative behavior. We calculate a combined democracy score based on the Polity IV database (Marshall, Gurr, and Jaggers 2013). Polity IV is time variant but our combined democracy score is based on an average value.

# <u>4.1.3.4.</u> _<u>Overall relations: Militarized history and trade</u>_

Overall political and economic relations between countries should also affect countries’ hydropolitical relations (Yoffe, Wolf, and Giordano 2003: 1117; Brochmann and Hensel 2009: 415). We use measures of a history of militarized disputes and trade to proxy for overall relations between states sharing a river.

Pertaining to militarized disputes, Hensel et al. (2008: 133-135) and Brochmann and Hensel (2011, 867-868) hypothesize that countries with overall unfriendly relations (due to a history of militarized conflict) may be less likely to cooperate over such contentious issues as water. Consequently, we hypothesize that a dyad with a more robust history of militarized disputes will be more likely to elicit conflictive events and their intensity shall be higher. A history of militarized disputes will likewise affect the incidence of cooperation. Furthermore, inclusion of a history of militarized conflict is an important control in terms of understanding the presence of certain types of institutional mechanisms. For instance, existence of a conflict dispute resolution mechansism may be more prevelant among dyads with a history of military conflict.

14
taking on this role. First, the WTO was not given the mandate to provide sufficiently detailed information on changes in policies to be of use to private sector agents – whether it be a firm or an advocate working on its behalf.<sup>13</sup> Second, given its other important roles in the system, it may be politically quite difficult for the WTO Secretariat to play an active monitoring and information generation role.

The global economic crisis took hold in late 2008, and by early 2009 a number of new initiatives provided additional information and data of use to the monitoring agenda. Much of the information could be especially useful for those interested in improving understanding of changes to the foreign market access opportunities facing exporters in developing countries. Table 1 presents a timeline of a number of major informationgenerating announcements in 2009 that focuses on three entities: the WTO Secretariat, the World Bank-sponsored _Global Antidumping Database_ , and the _Global Trade Alert_ .

The WTO Secretariat did respond in 2009 with more frequent updates and information useful for monitoring. In April 2009 it began to provide quarterly lists of trade and trade-related measures imposed during the crisis – with data taken from official and also _unofficial_ (news) sources. An explicit statement did accompany the lists, stating that, despite a measure being included in the document, the WTO itself was not making any judgment as to its potential consistency with any commitments or obligations.<sup>14</sup> 13 Consider a policy like antidumping – the information that the WTO reports on behalf of its member countries is neither timely (it arrives with a substantial lag) or detailed enough (no tariff-line codes of products affected) to be of use to analysts seeking to make sense of the economic market implications of the potential barrier.

14 Footnote 1 of the April 20 list (WTO document WT/TPR/OV/W/1) states: ‘This is intended to be a purely factual report and is issued under the sole responsibility of the Director-General. It has no legal 14