rafmacalaba/datause-encoder-data huggingface dataset:
**-** **<u>Table A3: Double difference regression of effects of participation on NREGS supported land investment</u>**

||**All**|**Scheduled Castes**<br>**and Tribes**|**Other**|
|---|---|---|---|
|If household participated in NREGS|0.229***<br>(10.46)<br>|0.226***<br>(6.97)<br>|0.228***<br>(8.86)<br>|
|If hhld ld i hl|0.00279|0.0196|-0.00753|
|ouso ocate n amet|(0.12)|(0.59)|(-0.28)|
|Being very poor|0.00837|0.0196|0.0264|
||(0.44)|(0.46)|(1.15)|
|Being poor|0.00558|0.0395|-0.00774|
||(0.32)|(0.98)|(-0.39)|
|Scheduled tribe|0.0539**|0.0986**||
||(2.31)|(2.56)||
|Scheduled caste|-0.0111<br>(-0.26)|||
|Non backward cates|-0.0529**||-0.0364|
||(-2.08)||(-1.41)|
|If any member can write|-0.0268|-0.0283|-0.0377|
||(-1.22)|(-0.85)|(-1.35)|
|Female headed|-0.0104|-0.00298|-0.0215|
||(-0.45)|(-0.08)|(-0.76)|
|Number of female adults|0.0115|0.00378|0.0163|
||(0.84)|(0.18)|(0.91)|
|Number of male adults|0.0165|0.0392**|0.00323|
||(1.55)|(2.19)|(0.25)|
|Household size|0.00489|0.00306|0.0160|
||(0.43)|(0.11)|(1.20)|
|Square term of household size|-0.000541|-0.00148|-0.000881|
||(-0.81)|(-0.60)|(-1.32)|
|Irrigated area of land owned|0.00585|0.00930|0.00177|
||(1.27)|(0.48)|(0.39)|
|Non-irrigated area of land owned|0.0125**<br>(1.98)|0.0180<br>(0.79)|0.00555<br>(0.85)|
|Square term of total land owned|-0.000204***|0.000882|-0.000122|
||(-2.66)|(0.42)|(-1.65)|
|District dummies and constant term|Yes|Yes|Yes|
|Number of observations|3904|1535|2369|

27
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# **II. Identification and Quantification of Restrictive Practices**

2.1 Examining specific cases is the best way to show how the different tools used in this study were employed. To describe the procedure one looks first at exports of glucose and glucose syrup from Morocco to Tunisia, selected because of what this product reveals about competitiveness. We then look at one case study by country for a selected product, with calculations presented in increasing order of detail.

2.2 Glucose and glucose syrup are processed foodstuffs manufactured from imported maize. They are not differentiated by quality, origin, and brand so the analysis and comparisons can be based mainly on prices. Although Morocco's unit prices are competitive (on an f.o.b. basis) with those of other suppliers, and supply is largely available, its market share in Tunisia imports is only 5.2% (Table 2.1).

**Table 2.1: Tunisian Imports of Glucose and Glucose Syrup, 1991**

|_Producers_|_f o. b. value_<br>_(thousand of_<br>_dollars)_|_Market share_<br>_(percent)_|_Quantity_|_Cost per unit_<br>_(dollars)_|
|---|---|---|---|---|
|Germany|383|39.4|1,133|0.34|
|Spain|193|19.9|539|0.36|
|Austria|104|10.7|102|1.02|
|France|92|9.5|160|0.58|
|Italy|70|7.2|194|0.36|
|Morocco|51|5.2|158|0.32|
|Belgium|43|4.4|84|0.51|
|Netherlands|36|3.7|100|0.36|

_Source: UNC7AD, Trade Analysis and Information System 1991_ Based on shipper's customs declarations 2.3 Maritime shipping and insurance costs could be the cause of Morocco's loss of competitive advantage in the Tunisian market. However, Tunisia requires all imported goods to be insured by Tunisian companies, and thus insurance costs do not affect the equation. Shipping costs remain as the only apparent cause of lost marketshare. This conclusion was verified with a model used to estimate maritime transport costs. The sea routes tested were Casablanca-Tunis (CTN and roll-on/roll-off (ro/ro) vessels), Cadiz-Tunis (CTN vessels), and Rotterdam-Tunis (CTN and Ahlerlines vessels). The findings indicate that:
to the EU.<sup>35</sup> This decline may be explained by the more stringent ROO in the EU which partially eroded the generous preferences it granted to African LDCs. Our finding of a negative effect of AGOA preferences on apparel exports to the EU stands in contrast with the finding of no trade redirection from the EU by Frazer and Van Biesebroeck (2010), possibly due to their use of a short period before and after AGOA (1999-2000 and 2002-2003).

We also consider potential trade redirection of African exports from non-EU destinations, relying on WITS data over the 1992-2016 period for imports by the US, EU and other countries designated as rest of the world (ROW).<sup>36</sup> Column (4) reports a negative and significant coefficient on the AGOA apparel term of imports by the EU plus ROW, indicating that the AGOA trade preference shock reduced African exports of apparel to all countries other than the US. This shows that US trade preferences did not immediately generate economies of scale that could spur growth of apparel exports also to other destinations, and that some of the increase in exports to the US was due to reorientation of installed capacity.<sup>37</sup> But column (5) shows that exports by African countries of AGOA apparel (as well as non-apparel) products and GSP LDC products to the combined US, EU plus ROW increase, suggesting that despite evidence of export diversion away from the EU and ROW towards the US, AGOA did lead to export creation reflected in growth in total exports of beneficiary countries.

# **_6.3 Robustness:  Omitted Variables_**

> 35 Column (1) replicates the estimates for the impact of GSP LDC and AGOA on US imports shown in Table 2 restricting the sample to that used for EU imports (i.e., dropping year 2017 and excluding all 28 EU countries from the control group). 36 Column (3) replicates the estimates for the impact of GSP LDC and AGOA shown in Table 2 on US imports from WITS data. 37 Columns (2) and (4) show that African exports to the EU or EU plus ROW of non-apparel products duty-free in the US under GSP LDC increased. This might suggest that export growth to the US helped African countries increase their exports to other destinations due to economies of scale or LBD mechanisms. But it is not clear why such mechanisms would operate for GSP LDC products and not for apparel products. The absence of cross-destination economies of scale could be due to the fragmentation of apparel manufacturing due to divergent ROO, differences in tastes, and vertical specialization of manufacturing and retail.

23
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Annual funding gap

Malawi’s infrastructure funding gap amounts to $0.3 billion per year, or about 10 percent of GDP. Almost half of the country’s entire funding gap is found in the WSS sector, where the annual shortfall in meeting the MDGs is $141 million (table 12). Another significant part of the gap is found in the power sector, where an additional $69 million is needed to meet the country’s development goals.

## **Table 12. Funding gaps by sector**

|$ millions|ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Spending needs|(41)|(61)|(190)|(84)|(188)|(585)|
|Existing spending|19|2|30|84|40|175|
|Efficiency gains|0|0|91|63|28|182|
|Funding gap|**(22)**|**(59)**|**(69)**|**63**|**(120)**|**(270)***|

_Source:_ Derived from Foster and Briceño-Garmendia, AICD Flagship Report, 2009. _Note:_ Potential overspending is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors.

# What else can be done?

The most obvious way to address the funding gap is by raising additional finance. While prospects for doing so may be limited, it is worth noting that (relative to its GDP) Malawi is capturing less external finance for infrastructure than other African low-income countries, whether through ODA, private investment, or non-OECD finance.

But even if additional finance is hard to secure, there is still much that Malawi can do to reduce the infrastructure funding gap through its own policy choices, and in particular the technology choices it makes to meet its infrastructure targets. The single-largest measure that Malawi could take to reduce its infrastructure spending needs would be to integrate itself more closely with the SAPP. As a result, the country would become a net importer of power—importing up to 1.5 TWh of electricity by 2015 from neighboring countries—and as a result would save $134 million of annual investment requirements. Another $30 million a year could be saved by adopting lower-cost technologies to meet the MDGs; for example, by placing greater emphasis on standposts, boreholes, and improved latrines. Finally, $15 million a year could be saved by adopting appropriate technologies for the surfacing of paved roads. If all these policy measures were adopted, Malawi could save $179 million a year, cutting its infrastructure funding gap by two-thirds and bringing it down to $91 million a year.

Finally, if all else fails, it may be necessary to extend the time horizon for meeting the infrastructure targets beyond the illustrative 10-year period considered here. Simulations suggest that if Malawi were unable to raise additional finance but addressed inefficiencies, the identified infrastructure targets could be achieved within a 16-year horizon. Without stemming inefficiencies, however, the existing resource envelope would not suffice to meet infrastructure targets in the medium term.

27
# **7. Conclusion**

The consequences that come with hosting refugees are driven by several factors which encompass the refugee policy in the hosting country, the sheer number of refugees being hosted, the duration of stay of the refugees and the coping strategies which the hosts employ given the refugee shocks, among others. Sub-Saharan Africa (SSA) has to cope with a large and rising number of refugees who live for protracted durations in their host countries. Therefore, understanding the consequences of hosting refugees in SSA has increasingly gained attention in the literature. Several studies have highlighted a net economic benefit. In this study, we contribute to the literature by exploring the potential channels of refugee influence on household welfare and the possible coping strategies employed by the refugee hosting households.

Our study centers on Uganda which has a unique refugee policy. The country hosts the most refugees in SSA and is the third largest refugee hosting nation globally. We use panel data to determine the effect of refugee presence on household welfare based on consumption per adult equivalent between 2009 and 2012. The refugees originate from various source countries. We concentrate our analysis at the enumeration area/cluster level and use a constructed refugee index which weights the number of refugees in the closest refugee settlements by the inversed distance from those settlements to the clusters. This is intended to capture the relative importance of proximity to the refugee settlement rather than the usual classification of refugee hosting districts versus non-refugee hosting districts. We employ a shift-share instrumental variable, which is based on the distance of to the closest border crossing points for each source country, in order to limit endogeneity concerns.

Our results indicate that the presence of refugees causes significant welfare benefits especially for households living closer to refugee settlements. However, it is the rural households, initially involved in subsistence agriculture who benefited from the refugee presence. Uganda is a case in point here. It contrasts with previous studies pointing to households with access to human and physical capital as those most likely to adapt to the refugee shock and respond optimally to changing economic opportunities (Maystadt et al. 2019). Our results indicate that those involved in subsistence agriculture benefit the most. Welfare improvements in refugee-hosting districts also correlate with a switch to commercial agriculture and to some extent wage employment. The way households are able to benefit is possibly through increased agricultural production, especially vegetable production for those who switch to commercial farming and increased labor force participation with reduced competition for those who switch to wage employment. The second effect is confirmed by the analysis at the individual level which shows that household members indeed participate more in the 35
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prove not to be sensitive to whether prices were specified in<sup>logarithms or</sup> levels. We, therefore, retain Ravallion's formulation in levels.

# RESULTS OF THE DYNAMIC INTERREGIONAL MODEL

As mentioned, we also follow Ravallion in instrumenting prices<sup>in the</sup> reference market in the estimation of eq. 4. That is, the right hand side variables for Techiman prices in the subsequent analysis are predicted rather than observed prices in the reference market. This was done using Sunyani current and lagged prices with a correction for first-order serial correlation.13 Whiile more markets might improve the efficiency of the instrumenting equations, all other relevant market series contain gaps that would require a reduction in the sample size. The fit in the instrumenting equation was good, with an r<sup>2</sup> over 0.90. Again, none of the results reported below were particularly sensitive to the use or exclusion of instrumental variable techniques.

The next consideration is the appropriate length of the lag structure in the estimates of eq. 4. Test 1 in Table one indicates that adding one<sup>period</sup> lagged prices to a base model which regresses only the current price in Bolgatanga on the current Techiman price results in a significant improvement of

**13** This was deemed warranted by conventional analysis of the DurbinWatson statistic. As this test is not appropriate when lagged values of the dep3ndent variables are included on the right-hand side, Durbin's h statistic was used for initial diagnostics of a model of Bolgatanga maize price with a one-period lag (Durbin 1970). No evidence of serial correlation was revealed with this test, which used instrumented Techiman prices corrected for auto correlation as the independent price.
participation. In addition, Treisman (2007) finds that perceptions data often do not correlate well with experienced-based measures of corruption. Thus, results from the CPI should be interpreted with caution. More recently, experience-based measures have come to replace perceptions-based measures in the literature.

The above is not to belittle CPI-based research, which has produced many important insights into the correlates of corruption. Among the many studies in this area are those showing a relationship between corruption and judicial effectiveness (Treisman 2000), fairness in the rule of law (Uslaner 2005), political institutions (Lederman et al. 2005), economic liberalization (Goldsmith 1999), education (Shleifer and Vishny 1993), economic growth and openness to trade (Svensson 2005; Mauro 1995; Ades and Di Tella 1999), inequality (Jong-Sung and Khagram 2005).

On close examination, the term „corruption‟ is somewhat nebulous, in part because it incorporates a variety of actions at different levels of public office. The extent to which corruption is pervasive varies by both the level of government at which it occurs, and the type of corruption which occurs. Furthermore, measures of corruption are flawed in that they contain systematic biases, and do not disentangle the different forms that corrupt acts can take.

# **Culture: Theory and Empirics**

Defining culture is a challenge in its own right.<sup>9</sup> Economists narrow the definition down to shared values and beliefs governing interaction among individuals (Huntington 2000; Greif 2004; Fernandez 2008; Barr and Serra 2010). In all definitions, culture is “shared” among group members, and indeed, the group may be defined by these shared values. We can further distinguish two channels through which culture operates: social norms and formal institutions (Elster 1989). Social norms are informal rules, driven by values and beliefs that govern interaction, and are both shared and sustained by group members.<sup>10</sup> Institutions are formal rules

> 9 Some examples of (non-exclusive) definitions: Geertz (1973) defines culture as a “historically transmitted pattern of meaning embodied in symbols... by means of which men communicate, perpetuate, and develop their knowledge about and attitudes toward life.”  Ross (1997) states that “culture is a framework for organizing the world...for making sense of the actions and interpreting motives of others.”  Hofstede (1980) defined culture as “the collective programming of the mind which distinguishes the members of one category of people from those of another.” Hofstede (1997) argues that culture has five dimensions: power distance, individualism, masculinity, uncertainty avoidance, and long term orientation (also called “Confucian dynamism”).

> 10 Bicchieri (2010) discusses social norms as informal rules of behavior arising spontaneously, and from decentralized actions of individuals.  Posner (2000) views social norms as “nonlegal mechanisms of cooperation.” Drobak (2006) argues that norms influence individuals toward conformity.

5
**Table 4.1 Short and long run impact of** adjustment policies on **nmacroeonomic** mggregmtes

||Impact<br>effect|Long run<br>effect|Ratio|
|---|---|---|---|
|Simulations using results of Corbo and Rojas||||
|Change in||||
|growth of GDP|0.016|0.020|1.2|
|investment rate|-0.035|-0.035|1.0|
|savings rate|0.014|_0.115_|8.2|
|export ratio|0.065|0.028|0.4|
|Simulations using results of Faini_et_at.||||
|Change in||||
|.growth<br>of;GDP|-0.001|-0.016|16.1|
|investment rate|-0.006|0.025|4.2|
|c-urrent-account (% of GDP)|0.034|0.063|1.9|
|inflation rate<br>--|0.00|-0.001|-0.1|

Source: own simulations based on resmuI reported in Corbo and Rojas (192)-and FPni-:d :a (1991a).

short and long run effects are, except in the case of investment, marked. The beneficial impact of adjustment policies on growth and savings are reinforced, especially that on the savings rate. On the other hand, the positive response of the export rate is more than halved.

The Faini _et al._ study is similar, except that rather than the savings rate and exports they have the rate of inflation and current account deficit (as a per cent of GDP) as endogenous variables. As shown in Table 4.1, the effect of the dynamic simulations is even more dramatic using the coefficients from their study.' The negadve impact on growth is strongly reinforced so that the growth rate falls by 1.6 percentage points, rather than only 0.1 percent. Despite this fact the impact on all of the other indicators is improved: the adverse impact on the investment rate and inflation are both reversed and the long run improvement in the current account is nearly double the impact effect.

77
specific sub-components of generic management scores and energy intensity in value and quantity terms, we find that the practices that matter most seem to differ for manufacturing and services sectors. These practices also influence energy intensity in expenditure and physical units differently. Specifications (1)-(3) in Table 4 show the relationship between management sub-scores (target-setting, monitoring, operations and incentives) and energy intensity in value terms. While target-setting practices matter for reducing energy intensity (expenditure) in manufacturing, these practices have statistically insignificant correlation for energy use in services. None of the other management practices seem to matter for energy use in services. Our results differ from Boyd and Curtis (2014) for the United States, who document that effective monitoring, incentive structures and particularly lean manufacturing operations are associated with a reduction in energy use, while the implementation of production targets (conditional on other practices) is found to increase energy consumption. The reason may be due to the fact that the World Management Survey, which was used to measure management practices in the United States in Boyd and Curtis (2014), refers to production targets, whereas the WBES definition is broader and explicitly includes examples such as “production volume, quality, efficiency, waste, or on-time delivery” in the question on target-setting practices. Appendix Table A10, columns (1)-(5), reveals that the target-setting practices matter especially for moderate and low energy intensive firms.<sup>15</sup> Specifications (4)-(6) in Table 4 show that there is a positive relationship in manufacturing firms between monitoring practices and energy intensity in physical quantity terms. While target-setting improves energy efficiency in value terms, monitoring production and performance of firms is actually associated with a worsening of the physical energy intensity in manufacturing, perhaps because more energy-intensive firms are more motivated to invest in monitoring capabilities. As for services, we do not find evidence of association of management with energy efficiency, both in value and in physical unit terms.

> 15Unlike high energy intensity firms, moderate and low energy intensive firms’ energy requirements are largely determined by core activities and long-lived capital equipment purchases, and thus may have less flexibility to adjust energy use through target setting.

21
respectively. Annual data from 1970 to 2014 are used in the analysis. The government consumption expenditure and real GDP in constant national currency are from the United Nations’ National Accounts Database. In general, most papers on fiscal cyclicality use the IMF’s databases, however, the UN statistics provide a more complete and consistent national accounts data for the time period covered. We apply the Hodrick-Prescott filter (HP) and use a value of λ equal to 6.25 in order to derive the cyclical components of the government consumption expenditure and real GDP (both in logs). General government gross debt as a percentage of GDP is from Abbas et al. (2010) and updated with the IMF’s World Economic Outlook. Natural resources rents (% of GDP) and net ODA received (% of GNI) are from the World Bank’s World Development Indicators (WDI). The instrumental variable is calculated with bilateral export values from the IMF’s Direction of Trade Statistics and UN COMTRADE, while the growth rates of trading partners are from the United Nations’ National Accounts Database updated with the WDI.

# **3. Results**

## **_3.1 Cyclical behavior of government consumption in 1970-2014_**

Before we conduct our regression analysis, we compute the degree of association between the cyclical components of real government consumption and real GDP from 1970 to 2014. The behavior of government consumption along the business cycle is defined as follows: government consumption expenditure is countercyclical (procyclical) if the correlation coefficient between the cyclical components of real government consumption expenditure and real GDP is negative (positive) and statistically significant at 5 percent. It is acyclical if that correlation is not statistically different from zero. Figure 2 shows that government consumption expenditure is procyclical for nearly 40 percent of SSA countries in our sample. Over the period 1970-2014, no country in the region exhibits countercyclical fiscal policies. The proportion of countries with a procyclical bias in fiscal policy is even larger in Latin America (60 percent). By contrast, 77 percent of industrial countries have exhibited an acyclical stance and 9 percent conducted countercyclical policies. The Appendix (Figure A.1) presents the correlation coefficients by region and country for the whole sample period.

9