rafmacalaba/datause-encoder-data huggingface dataset:
- 27 -

effective and knowledgeable media and other countervailing institutions outside the governmen **t** o provide the restraining influences.

The tension between "ownership" and "conditionalities" can arise when the recipient country agrees to undertake actions as part of the conditionalities under the Bank loan and/or the Fund arrangements or donor programs, and the loan conditions are either inappropriate to the economic context of the country or the country has no real intention of complying with those conditions.

Mosley, et al.,W in their study of nine countries receiving adjustment loans from the World Bank, illustrate this point by matching the commitment of reforms favored by the World Bank with the actual implementation of the loan conditions. The zero or partial implementation indicates low commitment, while full implementation indicates high commitment. But they argue that there are two other cases. First, there are cases where full implementation of a loan takes place but the commitment is low. This is the case where all formal conditions are scrupulously performed, but at the same time, or shortly afterwards, other actions are taken, the effect of which is to neutralize the result of implementing the original condition. The other situation arises when loan conditions are not fulfilled but commitmen **t** o reform is high. The authors hypothesize two causes for this deviation. The first concerns trivial conditions-either too broad in scope or peripheral-a matter of detail such as the hiring of consultants, the design of a minor study, etc. Second, when the required conditions are infeasible or improbable. In Ghana, the government agreed to meet an ambitious target of divesting the state enterprises without realizing the depth of the problem of inter-enterprise borrowing and the impediment which the inability to value each enterprise properly would place in the way of divestiture. The failure to implement loan conditions in this case was compatible with commitmen **t** o reform.

21/ Mosley P. et al., ibid.
allow for threshold effects. The results, accounting for both institutional quality and size of government, are presented in Tables 5 and 6.

Table 5. Economic Growth and the Size of Government By Government Effectiveness (WB Governance Indicator for Government Effectiveness)

|||All Countri|es|Relativ|elyMore Effectiv|e Government|Relativ|elyLess Effecti|ve Government|
|---|---|---|---|---|---|---|---|---|---|
||OLS<br>(1)|Fixed Effects<br>(2)|Random Effects<br>(3)|OLS<br>(1)|Fixed Effects<br>(2)|Random Effects<br>(3)|OLS<br>(1)|Fixed Effects<br>(2)|Random Effects<br>(3)|
|War dummy|-5.45*|-5.92***|-5.67***|-3.16|-1.69|-2.66*|-5.74|-4.7|-5.34*|
||(1.86)|(3.18)|(3.27)|(1.55)|(0.89)|(1.66)|(1.25)|(1.48)|(1.81)|
|Ln(inflation) (IMF)|-1.91***|-1.83***|-1.85***|-0.88***|-1.06***|-1.00***|-2.97***|-2.38***|-2.72***|
||(5.95)|(5.32)|(6.30)|(3.37)|(3.89)|(4.18)|(4.80)|(3.43)|(4.48)|
|Fiscal balance (%GDP) (EBRD)|0.51***|0.38***|0.49***|0.71***|0.73***|0.70***|0.47***|0.32|0.44***|
||(4.75)|(3.24)|(5.22)|(7.20)|(6.31)|(7.01)|(3.38)|(1.64)|(2.83)|
|Overall index of reform|-0.01|-0.11|-0.05|0.34***|0.36***|0.34***|-0.59*|-0.52|-0.62**|
||(0.08)|(0.68)|(0.40)|(3.94)|(2.90)|(3.49)|(1.66)|(1.41)|(1.99)|
|Expenditure (in percent of GDP) (<35%)|-0.14|-0.25|-0.17*|-0.08|-0.11|-0.12|0.00|-0.2|-0.08|
||(1.26)|(1.65)|(1.67)|(0.15)|(0.27)|(0.29)|(0.00)|(0.85)|(0.45)|
|Expenditure (in percent of GDP) (>=35%)|-0.13*|-0.41***|-0.26***|0.13**|0.19*|0.14*|-0.48***|-0.67***|-0.55***|
||(1.68)|(3.85)|(2.87)|(2.16)|(1.74)|(1.84)|(2.87)|(3.60)|(3.36)|
|Dummy (Expenditure>=35%)|-0.52|0.12|0.09|-1.18|-0.62|-0.81|0.08|-0.93|-0.45|
||(0.43)|(0.09)|(0.08)|(0.88)|(0.60)|(0.82)|(0.03)|(0.30)|(0.15)|
|Constant|16.10***|22.29***|18.14***|0.8|0.96|2.13|28.71***|31.18***|31.43***|
||(3.42)|(3.34)|(4.41)|(0.04)|(0.07)|(0.16)|(2.66)|(2.71)|(3.41)|
|Observations|295|295|295|160|160|160|135|135|135|
|R-squared|0.56|0.6||0.61|0.61||0.6|0.63||
|Number of countries||25|25||13|13||12|12|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% The results suggest the following: First, the impact of the size of government on economic growth is nonlinear. We experimented with plausible threshold values given the sample characteristics. We settled on a threshold value of 35 percent of GDP, which is roughly equal to the sample median and mean values and also yielded the strongest results.<sup>12</sup> The results in Table 1 provide strong evidence that at expenditure levels of 35 percent of GDP or higher, public spending negatively affects growth. At levels below 35 percent, public sector size has no robust measurable effect on growth (see also Figure 2).

Figure 2: Size of Government and Economic Growth: Threshold Effects

> 12 Threshold estimation procedures developed by Hansen (2000) using our data set were performed by Emilia Skrok and yielded a similar threshold (34 percent of GDP). This contribution to the analysis is gratefully acknowledged.

18
of the country’s low performance in terms of documentary and border compliance costs. Compared to other landlocked and Sahelian countries in the West African region, Mali's cost performance is below that of Burkina Faso but superior to that of Niger.

Table 4: Trading across border indicators

||Time to export:<br>border|Cost to export:<br>border|Time to export:<br>documentary|Cost to export:<br>documentary|Time to import:<br>border|Cost to import:<br>border|Time to import:<br>documentary|Cost to import:<br>documentary|
|---|---|---|---|---|---|---|---|---|
|Cameroon|202|983|66|306|271|1407|163|849|
|Chad|106|319|87|188|242|669|172|500|
|Central African Republic|141|280|48|60|98|209|120|500|
|Guinea|36|310|48|105|72|405|32|37|
|Côte d’Ivoire|239|423|84|136|125|456|89|267|
|Libya|72|575|72|50|79|637|96|60|
|Mali|48|242|48|33|98|545|77|90|
|Niger|48|543|41|39|78|462|156|457|
|Nigeria|135|786|131|250|284|1077|173|564|
|Senegal|61|547|26|96|53|702|72|545|
|Sudan|162|950|190|428|144|1093|132|420|
|Peers (Botswana, Kenya,<br>Uganda)|33|249|36|157|113|473|75|159|

_Source:_ World Bank’s Trading Across Border—time and cost--Index in Doing Business Indicators 2019.

Despite the multiplicity of ineffective bilateral and regional agreements,<sup>29</sup> several major issues prevent the availability of efficient transit corridors in order to connect to other countries. These are summarized below.

- Road Infrastructure is poor. In Niger, the transport infrastructure depends strongly on partly paved roads that suffer from lack of maintenance. The entire network is approximately 19,000 kilometers, of which less than 4,000 are paved. The Cotonou-Niamey corridor, almost entirely paved, is by far the most utilized, accounting for more than 65 percent of goods traffic but also very long. At 1,050 kilometers, the corridor is shorter than Lomé-Niamey. The Chadian road network is 42,000 km, out of which 6,200 km are primary roads and only 996 km are asphalted roads, which seem to be in poor condition, especially in the north and east of the country. Unpaved roads are often inaccessible during the wet season, especially in the southern half of the country. The N'Djamena to Douala port route (1,800 Km.) is currently the main way to open-up Chad. Nearly 90 percent of the total volume of international freight uses N'Djamena- Douala corridor.

- In relation to road infrastructure development in West Africa, is the PraiaDakarAbidjan-Lagos Corridor, which is 1,080 km in length, which is part of the Trans African Highways No. 7, and seeks to connect to Mombasa through Yaounde, Bangui, Kisangani, Kampala and Nairobi in East Africa through Central Africa. Part

16
14 oligopolistic market . Another monopolistic sector, preferred by domestic funds is energy. 1 7 HCC invested more than 70% of its voucher points in these two sectors.

**Table 7: Fund's investment in different industries**

|Fund category|do|mesti|c bank s|ponso|red|for<br>bank|eign<br>spons.|l non|-bank f|unds|
|---|---|---|---|---|---|---|---|---|---|---|
|Fund group|CS|IB_|J KB_C|P_[|AB|CA]|ZB|HCC|PPF|YSE|
|Food/consumer goods|26|16|9|**23**|n/a|30|40|8|30|2|
|Construction 1/|14|0|8|2|n/a|30|**17**|3|15|5|
|Financial institutions|**15**|**50**|**22**|**14**|n/a|0|0|**50**|0|**43**|
|Energy|19|10|**19**|7|n/a|0|0|23|5|10|
|Machinery/engineering|12|9|11|21|n/a|16|7|10|**35**|6|

1/ including construction materials.

Remark: for fund names and fund classification see Table 3. Bold figures represent significant investments.

Source: Fund interviews.

Since domestic banks had more information on individual companies than most other market participants, funds of this type also invested in more 'difficult' industries like machinery, engineering and heavy industiy (between 9 and 21% of their voucher points). Surprisingly, the non-bank fund PPF courageously invested considerably in this industry.

**Table 8: Diversification of fund's portfolio (June 30, 1994)**

|I__________|HCC|ZBC|AYSE|PPF||AB|CP||KBC|S|
|---|---|---|---|---|---|---|---|---|---|---|
|Numberof companies|46|50|72|126|186|215|241||320|245|476|
|Asset value of top ten|||||||||||
|<br>companies 1/|77|60|63|70|54|25|42|44|49|28|
|Fund category 2/|NB|FB|FB|NB|NB|DB|DB|DB|DB|DB|

1/ in % of asset value of total portfolio (at market prices).

2/ NB=non-bank; DB=domestic bank sponsored; FB=foreign bank sponsored. Remark: for fund names see Table 3. Source: Fund interviews.

> 17Ceska Sporitelna, the Czech Savings Bank, holds 40% of the savings in the country and the big commercial banks, Komercni Banka and Investicni Banka, hold significant portfolios of enterprise credit.
purchase of intermediate inputs, mainly paddy. The rest of the production value goes to the payments of production factors (nearly 21 percent) and taxes (1.4 percent). Although rice milling contributes for less than one percent to total GDP (0.6 percent), paddy production represents more than one percent of GDP so that overall, the rice sector accounts for more than 2 percent of GDP in Mali.

Additional insights on the Malian rice economy can be derived from the SAM using demand analysis. Total demand for rice represents approximately 5 percent of GDP, with a 2004 nominal value of nearly CFAF108 billions. Considering all forms of demands, more than one quarter of total demand (26.1 percent) comes from home consumption whereas the remaining three quarters are purchased on the market (table 1). Home consumption is even slightly more important if we only consider the demand by households (28.2 percent of total household demand for rice in 2004—see Column 2 in table 2). The SAM shows that households represented more than 90 percent of the market demand for rice in 2004, which represents more than 3 percent of GDP (table 1), since this also includes demand satisfied through rice imports which account for about 40 percent of total rice consumption. Regarding the value of market supply, it can be decomposed into domestic sales by rice producers (50 percent), imports (31 percent), marketing costs or margins (16.5 percent) and taxes (2.3 percent).

Although the SAM includes 11 household groups, which serve as the basis for poverty analysis, the presentation will focus on the six groups indicated in table 2. These include four urban and two rural household groups: (i) households in which the head live in urban area and works in industry; (ii) urban households with a head that is a public servant; (iii) urban households with heads working in private services; (iv) urban households with the heads 10
Three cautions should be considered in comparing results between these three data sources. First, the regional Laboratorio and Guatemala Laboratorio data include third and fourth grade students, while the Mexican National Standards data only represents fifth grade students. More students have dropped out of school by fifth than by third or fourth grade, so the Mexican sample offers a rosier picture of the well-being of indigenous and non-indigenous students than the Guatemalan or Peruvian samples offer. Since the grade difference between the Laboratorio data and the National Standards data is a single year, since we control for a student’s grade in the Laboratorio regressions, and since we are comparing groups within a country, this caveat is not critical.

Second, one could worry that the Mexican data are scaled using IRT while the Regional and Guatemalan Laboratorio data are not. That worry identifies a weakness of the Laboratorio exams – they may overweight easy questions and underweight hard questions, so the final score is an imperfect measure of a student’s ability. To address this concern, we use students’ z-scores in regressions and avoid comparison of raw scores between countries. Additionally, this difference in scaling should affect all students and hence is unlikely to impact the indigenous/non-indigenous test score gap.

Third, many students, teachers and principals gave no response to some background questions, forcing exclusion of some observations from multivariate analysis. The present analysis walks a balancing act: on one hand, adding explanatory variables to the regressions increases explanatory power and more effectively shows the determinants of learning. On the 13
Table 2 shows the descriptive statistics of our sample at each point in time. The combined household survey comprises of 9,228 observations from five VUP cohorts. For the analysis in this paper we focus on households in cohort 3 so that we can exploit baseline properties. Our sample thus amounts to 1,947 individual observations. Within this we have complete information from all three survey rounds for 615 out of a total of 618 households surveyed at baseline. The rate of attrition is less than 1 percent. Hence, systematic attrition is not a concern for our analysis. In later survey rounds households have been added to increase the sample size but we are not considering these households here since we do not have baseline information on them.

The descriptive statistics show that compared to other contexts the share of female headed households in the study location and in our sample is quite large with 40 percent of all reported heads of household being female.<sup>12</sup> The average age of household heads in our population was 48 years old at baseline. Heads of direct support households are older – 58 years at baseline – whereas heads of public works households are younger – 41 years at baseline.

Finally, the descriptive statistics also show that the sample size of the direct support and public works beneficiaries is very small, i.e. just over 12 percent of the sample population.<sup>13</sup> Overall the education level in the sample population is low with over half of the household heads never attending school. For direct support households it is even more than three-quarters of the heads of households. Disability in our study refers to physical disabilities or chronic inhibition such as continuous or debilitating pain or mental illness. The proportion of heads of households with a disability is also quite high in our context reaching almost 50 percent. Again, the shares are higher for direct support households, reflecting the eligibility criteria applied by the program. Within the education levels and reported disability status we see some changes over time. This is partly due to misreporting but also due to compositional changes and changes of the household head, particularly in direct support households where the household head died. The average household in our sample has 5 members; direct support households are however 18
**Table 2: Currency Distribution of Foreign Exchange Market Turnover, International Bond Issues, and Cross-Border Lending** **<u>(percentage)</u>**

|||**1980**<br>**Bretton**<br>**Woods 2**|**1992***<br>**Post-Plaza**<br>**Accord**|**2002**<br>**Eurozone**|**2008–2010**<br>**Financial**<br>**Crisis**|
|---|---|---|---|---|---|
||$||82.0|89.9|84.9|
|**Currency Distribution of Global**|£||13.6|13.0|12.9|
|**Foreign Exchange Market**|DM||39.6|||
|**Turnover****|€|||37.9|39.1|
||Other||64.8|59.2|63.1|
|**iii f**|$||40.4|51.6|36.8|
|**Currency Dstrbuton o**<br>**International Bond Issues**|£||7.3|6.7|7.9|
||€||25.2|32.4|46.8|
||Other||27.1|9.3|8.5|
|**C Ditibti f C**|$|63.8|52.3|49.9|39.2|
|**urrency sruon o ross-**<br>**Border Lending**|£|2.8|3.9|5.9|7.4|
||€|23.1|22.8|31.3|41.2|
||Other|10.3|21.0|12.9|12.2|

Source: Bank of International Settlements.

* Currency distribution of international bond issues data from 1993.

** Because two currencies are involved in each transaction, the sum of the percentage shares of individual currencies totals 200% instead of 100%. Data from Triennial Central Bank Survey. Note: ECU used as internal accounting unit for bond issues and lending in the European Community beginning in 1979.  It was later replaced by the Euro at equal parity in 1999.

18
We tease out the impact of the BLAs on migration after imposing a series of fixed effects to control for the various confounds. Our most stringent, and preferred, specification controls for corridor (origin country _×_ destination country) fixed effects, origin _×_ decade, and destination _×_ decade fixed effects. That is, if country _A_ signs a BLA with country _B_ , the identifying variation comes from comparing migration within the _AB_ corridor before and after the signage (corridor fixed effects); from comparing migration from country _B_ to country _A_ relative to all potential destinations for migrants in country _B_ (origin-time fixed effects); and from comparing migration to country _A_ from country _B_ relative to all potential origin countries for migrants to country _A_ (destination-time fixed effects).

These extensive sets of fixed effects ensure that some corridor-specific factors (e.g., historic bilateral relationships between two countries), or country-time-specific factors that can affect migrant labor demand or supply (such as economic cycles or the labor market situation in origin or destination countries) do not drive the relationship and that the impact can be attributed to the BLA itself. One of the key assumptions for the identification of the impact of the BLAs is that of parallel trends. We employ an event-study design to check for any pre-trends on migration prior to the signing of the BLAs (in addition to using this to explore persistence of effects).<sup>2</sup> However, as migration data is measured every ten years, the pre-trend test cannot test for anticipatory BLA signing in response to, say, temporary shocks within the decade of signing.

We find that signing a BLA increases migration from the origin country to the destination country by 76 percent (0.57 log points) in the decade of signing the BLA. The migration effect lasts up to three decades after signing a BLA. The impact of BLAs is muted at 25 percent (0.24 log points) for ‘regular’ corridors - corridors with at least 10 migrants in all periods.<sup>3</sup> The estimates are of similar magnitudes with alternative sets of fixed effects, and slightly larger with an instrumental variable specification where we use ‘leave-out’ mean as the instrument. These estimates can be considered as a lower-bound of the true impact of a BLA on facilitating migration as the data on migration only captures the stock of migration measured every ten years and misses temporary migration that might have happened between the periods. We also

> 2We also test the assumption of no pre-trends using a heterogeneity-robust estimator applied to gravity settings following the recent advancements in the difference-in-difference methodologies (as developed in, Nagengast and Yotov, Forthcoming, Nagengast, Rios-Avila and Yotov, 2024) and fail to reject the null of no pre-trends.

> 3The results are robust to alternative thresholds for defining which corridors count as ‘regular’.

4
23

# Notes

As noted by Leibbrandt et al (1996: 4), Rk is a form of rank correlation coefficient, because it measures the extent to which the relationship between Yk and the rank distribution of total income coincides with the relationship between Yk and its own rank distribution.

**2** This analysis ignores feedback effects, that is, the effects that a change in any source income share might have on distribution within any source income. Of course, such an assumption might be quite unrealistic for large changes in any source income share.

**3** This household survey was conducted by the International Food Policy Institute, working in collaboration with the Egyptian Ministry of Agriculture and the Egyptian Ministry of Trade and Supply.

**_4_** For more details on this 1997 Egypt Integrated Household Survey, see Datt, Joliffe and Sharma (1998).

5According to the data, 90 percent of rural households in the survey own their own house.

**6** Only the simple correlation between transfer income and total income (0.204) is lower than that for livestock income. The simple correlations for the other source incomes are: nonfarm income, 0.362; and rental income, 0.442.

7 It should be noted that the definition of nonfarm income used here is narrower than those used in other studies. For example, Reardon (1997) includes migration (remittances) income in nonfarm income while Chinn (1979) includes rental income.

8For other estimates of the share of nonfarn income in total rural income, see Reardon (1997) on Africa, and Hazell and Haggblade (1993) on Latin America.

91t should be noted, however, that these Ginis of per capita household income for Bolivia and Brazil are based on the distribution of overall (that is, urban and rural) incomes, while the income Ginis used in this study are based on rural household income. In theory, one would expect that the distribution of rural household income to be more egalitarian than that of overall household income.

'° Non-farm income is also important to the poor, when the poor are defined on the basis of expenditure rather than income data. Appendix Table I, where the quintile groups are based on total per capita expenditures, shows that the poor -those in the lowest quintile group - receive almost 45 percent of their total per capita income from nonfarm income.