rafmacalaba/datause-encoder-data huggingface dataset:
assessment stands at odds with UNDP who judges MDG4 to be very likely to be achieved. UNDP arrives at the same judgment for Madagascar, where we are a bit more pessimistic although some further improvement seems to be possible (however, less likely to be obtained under the current political and economic circumstances). In contrast, we are more optimistic than UNDP concerning Uganda.

In Guinea and Senegal the effects of the simultaneous interventions are close to the extrapolation of the current trend (see Table 1), which falls short of the reduction needed to achieve MDG4. In Guinea and Senegal, the assessments based on recent observed trends and on simulations converge to indicate that these two countries are unlikely to achieve MDG4 by 2015. This assessment contrasts with the UNDP judgment which is more encouraging for both countries.

Finally, Kenya, would only get half way to the target. For this comparably rich country, the simulated policy scenario do not appear to be overly ambitious. For Kenya, our assessments (based on recent observed trends and on simulations) converge and point to a very worrying situation although the UNDP judgment is more optimistic.

# Conclusion

This paper investigates the determinants of child mortality with a focus on MDG complementarities. More specifically, we examine how progress in other MDGs – in particular the expansion of female education, corresponding to MDG2 & MDG3 – would affect child mortality (MDG4). In particular, we analyze how so-called proximate determinants such as universal BCG vaccination and the elimination of short birth intervals are associated with mother’s education. The analysis is carried out on 16 DHS surveys collected in eight Sub-Saharan countries (Guinea, Senegal, Mali, Niger, Kenya, Uganda, Madagascar, and Mozambique) over the last 15 years and relies on the estimation of dicretetime hazard models of survival for children under five.

We show that mother’s education is indeed an important determinant of child survival. The effect of mother’s education runs through a number of channels. Our analysis shows that more educated mothers have larger birth intervals and that their children are more likely to be vaccinated. These findings suggest that complementarities between achieving MDG2 & MDG3, on the one hand, and MDG4, on the other, could be substantial. This is confirmed by our simulation results: The results illustrate that the impact of mother’s education expansion on child mortality is considerable but varies markedly across countries. In countries with considerable room for improvement, notably in the Sahel zone, the simulated reductions are 40
16 Figure 1 graphs the two sets of effective income tax rates: income-based and expenditure -based rates for all households. Two features of the figure are noteworthy. First, the distributional pattern of income tax does not differ in the two cases. The percentage of per capita income (expenditure) paid in income tax rises consistently with income (expenditure). Irrespective of the approach used, income tax falls most heavily on higher-income (expenditure) households -decile 7 to 10- with effective rates nearly four times that of poor households (lowest two decile). These results suggest that the present income tax system with marginal rates ranging from 20 percent to 52 percent is very progressive. Furthermore, the exclusion of in-kind income/expenditur **e** liminates much of the share income (expenditure) of the poor from taxation, increasing the progressivity. Second, although the effective income tax rates based on expenditure are higher than those based on income, the figure shows that the variation in expenditure shares across deciles is the same as the variation in income tax outlays as a share of income 1 The urban and rural effective income tax rates for each income (expenditure) decile are also given in Table 4. Urban households pay 5.3 percent of their per capita income in income tax, whereas the rural sector pays less than half that amount. The table shows that the percentage of per capita income or expenditure devoted to income tax is much higher for the urban sector than the rural one. This urban-rural disparity in income tax burden cuts across income as well as expenditure classes. The disparity is due, among other things, to 12/ Surprisingly, the household budget survey data show that household per capita expenditure is, on the average, lower than per capita income. For an economy in transition such as Bulgaria, one would expect a significant fraction of households to experience transitory low income, or have expenditure in excess of income as part of a lifetime plan.
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 5. Road standards in Senegal (as of 2006)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Under-engineered Correctly engineered Over-engineered<br>Nigeria Benin South Africa Rwanda Uganda Chad Ethiopia Senegal Tanzania Ghana Malawi Cote d'Ivoire Cameroon Burkina Faso Kenya Niger Lesotho Mozambique Madagascar Namibia Zambia Average<br><!-- End of picture text --> _Source_ : Gwilliam and others 2008. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data) using RONET.

The combination of good connectivity and adequate standard selection is well received by users. Therefore, despite the relatively poor conditions of the road network due to deferred maintenance, less than one-third of Senegal’s firms identify transport services as a major constraint for doing business—a rating that is within the average observed among low-income countries. Moreover, according to a recent survey on trade logistics that captures indicators including the availability and quality of the roads, Senegal’s Logistics Performance Index (LPI), at 2.86, is above the regional average of 2.46 (figure 6). The LPI is based on a worldwide survey of

**Figure 6. Senegal’s Logistics Performance Index is the highest in West Africa**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Source : World Bank 2010d<br>Note: SSA = Sub-Saharan Africa.<br>Logistics Performance Index<br>Senegal Benin Guinea Togo Nigeria Niger Cote d'Ivoire Gambia, The Ghana Liberia Mali Burkina Faso Guinea-Bissau Sierra Leone SSA<br><!-- End of picture text --> operators on the ground (global freight forwarders and express carriers), providing feedback on the logistics ―friendliness‖ of the countries in which they operate and those with which they trade. Such operators combine in-depth knowledge of the countries in which they operate with informed qualitative assessments of other countries with which they trade as well as the experience of a global logistics environment. By this ranking, Senegal’s capacity to move goods and connect manufacturers and consumers with international markets is the highest in West Africa.

But there is only so much a well-designed network can do, if it is not properly maintained over time. Road conditions are eroding due to the poor allocation of resources to maintenance and periodic rehabilitation (based on the roads’ natural life cycles), and this hinders Senegal’s connectivity. The 10
- - 77

In the first part of the 1980s decade has resulted In cost underruns (with costs expressed In dollars) for about one third of projects. Of course, one of the major problems of expressing all costs In US dollars Is that nominal costs will go up and down with the fluctuation In the US dollar which has been quite volatile over the last few years. Now, after the recent period of cost underruns, overruns can again be expected because of the recent tall In the dollar which may not have been anticipated In project cost estimates. It expressed In local currency, the impact of cost Increases and decreases will look very different from the behavior of the corresponding US dollar cost, especlally If most of the procurement was In currencies other than US dollars.

# Interest Rates

The high Interest rates In the early 19809 has obviously resulted In higher debt service obligations for power utilities and was given as one of the reasons for poor performance by nearly one third of utiilties. A moro detailed study would be required to determine the magnitude of the Increase In debt service obligations, for they are not so readily apparent In those cases where tariffs were Increased In line with Increased costs, Including debt service.

# 3.2 Potentially Controllable Factors In the National Environment

The performance of the power sector could be significantly Improved If governments were to take certain measures that would either remove restrictions on the utilities' activities or Improve coordinatlon and planning of the sector. Such measures are outside the control of the utility but completely within the purview of government. Government procurement procedures are often slow and cumbersome resulting In delays In the award of contracts. About one quarter of the projects with significant delays cited slow government procedures as one of the major reasons for the delays. There does not appear to have been any
Our empirical analysis proceeds in several steps. We first examine the effects of German acquisitions on firm performance. Results from a difference-in-differences approach point to sizeable positive effects of German acquisitions on firm size. In a second step, we examine whether German acquisitions following EU integration were greater in sectors in which Germany was more specialized at the moment of accession. Event studies point to a sharp increase in German acquisitions of Polish firms right after EU accession, and at a remarkably greater extent in sectors in which Germany had higher export shares. While these acquisitions began in 2001 in concomitance with the conclusions of the accession negotiations, it was only after joining the EU that FDI observed a sharp rise. Foreign acquisitions increased also from other EU countries, but to a much lower extent. In a final step, we examine if the evolution of firm performance in Poland following EU accession was also mediated by pre-determined measures of industrial specialization in Germany. Difference-in-differences estimates reveal that Polish manufacturing firms operating in sectors in which Germany was more specialized at the moment of accession experienced stronger output and productivity growth in the post-2004 period. These results capture not only direct effects of German acquisitions on firm performance, but also spillovers to other domestic firms operating in the same broad sector. The estimates remain qualitatively similar across different measures of comparative advantage, including output-based indicators of industrial specialization and measures of relative factor intensity in the sector. Reassuringly, placebo tests using similar measures of industrial specialization for Poland, the Russian Federation and other less developed non-EU neighbour countries fail to identify systematic links with the evolution of firm performance in the post-2004 period. Taken together, our findings suggest that deeper integration in goods and factor markets stimulates capital flows to the less advanced economy, that these flows occur predominantly in sectors in which the advanced economy had a comparative advantage, and that this contributes to narrow the productivity gap within industries.

This paper complements and extends several strands of literature. A number of cross-country studies have identified systematic empirical links between foreign direct investment and growth, including early work by Blomstrom et al. (1996) and Borensztein et al. (1998). The current paper contributes to this literature by providing microeconomic evidence on these links, exploiting Poland’s accession to the EU as a source of variation in the degree of market integration with a high-income country. In doing so, this paper also relates to recent research using firm-level data to document effects of foreign acquisitions on firm performance, including Arnold and Javorcik (2009), Bloom et al. (2012), Guadalupe et al. (2012) and Bastos et al. (2018). In contrast to this strand of literature, we emphasize the key role of initial specialization patterns in the advanced economy in driving the impacts of deeper 3
changes in government investment. We achieve this by exploiting the fact that (a) a significant fraction of government spending in developing countries is financed by loans from official creditors, and (b) these loans typically disburse over a period of many years following the initial approval of the loan. Drawing on a unique loan-level dataset on nearly 60,000 individual loans from official creditors to developing country governments, Kraay [2013] constructs a measure of predicted disbursements on loans from official creditors that reflects only fluctuations in loan approval decisions made in previous years, but not the current year.<sup>8</sup> The key identifying assumption is that loan approval decisions in a given year do not anticipate shocks to private investment or output in subsequent years. Given this identifying assumption, subsequent predicted disbursements are uncorrelated with contemporaneous macroeconomic shocks, and are a valid instrument for changes in government investment. Moreover, in countries where official creditor financing of government investment is large, predicted disbursements will also be a strong instrument for changes in government investment.<sup>9</sup>

> 8The predicted disbursements instrument is constructed as follows. We start with a set of approximately 60,000 loans from official creditors to developing country governments, as recorded in the Debtor Reporting System database of the World Bank. For each loan we have the year of the original commitment, and the schedule of subsequent annual actual disbursements. For each loan we construct a 10-year disbursement profile, i.e. the fraction of the original commitment that is disbursed in year 0, 1,2,...,10, with year zero corresponding to the approval year of the loan. We next sort loans into creditor-decaderegion-specific bins and average disbursement profiles across loans within bins. We then apply these typical disbursement profiles to individual loans within each bin, and construct predicted disbursements for each loan, i.e. the disbursements that would have occurred had the loan disbursed at the typical rate for all loans in the same bin. The rationale for this step is that it removes any country-specific source of variation in actual disbursements on previously-approved loans, which might respond endogenously to contemporaneous macroeconomic shocks. Finally, we aggregate the predicted disbursements across all loans for each country-year observation, but excluding loans approved in the same year. By construction, the only country-specific variation in this predicted disbursements measure reflects loan approval decisions from previous years, which we assume to be uncorrelated with contemporaneous macroeconomic shocks. For details, see Sections 2 and 3 of Kraay [2013].

> 9In addition, we are also implicitly assuming that the multiplier effect of government consumption expenditures on output and on private investment is zero. This assumption is

15
According to the CRMG survey, almost all coffee in the Dominican Republic is interplanted.<sup>32</sup> The tendency to interplant is weakly related to farm sizes; 99 % of the smallest producers reported inter-planting, compared to 95% in medium holdings and 93% in the largest (>10 hectares) group. Thus, despite different uses of technologies, virtually all producers in the Dominican Republic interplant, but diversified production systems are somewhat more important for smaller producers.

# **C.  Quantities Produced and Exported**

Dominican coffee production has varied substantially over the past decade, ranging between 72,000 and 125,000 qq or about 32,700 to 57,000 MT (see table III.5). Production variability is associated with pest (primarily broca) and disease problems, Hurricane Georges, and with fluctuating international prices. Hurricane Georges resulted in a production decline of about 40%. Recent price declines have also been associated with steep production declines, and a very sharp drop in the volume and value of coffee exports – especially when compared to the mid-1990s..

**Table III.5:  Coffee Production and Exports 1990/1-2001/2**

||**Produc**|**tion**|**Exports**<br>**%**|||**Value of**|
|---|---|---|---|---|---|---|
||**QQ**|**MT**|**Exported**|**QQ**|**MT**|**Exports US$**|
|1990/1991|900000|40823|66|597100|27084|43,241,360|
|1991/1992|948000|43000|44|420100|19055|25,648,700|
|1992/1993|922000|41821|64|586100|26585|32,095,400|
|1993/1994|721200|32713|50|362100|16425|34,126,000|
|1994/1995|934900|42406|61|574700|26068|92,881,300|
|1995/1996|1055900|47895|65|691000|31343|73,752,300|
|1996/1997|749500|33997|53|393900|17867|59,785,900|
|1997/1998|1255400|56944|44|553000|25084|83,211,900|
|1998/1999|763000|34609|28|213400|9680|23,934,000|
|1999/2000|1004100|45545|25|250440|11360|27,432,440|
|2000/2001|782100|35550|17|135600|6163|11,626,400|
|2001/2002|822980|37408|18|151600|6890|12,832,380|

Source: CODOCAFE Unlike many other coffee-producing countries, the Dominican Republic has a high per capita consumption, of about 5 pounds per year. The 2-3 million tourists who visit the country annually also account for some of this domestic consumption. These tourists drink, buy, and take coffee home. Domestic consumption of coffee has been more stable than production and has slowly increased in recent years, largely a function of population growth. Domestic consumption averaged about 50-75 % of total production during the 1990s, but, due to sharp declines in exports, the domestic consumption in 2000-2003 has averaged over 80% of total production. Coffee exports fluctuate more than total production. Export fluctuations are also influenced by the management of coffee stocks by INDUBAN, a company that handles about 95% of the domestic market and 20% of exports.

> 32 According to the CRMG survey, the most popular inter-planted trees were orange, avocado and plantain (about 70% of producers), followed by mango and lemon (about 20% of producers).  About 12% of coffee producers inter-planted with cocoa.

34
presented in table A2 in the appendix and the number<sup>of poor people are in table A3.</sup>

# For comparison, table 2 also shows the regional<sup>poverty rates according to BPS's</sup>

# approach.

Table 2: Regional Poverty Incidence (%) and Rank in<sup>February 1999</sup>

|||Ite|rative<br>|Method<br>|<br>|||<br>|BPS Me<br>|thod<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Province|Urb|an|Rur|al|Tot|al|Urba|n|Rur|al|Tot|al|
||Value|Rank|Value|Rank|Value|Rank|<sup>Value</sup>|<sup>Rank V</sup>|<sup>alue</sup>|<sup>Rank</sup>|<sup>Value</sup>|<sup>Rank</sup>|
|Jakarta|2.82|1|||2.82|1|6.59|1|-||6.59|1|
|Bali|10.67|11|15.61|5|13.62|6|9.80|3|9.89|1|9.85|2|
|Riau|8.53|7|9.62|1|9.21|2|11.43|4|14.98|2|13.65|3|
|Aceh|5.43|3|15.41|4|12.89|5|13.76|6|17.38|_5_|16.47|4|
|West Sumatera|8.78|9|9.74|2|9.47|3|17.43|12|16.48|3|16.75|5|
|Central Kalimantan|5.00|2|13.43|3|11.15|4|7.16|2|20.41|6|16.83|6|
|North Sumatera|10.81|12|18.91|6|15.27|7|17.50|13|16.64|4|17.03|7|
|East Kalimantan|8.74|8|35.06|15|21.67|10|12.65|5|22.83|9|17.65|8|
|WestJava|20.82|22|31.87|13|26.60|15|19.10|14|22.32|8|20.79|9|
|North Sulawesi|11.70|13|26.83|11|22.47|12|14.23|7|24.60|13|21.61|10|
|South Sulawesi|17.42|18|24.94|8|22.63|13|20.50|17|22.20|7|21.68|1I|
|South Kalimantan|7.99|6|26.38|10|20.64|9|16.37|11|25.03|15|22.33|12|
|Yogyakarta|22.12|23|36.78|16|26.95|16|20.13|16|27.68|18|22.62|13<br>|
|Bengkulu|10.41|<br>10|24.55|7|20.44|8|20.02|15|24.55|12|23.23|14|
|West Nusa Tenggara|30.17|27|44.71|22|41.78|23|25.94|23|23.42|10|23.93|is1<br>|
|Jambi|15.41|<br>16|25.25|91|22.18|11|23.27|21|24.63|14<br>|24.24<br>|16<br> <br>|
|South Sumatera|14.47|<br>15|27.93|12|23.81|14|24.77|22|24.27|11|24.42|<br>17|
|Central Sulawesi|16.72|<br>17|32.69|14|28.52|17|21.69|20|25.87|16<br>|24.78<br>|<br>18<br> <br>|
|Southeast Sulawesi|13.74|<br>14|44.44|21|36.61|21|14.28|8|29.34|20|25.50|<br>19|
|East Java|19.51|<br>20|40.87|20|33.31|20|21.55|19<br>|28.80<br>|19<br>|26.24<br>|<br>20<br> <br>|
|Central Java|23.72|<br>25|37.76|17|32.78|<br>19|26.06|24|27.52|17|27.01|<br>21<br>|
|West Kalimantan|6.17|5|38.04|18|30.76|<br>18|14.43|<br>10|34.25|22|29.72|<br>22<br>|
|Lampung|19.90|<br>21|40.57|193|6.80|22|21.14|183|2.92|21<br>|30.77<br>|<br>23<br> <br>|
|Maluku|18.64|<br>195|9.90|24|48.40|<br>24|28.52|25|41.50|<br>23<br>|37.88<br>|<br>24<br> <br>|
|East Timor|23.37|<br>24|59.38|<br>23|55.49|<br>26|39.35|<br>27|44.07|<br>24|43.56|<br>25|
|East Nusa Tenggara|28.67|<br>26|66.11|<br>25|61.18|<br>27|30.43|<br>26|47.15|<br>25|44.95|<br>26|
|Papua|6.07|<br>4|72.19|<br>26|54.89|<br>25|14.31|<br>9|59.30|<br>26|47.53|<br>27|
|Indonesia|16.34||34.10|_|27.13||19.98||25.85||23.55||
|Note: Sorted by aver|age pro|vincial p|overty<sup>b</sup>|<sup>y BPS</sup>|<sup>method</sup>||||||||

The results show that in February 1999, the poverty<sup>rate in Indonesia was</sup> 27.13 percent, implying around 55.8 million<sup>poor people. This poverty rate is</sup> 14
Table 2b: State-level distribution of female activity in services

||Estimat<br>owne|ed count of<br>d establish|female-<br>ments|Share of e<br>that a|stablishme<br>re female-o|nts in state<br>wned|Estimat|ed count o<br>employees|f female<br>|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|A & N Islands|245|277|691|0.105|0.113|0.149|744|1,001|2,157|
|Andhra Pradesh|153,000|167,854|220,891|0.118|0.120|0.125|717,846|722,063|793,474|
|Assam|22,774|73,445|14,517|0.059|0.131|0.047|37,017|106,430|33,603|
|Bihar|42,951|89,802|53,082|0.030|0.069|0.047|168,668|237,011|136,923|
|Chandigarh|1,703|1,881|1,779|0.100|0.098|0.075|5,351|5,705|5,578|
|Dadra & Nagar Haveli|224|162|664|0.100|0.105|0.154|392|258|1,525|
|Daman & Diu|255|265|393|0.132|0.148|0.151|421|334|723|
|Delhi|24,143|14,030|32,961|0.103|0.099|0.094|53,478|36,121|117,721|
|Goa|1,819|5,945|3,364|0.140|0.231|0.148|7,106|15,045|7,725|
|Gujarat|40,412|49,288|83,095|0.073|0.080|0.093|72,415|99,046|155,804|
|Haryana|15,697|23,107|24,146|0.074|0.075|0.072|57,954|72,922|82,845|
|Himachal Pradesh|6,733|6,697|12,029|0.088|0.063|0.093|13,188|19,912|22,959|
|Jammu & Kashmir|5,733|4,259|4,979|0.054|0.031|0.037|21,709|18,019|29,065|
|Karnataka|49,951|61,191|83,473|0.077|0.093|0.111|193,560|192,897|281,105|
|Kerala|69,391|103,849|84,644|0.125|0.140|0.135|196,098|282,835|224,618|
|Madhya Pradesh|38,807|37,898|47,107|0.067|0.068|0.064|128,332|138,866|148,570|
|Maharashtra|107,804|149,592|191,522|0.106|0.113|0.122|316,617|376,063|398,265|
|Manipur|3,407|3,727|5,087|0.145|0.151|0.209|10,221|10,957|13,412|
|Meghalaya|9,379|15,589|13,135|0.398|0.323|0.382|18,510|30,973|20,698|
|Nagaland|1,970|1,546|945|0.155|0.205|0.129|4,843|5,483|2,760|
|Orissa|37,435|29,991|22,424|0.062|0.059|0.043|245,378|126,914|80,543|
|Pondicherry|2,340|6,014|5,026|0.170|0.210|0.233|8,904|20,842|12,314|
|Punjab|27,942|38,415|38,581|0.081|0.088|0.097|88,075|100,772|95,305|
|Rajasthan|22,887|29,192|40,822|0.044|0.051|0.064|82,768|98,275|118,021|
|Tamil Nadu|116,992|122,916|155,113|0.122|0.124|0.125|497,407|514,582|539,777|
|Tripura|1,852|2,891|1,576|0.042|0.047|0.013|4,032|4,732|9,130|
|Uttar Pradesh|169,949|127,425|140,823|0.067|0.055|0.059|543,937|558,141|573,011|
|West Bengal|116,091|133,018|149,127|0.076|0.069|0.076|186,230|221,739|241,858|
|Nationwide|1,091,887|1,300,267|1,431,996|0.079|0.088|0.089|3,681,202|4,017,939|4,149,488|

Notes: See Table 1.