rafmacalaba/datause-encoder-data huggingface dataset:
Data for the six Gulf Cooperation Council (GCC) countries are estimated on the basis of their Labor Force Surveys. The data for the remaining countries were obtained from the OECD DIOC-E database.<sup>5</sup> When constructing such a large-scale data set, we have to deal with inevitable gaps in the data. Imputation and adjustment are preferred when census data are not sufficiently detailed to identify the gender and education characteristics of stock of migrants in particular bilateral corridors. We therefore distinguish between two types of data sources on the basis of the degree of imputation needed to construct our database.

First, we obtained highly detailed census data from 17 non-OECD countries in 1990 and 60 non-OECD countries in 2000. These countries are not marked with an asterisk in Table A.1. They require two minimal adjustments: (i) we use OPSW data to split the available data if there are geographic aggregations for certain regions of origin (e.g. South Asia) or else countries that no longer exist (e.g. ex-Czechoslovakia)<sup>6</sup> ; (ii) when the year of census differs from 1990 or 2000 by four years or more, which is not atypical since censuses are conducted over decennial cycles. We rescale data using the annual growth rate of the total immigrant stock provided by the United Nations database.<sup>7</sup> Similar adjustments were used in DLM for OECD destinations. We consider these adjustments as minor corrections.

For the remaining 10 non-OECD countries in 1990 and 6 countries in 2000, we obtained precise data by country of origin, which unfortunately lacked the education and/or gender

> 5http://www.oecd.org/migration/databaseonimmigrantsinoecdandnon-oecdcountriesdioc-e.htm

> 6These regional aggregates recorded in many destination countries’ data are usually small.

> 7This is the case of Burkina Faso 1996, Colombia 2005, Costa Rica 1984, Israel 1983, Israel 1995, Laos 1995, Malta 1995, Malta 2005, Peru 2007 and Uruguay 2006.

12
# **1. Introduction**

Central banks intervene in foreign exchange (FX) markets when they target particular levels of the exchange rate, through pegs, crawls, or bands with respect to other currencies or currency baskets. Even when they do not target a particular rate, they may intervene when there is excessive volatility in the exchange rate, which can be destabilizing for traders and ordinary consumers. When faced with current account surpluses or capital inflows, central banks that target the exchange rate end up accumulating large amounts of FX reserves. Accumulation of reserves can also be a symptom of an undervalued exchange rate that some governments have used to promote exports—or to prevent an appreciation that could undermine competitiveness. The extraordinary accumulation of reserves by the People’s Bank of China since the mid-1990s and by the Swiss National Bank in the aftermath of the 2008-09 global financial crisis— especially following the Swiss franc’s peg to the euro in September 2011—have been the focus of a lot of discussion and analysis.<sup>1</sup> By contrast, central banks that intervene to prevent exchange rate depreciation could see their FX reserves get depleted.

Central banks committed to a floating exchange rate intervene in foreign exchange markets for various reasons: When, for instance, a country’s currency appreciates because of some shock and the central bank believes that this is a temporary fluctuation, it may want to prevent the appreciation of the exchange rate that may undermine growth. The central bank may also want to contain excessive currency depreciation, if it is believed to be short-lived, when domestic agents have unhedged foreign currency borrowing exposure that may imperil financial stability. When such temporary shocks occur frequently, central banks may systematically intervene to contain exchange rate volatility. Central banks also intervene to manage FX reserves at levels deemed adequate against volatile capital flows and debt servicing obligations.

The present paper is an amoral one. It takes no stance on the goodness of interventions in the foreign exchange market by central banks. Its concern is with the efficacy of such interventions, no matter what prompts them. It is arguable that our understanding of the mechanics of central

> 1 See, for example, Ross and Jones, (2013), who point to evidence that, in the face of excessive accumulation of FX reserves in a near-zero interest rate environment for US dollar and euro denominated bonds, central banks have been increasingly searching for opportunities to diversify reserve holding into higher-yielding assets, including from emerging market economies. According to IMF statistics, worldwide FX reserve holdings had more than doubled, from US$ 5 trillion in 2006 to US$ 10.8 trillion in 2012.

2
# **<u>Graph 1.   INDECOPI – Flowchart*</u>**

<!-- Start of picture text --> EXECUTIVE BOARD<br>PRESIDENCY Advisory Council<br>Institutional<br>Control Body<br>GENERAL MANAGEMENT<br>Quality Management Unit<br>National Metrology Service<br>Technical Cooperation and<br>International Relations Area<br>OFFICES COMMISSIONS COURT MANAGEMENT<br>AREAS<br>Document Administration<br>Copyright Consumer Protection Competition Defence Administration Logistics and Asset Control<br>Courtroom Human Resources<br>Audit Area<br>Distinctive Unfair Competition Legal<br>Signs<br>Intellectual Property<br>Market Access Courtroom Institutional Consumer Support Systems<br>Inventions and Development Decentralized Offices<br>New<br>Technology Dumping and<br>Subsidies Transitory Bankrupcy Marketing and Press and Communications<br>Courtroom Communication Information and<br>s Documentation Center<br>Free Competition<br>Economic Planning and Statistics<br>Studies Market Research<br>Technical and<br>Commercial<br>Regulations Accounting and Budget<br>Finance Treasury<br>Collections<br>Bankrupcy IT and Systems<br>Procedures<br><!-- End of picture text --> 8
A. Sanghi and D. Johnson

# **3.2 Chinese companies in Kenya: From large state companies to small private ones**

Kenya currently hosts around 400 Chinese firms spread across every sector. In February 2014, the Sino-African Centre of Excellence (SACE) foundation launched the Business Perception Index (BPI) survey to learn the views and experiences of Chinese companies in Kenya. The BPI surveyed 75 companies: 25 state owned and 50 privately owned enterprises. Many firms in the sample are in the manufacturing, construction, and resources sectors; Chinese firms are also involved in trade, tourism, hotels, and restaurants. Chinese electrical companies are working with Kenya Power, the government-owned power company, to establish 33 power lines in five Kenyan towns: Kitale, Awendo, Konza and Kutus (Business Daily 2015). Oil companies are sure to be attracted to Kenya’s new oil discoveries in Turkana and the northwest part of the Rift Valley, but the UK based Tullow Oil Plc has rights for drilling.

## **3.2.1 Chinese invest the most in metals, communications, and automotive original equipment manufacturing (OEM)**

Figure 23a below gives the amount of Chinese investment by sector between 2003 and 2015. Chinese companies invested US $178.9 million in metals, US $150.9 in communications, and US $68 million in automotive original equipment manufacturing (OEM). Investment in metals fits with Kenya’s high exports of titanium ores and copper to China. Companies also want to take advantage of the growing telecommunications market in Kenya: the number of mobile telephone has grown significantly in the last five years, reaching over 33 million subscribers in 2014; Chinese firms such as Huawei and ZTE wish to compete with other major companies such as Nokia or Ericsson (see figure 22). Figure 23b shows the overall distribution of Kenya’s FDI. The top sectors are communications at US $2.51 billion, alternative and renewable energy at US $2.36 billion, and coal, oil and natural gas at US $1.35 billion. In total, Chinese companies contributed US $464.75 million in capital investment with an average investment of US $35.75 million per project; other foreign companies in Kenya had a similar project size of US $35.5 million.

<!-- Start of picture text --> 30<br>20<br>10<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>Subscribers (Millions)<br><!-- End of picture text --> Figure 22: Kenya’s mobile telephone subscriptions grew quickly over last decade Source: International Telecommunications Union 2016 24
varies with proxies for time discount. The evidence presented in column 1 in Table 10 shows that low time discount students display a very large and significant increase in self-reported effort, as opposed to a zero impact among the high discount students. Point estimates on the average learning score show larger coefficients for low time discount than high discount students (column 2 in Table 10), but we can not reject the null hypothesis of no differential In summary, both boys and girls update their priors about the labor market returns to education, but only the latter exert more effort, partly by choosing subtracks with higher mathematical content. We provide some suggestive evidence that part of the difference might be explained by gender differences in time preferences. However, given the small difference in the proportion of high discount students among boys and girls, and the fact that we can not reject the hypothesis that the effect on learning is the same for high and low time discount students, we conclude that the role of time preferences in explaining the gender-differentiated effect on learning is at most small.

There might be alternative explanations behind the gender-specific response to the information treatment. Information about future returns to children’s education might in principle affect parental expectations and, as a result, their investments into their children’s human capital. Parents might invest more in girls if they were underestimating their future labor market returns.<sup>27</sup> Similarly, teachers in treatment schools might have increased their effort, possibly as a result of a Hawthorne effect, but it is unclear why this would have a differential effect on boys and girls. We test whether the intervention led to teachers’ and parents’ responses that differ with student gender. In the _ENLACE de contexto_ students are asked a series of questions about their math teachers’ practices and parental investment. Evidence presented in Table 11 shows no effect of the program on students’ perceptions about teacher practices and parental involvement, either for boys or girls.

One conjecture behind the gender-specific effect of the intervention is that, ex-ante, boys might have had a lower scope for improving their effort through the subtrack choice. The fact that in the control group almost half of the boys, as opposed to 26% of the women, were opting for the most difficult subtrack, and they have math results 0.4 _σ_ higher than girls - while girls were doing better than boys in 9th grade - is suggestive that, at least in our sample, boys are already exerting a higher level of effort compared to girls, at least regarding math. This difference might be either the result of different preferences - for instance boys are less risk averse than girls (Charness and Gneezy 2012) and they are willing to attend more

> 27Bharadwaj et al. (2012), using data from Chile, find that parents invest more in math for boys, while the reverse is true for reading.

21
**Table 12:  Re-examine incumbent SOE outcomes after taking into account city capacity**

|**_Incumbent Firms_**|Log main<br>operating<br>revenue|Log main<br>operating<br>cost|Log main<br>operating<br>profit|Log # of<br>employees|Log labor<br>vocational|Log<br>wage per<br>employee|Log<br>export|
|---|---|---|---|---|---|---|---|
|**City**|-0.704|-0.685|-0.848|-0.255|-0.208|-0.209|0.0498|
||(0.760)|(0.767)|(0.906)|(0.443)|(0.444)|(0.294)|(1.045)|
|**Capacity**|-4.061|-5.576|4.035|8.095|-2.814|0.945|-5.989|
||(6.818)|(7.159)|(8.218)|(5.278)|(6.852)|(3.019)|(13.56)|
|**SOE**|-0.176|-0.206|0.263|0.721***|0.425**|-0.201**|-0.772|
||(0.218)|(0.226)|(0.242)|(0.172)|(0.213)|(0.0874)|(0.565)|
|**City × Capacity**|23.63|22.64|29.11|-0.644|-0.324|8.319|-7.217|
||(31.10)|(31.28)|(36.52)|(16.48)|(17.69)|(11.62)|(42.60)|
|**Capacity × SOE**|-19.84***|-18.57**|-25.01***|-26.82***|-15.12**|-1.173|10.27|
||(7.134)|(7.401)|(8.443)|(5.719)|(6.890)|(2.958)|(20.07)|
|**City × SOE**|-0.649|-0.721|-0.521|-0.138|0.173|-0.508|-1.901|
||(1.010)|(0.996)|(1.232)|(0.640)|(0.783)|(0.334)|(2.101)|
|**City × Capacity ×**<br>**SOE**|**30.70**|**33.60**|**22.80**|**17.64**|**6.978**|**18.52**|**80.49**|
||(39.36)|(38.63)|(47.66)|(24.26)|(30.54)|(12.77)|(80.46)|
|**Constant**|12.09***|11.70***|9.979***|6.097***|2.671***|8.573***|9.736***|
||(0.676)|(0.667)|(0.760)|(0.442)|(0.265)|(0.520)|(0.873)|
|Observations|32,517|32,428|21,283|18,193|3,892|17,496|4,759|
|R-squared|0.351|0.356|0.236|0.252|0.225|0.264|0.190|
|Block FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry**×**Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Standard errors are in parentheses and clustered at county-level.

3. Analysis is restricted to firms opened before 1997.

4. Vocational labor data is only available in 2004.

SOEs located in newly promoted cities outperform non-SOEs (see Table 9). However, once we account for city capacity, this result no longer holds – see Table 12. This might suggest that cities with more power but lower capacity are more likely to exploit the extra power to favor certain types of firms, such as SOEs.

We re-run the above exercise using another proxy for city capacity, i.e. total tax collection as a proportion of city GDP. We find similar results - only cities with both the scope and capacity to carry out their additional remit are able to help firms grow and help employees increase wages.

17
could be more suited to successful implementation of aid projects, thanks to higher levels of social and human capital. This would be consistent with recent evidence that more-developed areas receive more aid (Briggs, 2017; Nunnenkamp, Öhler, & Andrés, 2017).

The second part of the article (Section 3) puts this hypothesis to an empirical test, employing two different strategies. The first compares the performance of projects implemented in the vicinity of missions with those further away, using the ratings by the World Bank’s Independent Evaluation Group (IEG) as a proxy for project performance. There is no evidence of a correlation between proximity to mission stations and project ratings, but the estimates are rather imprecise and thus do not allow definitive conclusions to be drawn. The second strategy exploits information on start and end dates of the aid projects to implement a triple-differences design. In particular, areas that received aid at different points in time are compared to test whether aid arrival affects the level of development and, more importantly, whether the effect is higher in the vicinity of mission stations. Proxies of development are constructed with georeferenced survey data from the Demographic and Health Survey (DHS) and include measures of wealth and access to public utilities. Under the identifying assumption that development trends (not levels) between areas with and without missions as of 1903 are comparable in the period 1995–2014, there is no evidence that mission presence matters for aid effectiveness. As with the first strategy, it is not possible to precisely estimate the absence of an effect, and so the conclusion drawn from this exercise is suggestive rather than definitive.

The final part of the article (Section 4) investigates whether favoritism can explain the correlation between aid and missions. First, it examines the role of _political_ favoritism. There is evidence from Africa that funds from the World Bank and the African Development Bank have been diverted to politically important areas—competitive electoral districts (Masaki, 2018), strongholds of the incumbent regime (Briggs, 2014; Jablonski, 2014), or birth regions of presidents (Dreher, Fuchs, Hodler, Parks, Raschky, & Tierney, 2019). In light of the fact that areas close to historical missions have higher social capital and are more developed, it is reasonable to suspect that they are also politically more important. Mission areas may also have had strong connections with the central government in colonial times, and these connections may have persisted over time. The analysis puts this hypothesis to an empirical test by estimating the existence of a political aid cycle specific to mission areas. Findings from a specification that includes country-year 4
Although Lalmonirhat district received PRIME treatment the earliest, Kurigram district had the highest percentage of PRIME participants, followed by Nilphamari, Lalmonirhat, Rangpur, and Gaibandha (Table 2). Interestingly, Lalmonirhat has high participation in regular microfinance programs (36.9 percent), second only to Gaibandha.

Table 3 presents descriptive statistics of major outcomes by program participation status, while Table 4 shows those of household and community characteristics by program participation status for the 2008–09 survey of 5,207 households. Of particular importance are the poverty and seasonal deprivation measures. Three measures of poverty are calculated and presented in Table 3: moderate poverty, food poverty, and extreme poverty.<sup>13</sup> The two poverty measures (moderate and extreme poverty) are calculated using the year-round consumption data. In contrast, the measure of food poverty is calculated using food consumption during the week preceding the interview, which occurred during the non- _monga_ season (December–February). The extent of poverty of all three dimensions is about the same for all three household categories. For example, the share of moderate poverty is 86.1 percent among the regular microfinance participants, compared to 87.1 percent among PRIME participants and 89.2 percent among non-participants. By contrast, among regular microcredit programs, 67.6 percent are extremely poor, compared to 67.5 percent among PRIME participants and 72.7 percent among non-participants; that is, among the targeted

> 13 Poverty measures indicate the proportion (or percentage) of households in a sample or population that cannot meet certain predefined welfare requirements called poverty lines.  The food poverty line is based on a food basket needed to maintain the per-capita daily caloric requirement (2,120 calories) recommended by the UN Food and Agriculture Organization (FAO) and the World Health Organization (WHO) (FAO and WHO 1973).  For rural Bangladesh, the food basket includes mostly rice and other food items including pulses, milk, meat, fish, fruits, and vegetables in specific quantities.  The cost of such a food basket (food poverty line) is calculated using local food prices.  The non-food poverty line is estimated as 30 percent of the food poverty line.  Together, these two poverty lines constitute the moderate poverty line.  Thus, moderate poverty and food poverty are defined by the proportions of households whose total and food expenditures cannot meet the respective poverty lines, while extreme poverty is the dire situation that results when a household’s total expenditure cannot even meet the food poverty line.

19
# **1. Introduction**

The original 23 founding members (officially “Contracting Parties”) of the General Agreement on Tariffs and Trade (GATT) had swelled to 91 by September 1986, when the Uruguay Round negotiations began. Notwithstanding the GATT’s early reputation as a “rich man’s club,” by 1986 the majority of the members were poor countries, including many newly independent African nations. Still more poor nations joined the GATT during the protracted negotiations that produced the World Trade Organization (WTO). The WTO opened its doors on January 1, 1995 with 128 members. By July 2008, an overwhelming majority of the 153 WTO members were developing countries, with 32 of the poorest classified as least developed countries (LDCs). Yet many observers, and especially those representing the interests of poor countries, judge that participation in the Uruguay Round and in the WTO have so far yielded few benefits for these countries.

Of the accomplishments from the Uruguay Round, the eagerly sought dismantling of the Multifibre Arrangement (MFA) has been a major disappointment, as quota rents dissipated and China’s share of export markets exploded. The Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) is widely seen as causing, at least potentially, an adverse movement in the terms of trade of poorer countries, which are overwhelmingly importers of proprietary technologies created mainly in a few rich countries. Promised elimination of U.S. and European Community (EC) agricultural subsidies has stalled, disappointing middle-income developing countries with comparative advantage in sugar, rice, cotton, soybeans, and other agricultural products. And the Doha Development Round, aimed specifically at addressing concerns of poor countries, has been declared dead on several occasions. Almost completely overshadowed by laments regarding lack of progress in the Doha Round is the increasing benefit derived by developing countries from another achievement of the Uruguay Round: creation via the Dispute Settlement Understanding (DSU) of an enhanced process that allows members to self-enforce the market access to which their trading partners have agreed.

This paper begins by presenting historical data on WTO dispute settlement that document selfenforcement activities of developing countries. Viewed from the perspective of developing country 2
1

# 1. INTRODUCTION

Morocco's recent economic hlstory bears strlking resemblances with the one oF many countries in Africa. The origins of Morocco's economic difficulties can be traced to the commodity (phosphate) boom of the mild19<sup>7</sup> 0's which coincided wlth rislng government expenditure and an unprecedented expanslon of the public investment program signallng the end of the conservative Fiscal policies of the past. The sudden reversal in the terms of trade in the late 1970's as a result of the plunge In phosphate prices and the second oil shock prompted Morocco to resort Increasingly to external capital markets In order to malntaln an unabated level of public expenditure. However the continued deterloratlon of the terms of trade, the unanticipated rise in International Interest rates together with the severe drought In 1980-84 eroded debt service capacity and precipitated a major foreign exchange crlsls In 1983. In response to this crisis, Morocco launched a medium-term program of economic reforms and, In consultation with the International Monetary Fund and the World Bank, introduced a comprehensive set oF stabllizatlon and structural adjustment measures. Since 1983 Morocco has made great progress in allevlating both Internal and external disequllibria. The overall budget and current account deficits have been reduced from 9% and 12% of GDP in 1982 tc 4.5% and .4% respectlvely In 1988.

There are several interesting features In Morocco's adjustment experience. Despite the severity of the crisls and the size of the adjustment undertaken, growth has remained fairly high, at least In relation to other highly indebted countries, and inflatlon subdued. As measured by the CPI, inflation was equal to 6.2% in 1983, It Increased to 12.5% in 1984 but then steadily declined to reach 2.3% in 1988. During the same period real GDP growth averaged 4.3%. This performance seems to contradict the received wisdom that large budget deficits will foster inflation. The inFlation record Is particularly surprising if we also consider that Morocco achieved a 20% real depreciation during the eighties.

In this paper we try to uncover the reasons underlying the performance oF the Moroccan economy. To anticipate our concluslons, we argue that wage moderation and judiclous monetary pollcies were instrumental in restraining inflation. Wlth one brief exceptlon ln 1983, monetary authorities remained firmly committed to eschew any