rafmacalaba/datause-encoder-data huggingface dataset:
employed in the service sector. Most of the workers left the country over the last few years, during which period real wages also grew on average by more than 20 percent.<sup>6</sup> Anecdotal and theoretical evidence suggests that emigration-induced shock to labor supply could be a driving force behind this rapid wage increase.

The paper offers a number of contributions to the literature. First, it provides empirical evidence on the impact of emigration on source country wages outside the North American experience. Second, it makes use of a unique database on emigrants, documented by a nationally representative survey conducted in the source country. Third, for the reasons noted above, on the size of emigration, Moldova offers an ideal case to study the impact of emigration on source country wages. In addition, Moldova also represents what is arguably a more typical source country, with its migrant workers spread out across multiple host countries rather than residing in a single host country, as in the case in North America. The emigrant flow is thus diversified across a number of host countries, and though the majority of workers are in Russia, Moldova’s remittance inflows are not as exposed to country-specific downturns. Fourth, this study is unique in that it combines the previously studied net labor supply shock due to emigration with information on sector specific labor demand shocks.

The rest of the paper is organized as follows. Section II provides a simple and stylized theoretical introduction while Section III introduces empirical framework and some descriptive evidence. We then discuss empirical findings and robustness issues in section IV, which is followed by a concluding remark in section V.

# **II. A simple analytical framework**

The textbook model of a competitive labor market yields clear and unambiguous implications of a migration induced reduction in labor supply. Ceteris paribus, a reduction in the supply of labor outflows because of migration should increase the wage

> 6 Close to 80 percent of all emigrants left Moldova between 2000 and 2006. At the end of the 1990s, Moldova was the poorest country in the region, with over two-thirds of its population living below the national poverty line. This was at the close of nearly a decade of  economic decline due to the initial transition downturn, made worse by the 1998 Russian financial crisis.

4
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banks mostly readjusted prices to tighten the supply of credit and promptly correct for pre-crisis risk underpricing where margins were falling precipitously. More recently, margins continue to be the instrument of choice to restrict credit conditions, but there is also a significant role for shortening maturities, and decreasing loan size.

**Combined, tightening supply and demand factors triggered a downward trend of longterm loan flows since 2007 which are currently at their lowest since 2010, well-below shortterm loan flows.** This process accelerated by the end of 2011 and flows have become negative in 2012, marking the break of a pre-crisis pattern in which longer-term flows were dominant (Exhibit II.3). Medium-term flows have already been negative since 2010Q4. Shorter-term flows have behaved more pro-cyclically―they were hit disproportionately in 2009 and rebounded strongly in early 2011 and 2012 when credit conditions temporarily improved.

**Falling demand mainly drove the decrease in flows. However, under extreme financial conditions supply factors contributed significantly to the drop in long-term lending volumes.** A key question is whether the fall in long-term loan flows was driven by supply or demand factors. To gauge this, Exhibit II.5 shows the results of an illustrative regression analysis that disentangles both factors using ECB bank lending survey data. The simple model is able to explain almost 90 percent of the variation in quarterly long-term loan flows.<sup>9</sup> The exhibit displays the supply- and demand-driven parts of the flows that can be explained by the model. The results imply that falling demand mainly drove the decrease in flows. However, a sizeable portion of the fall in loan flows was supply driven during stress peaks in 2007Q3-2009Q3 and 20011Q4.

> 9 The regression is based on data from 2003Q1-2012Q3. The independent variable is long-term flows to nonfinancial corporations in Europe, taken from the ECB. The explanatory variables are the cumulative net percentage of banks reporting tightening conditions, the cumulative net percentage of banks reporting higher demand, taken from ECB bank lending surveys. The regression also includes season-fixed effects. The supply and demand factors are highly statistically significant using robust standard errors. The R-squared of the model is 0.90.

8
- 5 -

# **<u>Table 1 Labor Statistics</u>**

|County|Population<br>(1,000)|Labor for<br> <br>(1,000)|ce<br>Informal e<br> <sup>% of</sup><br>total<br>(1,000)|mployment<br> <sup>% of labor</sup><br>force|
|---|---|---|---|---|
|Bomi|101|<br>31|<br>30.7<br>20|<br>64.5|
|Bong|379|<br>179|<br>47.2<br>108|<br>60.3|
|Grand Bassa|201|<br>75|<br>37.3<br>51|<br>68.0|
|Grand Cape Mount|120|<br>62|<br>51.7<br>39|<br>62.9|
|Grand Gedeh|79|<br>27|<br>34.2<br>21|<br>77.8|
|Grand Kru|54|<br>26|<br>48.1<br>16|<br>61.5|
|Lofa|234|<br>135|<br>57.7<br>86|<br>63.7|
|Margibi|219|<br>75|<br>34.2<br>41|<br>54.7|
|Maryland|96|<br>28|<br>29.2<br>17|<br>60.7|
|Montserrado|1,152|<br>362|<br>31.4<br>197|<br>54.4|
|Nimba|427|<br>164|<br>38.4<br>77|<br>47.0|
|Rivercess|70|<br>35|<br>50.0<br>20|<br>57.1|
|Sinoe|90|<br>18|<br>20.0<br>9|<br>50.0|
|River Gee|55|<br>22|<br>40.0<br>17|<br>77.3|
|Gbarpolu|61|<br>34|<br>55.7<br>23|<br>67.6|
|Total|3,338|<br>1,273|<br>38.1<br>742|<br>58.3|

Source: Liberia Institute of Statistics and Geo-Information Services.

Economic activity is highly concentrated in Monrovia, where more than 1.2 million people, or about 30 percent of the total population and 55 percent of the total urban population, live. The distribution of firm location is even more skewed (figure 1). The government’s business registry database indicates that more than 41,000 firms were officially registered in Liberia as of 2016, of which approximately 80 percent were located in Montserrado County, where the capital city, Monrovia, is (table 2). Thus, formal firms are almost exclusively concentrated in Monrovia, but even while Liberia has been experiencing rapid urbanization, many people live in rural and remote areas.

The skewness of firm location is higher than in other countries (figure 2). This does not mean that there are few businesses in other areas; there are many informal businesses, but they have not been registered. As discussed above, informality is significant in the Liberian economy, particularly in rural areas.
25

**<u>Table 4 (Cont’d)</u>**

|_Variables_|_Return_<br>_Migrant_|_Non-_<br>_Migrant_|_Differ_|_ence_|
|---|---|---|---|---|
|_Household Social Capital_|||||
|Social Capital Index (PCA)|0.14|-0.02|0.16|***|
|_Location_|||||
|Tirana∆|0.13|0.14|-0.01||
|Coastal Urban∆|0.19|0.13|0.06|***|
|Coastal Rural∆|0.17|0.17|0.00||
|Central Urban∆|0.15|0.16|-0.02||
|Central Rural∆|0.31|0.29|0.03||
|Mountain Urban∆|0.01|0.02|-0.01|***|
|Mountain Rural∆|0.04|0.09|-0.04|***|
|_Observations_|853|8712|||

**Notes:** */**/*** indicate significance at the 10/5/1 percent level, respectively; ∆ denotes a dummy variable; ^ indicates that averages were computed among the employed population.
- 29 -

There are a number of examples of feedback mechanisms in online auctions that materialize the reputation effect where the quality of items being traded is ex ante unverifiable (Table 10). In the public procurement context, the U.S. government also discloses the list of vendors excluded from federal procurement on the website. Everyone can access to the black list of suppliers and contractors excluded from business with the federal government (Dellarcocas _et al_ ., 2006).

|Ta|ble 10. Selected examples o|f feedback mechanisms in online auctions|
|---|---|---|
|Web site|Category|Feedback mechanism|
|eBay|Online marketplace|Buyer and sellers rate one another following<br>transaction.|
|Amazon|Online marketplace|Buyer and sellers rate one another following<br>transaction.|
|Google|Search engine|Search results are ordered based on how many sites<br>contain links that point to them.|
|Slashdot|Online discussion board|Positing a prioritized list based on the ratings they<br>receive from readers.|
|Citysearch|Entertainment guide|Users rate restaurants, bars, hotels, etc.|
|eLance|Professional services<br>marketplace|Contractors rate their subcontractors.|
|Epinons|Online opinions forum|Users write reviews about products, and others rate<br>usefulness of reviews.|

Source: Dellarocas _et al_ . (2006).

Despite some of these possibilities to mitigate unobservability prior to signing a contract, ex post adjustments may still be unavoidable for some exogenous reasons. Because of contractual incompleteness and because governments can by no means transfer ultimate business risk to private operators (i.e., hold-up problem), bidders would also have a strong incentive to submit unrealistic bids, often referred to as “low balling” and initiate renegotiation afterward (Ware _et al_ ., 2007). In fact, Guasch (2004) shows that about 30 percent of PPP infrastructure transactions underwent renegotiation within two years after the awards in the Latin American and Caribbean region. In the United Kingdom 55 percent of public finance initiative (PFI) projects experienced some changes in the contracts (NAO, 2001). One may consider the flexibility to ex post adjust a contract as beneficial under highly uncertain environment. However, ex post adjustments are in general considered much costly. Cost overruns and delays in infrastructure projects incur massive economic costs (Flyvbjerg, 2005). Bajari _et al._ (2006) estimates the adaptation cost reflected in the real bids at about 2.7 dollars per one dollar of expected contract adjustment.
interactions of these variables with a dummy for LAC economies to test for heterogenous effects across different groups of developing countries. We also test the robustness of the KCM to the inclusion of proxies concerning the relative abundance of natural resources.

We find that there is no substitution effect between FCS in China and India and those in LAC or other hosts of FCS. When using sector-level FCS data from the U.S., we continue to find that there is no robust substitution effect from China or India on manufacturing-sector FCS in other countries, including LAC and Central America and Mexico. In some cases we cannot reject the hypothesis of no substitution effect of Chinese FCS on Central America and Mexico or of Indian FCS on FCS in the Southern Cone countries of LAC. But these results are not robust across econometric techniques. In fact, to our knowledge, this is the first study of determinants of foreign capital across countries that utilizes OLS as well as count-data estimators (Poisson and Negative Binomials), which are consistent in the presence of systematic heteroskedasticity that might render OLS and other linear estimators unreliable.

The rest of the paper is organized as follows. The next section reviews related literature. Sections 3 and 4 describe the methodology and data. Section 5 presents and discusses the econometric results. The last section concludes.

# **2. Related Literature**

There is a recent literature that estimates econometrically the impact of China’s emergence as a large host of foreign capital on the developing world. Chantasasawat et al. (2004) use data on aggregate FDI inflows for a group of Asian and Latin American countries. After controlling for other FDI determinants, they find that China’s FDI is 3
establishment of K-Rep bank in 1999 with a mandate focusing on the provision of sustainable microfinance.

The innovation pioneered by Kenyan banks has spread to other countries, as banks are expanding their footprint in the EAC and beyond. In Rwanda, competition for SMEs and retail customers has increased through the entrance of Kenya-based banks since 2010, and by now all commercial banks are offering products for SMEs. They are also using more innovative distribution channels such as agency banking, also pioneered by their Kenyan competitors.

In Nigeria and South Africa such competition through innovation has still not taken place. In Nigeria, the dominance of the oil and gas sectors and the unfavorable lending environment (see below) has resulted in most banks focusing on servicing only those SMEs directly related (as suppliers or distributors) to their blue-chip larger enterprise clients. Expansion by innovators, whether domestic or foreign, has likewise not taken place and competition from the microfinance sector remains limited, though slightly increasing. In South Africa, four big banks dominate the financial sector, holding about 80 percent of bank assets and dominating the market for (cheap) retail deposits. This bank asset concentration is higher than in all other countries and has affected innovation as competitors have difficulties to enter and grow in this highly contested market. While competition for retail lending has increased markedly in South Africa in recent years, partly because of new banks entering that space, lending to SMEs remains limited. Competition from the microfinance industry is also not to be expected as that market is not developed in South Africa.

# **_c. The enabling environment_**

The enabling environment can in theory reduce the costs of SME lending, particularly through providing information on prospective borrowers through credit bureaus, ensuring the availability of unique IDs, facilitating the use movable collateral as security through movable collateral registries, and strengthening the enforcement of contracts through alternative dispute resolution mechanisms.

The legal and regulatory framework in Rwanda is one of the most conducive to the provision of SME finance in Sub-Saharan Africa (see Table 3 for an overview of the enabling environment across countries). The country’s legal framework for creditor’s rights and financial 11
29 to decompose the time series into 'permanent' and 'temporary' components to test for the differential effects of each, it is not difficult to argue<sup>that the</sup> 33 . changes in Bangladesh's expenditure composition. is unsustainable. Nevertheless it is more than likely that private sector decisions affecting the RER were not influenced by that unsustainability.

Ideally the RER response to unsustainable shifts in composition should reflect private sector demand and supply decisions based on that "unsustainability." In that event, the RER appreciation in response to a given unsustainable shift in composition would be negligible. If a part of the change is temporary, the appreciation would be much smaller than if the same shift was wholly sustainable or permanent. However, if this unsustainability is not perceived by Bangladesh's private sector and their demand and supply responses are thus based only on the current not on the long-run values of public expenditure, the RER appreciation in response to a change in current composition of expenditure, would be larger than the equilibrium level. The same is true, if the private sector perceives the unsustainability but is unable to incorporate this information into its ictual demand and supply decisions because it is constrained by controls or by liquidity constraints.

In both situations the appreciated RER is misaligned or departs<sup>from long-</sup> run equilibrium but resources continue to be allocated on the basis of<sup>that</sup> misaligned RER if producers do not perceive the misalignment.<sup>Even if they</sup> perceive the misalignment and expect a future depreciation,domestic<sup>capital</sup> 33The FY 1991 budget of Bangladesh government and the country projections of donors confirm a reversal in the trend of the share of capital (ADP) expenditure over the next three years.
**<u>Table 5.1. Summary Impact Matrix</u>**

||**Sc. 1**|**Sc. 2a**|**Sc. 2b**|**Sc. 3a**|**Sc. 3b**|**Sc. 3c**|
|---|---|---|---|---|---|---|
|Absortion|**=>**|**<=**|**=>**|**==>**|**===>**|**==>**|
|p<br>Household Consumption|**=>**|**<=**|**<=**|**==>**|**==>**|**==>**|
|Investment|**==>**|**=>**|**==>**|**===>**|**===>**|**===>**|
|Government Consumption|**=>**|**<=**|**<=**|**===>**|**===>**|**==>**|
|GDP at Market Prices|**=>**|**<=**|**=>**|**==>**|**===>**|**==>**|
|Net Income Tax|**==>**|**=>**|**=>**|**==>**|<br>**==>**|**==>**|
|GDP at Factor Costs|**=>**|**<=**|**=>**|**==>**|**===>**|**==>**|
|Total Exports|**=>**|**=>**|**=>**|**===>**|**===>**|**===>**|
|Total Imports|**=>**|**=>**|**=>**|**===>**|**===>**|**===>**|
|Agricultural Exports|**<===**|**==>**|**===>**|**===>**|**===>**|**===>**|
||**===**|**<=**|**<=**|**<==**|**<==**|**<==**|
|Agricultural Imports|**>**||**=**|**=**|**=**|**=**|

_Note: Sc.1: World price increase; Sc. 2: Rural-urban water transfer; Sc. 3: Climate change._ The highest effects on major macroeconomic indicators occurs in the climate change simulations. Nominal GDP declines drastically, but the real impact is limited. The changes are relatively smaller in the world price increase scenario when compared to all climate simulations. This situation points out the importance of the climate change in the performance of the overall economy. It is obvious that the impact of the climate change will not only be confined to the agricultural sector. Through the interactions with the rest of the economy, the negative impact of the climate change will be amplified. Irrigation is considered the most important adaptation measure to ease the negative impact of climate change, especially on farmers’ income. Warming is also expected to affect the availability of water resources. This aspect of the climate change is 58
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22
data on villages. The EPM uses multi-stage sampling, splitting observations into strata and clusters. Strata are identified by province (“Faritany”) and by urban vs. rural area; clusters are identified by administrative units (“Fokontany”) randomly drawn in each stratum. The sample in this paper includes 5,951 agricultural housholds. Although repeated, the EPM is not a panel as only a small number of households were sampled repeatedly in 1997 and 1999.

Commune data (collected in 2001 and covering 1’385 communities at the level of administrative units above Fokontany) is from the commune census of Cornell University’s ILO program. It includes, inter alia, data on the commune’s level of development, infrastructure and aggregate agricultural production.

Producer prices are taken from the Food and Agriculture Organization (FAO), for all years between 1991 and 2001. Prices are in current Malagasy francs (MGF). Climatic data was taken on the one hand from the Ministry of Agriculture, Livestock and Fisheries of Madagascar and on the other hand from a paper on climatic perturbations (Rambeloalijaona & Randrianarivelo, 2003).

The first column of Table 1<sup>8</sup> shows descriptive statistics computed allowing for the sample design (weights, strata and clusters<sup>9</sup> ). The first striking feature of the data is agricultural household’s extreme poverty.<sup>10</sup> With a yearly income of US $566 for a family of five (implying about 30 cents per day per person) the average Malagasy household lives well under the international dollar-a-day poverty line.

The second is the degree of farm specialization. On average, a farm’s first crop accounts for 80 percent of agricultural income, and about 70 percent of total income comes from crop sales. With 7.5 percent of full income<sup>11</sup> from livestock sales, the diversification of sources of income is somewhat limited.

At about 1.6 hectares, the average Malagasy farm is very small, which means that all but the simplest capital equipment is prohibitively costly to buy at the farm level, and hence

> 8The second and third columns will be commented on later.

> 9For more on that, see Deaton (1997).

> 10A thorough analysis of poverty trends in Madagascar can be found in Paternostro et al. (2001).

> 11By “full”income we mean that our measure of income includes the value of the total harvest (evaluated at producer prices) and not only the value of what is sold in the market.

10