rafmacalaba/datause-encoder-data huggingface dataset:
Importantly for our purposes, the two transfer components are unbundled. Households declared eligible to receive benefits can take up food stipends, scholarships, or both. They can also choose to receive the scholarships for some but not all of their eligible children. Beyond transfer amounts, take-up decisions are largely dependent on the tightness of the conditions attached to each grant component. While nominally conditional, a substantial fraction of the transfers is _de facto_ unconditional. In particular, the conditions attached to the food stipends and scholarships for primary school children do not seem to incur a high cost to households, because school enrollment at that level is almost 100 percent. We use data to document take-up from the administration of the program on the distribution of the different transfers in the 320 treatment localities of the evaluation. This data confirms the complete take-up of the food stipends: at the end of 1998 and 1999, respectively 97.1 and 98.0 percent of eligible households in those localities received the transfers.

In contrast, the conditionality of the scholarships at the secondary level is binding for many households whose eligible school-age children would not have gone to school in the absence of the program. The same data indicates that, respectively, 83.0 and 91.3 percent of households that are eligible for a scholarship for at least one child enrolled at the primary or secondary level received one. However, only 63.7 percent of children who were eligible for a scholarship for secondary-level school attended school in 1998 with 61.9 percent attending in 1999.

Hence, partial take-up of the program benefits is prevalent in this setting whereby some eligible households comply with the food stipend conditions but refrain from enrolling some or all of their children in secondary school. However, once they are incorporated into the program, recipients can further adjust their behavior by enrolling some of their program-eligible children. While take-up of the food transfers is almost complete, there is thus a margin for increasing the take-up of the schooling component, which can be seen as an intensive margin of program participation.

# _Village Neighborhoods_

In this paper, we use the term ”neighborhood” to describe areas within a given radius around each evaluation village. We borrow this terminology from a literature based mainly on urban data, but, in our context, ”neighborhood” means an area or cluster of villages.

8
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# V. AN INSTITUTIONAL APPROACH: THEORY AND NEW DEVELOPMENTS

## **V.1** OVERVIEW

From our analysis of the credit polllels undertaken In rural markets in LDCs durlng the last three decades, we can Infer that by and large the root of the problem see,iie to be the following. Flrst, the objectives of those policies are not altogether clear or expilclt. Second, even when they are, there seem to be confiltIng goals stated or an underlying Inconsistency. Third, the mechanism or technology to Implemen **t** hese objectivos Is not well specifled. Furthermore, even when there Is a well-defined mechanism, the Incentive system for the Individuals or Institutions rosponsible for tholr undertaking Is not fully compatible wlth the proposed objectives. Therefore, the reported high failure rate of past credit pollcies should not be surprising. Perhaps the common perceptlon was that subsidized credit would be the magie wand which when waved would make everything turn out all right. But as we have seen, subsidized credit has made matters even worse.

The problems of objective definitlon ought to be easy to correct, but clearly conflicting goals cannot be expected to be obtalned simultaneously wlth a llmited set of pollcy Instruments. The main challenge, however, ought to come In the design of the Implementation technology, namely, the Institutional structure and the Incentive schemes most appropriate to Induce the desired objectives along with a strong enforcement policy. Unfortunately, llttle conceptual work to date has been devoted to this task In the economic professlon. Following the decline In attentlon to the "Institutlonal school", modern economic literature has largely overlooked the analysis of Institutions,
valuation is location specific. The option value of country _j_ is going to be larger if it provides easier access to other and possibly higher income countries. We argue and show that the option value of such locations (such as Canada, the United Kingdom and Australia) is one of the main motivations behind the observed transit and sequential migration patterns. Finally, we should emphasize that the option value of a location Ω<sup>_s,i,j_</sup> _t_ = _−_ log( _p_<sup>_s,i,j,j_</sup> _t_ ) decreases as the probability of moving decreases. If the agents were completely immobile and were stuck in _j_ forever, say due to complete border closures between _j_ and other destinations, then the option value of _j_ would decline to zero.

# **1.1 Functional Form of the Moving Costs**

The moving costs are one of the key determinants of the mobility patterns generated in the model. We assume that the fixed moving cost from country _j_ to country _k_ has two distinct components. The first component, denoted by _C_ 1<sup>_s,j,k_</sup> , is the cost of moving from _j_ to _k_ . This is assumed to be common for all agents whether they were born in _j_ (locals) or were born in another country _i_ and had migrated in an earlier period to _j_ (immigrants). The second component, denoted by _C_ 2<sup>_s,i,k_</sup> , is the additional moving cost for immigrants born in _i_ and are currently living in _j_ . We refer to this component as the cost of transit migration and do not have a priori expectation on its value. The data, however, indicate immigrants are more likely to move on to another country _k_ from _j_ when compared to locals, which implies this second component is likely to be negative. We assume that the transit cost is a function of birth country _i_ and destination country _k_ .<sup>6</sup> The common cost component is given by

> 6Unfortunately we cannot use destination specific parameters in these expressions, since we use only the transit migration data to the US. However, we can use bilateral and origin specific parameters.

12
accelerations and decelerations, most of the benefits of growth accelerations on Africa were offset by growth collapses, leading to an overall tepid rate of growth. Had Africa avoided its growth collapses, it would have grown at 1.7 percent a year in per capita terms instead of 0.7 percent. Figure 6 shows the actual and simulated GDP per capita at these growth rates. Income per capita would have been at least 30 percent higher in 2005 had bad times been avoided.<sup>24</sup> This finding is consistent with Easterly (1998), who argued that the incremental capital-output ratio, ICOR (or its inverse, the marginal returns of investment) of African countries such as Zambia fluctuated too much, so that growth opportunities were missed. Growth decelerations matter a great deal in the fight against poverty in Africa.

<!-- Start of picture text --> Figure 6: Actual and simulated GDP per capita ($)<br>3,600<br>3,400<br>Actual GDP per capita<br>3,200<br>3,000<br>GDP per capita growth at the<br>2,800 observed average (0.7<br>percent a year)<br>2,600<br>GDP per capita growth in the<br>2,400 no-collapse scenario (1.7<br>percent a year)<br>2,200<br>2,000<br>Source: Arbache and Page (2007b)<br>1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> The relative frequency of good and bad times over the past three decades follows Africa’s long-run pattern of growth. Accelerations were more frequent in 1995–2005; decelerations were more common in the two preceding decades. Forty two percent of the 494 country-year observations for 1995–2005 occurred in countries experiencing growth accelerations, and only 12 percent in countries undergoing growth decelerations. The remaining 46 percent of observations belong to years in which countries were experiencing neither growth acceleration nor deceleration. In 1975–1984, growth decelerations were 350 percent more frequent than accelerations.<sup>25</sup> PT In 1985–1994, this ratio had dropped to 71 percent, mainly because of a sharp rise in accelerations to 21 percent from 4 percent.

In 1995–2005, the average growth rate for countries during acceleration episodes was 3.8 percent, the second-highest average among the three 10-year periods.

> 24 The simulated growth rate without collapses takes into account the growth rate during all country-years but growth deceleration years. The additional GDP per capita results from the difference in compound growth at 1.7 percent and 0.7 percent in 1975–2005.

> 25 Calculated as ((0.18/0.04) − 1)*100.

13
The largest drops in output growth are expected to occur in various Asian countries including Japan, Thailand and Singapore as well as in Argentina and Venezuela.

# _Interest rates and Inflation_

Figure 3 shows that interest rates are close or below 1% in more than half of the countries of our sample. Only six of these countries have an inflation rate in excess of 1%, which means that many of the countries that have hit the lower bound still face positive real interest rates. The list of countries which are close to the zero lower bound includes most developed countries: the US, Japan, the UK, Canada, Sweden, Switzerland, Singapore and Hong Kong.

# _Deficits and debts_

Debt as a percent of GDP in our sample stands on average at 46%. Most prominently, in Japan and Singapore debt levels exceed 100% of GDP.<sup>6</sup> Both countries are expected to experience a severe output contraction in 2009 and have seen their competitiveness seriously eroded (Figure 7). European countries tend also to have a relative high debt level, in particular Italy, Greece and Belgium. Finally, some emerging markets including Hungary, Israel and India belong to the group of high debt countries. Given the recent world wide fiscal expansion these figures will increase over the next year by several percentage points.

**Figure 4: Debt and net foreign assets**

**Figure 5: Current account balance**

<!-- Start of picture text --> C ur. A cco unt and Expo rts (in % o f GD P )<br>P ubl. D ebt and N et Ext. P o s. (in % o f GD P )<br>-280 -240 -200 -160 -120 -80 -40 0 40 80 120 160 -50 -40 -30 -20 -10 0 10 20 30 40 50<br>SwitzerlandHong Kong Hong KongSingapore<br>Saudi ArabiaBelgium LuxembourgMalaysia<br>VenezuelaIsrael BelgiumIreland<br>South AfricaSingaporeGermanyRussian KuwaitJapan Saudi ArabiaNet herlandsCzech Rep.HungaryThailandVietnamKuwait<br>Argentina Switzerland<br>United. StatesTurkeyChina DenmarkSwedenAustria<br>ThailandM exico UkraineNorway<br>Philippines Chile<br>IndonesiaVietnamBrazilChile PhilippinesGermanyFinlandIsrael<br>M alaysia Korea, Rep.<br>India Poland<br>Canada China<br>Czech Re p. Canada<br>Finlan d Venezuela<br>Polan d Russian<br>Ukrai ne Mexico<br>Korea, Re p. Indonesia<br>Austr alia Portugal<br>Fra nce South Africa<br>Sw eden UK<br>Den mark Italy<br>G reece France<br>Italy Spain<br>No rway Argentina<br>A ustria Turkey<br>United Kin gdom Aust ralia<br>H ungary India<br>Ne therlandsSpain GreeceJapan Expor ts<br>Portugal Net Ext. Brazil CA<br>LuxembourgIreland Publ. Debt United Stat es<br>0 50 100 150 200 250 300 350 400 450 500<br>-120 -80 -40 0 40 80 120 160 200 240 280 320<br><!-- End of picture text --> Source: BIS (2008 Q3) and CIA Factbook (2008) Source: WEO, pre-crisis levels (2006)

> 6 Despite this fact, Japan already decided to increase its deficits to new record levels.

7
**Table 5: Heterogeneous Effects by Job Characteristics**

|||Days|Hours||Days|Hours|
|---|---|---|---|---|---|---|
|Dependent variable|1(Work)|worked|worked|1(Work)|worked|worked|
|Other worker ill|0.074|0.273|2.599|0.119**|0.486|6.108*|
||[0.058]|[0.325]|[3.750]|[0.047]|[0.305]|[3.477]|
|Other worker ill X Female|‐0.115|‐0.306|‐5.248|‐0.193**|‐1.151**|‐<br>13.278**|
||[0.111]|[0.597]|[6.189]|[0.085]|[0.539]|[5.957]|
|Self employed X Other worker ill|0.042|0.295|4.801||||
||[0.087]|[0.478]|[5.295]||||
|Self employed X Other worker ill X Female|‐0.047|‐0.425|‐3.088||||
||[0.126]|[0.760]|[7.506]||||
|Irregular wage work X Other worker ill|‐0.003|0.148|2.996||||
||[0.081]|[0.617]|[5.675]||||
|Irregular wage work X Other worker ill|0.078|0.741|5.26||||
|X Female|[0.159]|[0.877]|[7.896]||||
|Work close to home X Other worker ill||||‐0.069|‐0.181|‐2.883|
|||||[0.064]|[0.378]|[4.077]|
|Work close to home X Other worker ill||||0.119|1.054*|12.337**|
|X Female||||[0.098]|[0.630]|[6.401]|
|Net effect on Self Employed Male|0.116|0.568|7.400||||
|P‐value|0.051|0.128|0.000||||
|Net effect on Self Employed Female|‐0.046|‐0.163|‐0.936||||
|P‐value|0.294|0.542|0.168||||
|Net effect on Irreg Wage Employed Male|0.071|0.421|5.595||||
|P‐value|0.204|0.443|0.132||||
|Net effect on Irreg Wage Employed Female|0.190|0.856|5.607||||
|P‐value|0.209|0.000|0.084||||
|Net effect on Male Working Close to Home||||0.0499|0.305|3.225|
|P‐value||||0.295|0.29954|0.21324|
|Net effect on Female Working Close to Home||||‐0.0243|0.208|2.284|
|<br>P‐value||||0.597|0.40728|0.29809|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|3,951|3,948|3,948|
|R‐squared|0.507|||0.507|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. All specifications include individual fixed effects. Sample includes respondents employed at baseline. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Work close to home = 1 if the worker reported during the baseline survey that s/he normally works ʺclose to homeʺ. Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
its labor force so as to increase or maintain effort. The implications for firm growth are thus potentially quite significant.

The importance of labor market institutions relative to intra-firm organizational mechanisms, in terms of their effects on wage, is hard to assess based on the available research. What we do know is that the effects are economically large. In the case of Ghana, for instance, Söderbom and Teal (2004) estimate that if a firm with 350 employees faced the same wage as a firm with 20 employees, total costs in the large firm would fall by 20-25 percent.

# _iv) Infrastructure_

Based on several studies of growth determinants in Africa, Collier and Gunning (1999) argue that poor infrastructure is a serious constraint to growth in the continent. Compared to other regions public expenditure as a share of GDP has been higher in Africa, while service provision has been worse. The poor infrastructure in Africa is likely to be a particularly severe constraint to manufacturing growth. Many firms spend own resources directly on buying infrastructure services or providing it on their own. One reason for the prevalence of small manufacturing firms in Africa (see below) is that the transport infrastructure is poorly developed, which creates pockets of demand that tend to generate small-scale localized producers. To be able to take advantage of international trading opportunities and to engage actively in the emerging system of outsourcing requires reliable and cheap infrastructure. Poor infrastructure thus presents a significant problem for exporters.

The firm surveys have generated a lot of information about how firms perceive infrastructure problems and recently also about time and monetary costs of different bottlenecks. Bigsten et al. (1999b) show that attempts to use data on perceptions to explain productivity is problematic. It is shown that the firms that rank infrastructure problems as very severe are at the same time the most productive ones. This is due to the fact that these are the firms that sell on larger markets and they are thus more dependent on the infrastructure. The use of objective measures – e.g. days in customs, days without telephone connections, days without electricity etc. - is a more promising approach, but so far little analysis is available on the impact of infrastructure on manufacturing firm productivity.

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|Table 3 Average black mar|ket premi<br>**1980-89**|um _(perc_<br>**1990-93**|_ent)_<br>**1994-97**|
|---|---|---|---|
|Total<sup>a</sup>|82.0|78.2|20.3|
|East Asia|3.6|3.6|3.2|
|Middle East And North Africa|165.6|351.6|46.5|
|-Excluding outliers <sup>b</sup>|7.1|8.8|1.4|
|Latin America|48.7|13.1|4.4|
|South Asia|40.8|45.1|10.1|
|Africa|116.5|28.6|32.2|
|-ExcludingNigeria|112.1|25.8|9.6|
|_Notes:_<br>a<br>Sample of 41 develo<br>_Source:_<br>World Bank (2001)|ping count|ries. <sup>b</sup><br> Alge|ria and Iran|

There are good reasons to expect that, in this situation, a high protection regime will lock countries into continuing dependence on resource-based commodities which are typically less dependent on purchased intermediate inputs than is manufacturing, particularly in this era of production fragmentation. To allow further examination of this difference, Table 4 presents for a number of countries data on the cost structure of output and the effective rates of protection imposed on export-oriented activities. A striking feature is the top section of the tables is the much lower dependence of primary agriculture and resources commodities on intermediate inputs. This gives resource-based activities an opportunity to survive even in situations of very high protection.

It is, of course, possible that the greater vulnerability of manufacturers and agricultural processors to high protection regimes would be offset by a type of tariff escalation that involves lower than average protection on intermediate inputs to agricultural and manufacturing sectors. To see whether this is the case, the second panel of Table 4 examines the effective rates of protection applying to exporters. These effective rate calculations are done very simply, taking into account only the effects of intermediate input shares and tariff rates. They therefore ignore the additional burdens imposed on exporters by nontariff barriers on inputs, or by the real exchange rate appreciation associated with protection. What the results of these calculations strongly suggest is that the pattern of tariff protection does not provide any relief to exporters of manufactures or processed agricultural products. In fact, it appears that the pattern of 23
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- Teak (Thailand). Domestic wholesale price of teak logs. Data from 1965 to 1987.

- O Sapelli (Cameroon). The Cameroonian FOB price for high quality sapelli logs. DaLa from 1956 to 1989.

- O Niagon (Cote d'lvoire). Standard quality niagon logs, FOB Abidjan. Data from 1956 to 1989.

- O Samba (Cfte d'Ivoire). Standard quality samba logs, FOB Abidjan. Data from 1956 to 1989.

# Sawnwood

- Sawnwood (Malays;a). Dark red meranti, select and better quality of standard density, CIF French ports. Data from 1958 to 1989.

# Plywood

- Plywood. Lauan plywood, 3-ply extra, 91cm X 182 cm X 6mm, spot wholesale price in the Tokyo market. Data from 1963 to 1989.

All data series are quarterly.

16. The three prices for African logs are the FOB prices reported by French importers. The sawnwood price is for Malaysian sawnwood in France,
Finally, it should be noted that SAMs are part of the system of national accounts and that the information that they contain may be used to define national account indicators such as different measures of national income, product, and expenditures as well as their different components.<sup>7</sup>

# **3. Building a Macro SAM Using Cross‐Country Databases**

In this section, we describe a straightforward procedure for building macro SAMs. We present three alternative versions with gradually increasing data needs. Macro SAM I has a single sector (one activity and one commodity) and an aggregate treatment of savings and investment. In Macro SAM II, the sectors (activities and commodities) are disaggregated into private and government. Macro SAM III has the same structure as the SAM presented in Section 2; it differs from II in that the treatment of savings and investment is more disaggregated.

## **Macro SAM I**

Macro SAM I is presented in Table 3.1, using data for an archetype low‐income country for illustration.<sup>8</sup> The proposed procedure for building such a SAM for an individual country consists of two main steps. The first step – direct entry of data in raw form and computation of intermediate data ‐‐ is covered in Table 3.2. In the first section of the table (1‐20; here and in the following discussion of Macro SAMs I‐III, the parenthesized numbers refer to rows in relevant tables), data from World Bank and IMF sources are entered into the SAM directly without transformations – this should be done to the maximum extent possible. It is here possible for the indicators that define GDP at market (or purchasers’) prices from the expenditure side (domestic final demands, exports, and imports), disaggregated tax payments, and items in the balance of payments related to factor incomes and current transfers. Consistency between receiving and paying accounts is assured thanks to the fact that a single SAM entry applies to both; e.g. government consumption in the national

> 7 For example, see European Commission et al. (2009, Chapter 28).

> 8 More specifically, the data shown are the weighted average of macro SAMs for the 17 low‐income countries for which sufficient information was available in cross‐country databases. Each SAM was weighted by its share of GDP at market prices at current US dollars for the countries included.

‐9‐